Abnormal condition identification and safe control scheme for the electro-fused magnesia smelting process

被引：26

作者：

Li, Hui ^{[1
]}

Wang, Fuli ^{[1
]}

Li, Hongru ^{[1
]}

机构：

[1] Northeastern Univ, Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China

来源：

ISA TRANSACTIONS | 2018年 / 76卷

关键词：

Abnormal condition identification; Bayesian network; Case-based reasoning; Fused magnesium furnace; Safe control; FAULT-DIAGNOSIS; BAYESIAN NETWORKS; FURNACE; OPTIMIZATION; COMBINATION; INFORMATION;

D O I：

10.1016/j.isatra.2018.03.011

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a control framework to identify the abnormal condition and make safe control scheme by fusing the related multi-source information based on the analysis of the abnormal exhausting condition in the electro-fused magnesia smelting process. For the abnormal condition identification, the case-based reasoning is used to infer the degree of the abnormal condition. When the similarity of the matched case doesn't exceed the set threshold, Bayesian network is used to infer the degree. For the safe control scheme, taking advantage of the idea of the remaining lifetime predicting, and the adjustment of the control variables is obtained. The application results show that the proposed method is effective and it owns better performance than the manual and the existing research results. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：178 / 187

页数：10

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