Data-driven Prediction on Performance Indicators in Process Industry: A Survey

被引：0
|
作者：
Chen L. [1 ]
Liu Q.-L. [1 ]
Wang L.-Q. [1 ]
Zhao J. [1 ]
Wang W. [1 ]
机构：
[1] School of Control Science and Engineering, Dalian University of Technology, Dalian
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2017年 / 43卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Feature selection; Industrial big data; Parameter optimization; Prediction model; Production process;
D O I：
10.16383/j.aas.2017.c170136
中图分类号：
学科分类号：
摘要：
It is of great significance to predict production process indicators in process industry for production scheduling, safety production and energy saving. Currently, various data-driven approaches for predicting these indicators are proposed, including the following three aspects: feature selection, prediction model construction and model parameter optimization. This paper surveys the above three aspects and summaries the merits and demerits of these approaches. Finally, future research directions of production process prediction of key indicators in process industry are suggested with respect to industrial big data and knowledge automation. Copyright © 2017 Acta Automatica Sinica. All rights reserved.
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收藏
页码：944 / 954
页数：10
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