ISSN 1408-7073 RMZ - MATERIALS AND GEOENVIRONMENT PERIODICAL FOR MINING, METALLURGY AND GEOLOGY RMZ - MATERIALI IN GEOOKOLJE REVIJA ZA RUDARSTVO, METALURGIJO IN GEOLOGIJO RMZ-M&G, Vol. 58, No. 4 pp. 357-472 (2011) Ljubljana, December 2011 Historical Rewiev More than 80 years have passed since in 1919 the University Ljubljana in Slovenia was founded. Technical fields were joint in the School of Engineering that included the Geologic and Mining Division while the Metallurgy Division was established in 1939 only. Today the Departments of Geology, Mining and Geotechnology, Materials and Metallurgy are part of the Faculty of Natural Sciences and Engineering, University of Ljubljana. Before War II the members of the Mining Section together with the Association of Yugoslav Mining and Metallurgy Engineers began to publish the summaries of their research and studies in their technical periodical Rudarski zbornik (Mining Proceedings). Three volumes of Rudarski zbornik (1937, 1938 and 1939) were published. The War interrupted the publication and not untill 1952 the first number of the new journal Rudarsko-metalurski zbornik - RMZ (Mining and Metallurgy Quarterly) has been published by the Division of Mining and Metallurgy, University of Ljubljana. Later the journal has been regularly published quarterly by the Departments of Geology, Mining and Geotechnology, Materials and Metallurgy, and the Institute for Mining, Geotechnology and Environment. On the meeting of the Advisory and the Editorial Board on May 22nd 1998 Rudarsko-metalurski zbornik has been renamed into "RMZ - Materials and Geoenvironment (RMZ -Materiali in Geookolje)" or shortly RMZ - M&G. RMZ - M&G is managed by an international advisory and editorial board and is exchanged with other world-known periodicals. All the papers are reviewed by the corresponding professionals and experts. RMZ - M&G is the only scientific and professional periodical in Slovenia, which is published in the same form nearly 50 years. It incorporates the scientific and professional topics in geology, mining, and geotechnology, in materials and in metallurgy. The wide range of topics inside the geosciences are wellcome to be published in the RMZ -Materials and Geoenvironment. Research results in geology, hydrogeology, mining, geotechnology, materials, metallurgy, natural and antropogenic pollution of environment, biogeochemistry are proposed fields of work which the journal will handle. RMZ -M&G is co-issued and co-financed by the Faculty of Natural Sciences and Engineering Ljubljana, and the Institute for Mining, Geotechnology and Environment Ljubljana. In addition it is financially supported also by the Ministry of Higher Education, Science and Technology of Republic of Slovenia. Editor in chief Table of Contents - Kazalo Original Scientific Papers - Izvirni znanstveni članki Effect of cooling rate on the constitution of Al-Mn-Be-Cu 357 Vpliv ohlajevalne hitrosti na konstitucijo Al-Mn-Be-Cu Štrekelj, N., Naglic, I., Karpe, B., Markoli, B. Development of intelligent knowledge-based computing environment for 367 controling the proces parameters and nonmetalic inclusions in steels Razvoj inteligentnega, na znanje oprtega računalniškega okolja za kontrolo procesnih parametrov in nekovinskih vključkov v jeklih Krušic, U., Tercelj, M., Kugler, G., Peruš, I. Determination of hot workability and processing maps for AISI 904L 383 stainless steel Vroča preoblikovalnost nerjavnega jekla AISI 904L ter izračun procesnih map Fajfar, P., Bradaškja, B., Pirnar, B., Fazarinc, M. A comparison of parameters below the limit of detection in geochemical 393 analyses by substitution methods Primerjava ocenitev parametrov pod mejo določljivosti pri geokemičnih analizah z metodo nadomeščanja Verbovšek, T. Compositional appraisal and quality implications of a metacarbonate 405 deposit occurring in parts of southeastern Nigeria Presoja sestave in njenega vpliva na kakovost metakarbonatne kamnine iz nahajališča v jugovzhodni Nigeriji Ephraim, B. E. Hydrochemistry of the near shore marine bay, Calabar river 421 (South-eastern, Nigeria) Hidrokemične razmere v priobalni vodi morskega zaliva ob izlivu reke Calabar v jugovzhodni Nigeriji Ekwere, A. S., Edet, A., Ukpong, A. J. Modernization of technological process and equipment at floor level 437 roadways execution in Velenje coal mine Modernizacija tehnoloških procesov in opreme pri gradnji jamskih prog v Premogovniku Velenje Dervaric, E., Vukelič, Ž., Medved, M. Author s Index, Vol. 58, No. 4 451 Author s Index, Vol. 58 452 Contents, Volume 58, 2011/1, 2, 3, 4 455 Instructions to Authors 459 Template 467 Effect of cooling rate on the constitution of Al-Mn-Be-Cu Vpliv ohlajevalne hitrosti na konstitucijo Al-Mn-Be-Cu Neva Štrekelj1-*, Iztok Naglič1, Blaž Karpe1, Boštjan Markoli1 University of Ljubljana, Faculty of Natural Sciences and Engineering, Aškerčeva 12, SI-1000 Ljubljana, Slovenia corresponding author. E-mail: neva.strekelj@omm.ntf.uni-lj.si Received: November 4, 2011 Accepted: November 29, 2011 Abstract: In this article the constitution of the Al-Mn-Be-Cu alloys and the influence of the cooling rate on the constitution was studied. Cooling rates were determined through the measurements of the dendrite arm spacing and the heat calculations. By the use of computer program which is part of the light microscopy technique, individual fractions of i-phase were determined. It was found that at cooling rates between 500 K s-1 and 1 350 K s-1 the preferred phase formed was a quasic-rystalline phase - especially in the form of the quasicrystalline eutec-tic (aAl + i-phase). In addition to the quasicrystalline i-phase within eutectic, it is also formed as the primary phase. The fraction of the i-phase varied depending on the diameter of the castings (2 mm and 4 mm) and in most cases it increased with the decreasing diameter. Fractions of the i-phase in the individual castings were higher in the alloy with a higher content of manganese. Morphology of the i-phase depended on the content of the alloying elements and the cooling rate. Thus in the alloy with a higher mole content of the manganese (4.5 at. % Mn) i-phase possessed the dendritic form, while in the alloy with lower manganese content (1.8 at. % Mn) it was in form of needles, which were thicker at the tips. With the distance from the edge of the sample (with decreasing cooling rate) the dendrites grew, the primary branches were thicker and the secondary branches started to grow also. In the case of the needle morphology, the length and the thickness of needles grew with increasing distance from the sample edge. Izvleček: V članku je obravnavana konstitucija dveh zlitin Al-Mn-Be-Cu in vpliv ohlajevalne hitrosti nanjo. Opravljene so bile ocene ohlajevalnih hitrosti z merjenjem sekundarnih dendritnih razdalj in izračunov toplotne bilance strjevanja. Z računalniškim programom, ki je del svetlobnomikroskopske opreme, so bili določeni površinski deleži faze i v ulitkih. Ugotovljeno je bilo, da je pri ohlajevalnih hitrostih med 500 K s-1 in 1350 K s-1 prednostna tvorba kvazikristalne faze (zlasti v obliki kvazikristalnega evtektika (aAl + faza i)). Poleg kvazikristalne faze i v okviru heterogenega zloga se le-ta pojavlja tudi kot primarna faza. Delež faze i se spreminja glede na premer ulitih valjčkov (2 mm in 4 mm) in v povprečju raste s padajočim premerom. Deleži faze i v posameznih ulitkih so višji pri zlitini z višjim deležem mangana. Morfologija faze i se spreminja v odvisnosti od vsebnosti zlitinskih elementov in hitrosti ohlajanja. Tako je v zlitini z višjim deležem mangana (4,5 at. % Mn) faza i prisotna večinoma v dendritni obliki, medtem ko je v zlitini z nižjo vsebnostjo mangana (1,8 at. % Mn) v obliki iglic, ki so na koncu odebeljene. Z oddaljenostjo od roba vzorca, torej v odvisnosti od ohlajevalne hitrosti, pa dendriti rastejo, primarne veje se debelijo, sekundarne začno rasti. Pri igličasti morfologiji pa dolžina in debelina iglic z oddaljenostjo od roba vzorca rasteta. Key words: quasicrystals, microstructure, aluminum alloys Ključne besede: kvazikristali, mikrostruktura, aluminijeve zlitine Introduction Quasicrystals were first discovered by D. Shechtman in the year 1984 in an Al86Mn14 alloy.[1] They are considered to be the third state of matter in addition to the crystal and amorphous state. The atoms are ordered but with non-crystallographic rotational symmetry and without three-dimensional perio-dicity.[2] Quasicrystals have until now been found in more than a hundred binary and ternary metallic systems. About half of them are metastable and can only be obtained by rapid solidification techniques, such as melt spinning.[3] In binary Al-Mn alloys the metastable quasicrystalline phases form only at higher cooling rates. In some cases the addition of the third element (e.g. Be) causes the formation of quasicrystalline phases in Al-Mn alloys already at lower cooling rates,[4] that is why it was added in our case. Copper was selected since it has a good solubility in aluminum. In this paper the constitution of two Al-Mn-Be-Cu alloys was investigated (in as-cast state) mostly to establish the influence of the addition of beryllium and copper during casting with lower cooling rates. The emphasis was on the effect of cooling rate on the constitution of the Al-Mn-Be-Cu alloys. Materials and methods Materials Two different alloys were prepared (Table 1) using master alloys AlBe5.5, AlCu50 and AlMn20 and pure aluminum (Al 99.9). Major difference between the two Al-Mn-Be-Cu alloys was the content of manganese (4.5 at. % Mn and 1.8 at. % Mn). Synthesis and casting were performed in vacuum. Casting was done in water chilled copper mould at around 750 °C. Castings were cylindrical in shape with different diameters ((2, 4, 6 and 10) mm). By that it was possible to estimate the influence of the cooling rate and chemical composition on the formation, fraction and morphology of the quasic-rystalline phase, which in our case was i-phase. In this article only the smaller diameters (2 mm and 4 mm) of the two alloys are discussed, since the cooling rates were sufficient for the formation of more significant amounts of the i-phase. Methods for estimating the cooling rates Cooling rate was not monitored during the casting procedure. Instead we performed measuring of DAS (dendrite arm spacing) using LOM and SEM images. Measurements were performed with the Axio Vision software from the edge to the middle of the sample. According to reference[5] the cooling rates were evaluated through DAS measurements. Measurements were performed for the 62MCuKO alloy, while that was not possible for the 22MCuKO alloy because the morphology of the mi-croconstituents was not dendritic. Ad-ditionaly to DAS measurements heat calculations were carried out, where the unsteady transport of the heat was taken into account. For the calculations the approximate method - Lumped-System analysis - was used. Microstructural characterization Microstructural characterization of the two alloys was performed by light optical microscopy (LOM - ZEISS Axio Imager.Alm) and scanning electron microscopy (SEM - JEOL JSM-5610, JEOL JSM-5800 and JEOL JSM-7600F) in combination with the energy dispersive X-ray spectroscopy (SIRI- Table 1. Chemical composition of alloys in atomic fractions, x/% Alloy x(Al) x(Mn) x(Be) x(Cu) 62MCuKO 92.4 4.5 1.6 1.6 22MCuKO 95.4 1.8 1.2 1.5 US 10/SUTW (Gresham Scientific Instruments) and Oxford Isis 300). The specimens were prepared and etched using a solution of the distilled water and NaOH for both analytical methods, LOM and SEM. By the EDS analysis we determined only the amounts of the aluminum, manganese and copper within the individual phases, while for the beryllium that was not possible. Beryllium cannot be analyzed using EDS because it is a lightweight chemical element. Estimating the fraction of the i-phase in individual castings Determination of the fraction of the i-phase was carried out by a computer software Axio Vision and imported SEM images, which were captured from the sample edge towards the center of the sample. Selection of the SEM images was due to the good contrast between the different phases when using backscattered electrons. Principle of determining fraction of the i-phase with Axio Vision software is the separation of the phases by different colors. Results and discussion Alloy 62MCuKO Casting of diameter 2 mm (alloy 62MCuKO) In addition to the aAl matrix, phases present in the microstructure were: den- dritic i-phase within the quasicrystalline eutectic and the 9-Al2Cu phase as part of the (aAl + 9-Al2Cu) eutectic (Figure 1). From the edge to the center of the sample the dendrites of the i-phase grew and were more branched. In some places i-phase occured as a primary phase. In some cases the i-phase within the quasicrystalline eutectic was very fine (Figure 2). Phase aAl in average contained atomic fractions 98.0 % Al, 1.0 % Mn and 0.9 % Cu (Table 2), where the copper meant a high probability of 9-Al2Cu precipitates. Average composition o f i-phase was (Table 2): 82.7 % Al, 15.6 % Mn and 1.7 % Cu, which is quite similar to the results in reference.[6] It was found that the i-phase was also present in the center of the sample which indicated that the cooling rate was sufficient for its formation (refer-ence[7] states, that the critical cooling rate for the formation of the i-phase is 500-520 K s-1). Therefore an assessment of the cooling rate on the edge and the centre of the sample was carried out through DAS measurements and the heat calculations (Table 3). Assessed cooling rate via DAS measurements was: on the edge of the sample 1 350 K s-1 and in the middle of the sample 1 000 K s-1. Heat calculations of the edge of the sample (1 296 K s-1) well confirm value of 1 350 K s-1, while the heat calculations for the middle of the sample (1 036 K s-1) are decisive only when the average dT/dt is taken into account. Both methods for determining the cooling rate are suitable in the case of the sample of 2 mm in diameter because all SEM and LOM images confirm the existence of the i-phase in the middle of the sample (in addition to the presence on the edge of the sample). Figure 1. Microstructure of casting with 2 mm diameter (62MCuKO alloy) - SEM (BSE) The cooling rate was the highest on the edge of the sample, therefore it was expected that there will be the highest fraction of the i-phase. Measurements of the fraction showed that the average fraction on the edge of the sample was 26.7 % and in the middle of the Figure 2. Very fine i-phase within the qua-sicrystalline eutectic - SEM (BSE) Table 2. EDS analysis of the phases in the casting with 2 mm diameter (62MCuKO alloy); mass fractions w/% and atomic fractions x/% w(Al) w(Mn) w(Cu) x(Al) x(Mn) x(Cu) Phase 1 96.23 1.69 2.08 98.25 0.85 0.90 «Al 2 96.51 2.15 1.34 98.34 1.08 0.85 «Al 3 69.19 27.63 3.17 82.26 16.13 1.60 i-phase 4 71.74 24.92 3.61 83.85 14.36 1.80 i-phase 5 68.83 27.71 3.47 82.03 16.22 1.76 i-phase Table 3. Results of the heat calculations for the casting with 2 mm diameter (62MCuKO alloy) Solidification start t/s Solidification finish t/s Solidification time t/s Max. dT/dt K s-1 Average dT/dt K s-1 D = 2 mm (a = 2 500-1 700 v max W m-2 K-1) 0.16 0.76 0.6 1 296 1 036 sample it was 26 %. The difference is small probably due to the short size of the casting and due to the very small difference in time when the edge and the middle of the sample solidified. Casting of diameter 4 mm (alloy 62MCuKO) The microstructure consisted of the aAl matrix, small amount of 9-Al2Cu phase and dendritic i-phase (Figure 3). Dendrites had thick primary branches but they already grew the secondary ones. In the middle of the sample all dendrite branches were thicker, larger and longer and it was difficult to distinguish primary branches from the secondary. The i-phase in average contained atomic fractions 83.9 % Al, 13.2 % Mn and 1.2 % Cu (Table 4). Average composition of aAl phase was (Table 4): 97.8 % Al, 1.0 % Mn and 1.2 % Cu. Again the content of copper in the matrix indicated presence of the 9-Al2Cu precipitates. In addition to the microstructural characterization the estimations of the cooling rate on the edge and in the middle of the sample were made. The cooling rate on the edge of the sample was approximately 800 K s-1 according to DAS measurements and about 650 K s-1 according to the heat calculations (Table 5). In the middle of the sample this estimations were 600 K s-1 and 520 K s-1, respectively. It should be noted that the estimated values of cooling rates through DAS measurements are approximate, since dendritic morphology in some places was not representative. But from the SEM images it was obvious that the i-phase was present in the middle of the sample so the cooling rate should be at least 500 K s-1 in the middle of the sample (according to reference [7]). The calculated values were slightly lower that the cooling rates obtained from measurements of the DAS but in the same order of magnitude. The problem was that it is virtually impossible to determinate the accurate heat transfer coefficient which strongly depends on the given conditions of the casting. Furthermore, the model predicts the cooling of the sample without a temperature gradient in its cross-section. Influence of the cooling rate on the formation of the i-phase is crucial. Fraction of the i-phase is also dependent on the cooling rate, so the measurements Figure 3. Microstructure of casting with 4 mm diameter (62MCuKO alloy) - SEM (BSE) Table 4. EDS analysis of the phases in the casting with 4 mm diameter (62MCuKO alloy) w(Al) w(Mn) w(Cu) x(Al) x(Mn) x(Cu) Phase 1 95.68 1.99 2.33 97.98 1.00 1.01 «Al 2 94.71 2.10 3.19 97.54 1.06 1.40 «Al 3 72.98 19.75 7.27 85.09 11.31 3.60 i-phase 4 72.44 22.33 5.24 84.60 12.81 2.60 i-phase 5 68.87 26.91 4.94 82.15 15.34 2.50 i-phase Table 5. Results of the heat calculation for the casting with 4 mm diameter (62MCuKO alloy) Solidification start t/s Solidification finish t/s Solidification time t/s Max. dT/dt K s-1 Average dT/dt K s-1 D = 4 mm (a = 2500-1700 v max W m-2 K-1) 0.202 1.349 1.147 648 516 of the fraction of the i-phase on the edge and in the middle of the sample were carried out. From the edge of the sample towards the center values decreased from 28.6 % to 26.5 %, which is consistant with the decreasing cooling rate. Alloy 22MCuKO Casting of diameter 2 mm (alloy 22MCuKO) In addition to the aAl matrix. the primary i-phase was present (Figure 4) along with the i-phase within the qua-sicrystalline eutectic. There were traces of the 9-Al2Cu phase as well. The primary i-phase formed a 0.5 mm thick rim which extended from the surface into the specimen. The references[6, 7] also report the existence of only those phases. Quasicrystalline eutectic (aAl + i-phase) had a typical skeletal form, made of thin needles which were thicker at the end. From the edge of the sample to the center the skeletons of i-phase and 9-Al2Cu grew and interspaces were getting smaller. The matrix aAl in average contained atomic fractions 96.5 % Al, 2.3 % Mn and 1.3 % Cu. The EDS measurements were not performed for the i-phase, while the particles were too fine and small. Since the morphology of the i-phase was not dendritic, the DAS measurements were not possible. Cooling rates were estimated on the basis of the previous