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  • 1.
    Asghari, M.
    et al.
    University of Tehran, Tehran, Iran.
    Noaparast, M.
    University of Tehran, Tehran, Iran.
    Shafaie, S. Z.
    University of Tehran, Tehran, Iran.
    Ghassa, S.
    University of Tehran, Tehran, Iran.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, USA.
    Recovery of coal particles from a tailing dam for environmental protection and economical beneficiations2018Inngår i: International Journal of Coal Science & Technology, ISSN 2095-8293Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Considerable amounts of coal particles are accumulated in the tailing dams of washing plants which can make serious environmental problems. Recovery of these particles from tailings has economically and environmentally several advantages. Maintaining natural resources and reducing discharges to the dams are the most important ones. This study was examined the possibility to recover coal particles from a tailing dam with 56.29% ash content by using series of processing techniques. For this purpose, gravity separation (jig, shaking table and spiral) and flotation tests were conducted to upgrade products. Based the optimum value of these processing methods, a flowsheet was designed to increase the rate of recovery for a wide range of coal particles. Results indicated that the designed circuit can recover over 90% of value coal particles and reduce ash content of product to less than 14%. These results can potentially be used for designing an industrial operation as a recycling plant and an appropriate instance for other areas to reduce the environmental issues of coal tailing dams.

  • 2.
    Chehreh Chelgani, S.
    et al.
    Surface Science Western, Research Park, University of Western Ontario, Ontario, Canada.
    Hart, B.
    Surface Science Western, Research Park, University of Western Ontario, Ontario, Canada.
    Xia, L.
    Surface Science Western, Research Park, University of Western Ontario, Ontario, Canada.
    A TOF-SIMS surface chemical analytical study of rare earth element minerals from micro-flotation tests products2013Inngår i: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 45, nr May, s. 32-40Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Thor lake deposit is a world class resource of rare earth (REE) metals and minerals in Canada. Development work to optimize a REE mineral recovery process flow sheet is underway, however, given the ore mineralogy; the developed reagent scheme is relatively complex. As part of a research project, micro-flotation tests were conducted on a feed sample in order to examine factors affecting stream partitioning. SEM–EDX was performed to evaluate variability in grain composition between streams (concentrate and tails) and TOF-SIMS surface analysis was used to determine statistically significant differences in surface species particularly related to potential activation (or depression) of the examined mineral phases. SEM–EDX analysis reveal that the concentrate has a significantly higher proportion of REE bearing grains (carbonates and phosphates) relative to the tail (almost none were identified). Spectral fingerprinting by TOF-SIMS has allowed for the identification of all reagent species investigated. Reagent signal intensity discrimination on test stream mineral surfaces was observed by the TOF-SIMS analysis using reagents at plant concentration levels. TOF-SIMS analysis confirmed that REE bearing grains reporting to the concentrate are doing so in response to collector attachment whereas grains reporting to the tail are doing so in response to a lack of collector and/or in combination with the presence of the depressant. The surface analysis of gangue phases reveal a similar reagent discrimination; the signal intensity of collector species was significantly higher on the concentrate samples relative to the tails while depressant species were significantly enriched on the surface of the gangue phases in the tail samples. A detailed evaluation of the surface species representing the various reagents used in flotation scheme revealed a distinct competitive relationship between two of the reagents. The surface analysis identified that when used in concurrently, there appears to be a negative feedback resulting in a significant reduction in loading for several of the collectors on grains reporting to the concentrate. An evaluation of the effect of reagents on REE mineral in pilot plant is currently under way.

  • 3.
    Chelgani, Saeed Chehreh
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Exploring relationships of gross calorific value and valuable elements with conventional coal properties for North Korean coals2019Inngår i: International Journal of Mining Science and Technology, ISSN 2095-2686Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Coal in North Korean (NKC) is one of the most important products; however, based on various strategic policies its detail properties remain opaque even for general researchers. Since there are some signs for opening of the North Korea economy, this investigation as a modest effort is going to explore principle relationships among some essential parameters of NKCs such as gross calorific value (GCV), valuable elements and conventional properties by different statistical methods. Correlations indicated that ultimate parameters (carbon, nitrogen, and hydrogen) are the best GCV predictors for NKCs in comparison with proximate parameters (ash, moisture and volatile matter). Multivariable regression demonstrated that predicted GCV based on ultimate properties has a quite accuracy when correlation of determination was 0.99. Descriptive statistics processes showed that on average, the contents of valuable elements such as Ga and V for NKCs are higher than the world coal ranges and they can be considered as byproducts of combustion of NKCs. Pearson correlations indicated that Y may have a mixed organic-inorganic affinity while Ga and V mainly occur in the inorganic part (mineral matter) of NKCs. High inter-correlations between Ga-V and Al showed that aluminosilicates can be considered as their main bring minerals.

  • 4.
    Chelgani, Saeed Chehreh
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Investigating the occurrences of valuable trace elements in African coals as potential byproducts of coal and coal combustion products2019Inngår i: Journal of African Earth Sciences, ISSN 1464-343X, Vol. 150, s. 131-135Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    There is a growing attention in valuable trace elements (TEs) in coal and coal combustionproducts as they can potentially be future resources of valuable TEs. Therefore, understanding the mode of occurrences of valuable TEs in coal has several advantages for their economical and industrial extractions. Since there is limited information on the affinity of valuable TEs in the structure of African coals, this study explores correlations between conventional coal properties and concentration of vanadium, yttrium, gallium and lithium as valuable TEs for a wide range of African coal samples (139 samples) from South Africa, Botswana, Egypt, Tanzania, Nigeria and Zambia by statistical methods. Statistical assessments indicated that the concentrations of Y, V, Li and Ga for these countries are higher than their value in the world coal (on average). The outcomes of assessments showed that the Li, Ga and V are associated with the mineral matter fraction (inorganic affinity) of the coal where they have significant positive correlations with ash and Al (as a major element) and potentially clay minerals are their main bearing minerals. However, statistical explorations suggested that Y may have both the organic and inorganic occurrences in the African coal samples

  • 5.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, USA.
    Occurrences of valuable trace elements in Iranian coals as potential coal combustion byproducts2018Inngår i: International Journal of Coal Preparation and Utilization, ISSN 1939-2699Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In Iran, coal as one of the primary energy resources mainly has just one customer (Iranian Steel Corporation); therefore, coal and production of its combustion have recently attracted much attention as promising alternative sources for valuable trace metals (VTM). Since there has been few principle exploration on occurrence modes of VTM for Iranian coal, this investigation assessed possible interactions between various coal conventional properties (proximate and ultimate analysis) and Y, Li, Ga, and V as VTM for Iranian coals. Statistical analyses indicated that on average, the contents of all these elements are higher than the general world coal ranges. Inter-correlation assessments showed that Ga and V in the samples are mainly associated with mineral matter (inorganic fracture of coal), e.g., mostly adsorbed by aluminosilicate (clay minerals) while Y and Li have a mixed organic and inorganic affinity. Strong interactions of Ga and V with inorganic fraction for samples with over 70% ash content released that host rocks of Iranian coal seams may also have the potential for the extraction of these two VTM. On the other hand, the mixed organic and inorganic affinity of Y and Li showed high possibility of their extraction from the coal combustion products. These results could be a critical key not only for geological and environmental information but also for developing possible procedures for their extraction.

  • 6.
    Chelgani, Saeed Chehreh
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Prediction of specific gravity of Afghan coal based on conventional coal properties by stepwise regression and random forest2019Inngår i: Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, ISSN 1556-7036, E-ISSN 1556-7230Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Coal can be considered as the main fuel for electricity generation in Afghanistan. However, there is a quite limited data available about the overall quality, distribution, and character of coals in Afghanistan. Specific gravity (S.G) of coal as a key factor can be used for the estimation of potential tonnage production and be a fundamental parameter for the selection of coal washery process method. However, there is no investigation which comprehensively explores relationships between S.G and coal properties. In this investigation, the potential of S.G prediction based on conventional properties for Afghan coal samples was explored by stepwise regression and random forest. Pearson correlation (r) and variable importance measurement (VIM) of random forest (RF) were applied to select the most effective variables among conventional parameters for the S.G prediction. Results of VIM indicated that ash and carbon content of coal samples had the highest importance for the S.G prediction. Stepwise regression and RF models were developed based on these two coal variables. Testing the generated models indicated that S.G of Afghan coals can quite accurately predict by these models (R2 > 0.90). Modeling outcomes showed that the highest S.G (S.G > 2) for Afghan coal occurred when ash was higher than 40% and carbon was lower than 30%.

