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A Knowledge Base System for Operation Optimization: Design and Implementation Practice for the Polyethylene Process
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DOI：10.1016/j.eng.2019.09.004.png)
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Abstract
En 中文
Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the efficiency of reuse of information and knowledge-two critical elements in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowledge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized. (C) 2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.
Keywords:
Ontology
Operation optimization
Knowledge base system
Polyethylene process
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