Co-gasification of High Ash Coal–Biomass Blends in a Fluidized Bed Gasifier: Experimental Study and Computational Intelligence-Based Modeling
| Autor: | Suhas B. Ghugare, Gajanan Sahu, Sujan Saha, Sanjeev S. Tambe, Sudipta Datta, Prakash D. Chavan, Shishir Tiwary |
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| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Environmental Engineering Process modeling Wood gas generator Renewable Energy Sustainability and the Environment business.industry 020209 energy Biomass 02 engineering and technology 01 natural sciences Pilot plant 010608 biotechnology visual_art 0202 electrical engineering electronic engineering information engineering visual_art.visual_art_medium Coal gasification Environmental science Heat of combustion Coal Sawdust business Process engineering Waste Management and Disposal |
| Zdroj: | Waste and Biomass Valorization. 11:323-341 |
| ISSN: | 1877-265X 1877-2641 |
| DOI: | 10.1007/s12649-018-0378-7 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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