The Experts below are selected from a list of 6873 Experts worldwide ranked by ideXlab platform
Eisa Jorjani - One of the best experts on this subject based on the ideXlab platform.
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Studies of relationships between Free Swelling Index (FSI) and coal quality by regression and Adaptive Neuro Fuzzy Inference System
International Journal of Coal Geology, 2011Co-Authors: M. Tayebi Khorami, S. Chehreh Chelgani, James C Hower, Eisa JorjaniAbstract: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 Rmaxwere 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 R2of 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. © 2010 Elsevier B.V.
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studies of relationships between free Swelling Index fsi and coal quality by regression and adaptive neuro fuzzy inference system
International Journal of Coal Geology, 2011Co-Authors: Tayebi M Khorami, Chehreh S Chelgani, James C Hower, Eisa JorjaniAbstract: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 R max were applied for both methods. Non-linear regression achieved the correlation coefficients (R 2 ) 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 R 2 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.
Chehreh S Chelgani - One of the best experts on this subject based on the ideXlab platform.
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a new ensemble based multi agent system for prediction problems case study of modeling coal free Swelling Index
Applied Soft Computing, 2018Co-Authors: Mehdi Golzadeh, Esmaeil Hadavandi, Chehreh S ChelganiAbstract:Abstract 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.
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estimation of free Swelling Index based on coal analysis using multivariable regression and artificial neural network
Fuel Processing Technology, 2011Co-Authors: Chehreh S Chelgani, James C Hower, B R HartAbstract: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 (R 2 ) 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 R 2 = 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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studies of relationships between free Swelling Index fsi and coal quality by regression and adaptive neuro fuzzy inference system
International Journal of Coal Geology, 2011Co-Authors: Tayebi M Khorami, Chehreh S Chelgani, James C Hower, Eisa JorjaniAbstract: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 R max were applied for both methods. Non-linear regression achieved the correlation coefficients (R 2 ) 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 R 2 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.
James C Hower - One of the best experts on this subject based on the ideXlab platform.
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estimation of free Swelling Index based on coal analysis using multivariable regression and artificial neural network
Fuel Processing Technology, 2011Co-Authors: Chehreh S Chelgani, James C Hower, B R HartAbstract: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 (R 2 ) 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 R 2 = 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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Studies of relationships between Free Swelling Index (FSI) and coal quality by regression and Adaptive Neuro Fuzzy Inference System
International Journal of Coal Geology, 2011Co-Authors: M. Tayebi Khorami, S. Chehreh Chelgani, James C Hower, Eisa JorjaniAbstract: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 Rmaxwere 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 R2of 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. © 2010 Elsevier B.V.
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studies of relationships between free Swelling Index fsi and coal quality by regression and adaptive neuro fuzzy inference system
International Journal of Coal Geology, 2011Co-Authors: Tayebi M Khorami, Chehreh S Chelgani, James C Hower, Eisa JorjaniAbstract: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 R max were applied for both methods. Non-linear regression achieved the correlation coefficients (R 2 ) 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 R 2 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.
Hanafi Ismail - One of the best experts on this subject based on the ideXlab platform.
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The Effect of Dynamic Vulcanization on the Properties of Polypropylene/Ethylene-Propylene Diene Terpolymer/Natural Rubber (PP/EPDM/NR) Ternary Blend
Polymer-Plastics Technology and Engineering, 2009Co-Authors: Halimatuddahliana, Hanafi IsmailAbstract:The effects of dynamic vulcanization on the process development and some properties, such as tensile properties, Swelling Index, gel content, crystallinity, and morphology, of the polypropylene (PP)/ethylene-propylene diene terpolymer (EPDM)/natural rubber (NR) blends were investigated. Dynamically vulcanized blends show higher stabilization torque than unvulcanized blends. In terms of tensile properties, the tensile strength and tensile modulus (stress at 100% elongation, M100) of the vulcanized blends have been found to increase as compared with the unvulcanized blends, whereas the elongation at break is higher in the blend with richer EPDM content. These results can be attributed to the formation of cross-linking in the rubber phase. The formation of cross-links in the rubber phase has also been proved by Swelling Index and gel content. The percentage of crystallinity of the blends is decreased by dynamic vulcanization. Scanning electron microscopy (SEM) micrographs from the surface extraction of the blends support that the cross-links occurred during dynamic vulcanization.