measurements and estimations (62MCuKO alloy), since the diameter was the same. It was assessed that the cooling rate on the edge of the sample was about 1 300 K s-1 and in the middle of the sample around 1 000 K s-1. That is quite possible considering the presence of the i-phase in the whole cross-section of the sample. The 22MCuKO alloy contained considerably less manganese, therefore it was expected that the fraction of the i-phase in these castings will be lower than in the previously discussed castings (62MCu-KO alloy). Fraction of the i-phase was on the edge of the sample 8.7 % and in the middle 8.4 %. Once again the difference in the fractions between the edge and the middle of the sample was small, but it was large in comparison with the casting of the same diameter but different chemical composition. Casting of diameter 4 mm (alloy 22MCuKO) Casting of 4 mm diameter had microstructure composed of matrix aAl and two binary eutectics (aAl+ i-phase) and (aAl + 9-Al2Cu) (Figure 5). Primary i-phase was not detected like in the pre- vious sample. Towards the center of the sample dendrite networks grew. Considering the typical morphology of the quasicrystalline eutectic and due to the smallness of i-phase only EDS measurements of the matrix aAl were carried out. In average the aAl phase contained atomic fractions 96.4 % Al, 1.5 % Mn and 2.1 % Cu. Because of the absence of the dendrite morphology of the i-phase it was impossible to estimate cooling rate via DAS measurements. The estimates of the casting of the same diameter (4 mm) were taken; on the edge of the sample around 800 K s1 and in the center 600 K s-1. In addition to the value of 800 K s1, value of 600 K s1 in the middle of the sample is reasonable, since the i-phase was present also in the entire area of the sample's center. Differences in cooling rate at the edge and the center of the sample should be Figure 4. Microstructure of casting with 2 mm Figure 5. Microstructure of casting with 4 mm diameter (22MCuKO alloy) - SEM (BSE) diameter (22MCuKO alloy) - SEM (BSE) evident from measurements of fraction of the i-phase on the edge and in the middle of the sample. Average fraction of the i-phase on the edge of the sample was 6.1 % and then it increased (to 10.1 %), but at some point it started to decrease. In the middle of the sample fraction was 8.5 %. Obviously the points of measurements were not selected appropriately. Conclusions Based on the results of the constitution research, estimated cooling rates and fraction measurements of the i-phase, the following can be concluded: • the addition of the beryllium and copper enables the formation of the quasicrystalls at lower cooling rates than that of the casting processed via melt spinning (where the cooling rates are of the order of 106 K s-1) • in addition to the appropriate chemical composition the main influence on the fraction (shape, size distribution and size) of the i-phase is due to the cooling rate • with increasing cooling rate the fraction of the i-phase increase in general, but the additional further studies and measurements are necessary • i-phase dendrites in the case of the dendrite morphology with decreasing cooling rate grow, the primary branches are thicker and in some places already the second start to grow. At the highest cooling rate (edges of the castings with diameter of 2 mm) the i-phase is very small and fine, therefore it is difficult to distinguish its morphology i-phase occurs both in primary form such as the part of the quasicrystalline eutectic method used for the heat calculations is appropriate for estimating the cooling rate in case of the smaller samples (diameters of 2 mm and 4 mm), as the values agree well with the reference[7] and measurements DAS in all the samples inevitable 9-Al2Cu phase occurs. The desire is to eliminate and dissolve it all, which would additionaly contribute to the hardening when heat-treated. References [1] ZuPANIC, F., BoNCINA, T., SuSTERSIC, B., Anzel, I., Markoli, B. (2008): Microstructure of Al-Mn-Be melt-spun ribbons. Mater. Charact., 59. pp.1245-1251. [2] ROZMAN, N., ZuPANIC, F., BoNCINA, T., Grooger, W., Gspan, C., Hofer, F. (2009): Analytical TEM of Al-Mn-Be-Cu alloy, Materials Science, 3. pp. 205-206. [3] Steurer, W., Deloudia, S. (2008): Fascinating quasicrystals. Acta Crystallographica Section A. A64, pp. 1-11. [4] Bončina, T. (2006): Karakterizacija kvazikristalnih zlitin Al-Cu-Fe in Al-Mn-Be. Magistrsko delo. Ljubljana: Univerza v Ljubljani 2006; 72 p. [5] Spaic, S. (2002): Fizikalna metalurgi- ja. Zgradba kovinskih materialov. Strjevanje kovinskih talin. Ljubljana: Univerza v Ljubljani 2002; 328 p. [6] Bončina, T. (2010): Stabilization and identification quasicrystal- line phases in alloy of Al-Mn-Be. Ph. D. Ljubljana: University of Ljubljana 2010; 143 p. [7] Rozman, N. (2011): Razvoj visokotrd- nostnih aluminijevih zlitin s kvazikristali. Ph. D. Maribor: Univerza v Mariboru 2011; 143 p. [8] Strekelj, N. (2011): Influence of the cooling rate on the constitution of the Al-Mn-Be-Cu alloys. Diploma work. Ljubljana: University of Ljubljana 2011. 78 p. Development of intelligent knowledge-based computing environment for controling the proces parameters and nonmetalic inclusions in steels Razvoj inteligentnega, na znanje oprtega računalniškega okolja za kontrolo procesnih parametrov in nekovinskih vključkov v jeklih Uroš Krušič1-*, Milan Terčelj2, Goran Kugler2 & Iztok Peruš2 1Metal Ravne, d. o. o., Koroška cesta 14, SI-2390 Ravne na Koroškem, Slovenia ^University of Ljubljana, Faculty of Natural Sciences and Engineering, Aškerčeva 12, SI-1000 Ljubljana, Slovenia Corresponding author. E-mail: uros.krusic@metalravne.com Received: October 13, 2011 Accepted: December 6, 2011 Abstract: An intelligent knowledge-based computing environment for con-troling the steel production is proposed. CAE non-parametric mathematical model was developed based on the measured industrial data. Analysis of the obtained results reveals that there is a strong correlation between chemical composition of melts, Al blocs for dezoxida-tion and different supplements added at various stages, and nonmetal-ic inclusions. Relatively small number of input parameters taken into account in the existing model resulted in large scatter of the obtained results. Use of a higher number of input parameters will reduce the scatter and improve the prediction. It is evident that the standard ISO 4967 systematically overestimates some types of nonmetalic inclusions, which may be the result of a subjective human assessment or deliberately conservative estimate. The nonmetalic inclusions can be most effectively influenced at the early stages of the EAF process. As there is still long way to sufficiently describe the whole phenomenon of nonmetalic inclusions in steels, the results presented in this study are very promissing and they will eventualy lead us to the better models in the future. Izvleček: Predlagano je inteligentno, na znanje oprto računalniško okolje za kontrolo proizvodnje jekla. Na podlagi izmerjenih industrijskih podatkov je bil razvit neparametrični matematični model CAE. Analiza dobljenih rezultatov je pokazala, da obstaja močna korelacija med kemično sestavo šarže, Al bloki za dezoksidacijo in drugimi dodatki, dodanimi v različnih fazah procesa, in nekovinskimi vključki. Zaradi relativno majhnega števila upoštevanih vhodnih parametrov je raztros rezultatov dokaj velik. Upoštevanje večjega števila vhodnih parametrov bo zagotovo zmanjšalo raztros in izboljšalo napovedi. Očitno je, da standard ISO 4967 sistematično precenjuje nekatere tipe nekovinskih vključkov, kar je lahko posledica subjektivnega vrednotenja posameznikov ali pa namerno bolj konservativne ocene. Na nekovinske vključke lahko najbolj učinkovito vplivamo v zgodnjih fazah EAF-procesa. Čeprav je pred nami še dolga pot, preden bo mogoče v celoti opisati fenomen nekovinskih vključkov, so rezultati, predstavljeni v tem prispevku, zelo spodbudni in nas bodo v prihodnosti pripeljali do boljših modelov. Key words: steel production, SQL database, non-parametric models, CAE neural network, nonmetalic inclusions Ključne besede: proizvodnja jekla, SQL-baza podatkov, neparametrični modeli, nevronska mreža CAE, nekovinski vključki Introduction Production of steel, which includes many intermediate stages with high energy consumption, is a complex and costly procedure. Optimization of steel manufacturing process is therefore highly desirable. It can be achieved through better in-depth understanding of various influential parameters which determine the technological path of material in the production process. The problem is extremely complex due to the large number of influential parameters and consequently there is still lack of useful comprehensive solutions in the everyday steel production. Luckily, artificial intelligence and modern information and communication technologies now offer better opportunities for solution of this problem. In recent years we have witnessed an intensive development of both physical and metallurgical models which can adequately describe various processes taking place in steels during their thermo-mechanical processing as well as solutions in the field of artificial intelligence and optimization methods, which are possible by means of the modern computer technology. Numerous publications demonstrate that the methods of artificial intelligence provide a set of tools with great practical value for complex industrial processes.[1-5] Range of applications of artificial intelligence methods in materials research is wide.[6-10] Practical applications can be found both in research of metallic materials in virtually all phases of their production, such as casting, rolling, forging, heat treatment, etc.[1016] Especially popular are applications of neural networks, which are becoming an indispensable component of such systems for manufacturing automation and IT solutions that are designed to process control of metallurgical processes.[1719] Recently, Fazel-Zarandi and Ahmadpour[2] have used neural networks in developing expert system to control the parameters of electric arc furnaces in steel by means of a variety of independent modules, which coordinated the operation with regard to other modules. Zhou[3] used the dynamic neural networks and computer vision to predict the quality of the sintered products. Badheshia and colleagues used the methods of artificial intelligence in the development of materials and to find relations between the various parameters of their production. [6 8, 15, 16] Reviewed literature reveals that neural networks are often used to find the complex relations between the large number of influential parameters within individual processes. Research which address the entire manufactur- ing process or, where the method of artificial intelligence would be coupled with optimization methods that would allow the search of optimal values of influential parameters, are very rare. In this paper we present briefly the results of research which was focused on the development of intelligent knowledge-based computing environment for controling and optimizing the real industrial steel production. Due to the complexity of the problem only the solutions of aquirying and managing information from real steel production, development of non-parametric model and analysis of underlying trends in the formation of nonmetalic inclusions, defined by different standards, are presented. It solution for information management AND optimization IN STEEL PRoduction General In practice, a lot of the optimization in steel-making process is still based on »trail and error« procedures and/ or expert knowledge of process engineers, who based on their empirical experience of tuning the process parameters control the production. From a theoretical point of view it is the most appropriate to describe the manufacturing processes by means of abstract mathematical models in order to to represent mathematical relationships. However, due to the problems in the real steel production mentioned in the introduction section, these processes can be most effectively simulated by analogue models based on electronic devices (computers) using the measured data. Whitin this there are two main problems: (1) data aquisition and mathematical presentation of data as well as expert knowledge and (2) development of appropriate analog models with modern computer technology. It is clear that measurements and mathematical models represent mutually in- terdependent components in optimizing steel production. Data acquisition in Metal Ravne has been implemented several years ago. DBSteel is an integrated software solution for process control in the steel production. It was implemented by Siemens VAI Metals Technologies. The solution enables comprehensive data management process, quality control, planning process, steel production control per charge, networking and communication with the ERP (Enterprise Resource Planning) systems and to generate various reports. Figure 1. Schematic presentation of the main building blocks of DBSteel integrated software solution. Client - server environment The server part consists of a High-Availability (HA) cluster,[20] nine processing computers and fifteen workstations (Figure 1). HA cluster is based on IBM BladeCenter™ technology and is composed of three physical servers, where completely identical combination of 64-bit operating system Windows Server 2008 R2 and relational database Microsoft Windows SQL Server 2008 R2 is installed. All three servers connected into a HA cluster use database mirroring technique. One of the servers plays the role of principal database server, another server is a mirror site, while the third server provides a smooth transition to the secondary (MIRROR) server in the event of principal server failure. Processing computers Figure 1 shows different levels and connection between those levels of the DBSteel integrated software solution. At the zero-level there is a production unit. First level consists of programmable logic controllers (PLC's), e.g. for weighing system. PLC is special computer, that is used for automation of electromechanical processes and designed to operate in heavy industrial conditions (vibration, electrical noise and dust resistant). Processing computers on the second level control/support the production process, while the workstations on the third level enable the planning of charges. Processing computers control the following operations: • PC-scrap supports the preparation of inlay material (scrap metal, overhead cranes, computerized scales). By using the specific software the module operator determines the composition of the charge, reads data from the scale, records inlay material consumption, etc. • PC-MELT and PC-LEGI EAF support the melting and then alloying process on the electric arc furnace (EAF), respectively. The software is intended to record the various events (for example, charge start time), to record consumption of electricity, recording the results, obtained with CELOX device, etc. • PC-REFINE VD in PC-LEGI LF support the process of secondary treatment of steel in a vacuum ladle furnace (LF/VD). • PC-ESR LIGHT supports the electro slag remelting (ESR). • PC-LAB 1 in PC-LAB 2 are special-purpose computers for supporting the implementation of chemical analysis in the plant and a chemical laboratory. PC-LAB 1 is connected with the spectrometer at the plant site while PC-LAB 2 is connetced with two spectrometers located in the chemical laboratory. • PC-SPARE is a spare computer that is ready to replace any of the above eight processing computers in case of their failure. Data structure Physical partition of steel-making process is followed by the similar structure of the DBSteel database. Description of the tables revelas that each process is characterized by a specific prefix. Thus, for example, table, which is linked to the processes in the electric arc furnace, gets the prefix »EAF«, table which is linked to vacuum furnaces and ladle gets the prefix »LF/VD«, etc. The key tables are: • The main table of charges at the EAF • The table of events at the EAF • The main table in charges at the LF/VD • The table of events at the LF/VD Structure of the databases is well documented. After applying different scripts we got one database for further processing. It contains numerical empirical data which allow a mathematical description of the various phenomena in the steel production process (see Chapter 3). Matemathical tools for modeling of manufacturing processes Development of appropriate mathematical models is necessary to optimize the steel production at high-tech level. Today, in addition to the usual physical models, the models which exploit the principles of artificial intelligence, especially neural networks, are widely used. Among the neural networks is the most common use of BP neural networks, which describe the phenomenon on the basis of measured data and obtained results. Unfortunately, the rate of learning for very complex problem with large number of parameters and with large database is relatively slow and depends on settings of the parameters of learning. An additional weakness of these networks for »real-time« production is that the BP network must be constantly retrained with new data supply in order to improve the optimization. We have therefore in this study decided to use CAE neural network, [21-23] which enables faster analyses and is significantly more robust. Mathematical modeling of inclusions by using cae neural network Any type of nonmetalic inclusion (i.e. type according to the ISO 4967 standard [24]) of a specimen (e.g. charge) is characterized by a sample of observations/experiments on N test specimens. The mathematical description of the observation/experiment on a single specimen is called a model vector. Consequently, the whole phenomenon can be described by a finite set of model vectors {X1,...., Xn ,..., XN } (1) It is assumed that the observation/experiment on one particular specimen can be described by a number of variables, which are treated as components of a model vector X„ ={bnl nli ' he c ■ UD nl nki ' "nM Bn ={bn P =, K- abnD } and Cn ={cni ^^ cnk CnM } B = {bi,..., b,..., Öd} and C = {c, ...,< ■k-> • tabase. By using the conditional probability density function, the optimal estimator for the given problem can be expressed as = I An ^ nk A = a,„ n=1 I a. } (2) a1nd an = (4) The vector Xn can be further composed of two truncated vectors B and C (2*Y exp w -Z i=i (b - bnl )2 2w2 (3a) Vector Bn is comptl^m^ntary to vector C and therefore their concatenation n yields the complete data model vector Xn. The prediction vector, too, is composed of two truncated vectors, i.e. the given truncated vector B and the unknown complementary vector C (3b) The problem now is how an unknown complementary vector C can be estimated from a given truncated vector B and the model vectors {X, •••, X , 1n ..., Xw}, i.e. how the inclusion Ad can be estimated from known input parameters and the available data in the da- where c, is the estimate of the k-th output variable, cnk is the same output variable corre sponding to the n-th model ve ctor in the database, N is the number of model vectors rn the database, b , is 5 nl the l-th input variable of the n-th model vector in the