  • 7.
    Chelgani, Saeed Chehreh
    et al.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Dehghan, F.
    Department of Computer engineering, Jajarm Branch, Islamic Azad University, Iran.
    Hower, J. C.
    Center for Applied Energy Research, University of Kentucky, USA.
    Estimation of some coal parameters depending on petrographic and inorganic analyses by using Genetic algorithm and adaptive neuro-fuzzy inference systems2011Inngår i: Energy Exploration and Exploitation, ISSN 01445987, Vol. 29, nr 4, s. 479-494Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Adaptive neuro-fuzzy inference systems (ANFIS) in combination with genetic algorithm (GA); provide valuable modeling approaches of complex systems for a wide range of coal samples. Evaluation of this combination (GA-ANFIS) showed that the GA-ANFIS approach can be utilized as an efficient tool for describing and estimating some of coal variables such as Hardgrove grindability index, gross calorific value, free swelling index, and maximum vitrinite reflectance with various coal analyses (proximate, ultimate, elemental, and petrographic analysis). Statistical factors (correlation coefficient, mean square error, and variance accounted for) and differences between actual and predicted values demonstrated that the GA-ANFIS can be applied successfully, and provide high accuracy for prediction of those coal variables.

  • 8.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, Ann Arbor, USA .
    Hadavandi, E
    Birjand University of Technology, Birjand, Iran.
    Hower, James C
    University of Kentucky, Lexington, USA .
    Study Relationship Between the Coal Thermoplastic Factor With Its Organic and Inorganic Properties by the Support Vector Regression Method2017Inngår i: International Journal of Coal Preparation and Utilization, ISSN 1939-2699Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Metallurgical cokes, as fuel for blast furnaces, have certain properties which are directly related to their blended parental coal characters. The maximum fluidity (MF) of coal as an energy index is typically used to estimate the coke properties. In this investigation, Support Vector Regression (SVR), as an intelligent method, has been applied to link characteristics and pyrolysis properties of coal samples with their representative MFs. SVR variable importance measurement (VIM) through a wide range of coal properties indicated that volatile matter (VM) and maximum vitrinite reflectance (Rmax) are the most effective parameters for the MF prediction. The results indicated that low rank coal samples (VM>45% and Rmax>0.7) have log(MF) higher than 14 and high rank ones (VM<35% and Rmax<0.6) have log(MF) less than 4. The evaluation of the SVR model trained with these two selected input variables showed that SVR can predict MF quite accurately where the coefficient of determination (R2) between actual MF and SVR predicted was 0.86. According to these results, generation of SVR models which can predict and measure variable importance dependently, potentially may be applied for the scaling up of laboratory coal thermoplastic behavior to industrial levels, helping to sustainable development, and satisfactorily estimating coal consumption in the steel-making plants.

  • 9.
    Chelgani, Saeed Chehreh
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Hadavandi, Esmaeil
    Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran.
    Hower, James C.
    Center for Applied Energy Research, University of Kentucky, Lexington, KY, USA.
    Estimation of heavy and light rare earth elements of coal by intelligent methods2019Inngår i: Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, ISSN 1556-7036, E-ISSN 1556-7230Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Since last two decades, several investigations in various countries have been started to discover new rare earth element (REE) resources. It was reported that coal can be considered as a possible source of them. REE of coal occur in low concentrations, and their detection is a complicated process; therefore, their predictions based on conventional coal properties (proximate, ultimate and major elements (ME)) may have several advantages. However, few studies have been conducted in this area. This study examined relationships between coal properties and REE (HREE and LREE) for a wide range of coal samples (708 samples). Variable importance measure (VIM) by Mutual information (MI) as a new feature selection method was applied to consider the heterogeneous structure of coal and assess the individual relation between coal parameters and REE to select the compact subsets as input variables for modeling and improve the performance of prediction. VIM by MI showed that Si-Carbon, and Al-Hydrogen are the best subsets for the prediction of HREE and LREE concentrations, respectively. A boosted neural network (BNN) model as a new predictive tool was used for REE prediction. BNN can significantly reduce generalization of error. Results of BNN models showed that the HREE and LREE concentrations can satisfactory estimate (R 2 : 0.83 and 0.89, respectively). Results of this investigation were approved that MI-BNN can be used as a potential tool for prediction of other complex problems in energy and fuel areas.

  • 10.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, Ann Arbor, USA.
    Hart, B.
    University of Western Ontario, Ontario N6G0J3, Canada.
    Explaining surface interactions for common associated gangues of rare earth minerals in response to the oxalic acid2018Inngår i: International Journal of Mining Science and Technology, ISSN 2095-2686, Vol. 28, nr 2, s. 343-346Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In the flotation of rare earth minerals (REMs), oxalic acid is reportedly acting both as a depressant and pH modifier. Although results of testing have established the significance of oxalic acid in the flotation process, its specific role in either the recovery or selectivity of REMs over their common gangue minerals is not well understood. Pulp pH reduction trials with alternative acids have not shown the same effect on the REMs recovery or the depression of gangue phases. This work studies the effect of oxalic acid on the surface of common REMs gangue minerals (quartz and carbonates (dolomite and calcite)) in a series of conditioning tests. Gangue surface analyses by time of flight secondary ion mass spectroscopy (TOF-SIMS) indicate that oxalic acid inhibits the transfer of secondary ions generated during the conditioning process from one mineral to another. In this regard, the oxalate anion acts to fix ions in solution through chelation, limiting their participation in surface adsorption.

  • 11.
    Chelgani, Saeed Chehreh
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi. Surface Science Western, Research Park, University of Western Ontario, Canada.
    Hart, B.
    Surface Science Western, Research Park, University of Western Ontario, Candada.
    Grady, W. C.
    West Virginia Geological and Economic Survey, USA.
    Hower, J. C.
    Center for Applied Energy Research, University ofKentucky, USA.
    Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS2011Inngår i: International Journal of Coal Preparation and Utilization, ISSN 1939-2699, Vol. 31, nr 1, s. 9-19Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66 MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R 2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R 2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV.

  • 12.
    Chelgani, Saeed Chehreh
    et al.
    Surface Science Western, Research Park, University of Western Ontario, Canada.
    Hart, B.
    Surface Science Western, Research Park, University of Western Ontario, Candada.
    Marois, J.
    Niobec Inc., Canada.
    Ourriban, M.
    Niobec Inc., Canada.
    Study of pyrochlore matrix composition effects on froth flotation by SEM-EDX2012Inngår i: Minerals Engineering, ISSN 0892-6875, Vol. 30, s. 62-66Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM–EDX) was used to analyse pyrochlore grains from Niobec froth flotation plant. Approximately 200 pyrochlore gains from the mill feed, pyrochlore rougher feed, pyrochlore rougher concentrate, and tail were analysed in order to identify a potential relationship between pyrochlore matrix composition and selective separation. Analyses indicate that pyrochlore grains with high Fe content appear to be less recoverable than those with a lower Fe content. Furthermore, analysis indicates that the flotation response is related to matrix Fe rather than Fe occurring as inclusions within the pyrochlore. These mineralogical investigation results are from a much larger program where pyrochlore matrix composition will be examined in relation to surface chemistry and flotation selectivity.

  • 13.
    Chelgani, Saeed Chehreh
    et al.
    Surface Science Western, Research Park, University of Western Ontario, Canada.
    Hart, B.
    Surface Science Western, Research Park, University of Western Ontario, Candada.
    Marois, J.
    Niobec Inc., Canada.
    Ourriban, M.
    Niobec Inc., Canada.
    Study of pyrochlore surface chemistry effects on collector adsorption by TOF-SIMS2012Inngår i: Minerals Engineering, ISSN 0892-6875, Vol. 39, s. 71-76Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Time of flight secondary ion mass spectrometry (TOF-SIMS) was used to analyse the surface of two different types of pyrochlore, high Fe pyrochlore and low Fe pyrochlore, from Niobec Saint-Honore mine deposit. Pyrochlore grains were analysed in order to identify a potential relationship between pyrochlore matrix composition, the corresponding surface expression and the related effect on cationic collector adsorption. TOF-SIMS analyses of pyrochlore surfaces from a conditioning test show that the species indicative of the cationic collector, favour the surface of Fe poor pyrochlore relative to the Fe rich variety. Lower collector signals on the surface of the Fe-pyrochlore are matched by higher relative intensities of Fe, OH, O and FeOH. The TOF-SIMS results illustrate a negative relationship between a cationic collector adsorption and the presence of Fe and Fe oxidation species on the surface of pyrochlore grains, and supports previous work which identified a negative correlation between matrix Fe content and pyrochlore floatability. The surface analysis illustrates the link between pyrochlore matrix chemistry, the expression of surface species and their effect on collector adsorption.

