Version 1
: Received: 24 October 2024 / Approved: 25 October 2024 / Online: 25 October 2024 (08:14:01 CEST)
How to cite:
Yang, W.; Tang, J.; Tian, H.; Wang, T. Flue Gas Oxygen Content Model Based on Bayesian Optimiza-Tion Main-Compensation Ensemble Algorithm in Municipal Solid Waste Incineration Process. Preprints2024, 2024101999. https://doi.org/10.20944/preprints202410.1999.v1
Yang, W.; Tang, J.; Tian, H.; Wang, T. Flue Gas Oxygen Content Model Based on Bayesian Optimiza-Tion Main-Compensation Ensemble Algorithm in Municipal Solid Waste Incineration Process. Preprints 2024, 2024101999. https://doi.org/10.20944/preprints202410.1999.v1
Yang, W.; Tang, J.; Tian, H.; Wang, T. Flue Gas Oxygen Content Model Based on Bayesian Optimiza-Tion Main-Compensation Ensemble Algorithm in Municipal Solid Waste Incineration Process. Preprints2024, 2024101999. https://doi.org/10.20944/preprints202410.1999.v1
APA Style
Yang, W., Tang, J., Tian, H., & Wang, T. (2024). Flue Gas Oxygen Content Model Based on Bayesian Optimiza-Tion Main-Compensation Ensemble Algorithm in Municipal Solid Waste Incineration Process. Preprints. https://doi.org/10.20944/preprints202410.1999.v1
Chicago/Turabian Style
Yang, W., Hao Tian and Tianzheng Wang. 2024 "Flue Gas Oxygen Content Model Based on Bayesian Optimiza-Tion Main-Compensation Ensemble Algorithm in Municipal Solid Waste Incineration Process" Preprints. https://doi.org/10.20944/preprints202410.1999.v1
Abstract
Flue gas oxygen content is one of the important parameters in municipal solid waste incineration (MSWI) process. And its stable control is closely related to the incineration efficiency and pollutant emission. The construction of a high-precision and interpretable flue gas oxygen content model is the basis for achieving its optimal control. However, the existing methods have problems such as poor interpretability, low model accuracy, and complex manual hyperparameter adjustment. In view of the above problems, the article proposes a flue gas oxygen content model based on Bayes-ian optimization (BO) main-complement ensemble algorithm. Firstly, the ensemble TS fuzzy re-gression tree (EnTSFRT) is used to construct the main model. Then, the long short term memory network (LSTM) is used to construct the compensation model with the error of the EnTSFRT model as the true value. The weighted value of the main and compensation models is used as the output. Finally, based on the BO algorithm, the hyperparameters of the constructed main-complement ensemble model are optimized to achieve high performance model. Experi-mental results based on real MSWI process data show that the proposed method has good per-formance.
Keywords
Municipal solid waste incineration (MSWI); Flue gas oxygen content; Main-compensation tree ensemble model; Ensemble TS fuzzy regression tree (EnTSFRT); Long short term memory network (LSTM); Bayesian optimization (BO)
Subject
Environmental and Earth Sciences, Waste Management and Disposal
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