database (e.g. bnl, bn2, b3, ..., bnL), and bj is the l-th input variable corresponding to the prediction vector. D is the number of input variables, and defines the dimension of the sample space. Note that Equation (4) requires the input parameters to be normalized, generally in the range from 0 to 1 if we want to use the same width w of the Gaussian function for all of the input variables (dimensions). The Gaussian function is used for smooth interpolation between the points of the model vectors. In this context the width w is called the "smoothing" parameter. It determines how fast the influence of data in the 1 sample space decreases with increasing distance from the point whose coordinates are determined by the input variables of the prediction vector. A general application of the method does not include any prior information about the phenomenon. Equation suggest that the estimate of an output variable is computed as a linear combination of truncated vectors C, while the — coefficients A are non-linear functions n of all the input variables (Bn) in the database. Thus, non-linear phenomena can be modeled by this ap1pro-ach. The weights An depend on the similarity between the input variables of the prediction vector, and on the corresponding input variables pertinent to the1 model vectors stored in the database. Consequently, the unknown output variable is determined in such a way that the computed vector, composed of given and estimated data, is most co-sistent with the model vectors in thke database. An intermediate result in the computational process is the estimated probability density function p of known input variables 1 N A T P = — 7 N i 1 v n=1 more steel ingots (relatively to the total number of test steel ingots in database) with input parameters similar to the input parameters of the prediction vector exist in the database. In the case of using CAE for the estimation of the inclusions, which depends on, e.g. three input parameters, namely content of oxygen (O) and sulphur (S) in charge on one side and the amount of Al blocs (Al) relative to the total weight of charge on the other side, the equation for a can be written as: 1 an = (2n) 3/2 3 w exp (6) (5) It helps to detect the possible less accurate predictions due to the data distribution in the database and due to local extrapolation outside the data range. The higher the p value is, the (O-Oj + (S - Si )2 +( Al - Aln )2 2w2 Results and discussion Due to the complexity of the entire process of steel-making, in this paper only the problem of nonmetalic inclusions is studied and discussed. Note, that all chemical analyses for different charges, along with information about the plant where the sample was taken from, the grade of the sample, sample number, etc. are stored in DBSteel database as separate entries. Consequently, sample numbers in the range between 1 and 4 indicate charge sample taken during steel processing at EAF, while sample numbers in the range between 5 and 9 indicates charge samples taken during steel processing at LFVD. Standards used for the determining the inclusion content of steel Purity of steel is defined by the amount of nonmetallic inclusions. Nonmetallic inclusions can be found practically in any steel. The quantity, chemical composition and distribution of inclusions depends on the manufacturing process of steel. In general, nonmetallic inclu- ISO - K (N'lUKl. U-IIU1] sions lower the quality, workability and mechanical properties of steel. Different test methods for determination of content of nonmetalic inclusions exist (e.g. standards ISO 4967 [24], ASTM M45 [25] - ISO, DIN 50 602 [26] - M and K method). They cover a number of recognized procedures (macroscopic and microscopic methods) for determining the nonmetallic inclusion content of wrought steel. The methods K - M ii*>ioHTi"t7em Figure 2. Correlations between different standards, taking into consideration only the incidence but not the absolute value of the inclusions. Nand Nr indicate the number of charges and number of inclusions, respectively, used in the analysis. are primarily intended for rating inclusions. Constituents such as carbides, nitrides, carbonitrides, borides, and intermetallic phases may be rated using some of the microscopic methods. However, in order to model the phenomena of inclusions mathematicaly (i.e. developing of the non-parametric model), a mathematical representation of such knowledge is neeeded. To this end, we first look for correlations between different standards in order to identify the most appropriate method for modeling inclusionss. The purpose of this study was not to understand precisely the methods and physical background of each type, size and amount of inclusions, but the mathematical formalization of knowledge, contained in different standards. DBSteel database contains information of the same charges that were inspected using two or three standars simultaneously. Correlations between different standards, taking into consideration only the incidence but not the absolute value of the inclusions, were shown in Figure 2. Note that in one charge different type of inclusions may appear (therefore Nr > N). Graphs indicate that the standards differ from each other and describe inclusions in different ways. Consequently, customers according to their needs, require consideration of inclusions using the desired standard. Figure 2 indicates that mapping from one standard to another is more reliable than vice versa (e.g. mapping ISO to K or ISO to M). Effectivness of the non-parametric CAE model The effectivness of the CAE model can be estimated by the average prediction error E,. It is defined for k-th output variable and can be determined with the "leave one out cross validation method". The method computes the prediction of k-th inclusion for every charge sample, whereas the predicted k-th charge sample is excluded from the database. With averaging of the absolute errors of predictions for all N charge samples Ek is calculated as: E cj N fj( Ck Cnk ) (7) where ck is the average of the known k-th outputs of all the model vectors cnk (charge samples) and cnk is the prediction of the measured value c , of the nh k-th output (inclusion of some type) of the n-th model vector (charge sample). Figure 3 shows the results of »leave one out cross validation method« for two different smoothing parameters. Studied is the inclusion of type Ad according to the ISO 4967 standard. In the CAE non-parametric model some important chemical elements (C, S, Si, Mn, ...), some most influential suplements (FeSi, FeCrC, ...), Al blocs and oxygen were w = 0.01, E, = 3.598 w = 0.05, E, = 3.492 Figure 3. Results of the »leave one out cross validation method« for inclusion type Ad, using two different smoothing parameters. taken into account. The obtained results reveal large scatter. It can be concluded that (1) there is high uncertainty in the (subjective) determination of inclusions, and (2) in order to reduce the scatter in the predictions more influential input parameters must be taken into account. Nevertheless, existent model can give a sound qualitative relations between input parameters and different types of inclusions, as shown in the next section. The optimal smoothing parameter was found to amount around 0.05, however, in order to reveal clear qualitative relationships somewhat larger value was used (e.g. 0.1 or 0.2). Influence of important parameters on inclusions according to the ISO 4867 standard Due to the limited space only a few selected results are presented and discussed in this paper. In order to show different behaviour in nonmetalic inclusions, results for four different types, namely Ad, At, Dd and Dt are shown and discussed. Figure 4 reveals that higher content of sulphur increases nonmetalic inclusions of type At and Ad. Its influence is unfavourable, but more for inclusions of type Ad. Influence of oxygen (Figure 5) is very important and amounts up to 5 % of total contribution. In general, more oxygen increases nonmetalic inclusions of type Dd, whereas the influence of Al blocs is relatively small. Note, that use of smaller value of smoothing parameter reveals localy larger influence of Al blocs which may be taken into account when optimization is applied. Figure 6 reveals the influence of Moreover, in absolute terms, both oxygen and Al blocs on nonmetalic influences are small and relatively inclusions of type Dt. Influence of insignificant. both parameters is now reversed. Figure 4. Influence of sulphur (S) after taking first (left) and second (right) charge sample on nonmetalic inclusions At and Ad. w = 0.2 w = 0.05 Figure 5. Influence of Al blocs and oxygen (tapping weight in mass fractions w/%) on nonmetalic inclusion Dd after taking second charge sample. 0.2 0.3 AI blocs w = 0.2 0.2 0.3 AI blocs w = 0.1 Figure 6. Influence of Al aluminium blocs and oxygen (tapping weight in mass fractions w/%) on nonmetalic inclusion Dt after taking second charge sample. In general, it can be observed the important impact of different input parameters at different stages, suggesting that a specific physical phenomena is associated with a specific type of the nonmetalic inclusion. The results also suggest that some types of inclusions can be influenced more efficiently at earlier stages and some types of inclusions at later stages of the production process. Conclusions In the paper the development of intelligent knowledge-based computing environment for controling the processes in the real industrial steel production was presented. The problem addressed is extremely complex due to the large number of influential parameters. Application of modern information and communication technologies and some artificial intelligence methods enables us to develop and propose one possible solution to this problem. Within this study the CAE non-parametric mathematical model of nonmetalic inclusions was developed. Analysis of the obtained results lead us to the folowing conclusions: • There is a strong correlation between chemical composition of melts, Al blocs for dezoxidation and different supplements added at various stages, and nonmetalic inclusions. Relatively small number of input parameters (a limited number of elements of chemical com- position and a limited number of some most important suplements) taken into account in the existing model reveals large scatter of the obtained results. It is expected that use of a higher number of input parameters will reduce the scatter and improve the prediction. • It is evident that the standards ISO 4967 and ASTM E45 systematically overestimates the value of smaller At (between 0.5 and 1.5). This probably result from a subjective human assessment or deliberately conservative estimate of nonmet-alic inclusions by the producer. It should also be noted that the standard itself has shortcomings which may result in the above overesti-mations, when it tries to address a physical phenomenon, which is not linear. • The majority of nonmetalic inclusions can be most effectively influenced at the early stages of the EAF process (e.g. during the time of taking the first or second charge samples). But closer to the end of the process we approach the harder it become to influence the nonmet-alic inclusions. There is still long way to sufficiently describe the whole phenomenon of nonmetalic inclusions in steels. However, the results presented in this study show that the entire process can be fully mathematicaly described and then optimized in order to minimize the nonmetalic inclusions. Akcnowledgement The authors wish to thanks MSc. Rozman and other experts from Metal Ravne, whose help is greatly appreciated. One of the authors (U. Krusic) gratefully acknowledge the financial support of the European Union: »Operation part financed by the European Union, European Social Fund.« References [1] Schlang, M., Lang, B., Poppe, T., RuNKLER, T. & WEINZIERL, K. (2001): Current and future development in neural computation in steel processing. Control Engineering Practice, Vol. 9, pp. 975986. [2] Fazel Zarandi, M. H., Ahmadpour, P. (2009): Fuzzy agent-based expert system for steel making process. Expert Systems with Applications, Vol. 36 , No. 5, pp. 9539-9547. [3] ZHOU, S. M. (1995): Combining dynamic neural networks and image sequences in a dynamic model for complex industrial production processes. Expert Systems with Applications, Vol. 16, No. 1, pp. 13-19. [4] Canz, T. & Jagdale, S. (1995): De- cision support for manufacturing using artificial neural-networks. Journal of Materials Processing Technology, Vol. 52, No. 1, pp. 9-26. [5] D'Errico, G. E. (2001): Fuzzy con- trol systems with application to [13] machining processes. Journal of Materials Processing Technology, Vol. 109, No. 1-2, pp. 38-43. [6] Bhadeshia, H. K. D. H. (1999): Neu- ral Networks in Materials Science, ISIJ International, Vol. 39, pp. 966-979. [14] [7] Bhadeshia, H. K. D. H. (2009): Neu- ral networks and information in materials science. Statistical Analysis and Data Mining, Vol. 1, pp. 296-305. [8] Bhadeshia, H. K. D. H. 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Determination of hot workability and processing maps for AISI 904L stainless steel Vroča preoblikovalnost nerjavnega jekla AISI 904L ter izračun procesnih map Peter Fajfar1-*, Boštjan Bradaškja2, Boštjan Pirnar2 & Matevž Fazarinc1 University of Ljubljana, Faculty of Natural Sciences and Engineering, Aškerčeva 12, SI-1000 Ljubljana, Slovenia 2Acroni, Ltd., Research and Development, Cesta Borisa Kidriča 44, SI-4270 Jesenice, Slovenia Corresponding author. E-mail: peter.fajfar@omm.ntf.uni-lj.si Received: November 8, 2011 Accepted: December 19, 2011 Abstract: Hot workability and microstructural evolution in dependence to deformation conditions for AISI 904L, super-austenitic stainless steel has been investigated on the basis of hot compression tests conducted on a thermo-mechanical simulator, Gleeble 1500D. Processing maps were calculated for various deformation conditions and correlated to the stability maps. An extensive microstructural analysis was conducted and a map of microstructures in correlation to deformation conditions was structured. This will all serve as a basis for better understanding of microstructural processes involved during the production of this steel. Izvleček: S termomehanskim simulatorjem Gleeble 1500D sta bila preiskovana preoblikovalnost v vročem in razvoj mikrostrukture superavste-nitnega nerjavnega jekla AISI 904L. Procesne mape in mape nestabilnosti so bile preračunane za različne stopnje deformacije, prav tako je bila izdelana tudi karta mikrostruktur v odvisnosti od preoblikovalnih parametrov. Taka analiza se uporablja za boljše razumevanje mikro-strukturnih procesov, ki se pojavljajo med vročim preoblikovanjem preiskovanega jekla. Key words: AISI 904L stainless steel, processing maps, hot workability, hot compression testing Ključne besede: neijavno jeklo AISI 904L, procesne mape, preoblikoval-nost v vročem, tlačni preizkusi v vročem Introduction AISI 904L is a non-stabilized, low-carbon, high-alloy superaustenitic stainless steel. These types of austen-itic stainless steels are known to have very high ductility, formability and exceptional toughness.[1-4] These characteristics are present through a wide temperature range as a result of a single phase structure. Because of the excellent corrosion resistance AISI 904L is manly used in highly aggressive environments.[5, 6] It has a very good oxidation resistance and retains strength at elevated tem-peratures.[7] The widespread of use is limited by the production costs on account of high nickel and molybdenum content and can reach high prices on the market compared to similar stainless steels. Thus the knowledge about the processes involved during hot deformation is of a great importance to minimize the processing costs and optimize the processing parameters and insure the uniform characteristics of the end product. The literature overview has shown that no studies of its hot deformation behavior and microstructural evolution during hot deformation were conducted. The goal of this study was to investigate the hot workability and microstructural evolution during hot deformation, as a basis for process optimization and better understanding of the microstructural processes involved during production of this steel. The uniaxially deformed specimens were metallographically examined using light optical microscopy (LOM). Gathered data from the experiments was used to calculate the processing maps for different logarithmic deformations and coupled with instability, which can be used in correlation with the microstructure maps to determine the optimal processing parameters during the production of this steel grade. Materials and methods The material used in this study is super-austenitic stainless steel AISI 904L. The higher content of copper in the AISI 904L enhances its corrosion resistance to strong reducing acids, e.g., sulphuric acid. Cylindrical specimens with an initial diameter of 8 mm and a height of 12 mm were machined from a 20-mm-thick, rolled, annealed plate, with its chemical composition listed in Table 1. Table 1. Chemical composition of the AISI 904L stainless steels in mass fractions, (w/ %) C Cr Ni Si Mn Mo N Cu P S AISI 904L 0.0074 19.45 23.31 0.25 1.56 4.19 0.069 1.41 0.019 0.001 A computer-controlled, Gleeble 1500D thermo-mechanical simulator was used for the hot, uniaxial, isothermal compression tests. To reduce the friction between the specimen and the tool, and to avoid their mutual welding, a graphite lubricant foil and a 0.05 mm tantalum foil, respectively, were used. The temperature was measured at the central part of the specimen, using a spot-welded S-type thermocouple. The testing was performed in the temperature range between 850 °C and 1 200 °C in 50 °C steps and at five different strain rates (0.001 s-1, 0.01 s1, 0.1 s1, 1 s1 and 5 s1). The specimens were heated to 1 200 °C with a heating rate of 10 K/s, followed by a 5 min soaking time at 1 200 °C. The temperature was subse- quently lowered at 3 K/s to the deformation temperature, where it was held for another 5 min before deformation. The maximum true strain was set to 0.9. After the deformation the specimens were rapidly quenched into water (quench time <1 s). True stress vs. logarithmic strain curves were calculated, taking into account the heat generated due to the high strain rates. The temperature rise was compensated for by applying the method proposed by Liu et al. [9] The deformed specimens were cut along the mid-plane and metallo-graphically prepared by grinding, polishing and electrolytic etching. Light optical microscopy was used to observe the microstructural features in each sample. a) b) Figure 1. True stress vs. strain curves for the AISI 904L material deformed a) with strain rates of 0.1 s-1 and b) at 1 000 °C. Results and discussion Compression tests Typical true stress vs. trues strain curves are presented in Figure 1 for strain rates of 0.1 s-1 and deformed at 1 000 °C, respectively. After the initial deformation hardening, softening behavior occurs. It is attributed to dynamic recrystallization (DRX), which was later on confirmed by microstructural analysis. The flow curves of the tests performed at higher temperature and lower strain rates (Figure 1a) show steady