  • 14.
    Chelgani, Saeed Chehreh
    et al.
    Surface Science Western, Research Park, University of Western Ontario, Canada.
    Hart, B.
    Surface Science Western, Research Park, University of Western Ontario, Candada.
    Marois, J.
    Niobec Inc., Canada.
    Ourriban, M.
    Niobec Inc., Canada.
    Study the relationship between the compositional zoning of high iron content pyrochlore and adsorption of cationic collector2013Inngår i: Minerals Engineering, ISSN 0892-6875, Vol. 46-47, s. 34-37Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The matrix composition and surface chemistry of high iron pyrochlore (Fe pyrochlore) grains from Niobec (St-Horone carbonatite deposit) were analyzed, in order to identify a potential relationship between Fe pyrochlore matrix composition and the related effect on cationic collector adsorption (tallow diamine). SEM–EDX analyses indicate compositional zoning in the structure Fe pyrochlores. TOF-SIMS was used to analyse the surface of different compositional zones of Fe pyrochlore, in order to identify their related effects on tallow diamine adsorption. Surface analyses of high and low iron zones of treated Fe pyrochlore show that species indicative of the collector favour the regions of low iron content The low iron areas also show a lower relative proportion of species indicative of oxidation. This study identifies the link between Fe pyrochlore compositional zoning, surface oxidation and, area selective collector loading.

  • 15.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, Ann Arbor, USA.
    Hower, J. C.
    University of Kentucky, Lexington, USA.
    Relationships between noble metals as potential coal combustion products and conventional coal properties2018Inngår i: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 226, s. 345-349Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Increasing coal consumption has generated million tons of ash and caused various environmental issues. Exploring statistical relationships between concentrations of valuable metals in coal and other coal properties may have several benefits for their commercial extraction as byproducts. This investigation studied relationships between conventional coal concentrations and concentration of noble metals for a wide range (708 samples) of eastern Kentucky coal samples (EKCS) by statistical methods. The results indicate that there are significant positive Pearson correlations (r) > 0.90 among all noble metals (Au, Pt, Pd, Ru and Rh) except for Ag (r < 0.2). The results also showed that the noble metals (except Ag) are associated with the minerals of the coal and have high positive correlations with ash (and high negative correlations with the organic fraction). Modeling through the database demonstrated that the highest Au concentrations in the EKCS occur when Si is between 6000 and 8000 ppm and Fe is below 10000 ppm, and the highest Ag was observed when both Cu and Ni were over 40 ppm. Outcomes suggested that aluminosilicate minerals and pyrite are possibly the main host of noble metals (except Ag) in the EKCS whereas Ag might occur in various forms including organic association, mineral species, and as a native metal.

  • 16.
    Chelgani, Saeed Chehreh
    et al.
    Surface Science Western, University of Western Ontario, Canada.
    Hower, J. C.
    Center for Applied Energy Research, University of Kentucky, USA.
    Hart, B.
    Surface Science Western, University of Western Ontario, Canada.
    Estimation of free-swelling index based on coal analysis using multivariable regression and artificial neural network2011Inngår i: Fuel Processing Technology, ISSN 0378-3820, Vol. 92, nr 3, s. 349-355Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The effects of proximate, ultimate and elemental analysis for a wide range of American coal samples on Free-swelling Index (FSI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that variables of ultimate analysis are better predictors than those from proximate analysis. The non linear multivariable regression, correlation coefficients (R2) from ultimate analysis inputs was 0.71, and for proximate analysis input variables was 0.49. With the same input sets, feed-forward artificial neural network (FANN) procedures improved accuracy of predicted FSI with R2 = 0.89, and 0.94 for proximate and ultimate analyses, respectively. The ANN based prediction method, as a first report, shows FSI is a predictable variable, and ANN can be further employed as a reliable and accurate method in the free-swelling index prediction.

  • 17.
    Chelgani, Saeed Chehreh
    et al.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Hower, J. C.
    Center for Applied Energy Research, University of Kentucky, USA.
    Jorjani, E.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Mesroghli, Sh.
    Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Iran.
    Bagherieh, A. H.
    Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Iran.
    Prediction of coal grindability based on petrography, proximate and ultimate analysis using multiple regression and artificial neural network models2008Inngår i: Fuel Processing Technology, ISSN 0378-3820, Vol. 89, nr 1, s. 13-20Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The effects of proximate and ultimate analysis, maceral content, and coal rank (Rmax) for a wide range of Kentucky coal samples from calorific value of 4320 to 14960 (BTU/lb) (10.05 to 34.80 MJ/kg) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that the relationship between (a) Moisture, ash, volatile matter, and total sulfur; (b) ln (total sulfur), hydrogen, ash, ln ((oxygen + nitrogen)/carbon) and moisture; (c) ln (exinite), semifusinite, micrinite, macrinite, resinite, and Rmax input sets with HGI in linear condition can achieve the correlation coefficients (R2) of 0.77, 0.75, and 0.81, respectively. The ANN, which adequately recognized the characteristics of the coal samples, can predict HGI with correlation coefficients of 0.89, 0.89 and 0.95 respectively in testing process. It was determined that ln (exinite), semifusinite, micrinite, macrinite, resinite, and Rmax can be used as the best predictor for the estimation of HGI on multivariable regression (R2 = 0.81) and also artificial neural network methods (R2 = 0.95). The ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the hardgrove grindability index prediction.

  • 18.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, Ann Arbor, Michigan, USA.
    Hower, James C.
    University of Kentucky, Lexington, USA.
    Estimating REY content of eastern Kentucky coal samples based on their associated ash elements2018Inngår i: Journal of Rare Earths, ISSN 1002-0721, Vol. 36, nr 11, s. 1234-1238Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Coal and coal combustion byproducts can be considered as an alternative source of rare earth elements and yttrium (REY). The study of relationships between REY and other main coal properties could have several advantages such as estimating REY content of coal particles and designing beneficial extraction method. In this investigation, inter-correlations between REY content with coal parameters (proximate and ash elements) for a wide range of eastern Kentucky coal samples (708 records) were explored. Results demonstrate that zircon and monazite are the main source of heavy and light rare earth elements (HREE and LREE), respectively. Zr has the highest correlation with Y and Th has the strength relationship with Ce and La. In general, LREE have higher interaction with coal ash content in comparison with HREE. Results indicated that REY can be estimated quite satisfactorily by using their associated elements in coal ash.

  • 19.
    Chelgani, Saeed Chehreh
    et al.
    Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Iran.
    Jorjani, E.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Microwave irradiation pretreatment and peroxyacetic acid desulfurization of coal and application of GRNN simultaneous predictor2011Inngår i: Fuel, ISSN 0016-2361, Vol. 90, nr 11, s. 3156-3163Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Artificial neural network was used to predict the effects of operational parameters on coal desulfurization using peroxyacetic acid from microwave pretreated coal. Coal particle size (150–1125 μm), leaching temperature (25–85 °C), leaching time (0–120 min), microwave irradiation power (0–1000 W) and time (0–110 s) were used as inputs to the network. The outputs of the model were organic and inorganic sulfur reductions for 40 of the data sets. The GRNN artificial neural network with spread of 0.3 was used to estimate both organic and inorganic sulfur reduction from a combined database, which was established from microwave pretreatment and leaching experiments. Thirty-two data sets were used for training and eight data sets for testing. Simulated values obtained from the neural network, correspond closely to the experimental results. Satisfactory correlations of R2 = 0.99 and 0.97 were achieved during the testing stages of the prediction of inorganic and organic sulfur reductions respectively.

