Application progress of machine learning in the whole chain disposal of organic solid waste

Abstract

A series of complex thermochemical reactions occur in the thermal conversion process of organic solid waste, which brings challenges to the in-depth understanding of its mechanism, the optimization of technical parameters for the conversion process and the directional regulation of products. The data-driven machine learning modeling method can promote the intelligence and refinement of organic solid waste disposal. In this review, the application of the intelligent modeling method based on machine learning in the whole chain disposal of organic solid waste was summarized. The prediction of machine learning on the upstream production and physical and chemical characteristics of organic solid waste, the thermal conversion process and product characteristics in the middle stream, and the utilization and benefit evaluation of downstream products were reviewed. The applicable scenarios, advantages and disadvantages of different models were compared. The purpose was to construct an intelligent disposal scheme for the whole chain of organic solid waste, and realize an efficient disposal mode of organic solid waste integrating harmlessness, reduction, resource utilization, high value and intelligence, and to provide some guidance for the effective management of solid waste.

Keywords

• organic solid waste
• thermal conversion
• machine learning
• whole chain disposal