state behavior after initial softening. This effect is not present at the flow curves of samples deformed below 1 100 °C and at higher strain rate than 1 s-1. Processing maps With the use of data, gathered during compression tests, processing maps were calculated and the windows of instable deformations were calculated. Processing maps are developed on the basis of a dynamic material model (DMM) which has been suggested and widely used by the group of Prasad. [10-11] The processing maps of the material can be described as an explicit representation of its response to the imposed process parameters. It is a superimposition of the efficiency of power dissipation and an instability map. The work-piece under hot deformation conditions works as an essential ener- gy dissipater for this model. The constituent equation describes the manner in which energy (P) is converted at any instant into two forms, thermal energy (G) making temperature increase and microstructural change caused by transform of metallurgical dynamics (J), which are not recoverable. In general, most of the dissipation is due to a temperature rise and only a small amount of energy dissipates through microstructural changes. The power partitioning between G and J is controlled by the constitutive flow behavior of the material and is decided by the strain rate sensitivity (m) of flow stress as shown in the equation dJ s da sadlna Alga (1) dG a ds asdlns Algs where s is true deformation, s is true stress and S is strain rate. For an ideal dissipater it can be shown that both quantities J and G are equal in their amount, which means that m = 1 and J = J whereas the efficiency of power max J A dissipation n is given by: n = j 2m m +1 (2) The variation of power dissipation with temperature and deformation represents the relative value of energy dissipation occurring through microstructural changes. Microstructural changes, which include a dynamic recovery and dynamic recrystallization, are desired and values for efficiency of power dissipation n are in such cases in range 0.3-0.4. Lower values for n are less desired and often overlap with area of instable hot deformation (described bellow). Thus lower values for n refer to wedge cracking, void formation at hard particles, dynamic strain ageing and intercrystalline cracking, etc. High values of n refer to formation of new surface related to microcracking. The instability map is defined by a stability criterion for a dynamic materi al, whefe the differential quotient of its dissipative function has to satisfy an inequelity condition (Z), given by equation 3, to allow a stable flow. s V d ln(m / (m +1)) d lns + m > 0 (3) Areas with values for Z< 0 are considered as non-stable for hot deformation. These areas refer to adiabatic shear bands, flow localization, Luder's bands, etc. The maps, calculated for deformations of 0.1, 0.2, 0.4 and 0.6 are presented in Figure 2. They were calculated for the processing parameters applied during compression tests; for temperature range from 850 °C to 1 150 °C and strain rates 0.001 s-1 to 5 s-1. The dissipation maps are similar at various strains. The instable zone with Z < 0 (gray region) starts forming at defor- mation 0.1 in the temperature region around 1 050-1 100 °C, and strain rate of around 0.1 s-1. The instability region grows with the raise of deformation, covering a processing area of strain rates from around 0.01 s-1 to 1 s-1 and temperature range from 1 000 °C to 1100 °C. This processing area should be avoided; strain rates have to be high enough. From Figure 2 it is clearly seen that instability area overlaps with area with high value for n that although indicates on safe hot forming. Microstructural analysis Microstructures of deformed specimens were inspected in the center part of the specimen where the highest deformation was present during compression. The comparison of initial microstructure and the microstructure before deformation is presented in Figure 3. The initial microstructure shows a very evenly distributed, recrystallized microstructure with recrystallization twins present. The measured mean grain size was =80 mm. As expected, the grains had grown substantially in the pre-deformation heating stage. This is contributed to the minimization of surface energy through the increase of the grain size. The austenite grains contain annealing twins which are characteristic for the alloys with low stacking fault energy. 050 900 a) 950 1 000 1050 Temperature, TTC b) 950 1000 1050 1100 Temperature, 77°C 850 900 950 1000 1050 Temperature, 7/°C S 50 900 950 1000 1050 1100 d) Temperature, TTC c) Figure 2. Processing maps for AISI 904L stainless steel, at strains a) e = 0.1, b) e = 0.2, c) s = 0.4 and d) e = 0.6. a) ct '' * \\_if 1 / vCV b) as®» Figure 3. Microstructure of AISI 904L-stainless steel. a) as-received state, b) after soaking at 1 200 °C for 5 min and subsequent water quenching. Figure 4. Obtained microstructures after deformation at various strain rates and temperatures. Quenched after deformation to s = 0.9. The microstructural map, presented in dependence to processing parameters is presented in Figure 4. No recrys-tallized grains are to be found in the specimens deformed with higher strain rates (1 s-1 and 5 s-1) and below 950 °C. On the other hand, other specimens did recrystallize some partly, with only necklace type microstructure, with small recrystallized grains around deformed larger grains and some fully recrystallized. Appearance of necklace type microstructure indicates that DRX was not completed. The DRX necklace which follows dynamic nucleation usually takes place in a coarse-grained structure under deformation at low temperature or high strain rates. The region, marked with red border, shows the region where instability maps predicted low microstructural stability. It can be seen that the grain size is widely distributed, from very small recrystallized grains in necklace to large deformed primary grains. This could cause unstable deformation and as such should be avoided during processing. Conclusions Hot deformation behavior of AISI 904L-stainless steel was studied, using thermo-mechanical processing. From the gathered results, the following conclusions can be drawn: • After initial deformation hardening, softening mechanisms are observed, and steady state flow stress is achieved at the logarithmic strain of 0.9 below the strain rate of 1 s-1 and above 1 100 °C. • Processing maps, calculated on the basis of compression test show an instability region below 1 s-1 and between 1 000 °C and 1 100 °C. • Microstructural analysis showed a gradual increase of dynamically recrystallized material with a decrease of strain rate and increase of temperature. A fully recrystallized microstructure is found in specimens deformed with a strain rate of 0.001 s-1 and above 1 050 °C. • The comparison between results of the map of microstructural evolution and the processing maps are in good agreement, providing useful information for process guidance. • Highest values for efficiency of power dissipation were obtained around 1 050 °C and strain rate of 0.1 s-1. References [1] Belyakov, A., Miura, H. & Sakai, T. (1998): Dynamic recrystallization under warm deformation of a 304 type austenitic stainless steel, Material Science and Engineering, A, 255,pp.139-147. [2] Deghan-Manshadi, A., Barnett, M. R. & Hodgson, P. D. (2008): Re-crystallization in AISI 304 aus-tenitic stainless steel during and after hot deformation, Material Science and Engineering, A, 485, pp.664-672. [3] Frechard, S., Redjaimia, A., Lach, E. & Lichtenberger, A. (2008): Dynamical behavior and microstructural evolution of a nitrogen-alloyed austenitic stainless steel, Material Science and Engineering, A,480, pp.89-95. [4] Nemat-Nasser, S., Guo, W. G. & Kihl, D. P. (2001): Thermo-mechanical response of AL-6XN stainless steel over a wide range of strain rates and temperatures, Journal of Mechanical Physics of Solids, 49, pp.1823-1846. [5] Moayed, M. H. & Newman, R. C. (2006): Deterioration in critical pitting temperature of 904L stainless steel by addition of sulfate ions, Corrosion Science, 48, pp. 3513-3530. [6] IvERSEN, A. K. (2006): Stainless steels in bipolar plates—Surface resis- tive properties of corrosion resistant steel grades during current loads, Corrosion Science, 48, pp. 1036-1058. [7] Laycock, N. J. & Newman, R. C. (1998): Temperature dependence of pitting potentials for austenitic stainless steels above their critical pitting temperature, Corrosion Science, 40, pp. 887-902. [8] Stauffer, A. C., Koss D. A. & Mc- Kirgan, J. B. (2004): Mechanical Behavior - Microstructural Banding and Failure of a Stainless Steel, Metallurgical and Materials Transaction: A, 35A, pp. 1317-1324. [9] Liu, J., Chang, H., Wu, R., Hsu, T. Y. & Ruan, X. (2000): Investigation on hot deformation behavior of AISI T1 high-speed steel, Materials Characterization., 45, pp. 175-186. [10] Prasad, Y. V. R. K., Sasidhara, S. (1997): Hot Working Guide, Compendium of Processing Maps, ASM - International, OH, USA, 1997,pp.1-24. A comparison of parameters below the limit of detection in geochemical analyses by substitution methods Primerjava ocenitev parametrov pod mejo določljivosti pri geokemičnih analizah z metodo nadomeščanja Timotej Verbovšek1, * University of Ljubljana, Faculty of Natural Science and Engineering, Department for Geology, Aškerčeva 12, SI-1000 Ljubljana, Slovenia Corresponding author. E-mail: timotej.verbovsek@ntf.uni-lj.si Received: November 9, 2011 Accepted: November 25, 2011 Abstract: Paper focuses on the analysis of geochemical data with values below the limit of detection (LOD). Such values are treated as text and are difficult to use in further calculations of mean, standard deviation and other statistical parameters. To estimate several methods for substitution of values below the LOD with fractions of LOD (zero, LOD/2, LOD/V2, LOD and no data values), a large dataset of generated values with normal and lognormal distributions was tested for different percent of censoring from 1 % to 50 %, plus the censored data of five selected geochemical parameters. Results indicate that the best substitution method is as it produces the smallest er- rors. The greatest errors are found for substitution methods with zero or no data. This is valid both for normally and lognormally distributed data. Median is not affected by most methods for censoring level below 50 %. For real geochemical parameters, the interpretation is more complex. For datasets with low amount of censoring (NO3, O2), the errors are small. For others (Sr, F, Mn) the errors are larger, as several LODs exist for each parameter and the LOD is sometimes larger than the mean value. Izvleček: V prispevku je predstavljena in analizirana problematika geokemičnih podatkov pod mejo določljivosti (MD). Ti so obravnavani kot tekst in se s težavo uporabljajo v nadaljnjih statističnih izračunih (povprečje, standardni odklon ipd). Primerjane so bile različne metode nadomeščanja vrednosti pod MD s petimi deleži: nič, MD/2, MD/V2, MD in vrednosti brez podatkov. Sprva je bil analiziran velik nabor generiranih podatkov idealne normalne in lognormalne porazdelitve za različne stopnje okrnjenosti podatkov (od 1 % do 50 %), nato pa še pet izbranih geokemičnih parametrov. Rezultati kažejo, da je najboljše uporabiti metodo nadomeščanja z vrednostjo MD/V2, ker daje najmanjše napake, največje napake pa dajeta metodi nadomeščanja z nič ali brez podatkov. To velja tako za normalno in lognormalno porazdeljene podatke. Na mediano ne vpliva večina metod, če je okrnjenih manj kot 50 % podatkov. Za izmerjene geokemične parametre je interpretacija bolj zapletena. Za parametre z manjšim deležem okrnjenosti (NO3, O2) so napake majhne, za druge (Sr, F, Mn) pa večje zaradi različnih mej določljivosti za vsak parameter in nekaterih vrednosti MD, večjih od povprečja. Key words: hydrogeochemistry, limit of detection, statistics Ključne besede: hidrogeokemija, meja detekcije, statistika Introduction Parameters, calculated from geochemi-cal analyses, often lie below some limit which occludes true values. Such a limit is called limit of detection (LOD) or method detection limit (MDL) and is written with a symbol "<", i.e. "<0.005 mg/L". There are many reasons for laboratories to present the values below the limit, the most obvious being the non-ability of instruments to detect the low concentrations of parameters. Signal from the analyzed parameter can be too small for the instruments to discriminate it from the background noise and several other factors can influence the laboratory to report the values below the limit of detection (Lambert et al., 1991). Limit of detection is usually defined as the level at which a measurement has a 95 % probability of being different than zero (Croghan & Egeghy, 2003), but sometimes in the reports no indication at all is given what a detection limit is (Lambert et al., 1991). The major problem of such low reported values lies in further statistical analysis of data. First, low concentrations are reported as text values ("<0.005 mg/L") and consequently such values are not recognized as numbers in the analyses. Only in specialized databases (like AquaChem software) it is possible to enter and treat the values as the ones below the LOD. Second, the calculation of statistical moments (mean value, standard deviation ...) is problematic, as low values are truncated or censored. Such calcu- lations are critical when they are used to predict the water quality and one needs to report whether the concentrations of toxic elements lie below or above some critical level. Several methods exist to "replace" the unknown values below the LOD with such values that the committed errors are minimized when performing the statistical analyses (Chastain, 2007, Gilliom & Helsel, 1986, Glass & Gray, 2001, Gochfeld et al., 2005, Helsel, 1990, Helsel & Cohn, 1988, Helsel & Gilliom, 1986, Smith et al., 2006, Succop et al., 2004), and each method has some advantages and disadvantages: • Values below the limit of detection are replaced with a constant of zero (0). Calculated mean values are in this case lower than the real ones, as we create a set of artificially low numbers. This approach is not recommended. • Values are replaced with the values of limit of detection (LOD). Consequently, the mean values are higher than the real ones. This approach is also not recommended, as both methods represent the extreme possible values of true mean. • Values are replaced with some fraction of LOD. Usually, the replacement is performed with LOD/2 and LOD/V2 (Croghan & Egeghy, 2003). The error is much lower than in previous methods, and this approach is very common due to its simplicity. There is no agreement which substitution value is the correct one, for following reasons. Of a great importance is the distribution of data, as replacement with LOD/2 is by some authors (Hor-nung & Reed, 1990, Succop et al., 2004) recommended for normally distributed data and LOD/V2 for lognormal distribution. Another suggestion is to use the LOD/2 substitution for datasets with much censored data and LOD/V2 for datasets with relatively few data below the detection limit (Glass & Gray, 2001). Substitution with LOD/2 is used in Slovenia for statistical calculations for water quality reports in Decree on groundwater status (Uradni list RS, 25/2009). Values are simply ignored and are not included in the analysis. Values below the LOD are replaced with no data values. Such an approach is not recommended, as calculated mean values are always higher than the real ones (Gochfeld et al., 2005). Unknown values are estimated or extrapolated from the distribution curve or calculated from regression. These methods are known to perform best, but the distribution of data should be generally known and there is no agreement on the most suitable method. Several methods exist for the estimation (Succop et al., 2004), and only some can be used for multiple detection limits (Helsel & Cohn, 1988). Compared to these methods, substitution with any fraction values between zero and LOD is generally not recommended, as real data do not have exactly such values (Helsel & Cohn, 1988). However, the usage of substitution methods is still permissible for data with a few censored values (Croghan & Egeghy, 2003). • Other methods are seldom used. A possible approach is also not to use any statistical methods at all, but just to report the values being lower than LOD (Gochfeld et al., 2005). The last two approaches are not presented here, as they require special statistical methods and software and are therefore not comparable to substitution of LOD fractions, discussed in this paper. The goal of this study is first to analyze the ideal normal and lognormal distribution of generated data with different amounts of censored data (from 1 % to 50 %) and to compare the errors produced in all methods. Secondly, to use the replacement methods on an actual dataset of five geochemical parameters obtained from groundwater analyses and discuss their deviations. Other authors have used some other rather uncommon distributions (bimodal lognormal, gamma and delta; (Gilliom & Helsel, 1986), but generally lognormal distribution is regarded as more realistic than normal distribution for environmental and geochemical data (Helsel, 2005, Hornung & Reed, 1990). The novel approach is a systematic comparison of both normal and lognormal distributions (mean and median values) with various substitution methods of replacements of values below the detection limit by five constants: zero, LOD/2, LOD/V2, LOD and no data values, all for differently censored data (from 1 % to 50 %), along with a comparison of five geochemical parameters. Materials and Methods For the simulation of influence of different detection limits on the statistical calculations, a statistical dataset of normally distributed data was generated first. Data was generated in Microsoft Excel with an internal function NOR-MINV Number of data was chosen as N = 65 536, being the maximum possible to calculate further in the program Statistica (Statsoft, Inc.) for the calculations and histograms. Mean value was chosen as 1.00 and standard deviation as 0.25. These data were later transformed to create a lognormal distribution. Values were later censored at different levels, first by discarding the low- est 1 % data. These were replaced with values of 0, LOD/2, LOD/V2, LOD and with blank values (no data). Datasets with censored 5 %, 10 %, 25 % and 50 % were analyzed as well (Figure 1). Some authors have used data with up to 60 % of censored values (Hornung & Reed, 1990), up to 80 % censored values (Gilliom & Helsel, 1986) or even more than 90 % (Glass & Gray, 2001), but any conclusion based on the analysis of such dataset can be considered as highly inaccurate. Beside the mean value, the comparison of medians is also presented, as this statistical value is often used in non-parametric statistics for non-normally distributed data. For the analysis of real geochemical data, a subset of geochemical monitoring data (kindly provided by Slovenian Environmental Agency) for groundwaters in karstic and fractured aquifers was used. Analyses have been performed in years 1990-2009, according to national monitoring program and Decree on groundwater