  • 20.
    Chelgani, Saeed Chehreh
    et al.
    Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany.
    Leißner, T.
    TU Bergakademie Freiberg, Freiberg, Germany.
    Rudolph, M.
    Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany; TU Bergakademie Freiberg, Freiberg, Germany.
    Peuker, U. A.
    TU Bergakademie Freiberg, Freiberg, Germany.
    Study of the relationship between zinnwaldite chemical composition and magnetic susceptibility2015Inngår i: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 72, s. 27-30Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This study investigates the relationship between chemical analyses and magnetic susceptibility of zinnwaldite through magnetic separation of various size fractions. Statistical analyses were used to increase information about magnetic properties of this mineral as a future source of lithium. Statistical modeling indicated that magnetic susceptibility (as a main factor of magnetic separation) accurately can be predicted based on cations content of zinnwaldite. However the size of particles had a significant effect on magnetic susceptibility. The small difference between the estimated and measured values for the non-linear relationship of this prediction (less than 1 (10−8 m3/kg)) shows that these accurate theoretical techniques can be also applied to estimate magnetic properties of zinnwaldite in other resources, and in-situ analysis.

  • 21.
    Chelgani, Saeed Chehreh
    et al.
    Surface Science Western, Research Park, University of Western Ontario, Canada.
    Makaremi, S.
    Biomedical Engineering Graduate Program, University of Western Ontario, Canada.
    Explaining the relationship between common coal analyses and Afghan coal parameters using statistical modeling methods2013Inngår i: Fuel Processing Technology, ISSN 0378-3820, Vol. 110, s. 79-85Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This study investigates the effects of proximate, ultimate and elemental analysis for Afghan coal samples on Hardgrove grindability index (HGI), Gross calorific value (GCV), and Ash fusion temperatures (AFTs) by using multivariable regression (MR) and Adaptive neuro-fuzzy inference system (ANFIS) to increase information about the properties of the Afghan coal. Statistical modeling (MR, and ANFIS) indicated that coal parameters (HGI, GCV, AFTs) can be predicted with high accuracy, where GCV, AFTs, and HGI were estimated by R2 = 0.99, 0.95, and 0.94, respectively. The small difference between the estimated parameters and their actual values shows that these accurate results can be also applied to estimate coal properties in other coal resources of Afghanistan.

  • 22.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, Ann Arbor, USA; Islamic Azad University, Islamshahr, Iran.
    Matin, S. S.
    University of Michigan, Ann Arbor, USA; Islamic Azad University, Islamshahr, Iran.
    Study the relationship between coal properties with Gieseler plasticity parameters by random forest2018Inngår i: International Journal of Oil, Gas and Coal Technology, ISSN 1753-3309, E-ISSN 1753-3317, Vol. 17, nr 1, s. 113-127Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Gieseler fluidity provides thermoplastic information and the compatibility of blended coals for the cokemaking. A novel soft computing method, random forest (RF), for prediction of the softening temperature (Ts), the temperature of maximum fluidity (Tf), resolidification temperature (Tr) and maximum fluidity (MF) [Gieseler parameters (Gp)] was conducted based on the coal proximate analysis. Variable importance measurements were performed by RF to select the most effective variables for the prediction of Gp. Selected variables have been used as an input set of RF model for the modelling and prediction. Results of models indicated that RF can provide a satisfactory prediction of Gp with the correlation of determination R2: 0.64, 0.82, 0.90, and 0.86 for Ts, Tf, Tr and MF, respectively. Based on these results, it can be proposed that RF as a reliable non-parametric reliable predictive tool can be used for modelling of complex relationships in the fuel and energy investigations. 

  • 23.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, Ann Arbor, Michigan, USA.
    Matin, S. S.
    Islamic Azad University, Tehran, Iran.
    Hower, James C.
    University of Kentucky, Lexington, Kentucky, USA.
    Explaining relationships between coke quality index and coal properties by Random Forest method2016Inngår i: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 182, s. 754-760Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this study was shown that random forest (RF) can be used as a sensible new data mining tool for variable importance measurements (VIMs) through various coal properties for prediction of coke quality (Free Swelling Index (FSI)). The VIMs of RF within coal analyses (proximate, ultimate, and petrographic analyses) were applied for the selection of the best predictors of FSI over a wide range of Kentucky coal samples. VIMs assisted by Pearson correlation through proximate, ultimate, and petrographic analyses indicated that volatile matter, carbon, vitrinite, and Rmax (coal rank parameters) are the most effective variables for the prediction of FSI. These important predictors have been used as inputs of RF model for the FSI prediction. Outputs in the testing stage of the model indicated that RF can predict FSI quite satisfactorily; the R2 was 0.93 and mean square error from actual FSIs was 0.15 (had less than interval unit of FSI; 0.5). According to the result, by providing nonlinear inter-dependence approximation among parameters for variable selection and also non-parametric predictive model RF can potentially be further employed as a reliable and accurate technique for the determination of complex relationship through fuel and energy investigations.

  • 24.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, Ann Arbor, Michigan, USA.
    Matin, S. S.
    Islamic Azad University, Tehran, Iran.
    Makaremi, S.
    McMaster University, ON, Canada.
    Modeling of Free Swelling Index Based on Variable Importance Measurements of Parent Coal Properties by Random Forest Method2016Inngår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 94, s. 416-422Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Coke quality has a critical role in the steelmaking industry. The aim of this study is to examine the complex relationships between various conventional coal analyses using coke making index “free swelling index (FSI)”. Random forest (RF) associated with variable importance measurements (VIMs), which is a new powerful statistical data mining approach, is utilized in this study to analyze a high-dimensional database (3961 samples) to rank variables, and to develop an accurate FSI predictive model based on the most important variables. VIMs was performed on various types of analyses which indicated that volatile matter, carbon, moisture (coal rank parameters) and organic sulfur are the most effective coal properties for the prediction of FSI. These variables have been used as an input set of RF model for the FSI modeling and prediction. Results of FSI model indicated that RF can provide a satisfactory prediction of FSI with the correlation of determination R2 = 0.96 and mean square error of 0.16 from laboratory FSIs (which is smaller than the interval unit of FSI; 0.5). Based on this result, RF can be used to rank and select effective variables by evaluating nonlinear relationships among parameters. Moreover, it can be further employed as a non-parametric reliable predictive method for modeling, controlling, and optimizing complex variables; which to our knowledge has never been utilized in the fuel and energy sectors.

  • 25.
    Chelgani, Saeed Chehreh
    et al.
    Surface Science Western, University of Western Ontario, Canada.
    Mesroghli, S.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University, Iran.
    Hower, J. C.
    Center for Applied Energy Research, University of Kentucky, USA.
    Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network2010Inngår i: International Journal of Coal Geology, ISSN 0166-5162, Vol. 83, nr 1, s. 31-34Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Results from ultimate analysis, proximate and petrographic analyses of a wide range of Kentucky coal samples were used to predict coal rank parameters (vitrinite maximum reflectance (Rmax) and gross calorific value (GCV)) using multivariable regression and artificial neural network (ANN) methods. Volatile matter, carbon, total sulfur, hydrogen and oxygen were used to predict both Rmax and GCV by regression and ANN. Multivariable regression equations to predict Rmax and GCV showed R2 = 0.77 and 0.69, respectively. Results from the ANN method with a 2–5–4–2 arrangement that simultaneously predicts GCV and Rmax showed R2 values of 0.84 and 0.90, respectively, for an independent test data set. The artificial neural network method can be appropriately used to predict Rmax and GCV when regression results do not have high accuracy.

  • 26.
    Chelgani, Saeed Chehreh
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Parian, Mehdi
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Semsari, Parisa
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Ghorbani, Yousef
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Rosenkranz, Jan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    A comparative study on the effects of dry and wet grinding on mineral flotation separation: a review2019Inngår i: Journal of Materials Research and Technology, ISSN 2238-7854Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Water scarcity dictates to limit the use of water in ore processing plants particularly in arid regions. Since wet grinding is the most common method for particle size reduction and mineral liberation, there is a lack of understanding about the effects of dry grinding on downstream separation processes such as flotation. This manuscript compiles various effects of dry grinding on flotation and compares them with wet grinding. Dry grinding consumes higher energy and produces wider particle size distributions compared with wet grinding. It significantly decreases the rate of media consumption and liner wear; thus, the contamination of pulp for flotation separation is lower after dry grinding. Surface roughness, particle agglomeration, and surface oxidation are higher in dry grinding than wet grinding, which all these effects on the flotation process. Moreover, dry ground samples in the pulp phase correlate with higher Eh and dissolved oxygen concentration. Therefore, dry grinding can alter the floatability of minerals. This review thoroughly assesses various approaches for flotation separation of different minerals, which have been drily ground, and provides perspectives for further future investigations.

