Modeling, simulation and optimization of municipal solid waste gasification process through physics informed deep learning

Municipal solid waste (MSW) refers to recyclables and compostable materials, as well as garbage from homes, businesses, institutions, and construction and demolition sites. Disposal of MSW causes significant environmental problems. It is imperative to develop efficient environmental-friendly treatment technologies to tackle this global challenge. Among feasible technologies, gasification of treated MSW has been considered as a critical option since it could carry out the waste prevention and energy recovery from waste. However, the characteristics of MSW are hardly investigated due to complex organic matters, moisture content, carbon, nitrogen, and sulphur. While modeling and simulation of MSW gasification process is critical for controlling and optimizing the process operations, the traditional physics principle based process model is computationally expensive and fast meta-models are required. As the artificial intelligence (AI) technologies became one of the major defining attributes of competitive advantage across many manufacturing processes, this project aims at the development of physics-informed machine learning model for the prediction of syngas composition, gas production rate and heating value of gas produce in MSW gasifiers. The developed model will leverage the power of recent development in artificial intelligence to support the development of advanced MSW gasification process.

Faculty Supervisor:

Zukui Li

Student:

Partner:

National Cheng Kung University

Discipline:

Engineering

Sector:

Artificial Intelligence; Sustainability & the Environment; Clean Technology

University:

University of Alberta

Program: