Numerical simulation and intelligent prediction of a 500 t/d municipal solid waste incinerator

Teng Ma, Hongquan Zhou, Fang Xu, Dezhen Chen, Kezhen Qian and Lijie Yin

Energy, 2024, vol. 312, issue C

Abstract: After classifying municipal solid waste (MSW), understanding how changes in its characteristics affect the incineration process becomes essential. Optimizing and adjusting operating parameters is crucial to ensure the smooth operation of MSW incinerators. In this study, we developed a three-dimensional numerical simulation model for a 500 t/d MSW incinerator, based on a two-fluid model that accounts for grate motion. We examined the effects of load and moisture on the combustion process. Using the results from the numerical simulation and on-site operational data, we established a machine learning model to predict incinerator conditions based on load, waste particle size, and moisture content. The average relative errors for the training and test sets were approximately 0.33 % and 0.44 %, respectively. Finally, we compared the influence of different operating parameters on flue gas temperature. Our numerical simulation model and intelligent prediction method offer valuable guidance for optimizing existing MSW incinerators and designing new ones.

Keywords: MSW incinerator; Numerical simulation; Neural networks; Two-fluid model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:312:y:2024:i:c:s0360544224034248

DOI: 10.1016/j.energy.2024.133646

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