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
Data-driven based model for flow prediction of steam system in steel industry
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论文类型:期刊论文
发表时间:2012-06-15
发表刊物:INFORMATION SCIENCES
收录刊物:SCIE、EI、Scopus
卷号:193
页面范围:104-114
ISSN号:0020-0255
关键字:Steam system; Data-driven; Time series prediction; Bayesian ESN
摘要:The steam system is one of the main energy systems in steel industry, and its operational scheduling plays a crucial role for energy utility and resources saving. For a reasonable resources operation, the accurate prediction of steam flow is required. Considering the large amount of production data in energy system, a data-driven based model is proposed to perform a time series prediction for steam flow, in which a Bayesian echo state network (ESN) is established. This method combines Bayesian theory with ESN to obtain optimal output weight via maximizing the posterior probability density of the weights to avoid over-fitting in the training process of sample data. To pursue optimized hyper-parameters in the proposed Bayesian ESN, the evidence framework based on sample data is further adopted in this work. Experimental results using the real production data from Shanghai Baosteel show the validity and practicality of the proposed data-driven based model in providing scientific decision guidance for the steam system. Published by Elsevier Inc.