个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:控制科学与工程学院
学科:控制理论与控制工程
办公地点:创新园大厦A614
联系方式:刘全利 大连理工大学控制科学与工程学院 邮编:116024 电话:0411-84705516
电子邮箱:liuql@dlut.edu.cn
Hybrid Neural Prediction and Optimized Adjustment for Coke Oven Gas System in Steel Industry
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论文类型:期刊论文
发表时间:2012-03-01
发表刊物:IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
收录刊物:SCIE、Scopus
卷号:23
期号:3
页面范围:439-450
ISSN号:2162-237X
关键字:Data mining; echo state network; energy balance in steel industry; Gaussian process; regression prediction
摘要:An energy system is the one of most important parts of the steel industry, and its reasonable operation exhibits a critical impact on manufacturing cost, energy security, and natural environment. With respect to the operation optimization problem for coke oven gas, a two-phase data-driven based forecasting and optimized adjusting method is proposed, where a Gaussian process-based echo states network is established to predict the gas real-time flow and the gasholder level in the prediction phase. Then, using the predicted gas flow and gasholder level, we develop a certain heuristic to quantify the user's optimal gas adjustment. The proposed operation measure has been verified to be effective by experimenting with the real-world on-line energy data sets coming from Shanghai Baosteel Corporation, Ltd., China. At present, the scheduling software developed with the proposed model and ensuing algorithms have been applied to the production practice of Baosteel. The application effects indicate that the software system can largely improve the real-time prediction accuracy of the gas units and provide with the optimized gas balance direction for the energy optimization.