Co-gasification of High Ash Coal–Biomass Blends in a Fluidized Bed Gasifier: Experimental Study and Computational Intelligence-Based Modeling

• Published in: Waste and biomass valorization 2020, Vol.11 (1), p.323-341
• Main Authors: Tiwary, Shishir, Ghugare, Suhas B., Chavan, Prakash D., Saha, Sujan, Datta, Sudipta, Sahu, Gajanan, Tambe, Sanjeev S.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Artificial neural networks; Ashes; Biomass; Calorific value; Clean technology; Coal; Coal gasification; Cold gas; Computer applications; Engineering; Environment; Environmental Engineering/Biotechnology; Fluidized beds; Genetic algorithms; Industrial Pollution Prevention; Mixtures; Model accuracy; Neural networks; Original Paper; Performance prediction; Pollutants; Pressmud; Renewable and Green Energy; Sawdust; Support vector machines; Waste Management/Waste Technology
• ISSN: 1877-2641; 1877-265X
• DOI: 10.1007/s12649-018-0378-7

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.