status (Uradni list RS, 25/2009). Number of analyses was N = 942, and from available dataset, the following five were chosen for the analysis: NO3, O2, Sr, F and Mn. Selection was based on two criteria; first that the percentage of censored data was approximately the same as the vales of generated data (from 1 % to 50 %), with values of NO3: 0,5 %, O2: 5 %, Sr: 17 %, F: 25 % and Mn: 45 %, with intention to compare the real data Figure 1. Histograms for generated ideal normal data (N = 65 536) with data below the detection limit substituted by LOD/2 and censored for: A: no censoring, B: 1 %, C: 5 %, D: 10 %, E: 25 %, F: 50 % of all data. with generated ones. Secondly, the actual number of data must have been high, at least 70 % of all analyses (NO3: N = 942, O2: N = 942, Sr: N = 678, F: N = 858, Mn: N = 942). Regarding the analyzed parameters, it must be mentioned that from year 2003 on some major ions (Ca2+, Mg2+, HCO3-, ...) are missing from the analyses, as they are not anymore required to analyze according to Rules on drinking water (Uradni list RS, 19/2004). This is a major information loss, as geochemi-cal modeling cannot be performed with such missing data, and the cost of including these parameters into a complete analysis is relatively small. Results and discussion Analysis of normally distributed generated data (Figure 1A, Table 1) clearly shows that the replacement of censored data with values of LOD/V2 is the best among all substitution methods, as the error is lowest for this approach for all censoring levels (1 %, 5 %, 10 %, 25 % in 50 %). Error is here represented as a ratio between the calculated mean value and true mean value (1.00), in percent. Average error is -1.7 % for normal distribution (Table 1). Replacement with zero produces the greatest errors and should be avoided. Other methods lie in between and also should not be used. Replacement of values below the limit of detection with LOD/V2 underestimates the true mean value only slightly, substitution by LOD/2 produces greater underestimation and substitution by zero the greatest underestimation, which can be seen by the greatest deflection of the curve in Figure 2A. Contrarily, the replacement with LOD or no data values overestimated the true mean values. Similar conclusions are found for the lognormally distributed data (Figure 2B), where the distribution with LOD/V2 is again considered the best (average error is only 0.2 % for this method) and therefore recommended method for the substitution, and replacement with no data values behaves as the worst method. From the comparison of curves for both distributions, the major difference lies in the fact which method performed worst. For normal distribution, this is substitution by zero and for lognormal distribution, the substitution by no data values. The reason lies in the skewness of data, as much more data lies on the left side of the histogram for the lognormal data. As seen from both figures, error is steeply increasing with the percentage of censored data, up to 40 % when a half of data are censored. The increase is not linear and this holds for all methods. Figure 2A, 2B. Errors (ratios between the calculated mean value and true value in %) for replacement of values with no data, zero, data, mean values. B. Lognormal data, mean values. so% ne mu 0 LOD/2 LOD/« LCO noj 041» c.1% ojam o.o% o.i* -«—ra cai i «m» | '97 % CaCO3 compositions and LOI values <3 % are required for a carbonate materials to be considered suitable for used in the manufacture of lime (Boynton, 1980; Ofulume 1993; Ofulume et al., 2009, Power, 1985), and lime that is manufactured for fluxing in steelmak-ing is expected to exhibit at least 52 % CaO (about 92.8 % CaCO3) (British Standard Institute, 1982). Similarly, materials to be adopted for use as fillers suitable for the production of paper coatings, paints, rubber and plastics are expected to be of high chemical purity and to display maximum possible calcium carbonates content and minimum acid insoluble composition (Howse, 1994; Qadhi, 2008). In the light of the foregoing, the Nso-fang marble fall short of the specifications of materials to be used as flux and also lack the requirements for its application as raw material in the manufacture of lime and fillers. In particular, the relatively low CaO contents (mean concentration of 35.79 %) of the Nso-fang marble, together with the presence of acid insoluble minerals (such as quartz with a maximum content of 6.21 %) fall short of the requirements for adoption of the marble for these usages. Also, the high LOI values of between 40.8 % and 46.5 %, exhibited by the Nsofang marble is considered unacceptable, since LOI of materials to be processed for lime or flux in the Basic Oxygen Furnace (BOF) is expected to be less than 3 % (Ofolume, 1993). Excessive LOI values can have significant effects in reducing potential for scrap melting which may lead to uncontrolled foaming or stopping (Anderson & Vernon, 1971). Furthermore, a simple comparison of the chemical data of Nsofang marble with the compiled chemical specifica- tion of a number of industries that consume raw marble (Emofurieta & Ekua-jemi, 1995) indicate that the Nsofang marble may also not be appropriate for application in the cement, ceramics and glass industries. Nevertheless, despite these shortcomings, the Nsofang low grade dolomitic marble can be adapted for a variety of other uses, notably, as aggregate in mortar and concrete of the construction industry and as an abrasive product for the polishing of certain metals. Most importantly, the elevated concentration of MgO in the marble can be exploited for use as compensators for magnesium deficiencies in the agricultural industry, especially for neutralizing acidic soils for farming purposes, and may also be relevant in the sourcing of magnesium oxide for the chemical industry as well as in the production of chrome-based products. In addition, with appropriate beneficia-tion, the elevated MgO, moderate total carbon, and low Al2O3 and SiO2 composition of the Nsofang marble can be harnessed for application as MgO + C refractories for electrical arc furnace (EAF) linings. Impurities, such as pyrite and marcasite are undesirable in marbles to be used as dimension stones because on oxidation they can produce stains which is of particular concern for exterior work (Howse, 1994). Thus, the low total sulphur composition of the marble, which most likely reflects the absence of sul- phur-bearing minerals such as pyrite and marcasite (FeS2) can be exploited in the application of the marble as dimension stones. Also, with favorable physical attributes, the marble could be considered as a potential source of terrazzo chips and landscaping material and may be suitable for use as a decorative stone. Furthermore, when pulverized and ground to specified mesh size, the Nsofang marble can also be used as low grade carbonate fillers, for use in putty, caulking, sealing, venyl floor covering, carpet backing, asphal-tic products and adhesives. Conclusion and Recommendations Metacarbonate deposit occurring in Nsofang area of southeastern Nigeria has been classified as low grade dolo-mitic marble, based on inherent compositional, petrographic and field attributes. The marble is characterized as tainted because of its relatively low CaO contents and CaO/MgO ratio, and elevated MgO, LOI and trace elements composition. The observed compositional features of the marble have suggested a number of applications for the marble, but these are by no means exhaustive. More uses would still be revealed when physical attributes, notably bulk specific gravity and bulk density, water absorption, apparent porosity, reflectance/chromaticity, including brightness, decrepitation, me- chanical strength tests, notably principle schmidt hammer rebound, uniaxial compression strength test and strength point load index as well as surface area and reactivity, are determined. Subsequent research should also incorporate geophysical techniques so that the extent of the prospect can be adequately defined for reserve estimation purposes. Acknowledgements The Chiefs and people of Nsofang community in present-day Etung Local Government Area of Cross River State in southeastern Nigeria are gratefully acknowledged for their hospitality during the field aspects of this work. This work has also benefited immensely from contributions and assistance from Mr. Okokon Ndaw, the Director, Regional Geology Unit of the Nigerian Geological Survey Agency (NGSA) and Mr. Ajenikpa, an Assistant Director in the Nigerian Geological Survey Agency (NGSA). Furthermore, appreciation is due my three graduate students, Charles Umagu, Columbus Edet and Festus Uduma for the roles they played towards the successful completion of this work. 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Hydrochemistry of the near shore marine bay, Calabar river (South-eastern, Nigeria) Hidrokemične razmere v priobalni vodi morskega zaliva ob izlivu reke Calabar v jugovzhodni Nigeriji Ekwere, A. s.1' *, Edet, A.1 & Ukpong, A. J1 1 University of Calabar, Department of Geology, Nigeria Corresponding author. E-mail: zerratta77@yahoo.com Received: November 21, 2011 Accepted: December 21, 2011 Abstract: A study to assess the current hydrochemical status of the rapid developing marine bay of the Calabar River, south-eastern Nigeria, was carried out. The waters have pH values ranging from slightly acidic to slightly alkaline and defined as generally fresh based on TDS values (<1000 mg/L). Analysis reveals an abundance sequence of Na>Ca>Mg>K and Cl>SO4>HCO3>NO3 for cations and anions respectively. Averages were Ca2+ (15.15 mg/L), Mg2+ (2.93 mg/L), Na+ (21.27 mg/L) and K+ (2.86 mg/L) while for the anions it was Cl-(156.99 mg/L), SO42- (27.85 mg/L), HCO3- (14.98 mg/L) and NO3 (0.551 mg/L). The waters belong to the Na-Ca-Cl field on the Piper diagram plot, indicating influence of atmospheric precipitation. Trace elements Fe, Mn, Pb, Zn, Cu, Cd, Cr, Ni and Co show levels within world averages and acceptable limits. Hydrochemical modelling shows heavy metals to exist mainly in free mobile states, chlorides, sulphates, carbonates and hydroxides bound under prevailing pH-Eh conditions at unsaturated levels. Chemical contents and mineral phases are adjudged to be sourced from mineral dissolution, salinization and minimal anthropogenic input. Izvleček: Raziskava je bila opravljena z namenom oceniti sedanje hidrokemične razmere v naglo spreminjajočem se okolju morskega zaliva ob izlivu reke Calabar v jugovzhodni Nigeriji. Vrednosti pH vode nihajo med rahlo kislimi do rahlo alkalnimi. Vodo je mogoče splošno oceniti za sladko glede na vrednosti TDS (celotna raztopljena snov <1000 mg/L). Analiza razkriva naslednji zaporedji kationov in anionov: Na>Ca>Mg>K in Cl>SO4>HCO3>NO3. Povprečna koncentracija Ca2+ je 15,15 mg/L, Mg2+ 2,93 mg/L, Na+ 21,27 mg/L in K+ 2,86 mg/L in anionov Cl- 156,99 mg/L, SO42- 27,85 mg/L, HCO3- 14,98 mg/L in NO3 0,551 mg/L. Vode pripadajo Na-Ca-Cl-polju v Piperjevem diagramu, kar nakazuje vpliv atmosfernih padavin. Sledne prvine Fe, Mn, Pb, Zn, Cu, Cd, Cr, Ni in Co so na ravni svetovnih povprečij in v sprejemljivih koncentracijah. Hidrokemijsko modeliranje kaže, da so težke kovine navzoče pretežno v prostih mobilnih specijah kot kloridi, sulfati, karbonati in hidroksidi, ki so v sedanjih razmerah pH-Eh v nezasičenih koncentracijah. Vir raztopljenih snovi in mineralnih faz so raztapljanje mineralov, salinizacija in minimalen antropogeni vpliv. Key words: river, trace element, mineral species, geogenic, Calabar, Nigeria Ključne besede: reka, sledne prvine, mineralne specije, geogeno, reka Calabar, Nigerija Introduction Rivers transport more than just water from land to sea as river basins are not inert. River chemistry and particulate load reflect natural and human processes within the watershed. The progression of a river along its course leads to interaction with a variety of geological types and it may have input from aquifers not visible on the surface anywhere in the locality. In most developing communities, increase in human population geared by the quest for good water supply, irrigation, fish production, recreation and navigation has resulted in enormous pressure and stress on reservoirs. This is readily identified by variation of the natural chemistries of reservoirs as well as heavy metal contamination. Howev- er two major sources have been identified as possible sources of metal loads in rivers in proximity to urbanised areas. These are natural sources from rock weathering within the catchment and anthropogenic sources derived from human activities (Abrahim & parker, 2002). Mustapha (2008), reckon water quality deterioration in reservoirs as related to excessive nutrient inputs, eu-trophication, acidification, heavy metal contamination, organic pollution and obnoxious fishing practices. Indiscriminate disposal of industrial and urban waste into river catchment plays a major role in most environmental problems. Associated presence of nutrients and heavy metals especially at elevated levels, leads to accumulation in tissues of organisms. These metals are not readily assimilated for growth or excreted, resulting in amplifications of their concentrations along the food chain and exposure of humans at the apex to the risk of metal poisoning (Akpan et al., 2002). Physico-chemical properties have been used to assess water quality in reservoirs (Djukic et al., 1994). This gives a good impression of the status, productivity and sustainability of such water bodies. The estuarine Marine Bay of the Calabar River, south-eastern Nigeria, has experienced a significant increase in activities in recent times. These human perturbations include fishing, shipping, recreation, aquaculture etc. Discharge of effluents into the river basin has been recognized as an integral part of these operations. These apparently will culminate into stresses on the status of the water quality with time. The present study aims to ascertain the potability of the near-shore waters of this marine bay and provide data set for future environmental and resource management issues. DESCRIPTION OF STUDY AREA The study area belongs to what has been described as Calabar urban (Edet et al, 2003), situated between latitude 4°15'-5°15' North and longitude 8°15'-8°25' East (Figure 1). The study area receives an average rainfall of about 254 mm annually within two distinct seasons; dry and wet seasons. Mean annual air temperature and relative humidity are 26.8 °C and 84.6 % respectively. Geologically the study area is composed of Tertiary and Quaternary sediments referred to as Coastal Plain Sands of the broader Niger Delta basin (Short & Stauble, 1967). This formation consists of alternating sequence of gravel, sand, silt, clay and alluvium which are derived from the adjoining Precambri-an Basement and Cretaceous rocks. The Precambrian basement (Oban Massif) complex is made up of migmatites-gneisses, granites, schists, para-schists, pegmatites and a host of other ultra-mafic rock suites (Ekwueme, 2003). The Cretaceous sedimentary geologic unit is known as the Calabar Flank and is built up of limestones, sandstones, shales and marls (Reijers, 1996). Geomorphologically, the study area is of a coastal setting with surface elevations ranging from less than 10 m in the southern part to about 80 m in the north. Material and methods A total of twenty (20) water samples were collected from locations along the near shore of the marine bay. The sampling locations included but were not limited to sites in proximity to marine related human activities within the study area (Table 1). This was to assess possible impacts of anthropogenic activities on water chemistry. The sampling period was June-July 2009 at the peak of the wet season. Physical parameters (pH, temperature, TDS, Eh and conductivity) were measured in the field using standard equipment: PHT-027 multi-parameter water quality probe. Major and trace element contents were determined by atomic absorption spectrophotometer (AAS), model UNICAM 939. The anion contents of samples were determined by colorimetric method using UNICAM UV2 spectrophotometer. All instrumental analyses were carried out in the quality control laboratory of the Aluminium Smelter Company of Nigeria (ALSCON), Ikot Abasi, Nigeria. Saturation indices and mineral phases were determined using the VISUAL MINTEQ computer program. The em- Figure 1. Map of Calabar and environs including sampled river. [A] Index map of Nigeria, [5] geologic map, [C] Calabar River; modified after Edet & Worden (2009) Table 1. Some sampling locations with active potential sources of contamination. Location Surrounding Activities Henshaw town beach Sand mining, wood processing, domestic waste disposal Esuk Ifia (marina) Boat jetty, wood processing, out-board engine repairs, abandoned boats Marina Resort Tourist/leisure park, boating, food waste and effluent disposal Inland Water Authority Jetty, boat repairs UNICEM jetty Jetty, hauling of cement, boating Esuk Utan Sand mining, boating, domestic waste disposal, wood processing Tinapa Jetty, boating, waste disposal, oil/crude carrying vessels Adiabo bridge Boating, domestic waste disposal Ibonda (NPA Port) Boating and boat repairs, shipping, waste disposal Creek Town beach Boating, domestic waste disposal, wood processing ployment of VISUAL-MINTEQ as modelling tool was due to its extensive database of mineral and aqueous species for interpretation purposes. Results and discussion Table 2 outlines the descriptive statistics for physico-chemical and trace elements contents of samples in comparison to world averages and acceptable standards. Table 3 shows the correlations between major cations and anions as a means of establishing probable controls of chemical characteristics. Physical properties The temperature of the sampled waters ranged from 27-29 °C with a mean of 28.5 °C (Table 2). These values are consistent with mean air temperatures as they coincide with period of sampling and diurnal temperature variations within the locality (Edet & WoRDEN, 2009). The pH values ranged from 3.48-7.60 with an average of 6.27. The lowest pH of 3.48 was recorded at one of the most active sites (Esuk Utan) along the shore with great potency for pollution. The rest of the locations revealed pH values of slightly acidic - slightly alkaline with a greater percentage within the 6.5-8.5 WHO recommended standard. Electrical conductivity (EC) data showed a range of 460-2,240 pS/cm with an average of 1 080 pS/cm. the values were generally below the 1,400 pS/cm WHO limit, except for the site with the maximum value. Comparison of EC data from Edet & Worden (2009) shows the values are within background levels, reflecting minimal or absence of pollution effects. Total dissolved solids (TDS) ranged from 320-1140 x 10-6 with an average value of 745 x 10-6. With reference to quality, about 60 % of the sampled waters are freshwater with TDS values <1,000 mg/L (Freeze & cherry, 1979). The rest of the waters were within the lower limits of brackish water (TDS 1,000-10,000 mg/L). There was an observed direct relationship between TDS and EC data, reflecting equilibrium of abundance of free ions in water relative to soluble content (Hem, 1986). The redox potential values (Eh), ranged from 9-97 