  • 27.
    Chelgani, Saeed Chehreh
    et al.
    Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany; University of Michigan, Ann Arbor, Michigan, USA.
    Rudolph, M.
    Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany.
    Kratzsch, R.
    Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany; TU Bergakademie Freiberg, Freiberg, Germany.
    Sandmann, D.
    TU Bergakademie Freiberg, Freiberg, Germany.
    Gutzmer, J.
    Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany.
    A Review of Graphite Beneficiation Techniques2016Inngår i: Mineral Processing and Extractive Metallurgy Review, ISSN 0882-7508, E-ISSN 1547-7401, Vol. 37, nr 1, s. 58-68Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Graphite as the most common polymorph of naturally occurring crystalline carbon is required for many different applications such as batteries, refractories, electrical products, and pencils. Graphite resources are currently being subjected to intensive exploration to help meet rapidly growing global demand – and graphite has made it onto the list of critical raw materials as issued by the European Union. Graphite ore is mostly beneficiated using flotation separation techniques. The increasing demand for high-grade graphite products with up to 99.99% carbon has resulted in the development of various approaches to remove impurities even to parts per million range. This paper considers separation and purification techniques that are currently employed for graphite mineral beneficiation, and identifies areas in need of further research.

  • 28.
    Chelgani, Saeed Chehreh
    et al.
    Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz-Institute Freiberg for Resource Technology, Germany.
    Rudolph, M.
    Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz-Institute Freiberg for Resource Technology, Germany.
    Leistner, T.
    Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz-Institute Freiberg for Resource Technology, Germany.
    Gutzmer, J.
    Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz-Institute Freiberg for Resource Technology, Germany.
    Peuker, U. A.
    Institute of Mechanical Process Engineering and Minerals Processing, TU Bergakademie Freiberg, Germany.
    A review of rare earth minerals flotation: Monazite and xenotime2015Inngår i: International Journal of Mining Science and Technology, ISSN 2095-2686, Vol. 25, nr 6, s. 877-883Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper reviews rare earth minerals (monazite and xenotime) separation by flotation. A wide range of monazite and xenotime flotation test results are summarized including: reasons of variation in the point of zero charges on these minerals, the effects of various flotation conditions on zeta potential of monazite and xenotime, interactions of collectors and depressants on the surface of these minerals during flotation separation, relationship between surface chemistry of the minerals and different types of collector adsorptions and effects of the conditioning temperature on flotation of rare earth minerals. This review collects various approaches for the selective separation of monazite and xenotime by flotation and gives perspectives for further research in the future.

  • 29.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, USA.
    Shahbazi, B.
    Tarbiat Modares University, Tehran, Iran.
    Hadavandi, E.
    Birjand University of Technology, Birjand, Iran.
    Support vector regression modeling of coal flotation based on variable importance measurements by mutual information method2018Inngår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 114, s. 102-108Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Support vector regression (SVR) modeling was used to predict the coal flotation responses (recovery (R∗) and flotation rate constant (k)) as a function of measured particle properties and hydrodynamic flotation variables. Coal flotation is a complicated multifaceted separation process and many measurable and unmeasurable variables can be considered for its modeling. Therefore, feature selection can be used to save time and cost of measuring irrelevant parameters. Mutual information (MI) as a powerful variable selection tool was used through laboratory measured variables to assess interactions and choose the most effective ones for predictions of R∗ and k. Feature selection by MI through variables indicated that the best arrangements for the R∗ and k predictions are the sets of particle Reynolds number-energy dissipation and particle size-bubble Reynolds number, respectively. Correlation of determination (R2) and difference between laboratory measured and SVR predicted values based on MI selected variables indicated that the SVR can model R∗ and k quite accurately with R2 = 0.93 and R2 = 0.72, respectively. These results demonstrated that the MI-SVR combination can quite satisfactorily measure the importance of variables, increase interpretability, reduce the risk of overfitting, decrease complexity and generate predictive models for high dimension of variables based on selected features for complicated processing systems.

  • 30.
    DEHGHAN, S.
    et al.
    Mining Engineering Department, Mahallat Branch, Islamic Azad University, Iran.
    SATTARI, G.
    Mining Engineering Department, Mahallat Branch, Islamic Azad University, Iran.
    Chelgani, Saeed Chehreh
    Surface Science Western, University of Western Ontario, Canada.
    ALIABADI, M.A.
    Mining Engineering Department, Mahallat Branch, Islamic Azad University, Iran.
    Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks2010Inngår i: Mining Science and Technology, ISSN 1674-5264, Vol. 20, nr 1, s. 41-46Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both methods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear relations obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression results. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

  • 31.
    Ghassa, S.
    et al.
    University of Tehran, Tehran, Iran .
    Abdollahi, H.
    University of Tehran, Tehran, Iran .
    Gharabaghi, M.
    University of Tehran, Tehran, Iran .
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, Michigan, USA.
    Jafari, M.
    University of Tehran, Tehran, Iran .
    The surface chemistry characterization of pyrite, sphalerite and molybdenite after bioleaching2017Inngår i: Solid State Phenomena, ISSN 1012-0394, E-ISSN 1662-9779, Vol. 262 SSP, s. 487-491Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The mineral surface chemistry characterization is essential to describe the dissolution kinetics in leaching and bioleaching. Five different methods, including X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy, have been applied to study the surface chemistry changes during pyrite, sphalerite and molybdenite bioleaching. The surface characterizations have been done for samples before and after biological and chemical leaching. The SEM images illustrated that the minerals surfaces were smooth before processing, while they covered with an ash layer after biological treatment. Although EDS analysis and Raman spectrum demonstrated the potassium jarosite formation on the pyrite surface during bioleaching, the formation of jarosite layer did not occur on the sphalerite surfaces during bioleaching. On the other hand, a sulfur layer formation on the sphalerite surface was confirmed by mentioned characterization methods. Finally, according to the XRD and EDS spectrum the molybdenite surface had been covered both with sulfur and jarosite.

  • 32.
    Gibson, B.
    et al.
    University of Liberia, Monrovia, Liberia.
    Wonyen, D. G.
    University of Liberia, Monrovia, Liberia.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, USA.
    A review of pretreatment of diasporic bauxite ores by flotation separation2017Inngår i: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 114, s. 64-73Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Bayer process is a conventional method for refining bauxite in the production of alumina. The Al/Si ratio in bauxite before feeding to the process must be enriched to more than eight by reducing impurities (mainly aluminosilicates). Therefore, diasporic bauxite ores (Al/Si < 6) have to be upgraded by pretreatment methods to meet the required quality for the Bayer process. Flotation separation (direct or reverse) followed by flocculation as an efficient pretreatment method is the main beneficiation technique for upgrading diaspore. Diaspore pretreatment by flotation favors several conditions and possesses certain limitations. This study has systematically explored various effective flotation factors (particle size, surface electrical charge, collectors, depressants, dispersants, flocculation and aggregation) and limitations in the pretreatment of diaspore and has compiled optimum results for its beneficiation. The summary offers various approaches for the selective flotation of diasporic ores via different conditions and suggests perspectives for further investigation.

  • 33.
    Golshani, T.
    et al.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Jorjani, E.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Chelgani, Saeed Chehreh
    Young Researchers and Elites club, Science and Research Branch, Islamic Azad University, Iran.
    Shafaei, S. Z.
    School of Mining Engineering, University of Tehran, Iran.
    Nafechi, Y. H.
    epartment of Mining Engineering, Science and Research Branch, Islamic Azad University.
    Modeling and process optimization for microbial desulfurization of coal by using a two-level full factorial design2013Inngår i: International Journal of Mining Science and Technology, ISSN 2095-2686, Vol. 23, nr 2, s. 261-265Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The microbial sulfur removal was investigated on high sulfur content (1.9%) coal concentrate from Tabas coal preparation plant. A mixed culture of ferrooxidans microorganisms was isolated from the tailing dam of the plant. Full factorial method was used to design laboratory test and to evaluate the effects of pH, particle size, iron sulfate concentration, pulp density, and bioleaching time on sulfur reduction. Statistical analyses of experimental data were considered and showed increases of pH and particle size had negative effects on sulfur reduction, whereas increases of pulp density and bioleaching time raised microbial desulfurization rate. According to results of designing, and regarding statistical factors, the optimum values for maximum sulfur reduction were obtained; pH (1.5), particle size (−180 μm), iron sulfate concentration (2.7 mmol/L), pulp density (10%) and bioleaching time (14 d), which leaded to 51.5% reduction from the total sulfur of sample.

