Zongyang Hu , Jiuwen Fang , Ruixiang Zheng , Mian Li , Baosheng Gao , Lingcan Zhang
{"title":"基于 NARX 中性网络的锅炉燃煤高效模型预测控制","authors":"Zongyang Hu , Jiuwen Fang , Ruixiang Zheng , Mian Li , Baosheng Gao , Lingcan Zhang","doi":"10.1016/j.jprocont.2023.103158","DOIUrl":null,"url":null,"abstract":"<div><p><span><span><span>During coal-fired power generation, uniform </span>combustion temperature in the boiler is desired which will benefit both economical efficiency and pollution reduction. To this end, a </span>model predictive control<span> (MPC) algorithm based on the Nonlinear Auto-Regressive Exogenous Inputs (NARX) neural network and KS-function is proposed, and the uniform combustion in the boiler is realized by controlling the opening travel of secondary windgates. In the modeling process, a multi-input and multi-output(MIMO) NARX neural network is developed using the historical data of the real system The NARX neural network is then used to predict the state variables, and the optimal control input is achieved by applying sequential quadratic programming (SQP), comparing with linear MPC the mean temperature difference is reduced by 64.2%. In addition, this paper proposes a new method to reduce the computational time of the online optimization process based on KS-function, which greatly accelerates the searching speed of SQP by 67.3%. The proposed MPC algorithm is applied to a 660 MW power generating unit. The results show that by applying the proposed algorithm, the temperature difference in the boiler is kept within 100 °C, the average coal consumption of the power plant is reduced by 5.71 g/kWh, and the NOx emission is reduced to 23.84 mg/m</span></span><sub>3</sub>. It can be concluded that the proposed algorithm greatly improves the economical efficiency of the power plant and reduces the emission of pollutants.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient model predictive control of boiler coal combustion based on NARX neutral network\",\"authors\":\"Zongyang Hu , Jiuwen Fang , Ruixiang Zheng , Mian Li , Baosheng Gao , Lingcan Zhang\",\"doi\":\"10.1016/j.jprocont.2023.103158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span><span><span>During coal-fired power generation, uniform </span>combustion temperature in the boiler is desired which will benefit both economical efficiency and pollution reduction. To this end, a </span>model predictive control<span> (MPC) algorithm based on the Nonlinear Auto-Regressive Exogenous Inputs (NARX) neural network and KS-function is proposed, and the uniform combustion in the boiler is realized by controlling the opening travel of secondary windgates. In the modeling process, a multi-input and multi-output(MIMO) NARX neural network is developed using the historical data of the real system The NARX neural network is then used to predict the state variables, and the optimal control input is achieved by applying sequential quadratic programming (SQP), comparing with linear MPC the mean temperature difference is reduced by 64.2%. In addition, this paper proposes a new method to reduce the computational time of the online optimization process based on KS-function, which greatly accelerates the searching speed of SQP by 67.3%. The proposed MPC algorithm is applied to a 660 MW power generating unit. The results show that by applying the proposed algorithm, the temperature difference in the boiler is kept within 100 °C, the average coal consumption of the power plant is reduced by 5.71 g/kWh, and the NOx emission is reduced to 23.84 mg/m</span></span><sub>3</sub>. It can be concluded that the proposed algorithm greatly improves the economical efficiency of the power plant and reduces the emission of pollutants.</p></div>\",\"PeriodicalId\":50079,\"journal\":{\"name\":\"Journal of Process Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Process Control\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0959152423002469\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Process Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959152423002469","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Efficient model predictive control of boiler coal combustion based on NARX neutral network
During coal-fired power generation, uniform combustion temperature in the boiler is desired which will benefit both economical efficiency and pollution reduction. To this end, a model predictive control (MPC) algorithm based on the Nonlinear Auto-Regressive Exogenous Inputs (NARX) neural network and KS-function is proposed, and the uniform combustion in the boiler is realized by controlling the opening travel of secondary windgates. In the modeling process, a multi-input and multi-output(MIMO) NARX neural network is developed using the historical data of the real system The NARX neural network is then used to predict the state variables, and the optimal control input is achieved by applying sequential quadratic programming (SQP), comparing with linear MPC the mean temperature difference is reduced by 64.2%. In addition, this paper proposes a new method to reduce the computational time of the online optimization process based on KS-function, which greatly accelerates the searching speed of SQP by 67.3%. The proposed MPC algorithm is applied to a 660 MW power generating unit. The results show that by applying the proposed algorithm, the temperature difference in the boiler is kept within 100 °C, the average coal consumption of the power plant is reduced by 5.71 g/kWh, and the NOx emission is reduced to 23.84 mg/m3. It can be concluded that the proposed algorithm greatly improves the economical efficiency of the power plant and reduces the emission of pollutants.
期刊介绍:
This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others.
Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques.
Topics covered include:
• Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods
Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.