mV with a mean of 42 mV. These values are well within the baseline data as reported by Edet et al. (2003) and significant variation in future may indicate pollution effects. Chemical characteristics As revealed from mean values, the relative abundance sequence of the major cations was Na > Ca > Mg > K. Sodium concentration varied from 4.65-33.16 mg/L with a mean of 21.27 mg/L. From observations all the values were within the baseline data as reported by Edet et al. (2003), below world average and within the WHO limits. Calcium had concentrations of 7.0722.46 mg/L and a mean of 15.15 mg/L. The mean value is in proximity to world average of 15.0 mg/L (Turekian, 1977) and all values were below the WHO limits. Magnesium and potassium exhibited ranges of values within local baseline data, global averages and below WHO standards with means of 2.93 mg/L and 2.86 mg/L respectively (Table 2). The relative dominance of Na reflects the mixing of seawater with the freshwater within the estuarine coastal area. Ion exchange processes for sea-water cations (especially Na and Mg) for Ca are prevalent at exchange sites with Ca2+ going into solution (Edet & worden, 2009). The effect of this would probably be more defined with an assessment based on tidal regimes within the study area. Sources of Ca and Mg are attributed to the weathering of anorthite, pyroxene and amphibole from the overlying Oban massif in the northern parts of the study area (Edet et al., 2003, Ekwere, 2010). Mineral dissolution incepted by precipitation and weathering of hydroxyl-apatite, fluorite, dolomite, calcite, sylvite, epsomite, mira-bilite and halite (which are present in the catchment geology) are also probable sources of these cations (Ekwere 2010). However, the contributions of rainfall through marine input of sea salts (Berner & Berner, 1996) cannot be ruled out as high precipitation levels is characteristic of this marine coastal setting. Assessment of anion concentrations shows that chloride varied from 118.10-234.10 mg/L with a mean of 156.99 mg/L. All the concentration values of Cl were above the baseline data of Edet et al., (2003) but below the WHO limit of 250 mg/L. The Cl contents in the waters are attributable to seawater incursion into the fresh coastal waters as well as precipitation influence. River runoff dominated by precipitation is proportionally high in Cl (recycled salt) and low in total concentration. Sulphate concentration ranged between 12.10-52.40 mg/L with an average value of 27.88 mg/L. the concentration levels were within the local baseline and below the WHO limit (table 2). Sulphate concentrations are attributed to natural accentuation from pyrite (FeS) within this estuarine setting (Edet & Worden, 2009). The bicarbonate (HCO3) concentration levels varied from 10.56-24.32 mg/L with a mean value of 14.98 mg/L. The concentrations were within the local baseline and below world average value (Table 2). The dominant source of HCO3 is the dissolution of carbonate rocks within the Cretaceous sediments which forms part of the catchment geology of the study area. Nitrate had concentration values ranging from 0.298-0.862 mg/L with a mean of 0.551 mg/L. This shows that all values were below the WHO maximum permissible limit of 10 mg/L. The main source of nitrate in the water is the use of nitrogenous fertilizers for agricultural purposes as well as the indiscriminate dumping of human and animal waste into the environment. These are anthropogenic and though not posing any threat to health status, monitoring is duly advised. Ammonium concentration ranged from 1.12-3.73 mg/L with a mean of 2.54 mg/L. These concentration levels are the result of agricultural land scrubbing, human and animal waste disposal into the environment. Heavy metal trend Assessment of heavy metal data (Table 2) shows that iron and manganese had mean values of 0.95 mg/L and 0.16 mg/L respectively with ranges within world average (Turekian, 1977) but slightly above WHO limits. The relatively more toxic elements; Pb, Zn, Cu, Cd, Cr, Co and Ni, had mean values of 0.004, 0.054, 0.051, 0.004, 0.071, 0.004 and 0.026 mg/L respectively. These values were within world averages and WHO limits. Contents of trace elements reflect a dominant influence of rock weathering processes, as mineral phases laden with these elements constitute the rocks within the overlying basement and Cretaceous sediments (Ekwere, 2010). However, though minimal, the effects of anthropogenic activities can contribute to concentration levels of these potentially toxic elements. Hydrochemical facies and variable projections A plot of the data for the major cations and anions on the Piper (1944) diagram (Figure 2) shows that the waters fall within the Na - Ca - (Mg) - Cl field. This is in line with relative abundance sequence as revealed from the data set. The recognized water type is defined to be a major constituent of atmospheric precipitation (Davis & De Weist, 1966). Bivariate correlation analysis was also employed to assess relations between variables. From the correlation matrix (Table 3), Ca2+ exhibits a strong positive correlation with Mg2+ and Na+ (r > 0.70) and moderate correlation with NO3- (r = 0.50-0.70). Magnesium (Mg2+) has a strong positive correlation with Na+. These relationships between the major exchangeable cations Ca-Mg-Na represent effects of dissolution and precipitation reactions and concentration effects (Ekwere, 2010). Moderate correlation between Na+, K+ and Cl- suggests evap-orite dissolution and seawater mixing effects with the coastal freshwaters. The entire scenario reflects a dominance of geogenic influence over water chemistry than anthropogenic. Two factorial axes F1 and F2 (Table 4) were determined through factor analysis using the STATISTICA computer program, extracting 31.22 % and 17.13 % respectively of total data variance and accumulating to 48.35 %. Classification of factor loadings is described as 'strong', 'moderate' and 'weak', corresponding to absolute values of >0.75, 0.75-0.50 and 0.50-0.30, respectively (Liu et al., 2003). However for this study, factor loadings greater than 0.60, were considered significant. Factor 1 (Ca, Mg, Na, Cl, SO4 and Mn) projected a maximum of variables and defined by the majority of parameters that describe water quality. These are contents in calcium (0.676), magnesium (0.906), sodium (0.876), chloride (0.750) and on the negative pole sulphate (-0.805) and manganese (-0.662). This axis defines water of strong salinities and charged in suspended matter (Ben Garali et al., 2011). The composition of this factor reflects natural weathering from waterrock interactions in geo-matrix of the basement and inputs from primitive saline waters of Cretaceous limestones of the Calabar Flank (Ekwere, 2010). Factor 2 (HCO3, Cr, K and Zn) appears erratic in composition but apparently indicates dissolution effects of mineral phases that constitute the rocks of the catchment geology. The dissolution of carbonate rocks from the Calabar Flank Table 2. Comparison of physico-chemical parameters with baseline data, world average and WHO (2001) standards Stat Temp EC TDS PH Eh Ca Mg Na K Mean 28.75 1080 745 6.27 42 15.15 2.93 21.27 2.86 Min 28 460 320 3.48 9.0 7.07 0.78 4.65 1.08 Max 29 2240 1140 7.60 97 22.46 4.02 33.16 6.11 SD 0.42 578.90 340.63 1.21 29.80 4.94 1.01 9.45 1.47 BLD 41-264 4.0-6.0 31-143 1.73-4.76 0.51-6.10 9.05-48.2 1.3-3.6 WA 120a 13.4b 3.4b 5.2b 1.3b SW 35,000 412 1,280 10.780 399 WHO 1,400 1000 6.5-8.5 200 100 250 12 Stat CI SO4 hco3 no3 NH4 Fe Mn Mean 156.9 27.85 14.98 0.55 2.54 0.95 0.16 Min 118.1 12.10 10.56 0.30 1.12 0.13 0.003 Max 234.1 52.40 24.32 0.86 3.73 2.19 0.712 SD 40.34 11.51 4.05 0.21 0.92 0.60 0.25 BLD 0.78-69.9 0.18 12.2-18.3 1.3-2.7 WA 5.8b 8.3b 52b 100a 15a SW 19,350 2,710 123 WHO 250 400 50 0.3 0.1 Stat Pb Zn Cu Ni Cd Cr Co Mean 0.004 0.054 0.051 0.026 0.004 0.071 0.004 Min 0.002 0.023 0.014 0.003 0.001 0.006 0.001 Max 0.007 0.104 0.086 0.083 0.016 0.211 0.007 SD 0.002 0.023 0.036 0.023 0.003 0.052 0.002 BLD WA 3.0a 20a 3.0a 1.5a 0.03a 1.0a 0.1a SW WHO 0.05 5.0 0.005 0.05 Temp (°C), EC (ps/cm), Eh (mV), TDS (x 10-6), cations, anions and heavy metals (mg/L) 5Z,D-Baseline data (Edet et al., 2003) WA-World Averages a (Turekian, 1977) b (meybeck, 1979) SW-Sea Water (Millero, 1996) WHO-World Health Organization (2001) and ultra-mafic rocks from the basement are visible in this factor loading. Hydrochemical modelling The modelling package VISUAL-MINTEQ was used to calculate distributions of aqueous species and mineral saturation indices using the measured water compositions. The employment of VISUAL-MINTEQ as a compli- mentary interpretation tool was to assess chemical species of dissolved component, saturation indices and hazard prognosis. Results for selected parameters for the sampled waters are presented in Table 5. Mineral equilibrium calculations for water are useful in predicting the presence of reactive minerals in water sys- Figure 2. Piper trilinear diagram of sampled waters tem and estimating mineral activity; (Taylor et al, 1996; Deutsch, 1997 and Zhu et al, 2008).The solution is considered to be in equilibrium with regards to a particular mineral if the saturation index (SI) = 0. It is considered to be undersaturated if SI < 1 and oversatu-rated if the SI > 1. Metals can exist in water as many aqueous species and the type(s) of species can affect mineral solubility, adsorption/desorption behaviour and possibly bioavailability. The predicted predominant aqueous species from hydrochem-ical modelling are given below for the metals measured and detected in the water samples. Iron Iron existed as Fe2+, FeOH+, FeCl+, FeSO4(aq) and FeHCO3. Despite the many possible aqueous species for iron, the predominant ones were invariably predicted to be Fe2+ and FeSO4(aq) (melanterite) constituting 86 % and 8 Table 3. Correlation matrix for major constituents of water Ca Mg Na K HCO, Cl SO4 NO, NH4 Ca 1 0.74 0.83 0.44 0.23 0.28 -0.56 0.50 -0.29 Mg 1 0.90 0.52 0.18 0.62 -0.76 0.46 -0.48 Na 1 0.61 0.17 0.58 -0.63 0.40 -0.25 K 1 0.45 0.70 -0.24 -0.16 -0.07 hco3 1 0.09 -0.45 -0.17 -0.49 Cl 1 -0.44 0.31 -0.05 SO4 1 -0.55 0.62 NO, 1 -0.21 NH Table 4. Factor loadings of chemical parameters 1 Factor 1 Factor 2 Ca 0.676 Mg 0.906 Na 0.876 Cl 0.750 SO4 -0.805 Mn -0.662 HCO3 -0.648 Cr -0. 851 K -0.713 Zn -0.674 Eigenvalue 5.619 3.082 % total variance 31.22 17.13 Cumul. Eigenval Cumul. % 5.619 31.22 8.701 48.35 Table. 5 Major chemical species of trace elements. Element Dominant species mass fraction, w/% Fe Fe2+ 86.0 FeSO4( ) 4(aq) 8.0 Mn Mn2+ 92.0 MnSO ) 4(aq) 3.0 Ni2+ 88.0 Ni NiSO^, , 4(aq) NiHCO, 6.0 3.0 Pb2+ 64.0 Pb PbCl- 9.0 PbSO4 6.0 Zn Zn2+ 95.0 ZnSO„ , 4(aq) 3.0 Cu2+ 86.0 Cu CuOH+ 5.0 CuCO3( ) 3(aq) 4.0 Cd Cd2+ 71.0 CdCl- 23.0 Cr CrOH- 92.0 Co Co2+ 90.0 % respectively of the species. Under the prevailing Eh-pH condition, Fe2+ is immobile. Its mobility will further be hindered in the area by precipitation as Fe oxides (hematite, magnetite), oxy-hydroxides (goethite) and other co-precipitating metal phases (Edet et al, 2004). Manganese Manganese exhibited various species in the water including, Mn2+, MnCO3(aq), MnCl-, MnSO., , and MnHCO ". Mn ' 4(aq) 3 is non-toxic and the dominant phase was the ionic Mn2+ accounting for over 92 % in the waters. Lesser to this was MnSO4(aq) constituting about 3 %. Under the present pH- Eh of the waters, Mn2+ is immobile posing no problem in the area. Nickel The following species of Ni were identified through modelling, Ni2+, NiOH+, NiCl+, NiSO HCO, 4( v NiCO3( ) and Ni- 4(aq)' 3(aq) Ni2+ was the most dominant constituting 88 %, NiSO4(aq) (retger- :2+ site) about 6 % and NiHCO3+ 3 %. Ni compounds are non-toxic (Reimann & Caritat, 1998) and its immobility un- + der the present pH-Eh condition, Ni does not pose any threat to the waters in the study area. Lead Species of lead (Pb) included, Pb2+, PbOH+, PbCl+, PbCl( ), PbSO4( ), (aq)' 4(aq)' PbCO3(aq) and PbHCO3+. Though aqueous lead is predicted to be distributed amongst several species Pb2+ made up about 64 %, PbCl 9 %, while PbSO4(aq) (anglesite) and PbH-CO3 were 6 % each. The distributions of the species show no marked difference for the different locations. Pb2+ appears to be mobile but however poses no problem of contamination as Pb is low (mean 0.004 mg/L) and PbCO3 will restrict its mobility (Edet et al, 2004). Zinc Species of zinc included Zn2+, ZnOH+, ZnCl+, ZnSO , ZnCO and Zn- 4(aq) 3(aq) HCO3+. Zn2+ made up about 95 % of the species. Far less but next to this was ZnSO4(aq) (bianchite) constituting only about 3 %. Cadmium Cadmium existed as various species but major constituents included, Cd2+, CdCl+, CdOH, CdCl2( ), CdSO4( ), Cd' ' 2(aq)' 4(aq)' HCO3+ and CdCO3(aq). The speciation of cadmium is dependent on pH, chlorini-ty, sulphate and total cadmium concentrations. However Cd2+ was dominant with 71 % and CdCl with 23 %. At present pH-Eh condition, Cd2+ which is toxic and carcinogenic is immobile and if present in high concentrations may be precipitated, thus reducing its danger potential. Chromium Chromium with minimal concentrations in most samples, exhibited a predominance predicted to be CrOH- with about 92 % of total composition. The only other specie was Cr2+. This potentially toxic element is immobile posing no threat to water quality as its content was low at a mean of 0.071 mg/L. Cobalt Cobalt CoSO, (Co2+), CoCO CoOH+, CoCl-and CoHCO. Copper Various species of copper were revealed total composition. and included Cu2+ (86 %), CuOH+ (5 %), CuCO3(aq) (4 %), CuSO4(aq) (3 %) and Cu(OH)22+, Cu(OH) , CuCl+ 4(aq) 3(aq) 3 were the species of cobalt identified. Co2+ predominated with over 90 % of 2(aq)' CuNH32+ and CuHCO3+. The speciation of copper is controlled by pH, redox, chlorinity, sulphate and total copper concentrations. Mineral saturation states were calculated as part of the output from the modelling program as a means of indicating what minerals might be dissolving or precipitating into or from the water and controlling its composition. The cal- Table 6. Oversaturated mineral species Mineral species Chemical composition Saturation index Brochantite CuSO4( ) 4(aq) 1.423 Malachite CuCO3( ) 3(aq) 1.449 Ni-hydroxide Ni(OH)2 1.413 Tenorite Cu.H+(H2O) 2.302 culations are based on an equilibrium model, so the results are only an indication, as kinetic factors may inhibit approach to equilibrium. The modelling reveals that majority of the species of the metals (hydroxides, carbonates, sulphates, chlorides and bicarbonates) were under-saturated. However a few species were above equilibrium at very low levels of oversaturation (Table 6). These include aqueous Cu-sulphate (brochantite), Cu-carbonate (malachite), hydrous copper (tenorite) and hydroxides of Ni and Fe. Free metal species are the most bio-available and toxic form of trace elements that exist in natural water (Apte et al., 1995). Considered metals exist as free ions and are relatively mobile under prevailing Eh-pH conditions. However their low concentrations in addition to the presence of limiting mineral phases (carbonates and sulphates) reduce the risk of their contamination to the waters. Conclusion The waters of the marine bay of the Calabar River exhibit a compositional abundance sequence of Na>Ca>Mg>K for cations and Cl>SO4>HCO3>NO3 for anions. The waters are characterised as Na-Ca-Cl, indicating influence of saline water incursion and lesser effects of atmospheric precipitation. The water characteristics also reflect mixing of waters of multiple geogenic origin; Ca-Mg-HCO3 and Ca-Na-Cl waters from the basement and Cretaceous geologic units north of the study area (Ekwere, 2010). Heavy metals (Fe, Mn, Pb, Zn, Cu, Cd, Cr, Ni and Co) generally show ranges within world averages and acceptable limits. Spe-ciation modelling shows that metals exist as ions, chlorides, sulphates, carbonates and hydroxides, largely at unsaturated levels. Mineral dissolution and salinization are the main controls of water chemistry. References: Abrahim, G. & parker, R. (2002): Heavy metal contaminants in Tamaki estuary: impact of city development and growth, Auckland, New Zealand. Environmental Geology, Vol. 42, pp. 883-890. Akpan, E. R., Ekpe, u. J. & Ibok, u. J. (2002): Heavy metal trends in the Calabar River, Nigeria. Environmental Geology, Vol. 42, pp. 47-51. Apte, S. c., Benko, w. I. & Day, G. M. (1995): Partition and complexation of copper in the Fly River, Papua, New Guinea. J Geochem. Explor. 52:67-79 Ben Garali, A., Ouakad, M. & Gueddari, M. (2011): Geochemistry of Bizert lagoon affluent water, Northern Tunisia: principal component analysis. Arab. Jour. Geosci., Vol. 4, pp. 475-481. Berner, E. K. & Berner, R. A. (1996): Global environment: water, air and ge-ochemical cycles. Upper Saddle River, NJ. Prentice Hall, Inc. Davis, S. N. & De Weist, R. J. M. (1966): Hydrogeology. New York: John Wiley & Sons. Deutsch, W. J. (1997): Groundwater geochemistry, fundamentals and applications to contamination. Lewis Press, New York. Djukic, N., Maletin, S., Pujin, V., Ivanc, A. & Milajonovic, B. (1994): Ecological assessment of water quality of Tisze by physico-chemical and biological parameters. Tisze Szeged, Vol. 28, No. 1, pp. 37-40. Edet, A. E., Merkel, B. J., & Offiong, O. E. (2003): Trace element hydrochemi-cal assessment of the Calabar Coastal Plain Aquifer, SE Nigeria using statistical methods. Environmental Geology, Vol. 44, pp. 137-149. Edet, A. E., Merkel, B. J., & Offiong, O. E. (2004): Contamination risk assessment of fresh groundwater using the distribution chemical speciation of some potentially toxic elements in Calabar (southern Nigeria). Environmental geology, Vol. 45, pp. 10251035. Edet, A. & Worden, R. H. (2009): Monitoring of the physical parameters and evaluation of the chemical composition of river and groundwater in Calabar (south-eastern Nigeria). Environ. Monit. Assess., Vol. 157, pp. 243-258. Ekwere, A. S. (2010): Hydrogeological and Hydrogeochemical Framework of the Oban Massif, south-eastern Nigeria. PhD Thesis, Dept. of Geology, University of Calabar, Calabar, Nigeria. Ekwueme, B. N. (2003): The Precambrian geology and evolution of the Southeastern Nigerian basement complex. University of Calabar Press 135p. Freeze, R. A. & Cherry, J. A. (1979): Groundwater, 2nd edition (604pp). New Jersey: Prentice Hall. Hem, J. B. (1986): Study and interpretation of chemical characteristics of natural water. US Geological survey water supply paper, Vol. 2245, pp. 263. Liu, C. W., Lin, K. H., Kuo, Y. M. (2003): Application of factor analysis in the assessment of groundwater quality in a Blackfoot disease area in Taiwan. Sci. Total Environ, Vol. 313 (1-3), pp. 77-89. Meybeck, M. (1979): Concentration des eaux fluvales en elements majeurs et apports en solution aux oceans. Revue de Géologie Dynamique et de Géographie Physique, Vol. 21, pp. 215-246. Millero, F. J. (1996): Chemical oceanography. Boca Raton, FL: CRC Press. Mustapha, M. K. (2008): Assessment of the water quality of Oyun reservoir, Offa, Nigeria, using selected physico-chemical parameters. Turkish Journal of Aquatic Sciences, Vol. 8, pp. 309319. Piper, A. M. (1944): A graphic procedure in the geochemical interpretation of water analysis. Am. Geophys. Union Trans., Vol. 25, pp. 914-923. Reimann, C. & Caritat, P. (1998): Chemical elements in the environment. Fact sheets for the geochemist and environmental scientist. 