  • 34.
    Golzadeh, M.
    et al.
    University of Tehran, Tehran, Iran.
    Hadavandi, E.
    Birjand University of Technology, Birjand, Iran.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, United States.
    A new Ensemble based multi-agent system for prediction problems: Case study of modeling coal free swelling index2018Inngår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 64, s. 109-125Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this article, a new ensemble based multi-agent system called “EMAS” is introduced for prediction of problems in data mining. The EMAS is constructed using a four-layer multi-agent system architecture to generate a data mining process based on the coordination of intelligent agents. The EMAS performance is based on data preprocessing and prediction. The first layer is dedicated to clean and normalize data. The second layer is designed for data preprocessing by using intelligent variable ranking to select the most effective agents (select the most important input variables to model an output variable). In the third layer, a negative correlation learning (NCL) algorithm is used to train a neural network ensemble (NNE). Fourth layer is dedicated to do three different subtasks including; knowledge discovery, prediction and data presentation. The ability of the EMAS is evaluated by using a robust coal database (3238 records) for prediction of Free Swelling Index (FSI) as an important problem in coke making industry, and comparing the outcomes with the results of other conventional modeling methods Coal particles have complex structures and EMAS can explore complicated relationships between their structural parameters and select the most important ones for FSI modeling. The results show that the EMAS outperforms all presented modeling methods; therefore, it can be considered as a suitable tool for prediction of problems. Moreover, the results indicated that the EMAS can be further employed as a reliable tool to select important variables, predict complicated problems, model, control, and optimize fuel consumption in iron making plants and other energy facilities.

  • 35.
    Hadavandi, E.
    et al.
    Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran.
    Chelgani, Saeed Chehreh
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Estimation of coking indexes based on parental coal properties by variable importance measurement and boosted-support vector regression method2019Inngår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 135, s. 306-311Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Coke as a fuel has a critical role for steel making industries. Since coke is a product of blended coals, it is essential to study relationships between parental coal components with quality of their coke products. Free swelling index (FSI) and maximum fluidity (MF) are standard coking indexes that widely used for blending coals and measuring quality of products. This study has been explored interdependencies between measured coal components by mutual information (MI) method and evaluated their importance in the prediction of coking indexes for a wide range of Illinois coal samples. MI results indicated that the set of moisture-organic sulfur and moisture-nitrogen-sulfate sulfur were the best variables for predictions of log(MF) and FSI, respectively. Adaptive Boosting method based on support vector regression (SVR), called Boosted-SVR, was used the selected variable sets for predictions of coking indexes. In testing stage of models, correlation of determination (R2) between actual and predicted values for the log(MF) and FSI were 0.89 and 0.90, respectively. These results indicated that Boosted-SVR model could quite satisfactory predict coking indexes. In general, outcomes of this investigation demonstrated an appropriate potential of coking quality prediction with limited numbers of input variables and suggested that a combination of MI with Boosted-SVR model as a new powerful tool which can be used for the computation of other complex fuel and processing problems based on measurement of conventional properties.

  • 36.
    Hadavandi, E.
    et al.
    University of Technology, Birjand, Iran.
    Hower, James C.
    University of Kentucky, Lexington, USA.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, USA.
    Modeling of gross calorific value based on coal properties by support vector regression method2017Inngår i: Modeling Earth Systems and Environment, ISSN 2363-6203, E-ISSN 2363-6211, Vol. 3, nr 37Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Gross calorific value (GCV) is one the most important coal combustion parameters for power plants. Modeling of GCV based on coal properties could be a key for estimating the amount of coal consumption in the combustion system of various plants. In this study, support vector regression (SVR) as a powerful prediction method has been used to investigate relationships among coal sample properties with their GCVs for a wide range of records. Variable importance measurement by the SVR method throughout various coal analyses (proximate, ultimate, different sulfur types, and petrography) indicated that carbon, ash, moisture, and hydrogen contents are the most effective variables for the GCV prediction. Two models based on all variables and four the most effective ones are conducted. Outputs in the testing stage of both models verified that SVR can predict GCV quite satisfactorily where the correlations of determination (R2) for models was 0.99. Based on these results, development of a variable selection system among wide range of parameters, and also application of an accurate predictive model such as SVR, can potentially be further employed as a reliable tool for evaluation of complex relationships in earth and energy problems.

  • 37.
    Jafari, M.
    et al.
    University of Tehran, Tehran, Iran.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, USA.
    Pourghahramani, P.
    Sahand University of Technology, Tabriz, Iran.
    Ebadi, H.
    Sahand University of Technology, Tabriz, Iran.
    Measurement of collector concentrations to make an efficient mixture for flotation of a low grade apatite2018Inngår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 121, s. 19-25Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    It was well documented that a mixture of collectors could have synergistic advantages over the use of an individual collector for apatite flotation. Therefore, it would be an essential procedure to determine an optimum amount of each collector for development of an efficient mixture (collector). In this study, a mixture design (MD) model was used to find an optimum amount of different typical apatite anionic collectors (Atrac, Alke and Dirol) and make an efficient mixture for the direct flotation of a low grade apatite ore. Assessment of responses for apatite flotation tests which their collectors were designed by MD showed that Dirol has the highest selectivity whereas Alke has the highest collectivity for the direct flotation of apatite. According to the experiments, the MD model computed that a mixture collector with Dirol: 364 (g/t), Alke: 295.2 (g/t) and Atrac: 140.8 (g/t) concentrations can provide the most efficient responses through the apatite flotation. Results based on the purposed concentrations for the mixed collector demonstrated that higher apatite flotation responses (grade: 14%, recovery: 76%, and S.E.: 66%) in comparison with the performance of tests with a single collector. These results can be used to design flotation conditions for the apatite flotation-separation in the industrial scale and assessment of collector concentrations for other investigations.

  • 38.
    Jafari, M.
    et al.
    University of Tehran, Tehran, Iran.
    Shafaei, S. Z.
    University of Tehran, Tehran, Iran.
    Abdollahi, H.
    University of Tehran, Tehran, Iran.
    Gharabaghi, M.
    University of Tehran, Tehran, Iran.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, USA.
    Effect of Flotation Reagents on the Activity of L. Ferrooxidans2018Inngår i: Mineral Processing and Extractive Metallurgy Review, ISSN 0882-7508, E-ISSN 1547-7401, Vol. 39, nr 1, s. 34-43Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Froth flotation is the most preferred processing technique for the enrichment of low-grade sulfides. Bioleaching is an eco-friendly method for metallurgical extraction from flotation products. Flotation reagents (collectors, frothers, etc.) have various impacts on bioleaching and bacterial activities. In this investigation, the effect of a number of sulfide flotation collectors [potassium amyl-xanthate, potassium isobutyl-xanthate, sodium ethyl-xanthate, potassium isopropyl-xanthate, and Dithiophosphate (Aero3477)], and frothers (pine oil and methyl isobutyl carbinol) with different dosages is studied on Leptospirillum ferrooxidans activities. The results of various measurements indicated that these flotation chemicals can have positive or negative influences on the bacterial activities, based on their chemical compositions and/or concentrations. These results can extensively be used for the selection of flotation reagents when bioleaching is chosen as the metallurgical extraction method after flotation enrichment.

  • 39.
    Jafari, M.
    et al.
    University of Tehran, Tehran, Iran.
    Shafaei, S. Z.
    University of Tehran, Tehran, Iran.
    Abdollahi, H.
    University of Tehran, Tehran, Iran.
    Gharabaghi, M.
    University of Tehran, Tehran, Iran.
    Chelgani, Saeed Chehreh
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi. University of Michigan, Ann Arbor, USA.
    Ghassa, S.
    University of Tehran, Tehran, Iran.
    Examining the effects of typical reagents for sulfide flotation on bio-oxidation activity of ferrous iron oxidizing microorganisms2017Inngår i: Solid State Phenomena, ISSN 1012-0394, E-ISSN 1662-9779, Vol. 262 SSP, s. 84-87Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Mineral separation by froth flotation is widely used around the world for the beneficiation of sulfide ores. Flotation products (typically concentrate) are subjected to metallurgical processes for metal extractions. Bioleaching as a metallurgical procedure indicated many advantages over other traditional techniques (pyro- and hydro-metallurgy). However, organic flotation reagent residuals on the surface of minerals are effective on biological activities of microorganisms. In this work, to extensively study these effects, typical sulfide flotation collectors (Sodium ethyl-xanthate, Potassium isopropyl-xanthate, Potassium isobutyl-xanthate, Potassium amyl-xanthate, and Dithiophosphate (Aero)), and frothers (pine oil (PO) and methyl isobutyl Carbinol (MIBC)) were used in the presence of various bacteria (Acidithiobacillus ferrooxidans and Leptospirillum ferrooxidans) to investigate their effects on bio-oxidation. The results of this investigation can be used to better understand the mechanisms of bio-activities when reagent residues are on the surface of flotation products and they will feed to the bioleaching process.

