文献类型：期刊文献

英文题名：An integrated neural-network and expert-system approach to the supervision of reactor operating states in polyethylene terephthalate production

作者：Zhang, Jie[1,3]; Yang, Qiugui[1]; Zhang, Suzhen[1]; Howell, John[2]

机构：[1]E China Univ Chem Technol, Res Inst Automat Control, Shanghai 200237, Peoples R China;[2]Univ Glasgow, Dept Engn Mech, Glasgow G12 8QQ, Lanark, Scotland

年份：1998

卷号：6

期号：5

起止页码：581

外文期刊名：CONTROL ENGINEERING PRACTICE

收录：;EI(收录号：1999054494197);WOS:【SCI-EXPANDED(收录号:WOS:000076945400001)】；

语种：英文

外文关键词：artificial neural networks; quasi-Newton; expert systems; self-scaling variable metrics; pattern recognition; non-linear optimisation

摘要：With the emphasis on quality indices in polyethylene terephthalate (PET) production, it is highly desirable to assess, and subsequently to maintain, individual reactors at satisfactory operating states. To fulfil these aims, it is proposed to use an approach that integrates artificial neural networks (ANNs) with an expert system (ES); the purpose of the former is to estimate the quality indices from reactor process variables, whilst that of the latter is to assess current operation, and thence to advise on, for instance, the application of optimisation procedures to any of the reactor controllers. In addition, an expert system is used to filter plant measurements before they are input to the ANNs; the aim is to suppress gross errors that can cause the ANNs to output incorrect conclusions. The ANN training algorithm is based on a Quasi-Newton method with a self-scaling variable metric (SSVM), because simulation results show that the algorithm has high performance especially in terms of its speed of convergence. The work was implemented on a large-scale PET plant, with the software installed as an Application Module of a Honeywell TDC-3000 distributed control system. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.