| 1. |
- Chelgani, Saeed Chehreh, et al.
(författare)
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Estimation of free-swelling index based on coal analysis using multivariable regression and artificial neural network
- 2011
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Ingår i: Fuel Processing Technology. - : Elsevier. - 0378-3820. ; 92:3, s. 349-355
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Chelgani, Saeed Chehreh, et al.
(författare)
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Estimation of some coal parameters depending on petrographic and inorganic analyses by using Genetic algorithm and adaptive neuro-fuzzy inference systems
- 2011
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Ingår i: Energy Exploration and Exploitation. - : Multi-Science Publishing. - 0144-5987 .- 2048-4054. ; 29:4, s. 479-494
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Chelgani, Saeed Chehreh, et al.
(författare)
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Prediction of coal grindability based on petrography, proximate and ultimate analysis using multiple regression and artificial neural network models
- 2008
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Ingår i: Fuel Processing Technology. - : Elsevier BV. - 0378-3820. ; 89:1, s. 13-20
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Chelgani, Saeed Chehreh, et al.
(författare)
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Relationships between noble metals as potential coal combustion products and conventional coal properties
- 2018
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Ingår i: Fuel. - : Elsevier. - 0016-2361 .- 1873-7153. ; 226, s. 345-349
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Chelgani, Saeed Chehreh, et al.
(författare)
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Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network
- 2010
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Ingår i: International Journal of Coal Geology. - : Elsevier BV. - 0166-5162. ; 83:1, s. 31-34
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
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| 7. |
- Jorjani, E., et al.
(författare)
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Studies of relationship between petrography and elemental analysis with grindability for Kentucky coals
- 2008
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Ingår i: Fuel. - : Elsevier BV. - 0016-2361. ; 87:6, s. 707-713
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 8. |
- Khorami, M. T., et al.
(författare)
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Studies of relationships between Free Swelling Index (FSI) and coal quality by regression and Adaptive Neuro Fuzzy Inference System
- 2011
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Ingår i: International Journal of Coal Geology. - : Elsevier BV. - 0166-5162. ; 85:1, s. 65-71
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Tidskriftsartikel (refereegranskat)abstract
- The results of proximate, ultimate, and petrographic analysis for a wide range of Kentucky coal samples were used to predict Free Swelling Index (FSI) using multivariable regression and Adaptive Neuro Fuzzy Inference System (ANFIS). Three different input sets: (a) moisture, ash, and volatile matter; (b) carbon, hydrogen, nitrogen, oxygen, sulfur, and mineral matter; and (c) group-maceral analysis, mineral matter, moisture, sulfur, and Rmax were applied for both methods. Non-linear regression achieved the correlation coefficients (R2) of 0.38, 0.49, and 0.70 for input sets (a), (b), and (c), respectively. By using the same input sets, ANFIS predicted FSI with higher R2 of 0.46, 0.82 and 0.95, respectively. Results show that input set (c) is the best predictor of FSI in both prediction methods, and ANFIS significantly can be used to predict FSI when regression results do not have appropriate accuracy.
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| 9. |
- Matin, S. S., et al.
(författare)
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Explaining relationships among various coal analyses with coal grindability index by Random Forest
- 2016
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Ingår i: International Journal of Mineral Processing. - : Elsevier. - 0301-7516 .- 1879-3525. ; 155, s. 140-146
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Tidskriftsartikel (refereegranskat)abstract
- Application of Random Forest (RF) via variable importance measurements (VIMs) and prediction is a new data mining model, not yet wide spread in the applied science and engineering fields. In this study, the VIMs (proximate and ultimate analysis, petrography) processed by RF models were used for the prediction of Hardgrove Grindability Index (HGI) based on a wide range of Kentucky coal samples. VIMs, coupled with Pearson correlation, through various analyses indicated that total sulfur, liptinite, and vitrinite maximum reflectance (Rmax) are the most importance variables for the prediction of HGI. These effective predictors have been used as inputs for the prediction of HGI by a RF model. Results indicated that the RF model can model HGI quite satisfactorily when the R2 = 0.90 and 99% of predicted HGIs had less than 4 HGI unit error in the testing stage. According to the result, by providing nonlinear VIMs as well as an accurate prediction model, RF can be further employed as a reliable and accurate technique for the evaluation of complex relationships in coal processing investigations.
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