  • 40.
    Jafari, M.
    et al.
    University of Tehran, Tehran, Iran.
    Shafaie, S. Z.
    University of Tehran, Tehran, Iran.
    Abdollahi, H.
    University of Tehran, Tehran, Iran.
    Gharabaghi, M.
    University of Tehran, Tehran, Iran.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, Michigan, USA.
    Study of the effects of conventional reagents for sulfide flotation on bio-oxidation activity of Acidithiobacillus ferrooxidans2019Inngår i: Chemical Engineering Communications, ISSN 0098-6445, E-ISSN 1563-5201, Vol. 206, nr 3, s. 365-377Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Bioleaching as a low cost and environment-friendly process could be a promising option for the enrichment of froth flotation products. Flotation reagents (collectors, frothers, etc.) are effective on the bacteria growth and oxidation activity; however, their impact has not been widely investigated. In this study, the effect of conventional reagents for sulfide flotation; collectors: potassium amylxanthate (KAX), potassium isobutyl-xanthate (KIBX), sodium ethylxanthate (NaEX), potassium isopropyl xanthate (KIPX) and Dithiophosphate (Aero3477), and frothers; pine oil (PO) and methyl isobutyl carbinol (MIBC) in various concentrations have been examined on Acidithiobacillus ferrooxidans activities. The results of this study demonstrate these flotation surfactants may have positive or negative influences on the bio-oxidation, based on their chemical compositions and/or concentrations. In general, the inhabitation effects of collectors would be increased in higher dosages and based on differences between results of various conditioning tests with the control test (without reagent) in different days, this effect could be considered by the following order: for 0.01 g/L: KAX > KIPX > KIBX > Aero3477 > NaEX, 0.1 g/L: NaEX > KIPX > KAX > KIBX > Aero3477, and 1 g/L: NaEX > KIPX > KIBX > KAX > Aero3477, and for frothers: MIBC > PO in all concentrates. These outputs potentially can be used for the selection of flotation surfactants when the flotation products are going to be further processed by bioleaching for the metallurgical extraction.

  • 41.
    Jafari, Mohammad
    et al.
    School of Mining, College of Engineering, University of Tehran, Tehran, Iran.
    Chelgani, Saeed Chehreh
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
    Shafaie, S.Z.
    School of Mining, College of Engineering, University of Tehran, Tehran, Iran.
    Abdollahi, H.
    School of Mining, College of Engineering, University of Tehran, Tehran , Iran.
    Hadavandi, E.
    Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran.
    Study effects of conventional flotation reagents on bioleaching of zinc sulfide2019Inngår i: Journal of Industrial and Engineering Chemistry, ISSN 1226-086X, E-ISSN 1876-794X, Vol. 78, s. 364-371Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Although flotation and bio-extraction of metals from its products are extensively investigated, there are few studied which evaluated the effects of reagents on bioleaching process. Both structure and concentration of flotation reagents are effective factors on microorganism activities. In this study, Kendall’s tau (τ) as a statistical method was used to statistically access the effect of typical sulfide flotation surfactants (collectors: potassium amyl-xanthate, potassium isobutyl-xanthate, sodium ethyl-xanthate, potassium isopropyl-xanthate, and Dithiophosphate), and frothers: pine oil and methyl isobutyl carbinol) on the bioleaching of Zn sulfides in a mixed culture (Leptospirillum ferrooxidans, Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans). To consider both structure and concentration of these reagents, their molarities were used for the statistical evaluations. The Kendall assessments indicated that by increasing in the molarity of reagents, the pH value (the most effective factors of bioleaching) was increased (τ: 0.56) while the ORP value (τ: -0.54), Fe ratio (τ: -0.51) and numbers of oxidizing bacteria (τ: -0.38) in the solution were decreased. Therefore, as a result of these multi-interactions, by increasing the molarity of reagents, Zn recovery was decreased (τ: -0.45). These results potentially can be used for selection of flotation reagents when bioleaching would be the metallurgical metal extraction method.

  • 42.
    Jafari, Mohammad
    et al.
    School of Mining Engineering, University of Tehran, Tehran, Iran.
    Shafaei, Said Zia Aldin
    School of Mining Engineering, University of Tehran, Tehran, Iran.
    Abdollahi, Hadi
    School of Mining Engineering, University of Tehran, Tehran, Iran.
    Gharabaghi, Mahdi
    School of Mining Engineering, University of Tehran, Tehran, Iran.
    Chelgani, Saeed Chehreh
    Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, USA.
    A comparative study on the effect of flotation reagents on growth and iron oxidation activities of Leptospirillum ferrooxidans and Acidithiobacillus ferrooxidans2017Inngår i: Minerals, ISSN 2075-163X, E-ISSN 2075-163X, Vol. 7, nr 1, artikkel-id 2Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Recently, extraction of metals from different resources using a simple, efficient, and low-cost technique-known as bioleaching-has been widely considered, and has turned out to be an important global technology. Leptospirillum ferrooxidans and Acidithiobacillus (Thiobacillus) ferrooxidans are ubiquitous bacteria in the biomining industry. To date, the effects of commercial flotation reagents on the biooxidation activities of these bacteria have not been thoroughly studied. This investigation, by using various systematic measurement methods, studied the effects of various collectors and frothers (collectors: potassium amylxanthate, potassium isobutyl-xanthate, sodium ethylxanthate, potassium isopropylxanthate, and dithiophosphate; and frothers: pine oil and methyl isobutyl carbinol) on L. ferrooxidans and A. ferrooxidans activities. In general, results indicate that in the presence of these collectors and frothers, L. ferrooxidans is less sensitive than T. ferrooxidans. In addition, the inhibition effect of collectors on both bacteria is recommended in the following order: for the collectors, potassium isobutyl-xanthate > dithiophosphate > sodium ethylxanthate > potassium isobutyl-xanthate > potassium amylxanthate; and for the frothers, methyl isobutyl carbinol > pine oil. These results can be used for the optimization of biometallurgical processes or in the early stage of a process design for selection of flotation reagents.

  • 43.
    Jorjani, E.
    et al.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Asadollahi Poorali, H.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Sam, A.
    Department of Mining Engineering, Shahid Bahonar University of Kerman, Iran.
    Chelgani, Saeed Chehreh
    Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Iran.
    Mesroghli, Sh.
    Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Iran.
    Shayestehfar, M. R.
    Department of Mining Engineering, Shahid Bahonar University of Kerman, Iran.
    Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network2009Inngår i: Minerals Engineering, ISSN 0892-6875, Vol. 22, nr 11, s. 970-976Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate were predicted by regression and artificial neural network based on proximate and group macerals analysis. The regression method shows that the relationships between (a) ln (ash), volatile matter and moisture (b) ln (ash), ln (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R2) of 0.8 and 0.79, respectively. In addition, the input sets of (c) ash, volatile matter and moisture (d) ash, liptinite and fusinite can predict the combustible recovery with the correlation coefficients of 0.84 and 0.63, respectively. Feed-forward artificial neural network with 6-8-12-11-2-1 arrangement for moisture, ash and volatile matter input set was capable to estimate both combustible value and combustible recovery with correlation of 0.95. It was shown that the proposed neural network model could accurately reproduce all the effects of proximate and group macerals analysis on coal flotation system.

