Prediction of NOx concentration using modular long short-term memory neural network for municipal solid waste incineration

doi:10.1016/j.cjche.2022.06.028

中国化学工程学报 ›› 2023, Vol. 56 ›› Issue (4): 46-57.DOI: 10.1016/j.cjche.2022.06.028

• Full Length Article • 上一篇下一篇

Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration

Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China;Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education, Beijing 100124, China

收稿日期:2021-12-18 修回日期:2022-05-18 出版日期:2023-04-28 发布日期:2023-06-13
通讯作者: Junfei Qiao,E-mail:adqiao@bjut.edu.cn
基金资助:
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (62021003, 61890930-5, 61903012, 62073006), Beijing Natural Science Foundation (42130232), the National Key Research and Development Program of China (2021ZD0112301, 2021ZD0112302).

Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration

Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China;Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education, Beijing 100124, China

Received:2021-12-18 Revised:2022-05-18 Online:2023-04-28 Published:2023-06-13
Contact: Junfei Qiao,E-mail:adqiao@bjut.edu.cn
Supported by:
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (62021003, 61890930-5, 61903012, 62073006), Beijing Natural Science Foundation (42130232), the National Key Research and Development Program of China (2021ZD0112301, 2021ZD0112302).

摘要/Abstract

摘要： Air pollution control poses a major problem in the implementation of municipal solid waste incineration (MSWI). Accurate prediction of nitrogen oxides (NO_x) concentration plays an important role in efficient NO_x emission controlling. In this study, a modular long short-term memory (M-LSTM) network is developed to design an efficient prediction model for NO_x concentration. First, the fuzzy C means (FCM) algorithm is utilized to divide the task into several sub-tasks, aiming to realize the divide-and-conquer ability for complex task. Second, long short-term memory (LSTM) neural networks are applied to tackle corresponding sub-tasks, which can improve the prediction accuracy of the sub-networks. Third, a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage. Finally, after being evaluated by a benchmark simulation, the proposed method is applied to a real MSWI process. And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

关键词: Municipal solid waste incineration, NO_x concentration prediction, Modular neural network, Model

Abstract: Air pollution control poses a major problem in the implementation of municipal solid waste incineration (MSWI). Accurate prediction of nitrogen oxides (NO_x) concentration plays an important role in efficient NO_x emission controlling. In this study, a modular long short-term memory (M-LSTM) network is developed to design an efficient prediction model for NO_x concentration. First, the fuzzy C means (FCM) algorithm is utilized to divide the task into several sub-tasks, aiming to realize the divide-and-conquer ability for complex task. Second, long short-term memory (LSTM) neural networks are applied to tackle corresponding sub-tasks, which can improve the prediction accuracy of the sub-networks. Third, a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage. Finally, after being evaluated by a benchmark simulation, the proposed method is applied to a real MSWI process. And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

Key words: Municipal solid waste incineration, NO_x concentration prediction, Modular neural network, Model

Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao. Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration[J]. 中国化学工程学报, 2023, 56(4): 46-57.

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Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration

Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration

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