@article { ISI:000346322200010, title = {Artificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier}, journal = {Industrial \& Engineering Chemistry Research }, volume = {53}, number = {49}, year = {2014}, month = {DEC}, pages = {18678-18689}, publisher = {AMER CHEMICAL SOC}, type = {Article}, address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA}, abstract = {

The quality of coalespecially its high ash contentsignificantly affects the performance of coal-based processes. Coal gasification is a cleaner and an efficient alternative to the coal combustion for producing the syngas. The high-ash coals are found in a number of countries, and they form an important source for the gasification. Accordingly, in this study, extensive gasification experiments were conducted in a pilot-plant scale fluidized-bed coal gasifier (FBCG) using high-ash coals from India. Specifically, the effects of eight coal and gasifier process related parameters on the four gasification performance variables, namely CO+H-2 generation rate, syngas production rate, carbon conversion, and heating value of the syngas, were rigorously studied. The data collected from these experiments were used in the FBCG modeling, which was conducted by utilizing two artificial intelligence (AI) strategies namely genetic programming (GP) and artificial neural networks (ANNs). The novelty of the GP formalism is that it searches and optimizes both the form and parameters of an appropriate linear/nonlinear function that best fits the given process data. The original eight-dimensional input space of the FBCG models was reduced to three-dimensional space using the principal component analysis (PCA) and the PCA-transformed three variables were used in the AI-based FBCG modeling. A comparison of the GP and ANN-based models reveals that their output prediction accuracies and the generalization performance vary from good to excellent as indicated by the high training and test set correlation coefficient magnitudes lying between 0.92 and 0.996. This study also presents results of the sensitivity analysis performed to identify those coal and process related parameters, which significantly affect the FBCG process performance.

}, issn = {0888-5885}, doi = {10.1021/ie500593j}, author = {Patil-Shinde, Veena and Kukarni, Tejas and Kulkarni, Rahul and Chavan, Prakash D. and Sharma, Tripurari and Sharma, Bijay Kumar and Tambe, Sanjeey S. and Kulkarni, B. D.} } @article {44432, title = {Co-gasification of high ash coal{\textendash}biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling}, journal = {Waste and Biomass Valorization}, volume = {11}, year = {2018}, month = {JUN}, pages = {1-19}, type = {Article}, abstract = {Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.}, keywords = {Artificial neural networks, Co-gasification, Computational intelligence, Fluidized bed gasifier, genetic programming, support vector regression}, issn = {1877-2641}, doi = {10.1007/s12649-018-0378-7}, author = {Tiwary, Shishir and Ghugare, Suhas B. and Chavan, Prakash D. and Saha, Sujan and Datta, Sudipta and Sahu, Gajanan and Tambe, Sanjeev S.} } @article {47151, title = {Co-gasification of high ash coal{\textendash}biomass blends in a fluidized bed gasifier: }, journal = {Waste and Biomass Valorization }, volume = {11}, year = {2020}, month = {JAN}, pages = {323{\textendash}341}, type = {Article}, abstract = {

Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the\ product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three\ computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.

}, author = {Tiwary, Shishir and Ghugare, Suhas B. and Chavan, Prakash D. and Saha, Sujan and Datta, Sudipta and Sahu, Gajanan and Tambe, Sanjeev S.} }