A Soft Sensing Method for Operation Optimization of Coke Dry Quenching Process
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- 英文论文题名：A Soft Sensing Method for Operation Optimization of Coke Dry Quenching Process
- 论文作者：Jian-Guo Wang ; Zhi-Duo Cao ; Li-Lan Liu ; Bang-Hua Yang ; Shi-Wei Ma ; Min-Rui Fei ; Zhi-Fu Deng ; Yuan Yao ; Yi-Ping Wu
- 英文论文作者：Jian-Guo Wang ; Zhi-Duo Cao ; Li-Lan Liu ; Bang-Hua Yang ; Shi-Wei Ma ; Min-Rui Fei ; Zhi-Fu Deng ; Yuan Yao ; Yi-Ping Wu ; School of Mechatronical Engineering and Automation ; Shanghai University ; Shanghai Key Lab of Power Station Automation Technology ; Department of Chemical Engineering ; "Tsing-Hua University" ; Shanghai Baosight Software Co Ltd
- 年：2017
- 作者机构：School of Mechatronical Engineering and Automation,Shanghai University,Shanghai Key Lab of Power Station Automation Technology;Department of Chemical Engineering,"Tsing-Hua University";Shanghai Baosight Software Co Ltd;
- 英文论文关键词：Soft sensor ; Coke dry quenching ; Data-driven ; Variable selection ; Autoregressive integrated moving average
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TQ522.16
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：4243k
- 原文格式：D
- 会议级别：全国
摘要
Based on the actual data analysis, it is found in this paper that the supplementary air flow rate in the CDQ operation didn't follow the variation of the discharge rate of incandescent coke well, which results in the concentration increase of combustible gas in the exhaust gas and the decrease of economic efficiency. The correlation analysis results show that the introduced derived variables are more useful than some plain variables for the purpose of prediction. Next, to handle the contradiction between the steam productivity and the coke burning loss, a new economic efficiency index is introduced by synthesizing the two competing aspects. A kind of soft sensor of economic efficiency is put forward by combining nonnegative garrote(NNG) variable selection algorithm with the autoregressive integrated moving average(ARIMA) model, which gives a good solution to the economic efficiency real-time prediction problem of CDQ system. Then, the implementation of model-based optimization is studied based on the actual operation data. The results show that there exists large room for economic efficiency promotion.
Based on the actual data analysis, it is found in this paper that the supplementary air flow rate in the CDQ operation didn't follow the variation of the discharge rate of incandescent coke well, which results in the concentration increase of combustible gas in the exhaust gas and the decrease of economic efficiency. The correlation analysis results show that the introduced derived variables are more useful than some plain variables for the purpose of prediction. Next, to handle the contradiction between the steam productivity and the coke burning loss, a new economic efficiency index is introduced by synthesizing the two competing aspects. A kind of soft sensor of economic efficiency is put forward by combining nonnegative garrote(NNG) variable selection algorithm with the autoregressive integrated moving average(ARIMA) model, which gives a good solution to the economic efficiency real-time prediction problem of CDQ system. Then, the implementation of model-based optimization is studied based on the actual operation data. The results show that there exists large room for economic efficiency promotion.
Based on the actual data analysis, it is found in this paper that the supplementary air flow rate in the CDQ operation didn't follow the variation of the discharge rate of incandescent coke well, which results in the concentration increase of combustible gas in the exhaust gas and the decrease of economic efficiency. The correlation analysis results show that the introduced derived variables are more useful than some plain variables for the purpose of prediction. Next, to handle the contradiction between the steam productivity and the coke burning loss, a new economic efficiency index is introduced by synthesizing the two competing aspects. A kind of soft sensor of economic efficiency is put forward by combining nonnegative garrote(NNG) variable selection algorithm with the autoregressive integrated moving average(ARIMA) model, which gives a good solution to the economic efficiency real-time prediction problem of CDQ system. Then, the implementation of model-based optimization is studied based on the actual operation data. The results show that there exists large room for economic efficiency promotion.
引文
[1]M.Arens and E.Worrell,“Diffusion of energy efficient technologies in the German steel industry and their impact on energy consumption,”Energy,vol.73,968-977,2014.
[2]H.Zhang,L.Dong,H.Li,T.Fujita,S.Ohnishi,and Q.Tang,“Analysis of low-carbon industrial symbiosis technology for carbon mitigation in a Chinese iron/steel industrial park:A case study with carbon flow analysis,”Energy Policy,vol.61,1400-1411,2013.
[3]J.Oda,K.Akimoto,F.Sano,T.Homma,and T.Tomoda,“Evaluation of energy saving and CO2 emission reduction technologies in energy supply and end-use sectors using a global energy model,”IEEE Transactions on Electrical and Electronic Engineering,vol.2,72-83,2007.
[4]D.Hayashi and M.Krey,“Assessment of clean development mechanism potential of large-scale energy efficiency measures in heavy industries,”Energy,vol.32,1917-1931,2007.
[5]G.Jian and C.Xianqiao,“CDQ System Designing and Dual-Loop PID Tuning Method for Air Steam Temperature,”in Proc.12th International Symposium on Distributed Computing and Applications to Business,Engineering&Science,Xianning,2013,153-156.
[6]M.Jia and H.Chi,“The research of temperature control system for cycle gas of CDQ export,”in Proc.International Conference on Electronic&Mechanical Engineering and Information Technology,Harbin,2011,3666-3668.
