Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration  

作　　者：Haoshan Duan Xi Meng Jian Tang Junfei Qiao 

机构地区：[1]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China [2]Beijing Laboratory of Smart Environmental Protection,Beijing 100124,China [3]Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China [4]Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education,Beijing 100124,China

出　　处：《Chinese Journal of Chemical Engineering》2023年第4期46-57,共12页中国化学工程学报（英文版）

基　　金：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：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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] X799.3[自动化与计算机技术—控制科学与工程]