Feng Gao1,2,*, Liang Bao3, Qin Wang4
Journal of Renewable Materials, Vol.11, No.6, pp. 2771-2786, 2023, DOI:10.32604/jrm.2023.027325
Abstract Gasification of organic waste represents one of the most effective valorization pathways for renewable energy and resources recovery, while this process can be affected by multi-factors like temperature, feedstock, and steam content, making the product’s prediction problematic. With the popularization and promotion of artificial intelligence such as machine learning (ML), traditional artificial neural networks have been paid more attention by researchers from the data science field, which provides scientific and engineering communities with flexible and rapid prediction frameworks in the field of organic waste gasification. In this work, critical parameters including temperature, steam ratio, and feedstock during gasification of organic… More >
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