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 s...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 waste were reviewed in three scenarios including steam gasification,air gasification,and oxygen-riched gasification,and the product distribution and involved mechanism were elaborated.Moreover,we presented the details of ML methods like regression analysis,artificial neural networks,decision trees,and related methods,which are expected to revolutionize data analysis and modeling of the gasification of organic waste.Typical outputs including the syngas yield,composition,and HHVs were discussed with a better understanding of the gasification process and ML application.This review focused on the combination of gasification and ML,and it is of immediate significance for the resource and energy utilization of organic waste.展开更多
基金This work is supported by Sichuan Science and Technology Program(2021JDR0343)the Project Fund of Chengdu Science and Technology Bureau(2019-YF09-00086-SN).
文摘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 waste were reviewed in three scenarios including steam gasification,air gasification,and oxygen-riched gasification,and the product distribution and involved mechanism were elaborated.Moreover,we presented the details of ML methods like regression analysis,artificial neural networks,decision trees,and related methods,which are expected to revolutionize data analysis and modeling of the gasification of organic waste.Typical outputs including the syngas yield,composition,and HHVs were discussed with a better understanding of the gasification process and ML application.This review focused on the combination of gasification and ML,and it is of immediate significance for the resource and energy utilization of organic waste.