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The Effect of Dynamic Vulcanization on the Properties of Polypropylene/Ethylene-Propylene Diene Terpolymer/Natural Rubber (PP/EPDM/NR) Ternary Blend
Polymer-plastics Technology and Engineering, 2008Co-Authors: Halimatuddahliana, Hanafi IsmailAbstract:The effects of dynamic vulcanization on the process development and some properties, such as tensile properties, Swelling Index, gel content, crystallinity, and morphology, of the polypropylene (PP)/ethylene-propylene diene terpolymer (EPDM)/natural rubber (NR) blends were investigated. Dynamically vulcanized blends show higher stabilization torque than unvulcanized blends. In terms of tensile properties, the tensile strength and tensile modulus (stress at 100% elongation, M100) of the vulcanized blends have been found to increase as compared with the unvulcanized blends, whereas the elongation at break is higher in the blend with richer EPDM content. These results can be attributed to the formation of cross-linking in the rubber phase. The formation of cross-links in the rubber phase has also been proved by Swelling Index and gel content. The percentage of crystallinity of the blends is decreased by dynamic vulcanization. Scanning electron microscopy (SEM) micrographs from the surface extraction of the b...
Z. A. Mohd Ishak - One of the best experts on this subject based on the ideXlab platform.
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Rheological and mechanical properties of dynamically cured poly(vinyl chloride)/nitrile butadiene rubber thermoplastic elastomers
Polymer International, 2020Co-Authors: Ahmad Mousa, U. S. Ishiaku, Z. A. Mohd IshakAbstract:Plasticized poly(vinyl chloride)/nitrile butadiene rubber (PVC/NBR) thermoplastic elastomers (TPEs) were dynamically cured in the melt stage with the incorporation of a semi-efficient curing system using a Brabender Plasticorder at 150 °C and rotor speed of 50 rev min−1. Sulfur concentration was progressively increased from zero to 1 part per hundred NBR to study the effect of dynamic curing on mechanical and rheological behaviour of the TPEs. The compounds were characterized in respect of their rheological and mechanical properties. The effectiveness of dynamic curing was studied using Brabender plastograms, a moving disc rheometer and Swelling Index measurement. The mechanical properties investigated include tensile strength, elongation at break, modulus at 100% elongation, tear strength and hardness. The influence of thermooxidative ageing on the mechanical properties was investigated by incubating the PVC/NBR TPEs in an air oven at 80 °C for 168 h. The torque values obtained from both rheometers increased with increasing sulfur dosage, while the Swelling Index decreased. The significant increase in the degree of curing evidenced by the steady reduction in the Swelling Index provided excellent proof of the efficiency of the dynamic curing technique. Thermo-oxidative ageing resulted in a pronounced enhancement in mechanical properties as a function of sulfur content. This observation seems to indicate that some microstructural changes, such as the formation of new crosslinks, occur in the thermo-oxidatively aged TPEs. This trend was supported by further reduction in the Swelling Index of the aged TPEs. © 2003 Society of Chemical Industry
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Dynamic vulcanisation of poly(vinyl chloride)-epoxidised natural rubber thermoplastic elastomers. Part 1 - Mixing rheology
Plastics Rubber and Composites Processing and Applications, 1997Co-Authors: Ahmad Mousa, Umaru Semo Ishiaku, Z. A. Mohd IshakAbstract:Thermoplastic elastomers have been prepared by blending poly(vinyl chloride) and epoxidised natural rubber in the presence of a plasticiser. The blends were melt mixed using a Brabender plasticorder torque rheometer. To effect dynamic vulcanisation, varying concentrations of a semi-efficient sulphur curative system were added during the mixing stage. The effectiveness of dynamic vulcanisation was studied using Brabender plastograms, a moving dic rheometer, Swelling Index measurement, and Fourier transform infrared spectroscopy. The values of torque obtained from both rheometers increased with an increase in curative concentration, while the Swelling Index decreased. The difference absorbance spectra from the Fourier transform infrared spectroscopy indicated sulphur crosslink formation. Processibility studies of the dynamically vulcanised thermoplastic elastomers showed that they have good processing stability and are processible as thermoplastics.
