02720nas a2200229 4500008004100000022001400041245011900055210006900174260007100243300001600314490000700330520185000337100002402187700001902211700002002230700002402250700002202274700002502296700002302321700002102344856012502365 2014 eng d a0888-588500aArtificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier0 aArtificial intelligencebased modeling of high ash coal gasificat a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcDEC a18678-186890 v533 a

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.

1 aPatil-Shinde, Veena1 aKukarni, Tejas1 aKulkarni, Rahul1 aChavan, Prakash, D.1 aSharma, Tripurari1 aSharma, Bijay, Kumar1 aTambe, Sanjeey, S.1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/artificial-intelligence-based-modeling-high-ash-coal-gasification-pilot-plant-scale-002503nas a2200289 4500008004100000022001400041245014800055210007100203260000800274300000900282490000700291520147100298653003101769653002001800653003101820653002701851653002401878653003001902100002001932700002301952700002401975700001601999700001902015700001802034700002302052856013802075 2018 eng d a1877-264100aCo-gasification of high ash coal–biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling0 aCogasification of high ash coal–biomass blends in a fluidized be cJUN a1-190 v113 aCo-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.10aArtificial neural networks10aCo-gasification10aComputational intelligence10aFluidized bed gasifier10agenetic programming10asupport vector regression1 aTiwary, Shishir1 aGhugare, Suhas, B.1 aChavan, Prakash, D.1 aSaha, Sujan1 aDatta, Sudipta1 aSahu, Gajanan1 aTambe, Sanjeev, S. uhttps://library.ncl.res.in/content/co-gasification-high-ash-coal%E2%80%93biomass-blends-fluidized-bed-gasifier-experimental-study-and02221nas a2200205 4500008004100000245008400041210007100125260000800196300001400204490000700218520153200225100002001757700002301777700002401800700001601824700001901840700001801859700002301877856011501900 2020 eng d00aCo-gasification of high ash coal–biomass blends in a fluidized bed gasifier: 0 aCogasification of high ash coal–biomass blends in a fluidized be cJAN a323–3410 v113 a

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.

1 aTiwary, Shishir1 aGhugare, Suhas, B.1 aChavan, Prakash, D.1 aSaha, Sujan1 aDatta, Sudipta1 aSahu, Gajanan1 aTambe, Sanjeev, S. uhttps://library.ncl.res.in/content/co-gasification-high-ash-coal%E2%80%93biomass-blends-fluidized-bed-gasifier