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Modernization of technological process and equipment at floor level roadways execution in Velenje coal mine Modernizacija tehnoloških procesov in opreme pri gradnji jamskih prog v Premogovniku Velenje EVGEN Dervarič1'*, ŽELJKO VUKELIC1, MILAN MEDVED2 University of Ljubljana, Faculty of Natural Sciences and Engineering, Aškerčeva 12, SI-1000 Ljubljana, Slovenia 2Velenje Coal Mine, Partizanska 78, SI-3320 Velenje, Slovenia Corresponding author. E-mail: evgen.dervaric@ntf.uni-lj.si Received: November 30, 2011 Accepted: January 18, 2012 Abstract: In the period from 2008 to 2010 Premogovnik Velenje (Velenje Coal Mine) carried out an extensive research and development project on modernization of technological processes, materials and equipment needed in construction of the floor level roadways. The main purpose of this research project was a comprehensive update of all key processes in construction of roadways, which contribute to increasing advances in highly productive extraction by divergent method of mining, especially in the working phase of dismantling the steel arch support units on the accession lines during extraction. In the technological process of building floor level roadway elements such as a steel arch supports, wooden panels, composite bolts, composite nets and various insulating coatings that prevent the formation of oxidation processes are used for support. The bright roadway diameter lines represent the modified steel arches shape where the cross section ranges from 13 to 18 m2. Due to technological changes and new support elements a new appropriate shape of the roadway profile was constructed, tested and introduced. As well, new screw clamps, wire clamps for screw clamps and the new struts for connecting steel arch line frame supports were introduced. Analysis and the load bearing capacity tests of supports were carried out with intention of re-installing. There were several experiments, on the surface and underground, on materials and technologies which are used for isolation procedures. Experimental results during the construction phase were rather excellent, the situation will 438 Dervaric, E., Vukelič, Ž., Medved, M. be monitored further on in 2011, so a comprehensive technical evaluation could be defined. In case of equipment an extensive work has been done. A new conveyer, a mobile conveyor belt with continuous lengthening, won all the parameters which enable continuous operation without physical assistance. New feeder bars were introduced. In the field of equipment development the research was primarily aimed on complete mechanization and automation of all phases of roadway construction work and thus greatly improved security, technical and economic parameters. In this paper all the important achievements in the floor level roadways construction are presented. Izvleček: Premogovnik Velenje je v letih od 2008 do vključno 2010 izvedel obsežen razvojno-raziskovalni projekt pri modernizaciji tehnoloških procesov, materialov in opreme pri gradnji etažnih jamskih prog. Temeljni cilj razvojnega projekta je bil celovita posodobitev vseh ključnih procesov gradnje jamskih prog, ki bistveno pripomorejo k povečanju napredkov na visokoproduktivnih odkopih z odstopajočim načinom odkopavanja, predvsem pri delovni fazi demontaže jeklenega ločnega podporja v pristopnih progah ob napredovanju odkopa. V tehnološkem procesu gradnje jamskih prog se uporabljajo podgradni elementi v obliki jeklenega ločnega podporja, lesenega opaža, kom-pozitnih mrež in kompozitnih sider ter različnih izolacijskih oblog, ki preprečujejo nastanek oksidacijskih procesov. Svetli premer jamske proge so modificirani profili dimenzij od 13 m2 do 17 m2. Na področju tehnoloških sprememb in novih podgradnih elementov je bil konstruiran, preizkušen in uveden nov ustreznejši profil gradnje jamskih prog. Poleg tega so bile uvedene nove vijačne objemke, žične spone za vijačne objemke in nove veznice za povezovanje okvirjev jeklenega ločnega podporja. Izvedena je bila analiza in preizkusi nosilnosti ločnega podporja, ki je namenjeno za ponovno vgradnjo. Na področju materialov in tehnologij izdelave izolacijskih oblog je bila izvedena vrsta poizkusov na površini in v jami. Rezultati poizkusov so bili v fazi gradnje dobri, stanje bomo spremljali še ob prehodu odkopa ob koncu leta 2011 in tako podali celovito tehnično oceno. Na področju opreme za delovišča je bilo opravljeno obsežno delo. Uveden je bil nov napredovalni stroj, pomični transporter, trak s kontinuiranim daljšanjem, osvojeni so bili vsi parametri avtomatizacije odvozov, ki omogočajo obratovanje odvoza brez posadk. Uveden je bil novi poda-jalnik lokov. Na področju opreme je bilo razvojno-raziskovalno delo usmerjeno predvsem v popolno mehanizacijo in avtomatizacijo vseh delovnih faz procesa gradnje jamskih prog in tako močno izboljšanih varnostnih, tehničnih in ekonomskih parametrov. V prispevku smo celovito predstavili vse dosežke pri gradnji jamskih prog. Key words: floor level roadways, steel arches, composite bolts, roadheader, conveyer, crusher Ključne besede: etažne jamske proge, jekleno ločno podporje, kompozitna sidra, napredovalni stroj, transporter, drobilec Introduction The starting point of the face development and open-up work is dictated by extreme thickness and geometry of the Velenje coal seam. As these conditions are unique in the world, it has not been possible to select an optimum mining method for years. Coal extraction is carried out from the hangingwall to footwall of the seam, and it is based on the retreating mining method. This means that it is necessary to prepare all the roadways before starting to extract the coal. As the face advances, the roadways are simultaneously liquidated. Development constructions, which enable access to the deposit and coal exploitation, are driven partly in the rock mass and mainly in the coal seam. At present, there are about 50 km of roadways driven in the coal deposit. A great concentration of roadways is typical, and all roadways are driven in the exploitation area that ensures nowadays the planned output (4 000 000 t per year) of the coal mine. With regard to the mining system and the mining output it is necessary to drive about 7 km of roadways a year. The designing of individual floor levels and the sequence of extraction from hangingwall towards the footwall of the seam it is usually limited to the floor level working heights of 15 m. The designing method is conceived so that the level of coal seam exploitation is a maximum one. Each floor level is confined by two drift roads used for airing and for the transportation of coal and material. A great number of underground structures in a limited exploitation field and their interaction during the exploitation process demand a systematic designing approach in order to achieve the necessary stability of the underground constructions. While designing and driving the roadways the basic principle is taken into consideration, namely roadways have to remain stable without being additionally maintained or repaired during their life period. This demand can be 440 Dervaric, E., Vukelic, Z., Medved, M. fully satisfied only by systematic evaluation of geological and geotechnical conditions, by evaluating other factors which have a significant influence on designing and roadway drivage, and by verification and improving the existent roadway drivage procedures. The qualitative and quantitative parameters collected in this way represent a good basis for the selection of optimal technically technological and economically most favourable solutions which can be used at designing method, support elements and at roadway drivage. Influencing factors in building underground STRUCTURES Building floor level roadways at PV is affected by many factors[1], which are mutually intertwined, they can be defined as follows: • natural, • planning, • designing, • technological and • organizational. Natural factors Natural factors are set out in layer of coal and other materials where facilities are made. These are mainly: • depth, • depth inside the layer, • geomechanical properties, • tectonics and faults, • microstructural properties, • content of gas, • water content, • ash content, • oxidation process susceptibility, • and others. Planning factors Planning factors are determined by the method of planning and executing works. These are mainly: • timeliness of preparation works before starting mining, • the timely start of construction of the roadways, • assurance of all resources (material, equipment, team), • the adequacy of planning according to the number construction teams, • interactions between the structures in terms of planning face out. Designing factors in terms of required lining capacity Designing factors are determined by load bearing capacity of the lining. These are mainly: • distance from the hangingwall of the seam, • distance from the old works, • interaction of the roadways and the longwall face in terms of design, • location of the roadway in the coal layer, • method of excavation. Technological factors Technological factors occur in all phases of construction works during the roadway drivage procedures. These are mainly: • cutting and preparing the space for the new section, • supporting, • building a fire covering and back filling, • coal transportation, • material delivery, • energy supply, • water drainage, • ventilation, • factors affecting the security of the working area. Organizational factors Organizational factors are related to the organization of work and contractors in the construction of underground objects. These are mainly: • corresponding numerical and structural ensemble of performers, • appropriate knowledge and practical skills of operators, • adequate motivation of contractors, • an appropriate organization of working time, • an appropriate organized execution of the various stages of technological process. In separate groups the presupposed influential parameters were selected on the basis of mathematical analysis. The link between natural, designing and technological conditions is worked out in the form of a mathematical model. The right choice and selection of in- fluential parameters was confirmed by comparing the measured convergence with the convergence calculated on the basis of model. The research of natural, designing and technological conditions resulted in the elaboration of systematization of supporting methods, and in the calculation of the supporting load bearing capacity and of the combination of supporting elements. Categorisation of geological and geo-technical conditions and the necessary supports load bearing capacity is dealt with separately for three basic groups (trunk, access, gate) of underground roadways. Underground structures and execution methods PV underground structures are divided into three groups[3]: • the main or trunk underground roadways, • important or access underground roadways and • floor level or gate objects (allowed convergence 20 %). Trunk roadways Trunk roadways are designed at safety pillar distance totalling 120 m. The allowed common convergence ensuring permanent roadway stability is estimated at 5 %. In order to ensure the stability of the roadways we have to select the support system with load bearing capacity of at least 450 kPa at 8,35 MPa compressive strength, at least 500 kPa at 5,43 MPa compressive strength, and at least 650 kPa at 3 MPa compressive strength. Access roadways They are designed at safety pillar distance which totals 50 m for such roadways. The allowed common convergence of access roadways is 10 %. To ensure the stability at this convergence the load bearing capacity of the supporting system has to total 300 kPa at 8,35 MPa compressive strength, at least 350 kPa at 5,43 MPa compressive strength, and at least 450 kPa at 3MPa compressive strength. Gate roadways Besides taking over the influences caused by face advance during the roadway drivage, gate roads have to take load also dynamic tensions developing in front of the active coal face. So the worst case is considered in the categorisation, namely when the face crosses over the roadway. The allowed convergence still enabling the use of roadway is 20 %. To ensure this stability we have to select the support system with load bearing capacity totalling at least 150 kPa at 8,35 MPa uniaxial compressive strength, at least 200 kPa at 5,43 MPa compressive strength, and at least 300 kPa at 3MPa compressive strength. Roadway drivage Technological procedures of driving roadways depend on the use and the life period of roadway. Gate roads are developed exclusively in the coal seam, their life period is on average 1 year. They are supported by girders and wooden struts. The density of building in girders is from 0,83 of girders set per one meter of roadway to 2,5 of set per one meter of driven roadway. As additional supporting measures anchor systems with composite and rope bolts are used, or the isolation of roadway frame with backfilling mixtures made of electrofilter ash functioning at the same time as prevention against the oxidation processes in roadways. Successful use of backfilling mixtures has decreased the range of coal mine fires by more than 95 %, whereas the reconstructions of roadways has been almost completely reduced. Access roads are supported in a similar way as gate roads, the minimum density of building in girders amounts to 1,66 of set per one meter of driven roadway. Besides this, different anchor systems are included in supporting the roadways. Trunk roadways are supported by girders, bolts, and shotcrete. The life period of this roadways is up to 10 years. Technological procedures of trunk roadway drivage are represented by the driving technology using concrete or shotcrete combined with anchoring systems. The life period of trunk roadways is more than 10 years, and even to the very end of coal mine exploitation. Technology of building gate roadways with steel arches, isolation and backfilling the frame with fly ash mixtures The basic feature of the technology[2] is primarily linked on supporting roads with a steel arch supports and filling edge lines with different mixtures of fly ash. This kind of technology is ba-sicaly used in execution of floor level roadways and also in making access roadways with a shorter life time. By filling voids between wooden panels and the coal in particular security against the development of oxidation processes in coal and the stability of the road is improved. Excavation of profile is achieved by using roadheader. Profile cutting is usually performed in three phases, which are divided onto the ceiling, side and floor of profile. Step advancement depends of the conditions at working area, ranging from 0.8 m to 1.6 m. Roadway support consists of steel arches supports and wooden panels placed successively after each phase of the cutting profile of a roadway. There are severe types of steel arches support profiles used, they consist of 6 or 7 modified profiles. The advantage of the modified (bell shaped) profile can be determined by providing the same support quality as a circular profile and also offers space advantages in terms of required usable area of the constructed roadway and in phase of excavation The designed profile area is between 13 m2 and 18 m2. The over all design profile radius is 2 200 mm or 2500 mm. In accordance with increase of a profile the length and the overall weight of support system increases. At present we use K 24 steel arch supports, 24 kg/m weight. In the past, several types of steel arches supports were tested. In 2011, we installed TH29 steel arches support. First results of the experiment show that this kind of support could be a good choice in replacing the existing K24 steel arches support. Joints in steel arches support units are interconnected with body clamps. Due to the studies and research of capacity of the clamp, the result is that screw clamps which have been tested and implemented had a better performance. Their advantage is represented by a greater strenght of screw clamps, easier remove and when overstressed, it does not fail instantly. Clamp is shown in Figure 1. Figure 1. Screw Clamp Figure 2. Strut for connecting steel frames of arch support units ^ COMPOSITE ARCHES / / Figure 3. Cross section of the roadway To achieve greater stability of roadways steel arches are interconnected on four sides with struts. By replacing the clamp it was necessary to design a new strut, which is shown in Figure 2. It is possible to combine steel arch support with other types of anchors or bolts. To protect the head wall during construction phase of roadway (from falling pieces of coal) we temporary use composite bolts, which are combined with wooden paneling. For anchoring in the roadway we use steel anchors, composite bolts and plain strand cables. Composite bolts are used in gate roads of the longwall face panel because steel anchors can not be cut by shearer. Composite bolts provide similar load bearing capacity as steel anchors. As using composite bolts the advancement of the longwall face rapidly increases because of the ability of the longwall shearer to cut them. Loading of bore holes for composite bolts is carried out with artificial quick-setting materials. With composite bolts we managed to reduce the overall installation of steel arches supports because the distance between two sets of support is longer. (from 0.4 m to 0.6 m in some cases 0.75 m) Wood turned out to be the most effective material for framework of the roadway, it provides sufficient support for the pressure of shotcrete filling mixture and it is easier to remove when necessary. Using this technology wood is economically accepted. For execution of framework we use oak panels, acacia and pine trees, thickness of the panels ranges from 40 mm to 100 mm. As an alternative material for the framework, it is possible to use a composite mash, which is processed in order of good connection with shot-crete materials for outdoor insulation paneling. With the aim of preventing leakage of thin mixture at filling voids between the framework and the outside material layers it is necessary to seal the framework. It is done by making a thin lining over the entire surface of the roadway. Two procedures at the same time are provided, excavation and supporting on one hand and isolating and filling-in on the other. Disposition of a roadway is shown in Figure 3. The results of geotechnical research show that the interaction between installed liner and surrounding materials is efficient when the filling-in of space between them is carried out right after the installation of the liner.