基于数据的流程工业生产过程指标预测方法综述

陈龙; 刘全利; 王霖青; 赵珺; 王伟

doi:10.16383/j.aas.2017.c170136

基于数据的流程工业生产过程指标预测方法综述

doi: 10.16383/j.aas.2017.c170136

陈龙^1,,
刘全利^1, ,,
王霖青^1,,
赵珺^1,,
王伟^1,

1.
大连理工大学控制科学与工程学院大连 116024

基金项目:

国家自然科学基金 61473056

国家自然科学基金 61522304

国家科技支撑计划 61522304

国家自然科学基金 61533005

中央高校基本科研基金 (DUT16RC(3)031

国家自然科学基金 U1560102

详细信息

作者简介:
陈龙  大连理工大学控制科学与工程学院博士研究生.主要研究方向为流程工业生产计划与优化调度和机器学习.E-mail:chl1207@aliyun.com

王霖青   大连理工大学控制科学与工程学院讲师.主要研究方向为流程工业生产计划与优化调度, 计算机集成制造, 机器学习.E-mail:wanglinqing@dlut.edu.cn

赵珺   大连理工大学控制科学与工程学院教授.主要研究方向为流程工业生产计划与优化调度, 计算机集成制造, 智能优化, 机器学习和知识自动化.E-mail:zhaoj@dlut.edu.cn

王伟  大连理工大学控制科学与工程学院教授.主要研究方向为复杂系统建模、控制与优化, 流程工业生产计划与优化调度, 知识自动化.E-mail:wangwei@dlut.edu.cn

通讯作者:
刘全利大连理工大学控制科学与工程学院教授.主要研究方向为流程工业生产计划与优化调度, 嵌入式系统研究、设计及应用.E-mail:liuql@dlut.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2017-03-13
- 录用日期: 2017-05-06
- 刊出日期: 2017-06-20

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

CHEN Long^1
,,
LIU Quan-Li^{1
, ,},
WANG Lin-Qing^1
,,
ZHAO Jun^1
,,
WANG Wei^1
,

1.
School of Control Science and Engineering, Dalian University of Technology, Dalian 116024

Funds:

National Natural Science Foundation of China 61473056

National Natural Science Foundation of China 61522304

National Key Technology Support Program 61522304

National Natural Science Foundation of China 61533005

Fundamental Research Funds for the Central Universities (DUT16RC(3)031

National Natural Science Foundation of China U1560102

More Information

Author Bio:
Ph. D. candidate at the School of Control Science and Engineering, Dalian University of Technology. His research interest covers production planning and scheduling optimization for process industry, and machine learning

Lecturer at the School of Control Science and Engineering, Dalian University of Technology. His research interest covers production planning and scheduling optimization for process industry, computer integrated manufacturing, and machine learning

Professor at the School of Control Science and Engineering, Dalian University of Technology. His research interest covers production planning and scheduling optimization for process industry, computer integrated manufacturing, intelligent optimization, machine learning, and knowledge automation

Professor at the School of Control Science and Engineering, Dalian University of Technology. His research interest covers complex system modeling, control and optimization, production planning and scheduling optimization for process industry, and knowledge automation

Corresponding author: LIU Quan-Li Professor at the School of Control Science and Engineering, Dalian University of Technology. His research interest covers production planning and scheduling optimization for process industry and embedded systems research, design, and application. Corresponding author of this paper

摘要

摘要: 生产过程关键指标的预测对于流程工业生产调度，安全生产和节能环保有着重要作用.目前，已有多种基于工业生产数据提出的生产过程指标预测方法，主要涉及特征（变量）选择，预测模型构建及其模型参数优化这三方面.本文分别针对以上三方面论述了基于数据的工业生产过程指标预测国内外研究现状，分析了各种方法的优缺点.最后，指出了流程工业生产过程指标预测方法在工业大数据及知识自动化等方面的未来研究方向和前景.
- 生产过程 /
- 特征选择 /
- 预测模型 /
- 参数优化 /
- 工业大数据
Abstract: 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.
- Production process /
- feature selection /
- prediction model /
- parameter optimization /
- industrial big data

HTML全文

图 1 基于数据生产关键指标预测的基本流程图

Fig. 1 A flow chart of data-based prediction on performance indicators in process industry

下载: 全尺寸图片幻灯片

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