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 Haoshan Duan;Xi Meng;Jian Tang;Junfei Qiao

作者机构：Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental ProtectionBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of EducationBeijing 100124China 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2023年第56卷第4期

页      面：46-57页

核心收录：

学科分类：12[管理学] 083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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) 

主　　题：Municipal solid waste incineration NO_(x)concentration prediction Modular neural network Model 

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 *** this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)***,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 ***,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the ***,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application ***,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI *** the experimental results demonstrate the considerable prediction ability of the M-LSTM network.