[6] According to those researches PV changed the roadway excavation procedure and the installation of the necessary liner support. The excavation area of the roadway face is more extensive because of the addi- tional installation of shotcrete fly ash filling. Radius of the roadway increases by 150 mm. However, this method is not suitable for the roadway excavation in faulted coal layers where cracking begins right after cutting out the profile. After cutting the roadway, face steel supports are installed into the segments. Then we install composite mash which is covered with fabrics. The fabrics prevent from the leakage of fly ash filling mixture. Another alternative is installation of wooden panels, where the outer layer is fabrics with the same assignment as before. When the framework in length between 0.8 m and 1.6 m is finished we immediately fill-in with fly ash filling mixture from the bottom to the top of the framework. Fly ash filling is distributed through high pressure pipelines from the mixing device through the pump to the side. The overall density of the thick filling material consists of a mixture of fly ash, lime, cement, clay, and water in accordance to appropriate recipe.[5] Filling-in is carried out on a distance of 30 m to 50 m from the roadway face. We use a thinly mixture of fly ash and water. Development and technological improvements of equipment used in construction of the roadway Analysis of equipment at the roadway excavation side showed that it is nec- essary to achieve greater efficiency and humanization of the technology, and that a comprehensive renewal of equipment is necessary. GPK road-header was an appropriate solution for the construction of roadways in PV, but it was technologically obsolete. Removal of coal from the roadway excavation site has been edited by a chain conveyor, which length was manually increased or decreased, similar to the belt conveyor. It was also necessary to allocate workers at each conveyor intersection. This method of removal has proven to be economically unap-propriate solution. Accordingly the delivery of the materials to the site was arranged by diesel locomotives and monorail transportation and it was proper solution. Delivery of material to the excavation site has been edited by hand, which represented a lot of hard physical work to be carried out for the employees. In Figure 4 there is a drawing of equipment prior to the technological modernization. Update of the existing and in development of new equipment for the construction of roadways has been linked to all stages of technological processes. The update was made on the cutting roadway face with the new roadhead-er, on installation of support elements with steel sets of feeding equipment and drilling rig for drilling anchor boreholes, on the crushing and conveying system with a continuous extension of Figure 4. Disposition of equipment on site (before the update) the transport. The system of roadway excavation has been fully automated. In this paper, we briefly summarize the achievements in the development of equipment in technological processes. [4] New GPK PV roadheader is the product of Coal Mine Velenje which meets the requirements of the technological process of roadway building from 13 m2 to 18 m2. Electrical equipment for the roadheader has been developed in cooperation with Bartec company. Hydraulic steering system is our own Coal Mine Velenje development result. Steel base structure was built by Russian manufacturer. Hazemag MCS 27 crushing and conveying system equipped with a continuous extension of the transportation is the product of collaboration between our own experts and the German manufacturer Hazemag. It serves to crush and transport coal between the roadway Figure 5. Automating removal excavations from roadway site excavation machine and belt conveyor. Crushed coal is transported by a chain conveyor to a belt conveyor. Hazemag MCS 27 crushing and conveying system with a continuous extension of the transportation is self-advanced unit pulled behind belt conveyor 800 AST transporter with a belt width of 800 448 Dervaric, E., Vukelic, Z., Medved, M. Figure 6. Disposition of equipment at excavation site (after comprehensive technological renovation) mm designed for conveying coal from excavation site. Conveyor consists of the main steel segments, the drive and convey unit. The unit has ability of continuous belt conveyor lengthening. Removal of the excavated material with the use of Hazemag unit is fully automated by implemented steering sistem Mincos. Figure 5 shows the control unit. For updated delivery and installation of material has been developed a new steel sets feeding equipment PL 08 PV. Development of this equipment is the result of collaboration between experts in Coal Mine Velenje and companies DBSS (steel works) and Bartec (steering system). The GTA company has developed a mobile drilling unit for drilling boreholes in the roadway face and anchoring boreholes around the roadway liner. In Figure 6 there is a drawing of equipment at the roadway excavation site undergoing update of equipment and facilities. Conclusion In the years from 2008-2010 there has been an extensive research development in roadway excavation. Update of the existing and development of new equipment for the construction of roadways has been linked to all stages of technological process. Numerous applications of research developments improved all stages of technological processes of roadway excavation. It is absolutely necessary to continue the research development in the future. References [i] Dervaric, E. (1997): Rock categorization for roadway support design and roadway development. Dis- sertation thesis, Ljubljana. [2] Dervaric, E., Kemperle, C., Hace, M. (1994): Fire prevention system at Velenje coal mine. Pasamehme-toglu, A. Gunhan. Mine planning and equipment selection 1994. Brookfield: A. A. Balkema, Rotterdam; 895-898. [3] Dervaric, E. (1997): Roadway [5] drivage designing and development model at Velenje coal mine. Panagiotou, G. N., Sturgul, J. R. Mine simulation: proceedings of the First international symposium [6] on mine simulation via the INTERNET / 2-13 December 1996. (1997) Rotterdam, A. A. Balkema, 90. [4] Vukelic, Z., Dervaric, E., Likar, J., KoLENC, M., ClŽMEK, D., POHOREC I., Mayer, J., Sotler, B. (2010): Izgradnja etažnih jamskih prog v Premogovniku Velenje. (2010): raziskovalno-razvojni projekt: Poročilo o delu za leto 2010. Ljubljana: Univerza v Ljubljani, NTF-OGR. Ternik, P., Marn, J., Kanduti, D., Dervarič, E. (2001): The power law as a model for an electrostatic filter ash and water mixture. Stroj. Vestn., 47(6), pp. 248-262. Likar, J., Dervarič, E., Medved, M., Čadež, J., Jeromel, G. (2006): Some methods of analysing caving processes in sublevel coal mining. RMZ-mater. geoenviron., Vol. 53, No. 2, pp. 203-220. Authors Index, Vol. 58, No. 4 Bradaškja Boštjan Dervarič Evgen Edet A. Ekwere A. S. Ephraim Bassey Edem Fajfar Peter Fazarinc Matevž Karpe Blaž Krušič Uroš Kugler Goran Markoli Boštjan Medved Milan Naglič Iztok Peruš Iztok Pirnar Boštjan Štrekelj Neva Terčelj Milan Ukpong A. J. Verbovšek Timotej Vukeljic Željko bostjan.bradaskja@acroni.si evgen.dervaric@ntf.uni-lj.si basifrem@yahoo.com peter.fajfar@omm.ntf.uni-lj.si matevz.fazarinc@omm.ntf.uni-lj.si blaz.karpe@omm.ntf.uni-lj.si uros.krusic@metalravne.com goran.kugler@omm.ntf.uni-lj.si bostjan.markoli@omm.ntf.uni-lj.si milan.medved@rlv.si iztok.naglic@omm.ntf.uni-lj.si iztok.persuh@ntf.uni-lj.si neva.strekelj@omm.ntf.uni-lj.si milan.tercelj@omm.ntf.uni-lj.si zerratta77@yahoo.com timotej.verbovsek@ntf.uni-lj.si zeljko.vukelic@ntf.uni-lj.si Antic Aco Akanji A. Olusoji Akinmosin Adewale Avdušinovic Hasan Bisht Sandeep Bončina Tonica Bradaškja Boštjan Burič Edi Chen Jiawen Choubey V. K. Čenčur Curk Barbara Dervarič Evgen Dragojevic Vukašin Edet A. Ekwere A. S. Ephraim Bassey Edem Fajfar Peter Fazarinc Matevž Gigovic-Gekic Almaida Glavaš Zoran Godec Matjaž Gojic Mirko Huis Melanija Kanduč Tjaša Karpe Blaž Kosec Borut Kosec Gorazd Kosec Ladislav Kovačevic Dušan Kovačič Miha Kožuh Stjepan Krušič Uroš Kugler Goran Kumar Vivek Author's Index, Vol. 58 antica@uns.ac.rs tonica.boncina@uni-mb.si bostjan.bradaskja@acroni.si jiawen.chenn@gmail.com barbara.cencur@guest.arnes.si evgen.dervaric@ntf.uni-lj.si vukasin.dragojevic@impol.si basifrem@yahoo.com peter.fajfar@omm.ntf.uni-lj.si matevz.fazarinc@omm.ntf.uni-lj.si almaida.gigovic@famm.unze.ba glavaszo@simet.hr matjaz.godec@imt.si gojic@simet.hr melanija@geoportal.si tjasa.kanduc@gmail.com blaz.karpe@omm.ntf.uni-lj.si borut.kosec@omm.ntf.uni-lj.si gorazd.kosec@acroni.si ladislav.kosec@omm.ntf.uni-lj.si miha.kovacic@store-steel.si uros.krusic@metalravne.com goran.kugler@omm.ntf.uni-lj.si Lamut Martin Lazic Ladislav Likar Andrej Likar Jakob Markoli Boštjan Medved Milan Milačič Radmila Mladenovič Ana Mrvar Primož Naglič Iztok Nagode Aleš Novak-Marcinčin Jozef Nton M. E. Oblak Tina Ogungbemi Tope Shade Oruč Mirsada Osinowo O. Olawale Pečlin Polona Peruš Iztok Pezdič Jože Pirnar Boštjan Purandara B. K. Ribarič Samo Rimac Milenko Rogan Šmuc Nastja Rozman Janez Rozman Niko Sati Archna Sharma Shivesh Smolej Anton Sood Anchal Steinacher Matej Šarler Božidar Ščančar Janez Štrekelj Neva Talabi Abel O. martin.lamut@space.si lazic@siscia.simet.hr andrej@geoportal.si jakob.likar@ntf.uni-lj.si bostjan.markoli@omm.ntf.uni-lj.si milan.medved@rlv.si radmila.milacic@ijs.si primoz.mrvar@omm.ntf.uni-lj.si iztok.naglic@omm.ntf.uni-lj.si ales.nagode@omm.ntf.uni-lj.si ntonme@yahoo.com wale.osinowo@mail.ui.edu.ng iztok.persuh@ntf.uni-lj.si purandarabk@yahoo.com nastja.rogan@guest.arnes.si jnzrzmn6@gmail.com dr.shiveshsharma@gmail.com anton.smolej@omm.ntf.uni-lj.si matej.steinacher@omm.ntf.uni-lj janez.scancar@ijs.si neva.strekelj@omm.ntf.uni-lj.si soar_abel@yahoo.com Terčelj Milan Terzic Katarina Tijani Moshood N. Ukpong A. J. Unkic Faruk Vahčič Mitja Večko Pirtovšek Tatjana Venkatesh B. Verbovšek Timotej Vukeljic Željko Zavšek Simon Zeljko Snježana Zeljkovic Milan Zuliani Tea Zupanič Franc Žula Janja milan.tercelj@omm.ntf.uni-lj.si mn.tijani@mail.ui.edu.ng zerratta77@yahoo.com timotej.verbovsek@ntf.uni-lj.si zeljko.vukelic@ntf.uni-lj.si simon.zavsek@rlv.si franc.zupanic@uni-mb.si janja.zula@rlv.si RMZ MATERIALS AND GEOENVIRONMENT Contents Volume 58, 2011/1, 2, 3, 4 58/1 Quasicrystal-strengthened cast Al-alloys 1 Zupanic, F., Boncina, T., Rozman, N., Markoli, B. Wear level influence on chip segmentation and vibrations 15 of the cutting tool Antic, A., Kovacevic, D., Zeljkovic, M., Kosec, B., Novak-Marcincin, J. Soft annealing productivity optimization 29 Kovacic, M., Sarler, B. Cell for electrochemical and electrophysiological 39 measurements in peripheral nerves Peclin, P., Ribaric, S., Rozman, J. Assessment of heavy metal contamination in paddy soils from 47 Kocani Field (Republic of Macedonia): part II Rogan Smuc, N. Investigations of carbide precipitates in modified 9 % Cr 59 steel using different electron spectroscopy techniques Nagode, A., Godec, M., Kosec, G., Kosec, L. Reconstruction of two road tunnels Likar, J., Huis, M., Likar, A. 58/2 71 Microstructural stability of gray iron thin section castings 101 for enameling Zeljko, S., Glavas, Z., Terzic, K., Unkic, F. The effect of cooling rates on microstructures and hot 113 workability of BRCMO2 tool steel Večko Pirtovšek, T., Fazarinc, M., Kugler, G., Mrvar, P., Terčelj, M. Effect of heat treatment and test temperature on fracture 121 type of steel Nitronic 60 Gigovic-Gekic, A., Oruč, M., Nagode, A., Avdušinovic, H. Bacterial indicators of faecal pollution and physiochemical assessment 129 of tributaries of Ganges River in Garhwal Himalayas, India Sati, A., Sood, A., Sharma, S., Bisht, S., Kumar, V. Integrated geophysical and geotechnical investigation of the failed 143 portion of a road in basement complex Terrain, Southwest Nigeria Osinowo, O. Olawale, Akanji, A. Olusoji, Akinmosin Adewale Sequence stratigraphic framework of K-field in part of 163 Western Niger delta, Nigeria Nton, M. E., Ogungbemi, T. S. Environmental impacts of asphalt and cement composites 181 with addition of EAF dust Oblak, T., Ščančar, J., Vahčič, M., Zuliani, T., Mladenovič, A., Milačič, R. Adsorption capacity of the Velenje lignite: methodology 193 and equipment Žula, J., Pezdič, J., Zavšek, S., Burič, E. Strokovni posvet Podnebni ekstremi in varna oskrba s pitno vodo 217 Čenčur Curk, B. 58/3 The effect of defects on tensile strength of the continuous 241 steel casting products Gojic, M., Lazic, L., Kožuh, S., Kosec, L. An analysis of the quasi-chemical model of a 253 ternary solution: On the counting of pairs Chen, J. Finite element solution strategy to analyze heterogeneous structures 259 Lamut, M. Tracing coalbed gas dynamics and origin of gases in advancement of 273 the working faces at mining areas Preloge and Pesje, Velenje Basin Kanduč, T., Žula, J. Zavšek, S. Sediment transport and sedimentation in a 289 coastal ecosystem - a case study Purandara, B. K., Venkatesh, B., Choubey, V. K. Integrated remote sensing and GIS approach to groundwater potential 303 assessment in the basement terrain of Ekiti area southwestern Nigeria Talabi, A. O., Tijani, M. N. Influence of the heat treatment and extrusion process on the mechanical 329 and microstructural properties of the AISilMgMn Alloy Steinacher, M., Dragojevic, v., Smolej, A. Petdeset let delovanja Metalurškega instituta 339 "Kemal Kapetanovic" v Zenici Kosec, B. 58/4 Effect of cooling rate on the constitution of Al-Mn-Be-Cu 357 Štrekelj, N., Naglič, I., Karpe, B., Markoli, B. Development of intelligent knowledge-based computing environment for 367 controling the proces parameters and nonmetalic inclusions in steels Krušič, u., Terčelj, M., Kugler, G., Peruš, I. Determination of hot workability and processing maps 383 for AISI 904L stainless steel Fajfar, P., Bradaskja, B., Pirnar, B., Fazarinc, M. A comparison of parameters below the limit of detection in 393 geochemical analyses by substitution methods Verbovsek, T. Compositional appraisal and quality implications of a metacarbonate 405 deposit occurring in parts of southeastern Nigeria Ephraim, B. E. Hydrochemistry of the near shore marine bay, Calabar river 421 (South-eastern, Nigeria) Ekwere, A. S., Edet, A., Ukpong, A. J. Modernization of technological process and equipment at 437 floor level roadways execution in Velenje coal mine Dervaric, E., Vukelic, Z., Medved, M. 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Ass. Petrol. Geol. Bull.; Vol. 43, No. 1, pp. 1-38, Tulsa. SECOND OPTION - in numerical order [1] Trcek, B. (2001): Solute transport monitoring in the unsaturated zone of the karst aquifer by natural tracers. Ph. D. Thesis. Ljubljana: University of Ljubljana 2001;125 p. [2] Higashitani, K., Iseri, H., okuhara, K., Hatade, S. (1995): Magnetic Effects on Zeta Potential and Diffusivity of Nonmagnetic Particles. Journal of Colloid and Interface Science, 172, pp. 383-388. Citing the Internet site: CASREACT-Chemical reactions database [online]. Chemical Abstracts Service, 2000, updated 2. 2. 2000 [cited 3. 2. 2000]. Accessible on Internet: http://www.cas.org/ CASFILES/casreact.html. Texts in Slovene (title, abstract and key words) can be written by the author(s) or will be provided by the referee or by the Editorial Board. PREDLOGA ZA SLOVENSKE ČLANKE Naslov članka (Times New Roman, 14, Na sredino) The title of the manuscript should be written in bold letters (Times New Roman, 14, Center) Ime Priimek1, ..., Ime Priimekx (Times New Roman, 12, Na sredino) XUniverza..., Fakulteta., Naslov., Država. (Times New Roman, 11, Center) *Korespondenčni avtor. E-mail: ... (Times New Roman, 11, Center) Izvleček (Times New Roman, Navadno, 11): Kratek izvleček namena članka ter ključnih rezultatov in ugotovitev. Razen prve j bo tekst zamaknjen z levega roba za 10 mm. Dolžina naj ne presega petnajst (15) vrstic (10 je priporočeno). Abstract (Times New Roman, Normal, 11): The abstract should be concise and should present the aim of the work, essential results and conclusion. It should be typed in font size 11, single-spaced. Except for the first line, the text should be indented from the left margin by 10 mm. The length should not exceed fifteen (15) lines (10 are recommended). Ključne besede: seznam največ 5 ključnih besed (3-5) za pomoč pri indeksiranju ali iskanju. Uporabite enako obliko kot za izvleček. Key words: a list of up to 5 key words (3 to 5) that will be useful for indexing or searching. Use the same styling as for abstract. Uvod (Times New Roman, Krepko, 12) Dve vrstici pod ključnimi besedami se začne Uvod. Uporabite pisavo Times New Roman, velikost črk 12, z obojestransko poravnavo. Naslovi slik in tabel (vključno z besedilom v slikah) morajo biti v slovenskem jeziku. Slika (Tabela) X. Pripadajoče besedilo k sliki (tabeli) Obstajata dve sprejemljivi metodi navajanja referenc: 1. z navedbo prvega avtorja in letnice objave reference v oklepaju na ustreznem mestu v tekstu in z ureditvijo seznama referenc po abecednem zaporedju prvih avtorjev; npr.: "Detailed information about geohistorical development of this zone can be found in: Antonijevic (1957), Grubic (1962), ..." "... the method was described previously (Hoefs, 1996)" 2. z zaporednimi arabskimi številkami v oglatih oklepajih na ustreznem mestu v tekstu in z ureditvijo seznama referenc v številčnem zaporedju navajanja; npr.; "... while the portal was made in Zope[3] environment." Materiali in metode (Times New Roman, Krepko, 12) Ta del opisuje razpoložljive podatke, metode in način dela ter omogoča zadostno količino informacij, da lahko z opisanimi metodami delo ponovimo. Rezultati in razprava (Times New Roman, Krepko, 12) Tabele, sheme in slike je treba vnesti (z ukazom Insert, ne Paste) v tekst na ustreznem mestu. Večje sheme in tabele je po treba ločiti na manjše dele, da ne presegajo ene strani. sklepi (Times New Roman, Krepko, 12) Povzetek rezultatov in sklepi. Zahvale (Times New Roman, Krepko, 12, Na sredino - opcija) Izvedbo tega dela je omogočilo......... viri (Times New Roman, Krepko, 12) Glede na uporabljeno metodo citiranja referenc v tekstu upoštevajte eno od naslednjih oblik: PRVA MOŽNOST (priporočena) - v abecednem zaporedju Casati, P., Jadoul, F., Nicora, A., Marinelli, M., Fantini-Sestini, N. & Fois, E. (1981): Geologia della Valle del'Anisici e dei gruppi M. Popera - Tre Cime di Lavaredo (Dolomiti Orientali). Riv. Ital. Paleont.; Vol. 87, No. 3, pp. 391-400, Milano. Folk, R. L. (1959): Practical petrographic classification of limestones. Amer. Ass. Petrol. Geol. Bull.; Vol. 43, No. 1, pp. 1-38, Tulsa. DRUGA MOŽNOST - v numeričnem zaporedju [1] Trček, B. (2001): Solute transport monitoring in the unsaturated zone of the karst aquifer by natural tracers. Ph. D. Thesis. Ljubljana: University of Ljubljana 2001;125 p. [2] Higashitani, K., Iseri, H., okuhara, K., Hatade, S. (1995): Magnetic Effects on Zeta Potential and Diffusivity of Nonmagnetic Particles. Journal of Colloid and Interface Science, 172, pp. 383-388. Citiranje spletne strani: CASREACT-Chemical reactions database [online]. Chemical Abstracts Service, 2000, obnovljeno 2. 2. 2000 [citirano 3. 2. 2000]. Dostopno na svetovnem spletu: http://www. cas.org/CASFILES/casreact.html. Znanstveni, pregledni in strokovni članki ter predhodne objave se objavijo v angleškem jeziku. Izjemoma se strokovni članek objavi v slovenskem jeziku. /ëe Inženirska geologija Hidrogeologija Geomehanika Projektiranje Tehnologije za okolje Če se premakne, boste ¡zvedeli prvi Le i ta Geosvstems rešitve ¿a opazovanje premikov ■ Geodetski senzorji samodejni tahlmetri, GPS in GNSS senzorji ■ Geotehnični senzorji senzorji nagiba Campbell datalogger ■ Drugi senzorji meteo, senzorji nivoja • Programska oprema za zajem in obdelavo podatkov, analizo opazovanj, alarmiranje, predstavitev rezultatov servis vvhen il Grrrterviv d.o.o. 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