[7]Y.H.Feng,X.X.Zhang,and M.L.Wu,“Experimental study on the effects of blast-cap configurations and charge patterns on coke descending in CDQ cooling shaft,”Heat Transfer-Asian Research,vol.37,352-358,2008.
[8]K.Sun,C.T.Tseng,D.Shan Hill Wong,S.S.Shieh,S.S.Jang,J.L.Kang,et al.,“Model predictive control for improving waste heat recovery in coke dry quenching processes,”Energy,vol.80,275-283,2015.
[9]Z.Song and A.Kusiak,“Constraint-Based Control of Boiler Efficiency:A Data-Mining Approach,”IEEE Transactions on Industrial Informatics,vol.3,73-83,2007.
[10]J.Q.Li,J.J.Gu,and C.L.Niu,“The Operation Optimization based on Correlation Analysis of Operation Parameters in Power Plant,”in Proc.International Symposium on Computational Intelligence and Design,Hangzhou,2008,138-141.
[11]J.G.Wang,S.S.Shieh,S.S.Jang,D.S.H.Wong,and C.W.Wu,“A two-tier approach to the data-driven modeling on thermal efficiency of a BFG/coal co-firing boiler,”Fuel,vol.111,528-534,Sep 2013.
[12]J.G.Wang,S.S.Jang,D.S.H.Wong,S.S.Shieh,and C.W.Wu,“Soft-sensor development with adaptive variable selection using nonnegative garrote,”Control Engineering Practice,vol.21,1157-1164,2013.
[13]L.Breiman,“Better Subset Regression Using the Nonnegative Garrote,”Technometrics,vol.37,p.373,1995.
[14]K.Sun,J.Liu,J.L.Kang,S.S.Jang,D.S.H.Wong,and D.S.Chen,“Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote,”Journal of Process Control,vol.24,1068-1075,2014.
[15]J.Liu,“Developing a soft sensor based on sparse partial least squares with variable selection,”Journal of Process Control,vol.24,1046-1056,2014.
[2]H.Zhang,L.Dong,H.Li,T.Fujita,S.Ohnishi,and Q.Tang,“Analysis of low-carbon industrial symbiosis technology for carbon mitigation in a Chinese iron/steel industrial park:A case study with carbon flow analysis,”Energy Policy,vol.61,1400-1411,2013.
[3]J.Oda,K.Akimoto,F.Sano,T.Homma,and T.Tomoda,“Evaluation of energy saving and CO2 emission reduction technologies in energy supply and end-use sectors using a global energy model,”IEEE Transactions on Electrical and Electronic Engineering,vol.2,72-83,2007.
[4]D.Hayashi and M.Krey,“Assessment of clean development mechanism potential of large-scale energy efficiency measures in heavy industries,”Energy,vol.32,1917-1931,2007.
[5]G.Jian and C.Xianqiao,“CDQ System Designing and Dual-Loop PID Tuning Method for Air Steam Temperature,”in Proc.12th International Symposium on Distributed Computing and Applications to Business,Engineering&Science,Xianning,2013,153-156.
[6]M.Jia and H.Chi,“The research of temperature control system for cycle gas of CDQ export,”in Proc.International Conference on Electronic&Mechanical Engineering and Information Technology,Harbin,2011,3666-3668.
[7]Y.H.Feng,X.X.Zhang,and M.L.Wu,“Experimental study on the effects of blast-cap configurations and charge patterns on coke descending in CDQ cooling shaft,”Heat Transfer-Asian Research,vol.37,352-358,2008.
[8]K.Sun,C.T.Tseng,D.Shan Hill Wong,S.S.Shieh,S.S.Jang,J.L.Kang,et al.,“Model predictive control for improving waste heat recovery in coke dry quenching processes,”Energy,vol.80,275-283,2015.
[9]Z.Song and A.Kusiak,“Constraint-Based Control of Boiler Efficiency:A Data-Mining Approach,”IEEE Transactions on Industrial Informatics,vol.3,73-83,2007.
[10]J.Q.Li,J.J.Gu,and C.L.Niu,“The Operation Optimization based on Correlation Analysis of Operation Parameters in Power Plant,”in Proc.International Symposium on Computational Intelligence and Design,Hangzhou,2008,138-141.
[11]J.G.Wang,S.S.Shieh,S.S.Jang,D.S.H.Wong,and C.W.Wu,“A two-tier approach to the data-driven modeling on thermal efficiency of a BFG/coal co-firing boiler,”Fuel,vol.111,528-534,Sep 2013.
[12]J.G.Wang,S.S.Jang,D.S.H.Wong,S.S.Shieh,and C.W.Wu,“Soft-sensor development with adaptive variable selection using nonnegative garrote,”Control Engineering Practice,vol.21,1157-1164,2013.
[13]L.Breiman,“Better Subset Regression Using the Nonnegative Garrote,”Technometrics,vol.37,p.373,1995.
[14]K.Sun,J.Liu,J.L.Kang,S.S.Jang,D.S.H.Wong,and D.S.Chen,“Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote,”Journal of Process Control,vol.24,1068-1075,2014.
[15]J.Liu,“Developing a soft sensor based on sparse partial least squares with variable selection,”Journal of Process Control,vol.24,1046-1056,2014.