Prediction of NO<sub><i>x</i></sub> concentration using modular long short-term memory neural network for municipal solid waste incineration

Haoshan Duan; Xi Meng; Jian Tang; Junfei Qiao

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

Full Length Article | Updated：2026-01-06

- Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration
- Chinese Journal of Chemical Engineering Vol. 56, Issue 4, Pages: 46-57(2023)
- Affiliations：
  
  1. Faculty of Information Technology, Beijing University of Technology,Beijing,China,100124
  2. Beijing Laboratory of Smart Environmental Protection,Beijing,China,100124
  3. Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing,China,100124
  4. Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education,Beijing,China,100124
- Author bio：
- Funds：
- DOI：
  CLC：
- Published：2023
- Accepted：
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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.
DOI：

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. DOI：

摘要

Abstract

Air pollution control poses a major problem in the implementation of municipal solid waste incineration (MSWI). Accurate prediction of nitrogen oxides (NO

) concentration plays an important role in efficient NO

emission controlling. In this study

a modular long short-term memory (M-LSTM) network is developed to design an efficient prediction model for NO

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.

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

DOI：

摘要

Abstract

关键词

Keywords

references

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