  • 44.
    Jorjani, E.
    et al.
    Mining Engineering Department, Science and Research Branch Islamic Azad University, Iran.
    Bagherieh, A. H.
    Mining Engineering Department, Science and Research Branch Islamic Azad University, Iran.
    Chelgani, Saeed Chehreh
    Mining Engineering Department, Science and Research Branch Islamic Azad University, Iran.
    Rare earth elements leaching from Chadormalu apatite concentrate: Laboratory studies and regression predictions2011Inngår i: Korean Journal of Chemical Engineering, ISSN 0256-1115, Vol. 28, nr 2, s. 557-562Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The extraction of rare earth elements from apatite concentrate of Chadormalu plant of Iran was studied with the dissolution of ore in nitric acid. The parameters of acidity: 60%, solid to liquid ratio: 30%, leaching time: 30 minute, agitation rate: 200 rpm, temperature: 60 °C and particle size (d80): 50 microns were determined as the optimum operational conditions. The recoveries of lanthanum, cerium, neodymium and yttrium were achieved at 74, 59, 72 and 73%, respectively, in the optimized conditions. Multivariable regression was used to predict La, Ce, Nd, Y and total REEs (Y+Nd+Ce+La) leaching recoveries, using experimental data from laboratory studies. It was achieved quite satisfactory correlations of 0.93, 0.98, 0.99, 0.97 and 0.99 for the prediction of Y, Nd, Ce, La and total REEs recoveries, respectively. It was shown that the proposed equations accurately reproduce the effects of operational variables on the different REEs recoveries, and can be used to optimize the REEs leaching plant.

  • 45.
    Jorjani, E.
    et al.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University, Iran.
    Bagherieh, A. H.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University, Iran.
    Mesroghli, Sh.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University, Iran.
    Chelgani, Saeed Chehreh
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University, Iran.
    Prediction of yttrium, lanthanum, cerium, and neodymium leaching recovery from apatite concentrate using artificial neural networks2008Inngår i: Journal of University of Science and Technology Beijing: Mineral Metallurgy Materials (Eng Ed), ISSN 1005-8850, Vol. 15, nr 4, s. 367-374Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The assay and recovery of rare earth elements (REEs) in the leaching process is being determined using expensive analytical methods: inductively coupled plasma atomic emission spectroscopy (ICP-AES) and inductively coupled plasma mass spectroscopy (ICP-MS). A neural network model to predict the effects of operational variables on the lanthanum, cerium, yttrium, and neodymium recovery in the leaching of apatite concentrate is presented in this article. The effects of leaching time (10 to 40 min), pulp densities (30% to 50%), acid concentrations (20% to 60%), and agitation rates (100 to 200 r/min), were investigated and optimized on the recovery of REEs in the laboratory at a leaching temperature of 60°C. The obtained data in the laboratory optimization process were used for training and testing the neural network. The feed-forward artificial neural network with a 4-5-5-1 arrangement was capable of estimating the leaching recovery of REEs. The neural network predicted values were in good agreement with the experimental results. The correlations of R=1 in training stages, and R=0.971, 0.952, 0.985, and 0.98 in testing stages were a result of Ce, Nd, La, and Y recovery prediction respectively, and these values were usually acceptable. It was shown that the proposed neural network model accurately reproduced all the effects of the operation variables, and could be used in the simulation of a leaching plant for REEs.

  • 46.
    Jorjani, E.
    et al.
    Department of Mining Engineering, Research and Science Campus, Islamic Azad University.
    Chelgani, Saeed Chehreh
    Department of Mining Engineering, Research and Science Campus, Islamic Azad University.
    Mesroghli, Sh.
    Department of Mining Engineering, Research and Science Campus, Islamic Azad University.
    Application of artificial neural networks to predict chemical desulfurization of Tabas coal2008Inngår i: Fuel, ISSN 00162361, Vol. 87, nr 12, s. 2727-2734Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a neural network model to predict the effects of operational parameters on the organic and inorganic sulfur removal from coal by sodium butoxide. The coal particle size, leaching temperature and time, sodium butoxide concentration and pre oxidation time by peroxyacetic acid (PAA) were used as inputs to the network. The outputs of the models were organic and inorganic sulfur reduction. Feed-forward artificial neural network with 5-7-10-1 arrangement, were capable to estimate organic and inorganic sulfur reduction, respectively. Simulated values obtained with neural network correspond closely to the experimental results. It was achieved quite satisfactory correlations of R2 = 1 and 0.96 in training and testing stages for pyritic sulfur and R2 = 1 and 0.97 in training and testing stages, respectively, for organic sulfur reduction prediction. The proposed neural network model accurately reproduces all the effects of operational variables and can be used in the simulation of Tabas coal desulfurization plant.

  • 47.
    Jorjani, E.
    et al.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Chelgani, Saeed Chehreh
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Mesroghli, Sh.
    Department of Mining Engineering, Science and Research Branch,Islamic Azad University.
    Prediction of microbial desulfurization of coal using artificial neural networks2007Inngår i: Minerals Engineering, ISSN 0892-6875, Vol. 20, nr 14, s. 1285-1292Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Artificial neural networks procedures were used to predict the organic and inorganic sulfur reduction from coal using mixed culture consisted ferrooxidans species extracted from coal washery tailings, for pyritic sulfur, and Rhodococcus species, extracted from oily soils, for the organic sulfur removal. The particle size, pulp density, initial pH, shaking rate, leaching time and temperature, in pyritic sulfur removal prediction, and pulp density, shaking rate, leaching time and temperature, in organic sulfur removal prediction, were used as inputs to the network. Feed-forward artificial neural networks with 4-8-4-1 and 3-5-6-1 arrangements, were capable to estimate organic and inorganic sulfur removal, respectively. The outputs of the models were percentage of organic and inorganic sulfur reduction. It was achieved quite satisfactory correlations of R2 = 1.00 and 0.98 in training and testing stages for pyritic sulfur removal prediction and R2 = 1.00 and 0.97 in training and testing stages, respectively, for organic sulfur removal prediction. The proposed neural network models accurately estimate the effects of operational variables in organic and inorganic desulphurization plants and can be used in order to optimize the process parameters without having to conduct the new experiments in laboratory.

  • 48.
    Jorjani, E.
    et al.
    Department of Mining Engineering, Research and Science Campus, Iran.
    Hower, J. C.
    Center for Applied Energy Research, University of Kentucky, USA.
    Chelgani, Saeed Chehreh
    Department of Mining Engineering, Research and Science Campus, Iran.
    Shirazi, M. A.
    Industrial Engineering Department, K.N. Toosi University of Technology, Iran.
    Mesroghli, Sh.
    Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Iran.
    Studies of relationship between petrography and elemental analysis with grindability for Kentucky coals2008Inngår i: Fuel, ISSN 0016-2361, Vol. 87, nr 6, s. 707-713Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The effects of macerals, ash, elemental analysis and moisture of wide range of Kentucky coal samples from calorific value of 23.65–34.68 MJ/kg (10,170–14,910 (BTU/lb)) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression method. Two sets of input: (a) macerals, ash and moisture (b) macerals, elemental analysis and moisture, were used for the estimation of HGI. The least square mathematical method shows that increase of the TiO2 and Al2O3 contents in coal can decrease HGI. The higher Fe2O3 content in coal can result in higher HGI. With the increase of micrinite and exinite contents in coal, the HGI has been decreased and higher vitrinite content in coal results in higher HGI. The multivariable studies have shown that input set of macerals, elemental analysis and moisture in non-linear condition can be achieved an acceptable correlation, R = 90.38%, versus R = 87.34% for the input set of macerals, ash and moisture. It is predicted that elemental analysis of coal can be a better representative of mineral matters for the prediction of HGI than ash.

  • 49. Jorjani, E.
    et al.
    Hower, J. C.
    Center for Applied Energy Research, University of Kentucky, USA.
    Mesroghli, Sh.
    Shirazi, M. A.
    Chelgani, Saeed Chehreh
    Bagherieh, A. H.
    Estimation of coal calorific value with petrography, ultimate analysis, moisture, Rmaxand ash using regression and artificial neural network methods2007Inngår i: 24th Annual International Pittsburgh Coal Conference 2007, 2007, Vol. 2, s. 1003-1015Konferansepaper (Fagfellevurdert)
  • 50.
    Jorjani, E.
    et al.
    Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Iran.
    Mesroghli, Sh.
    Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Iran.
    Chelgani, Saeed Chehreh
    Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Iran.
    Prediction of operational parameters effect on coal flotation using artificial neural network2008Inngår i: Journal of University of Science and Technology Beijing: Mineral Metallurgy Materials (Eng Ed), ISSN 1005-8850, Vol. 15, nr 5, s. 528-533Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Artificial neural network procedures were used to predict the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density, pH, rotation rate, coal particle size, dosage of collector, frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

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