返回
Optimal operational control for complex industrial processes
DOI:10.1016/j.arcontrol.2014.03.005.png)
摘要
En 中文
Process control should ensure not only controlled variables to follow their setpoint values, but also the whole process plant to meet operational requirements optimally (e.g., quality, efficiency and consumptions). Process control should also enable that operational indices for quality and efficiency be improved continuously, while keeping the indices related to consumptions at the lowest possible level. This paper starts with a survey on the existing operational optimization and control methodologies and then presents a data-driven hybrid intelligent optimal operational control for complex industrial processes where process operational models are difficult to obtain. Applications via a hybrid simulation system and an industrial roasting process for hematite ore mineral processing are presented to demonstrate the effectiveness of the proposed operational control method. Issues for future research on the optimal operational control for complex industrial processes are outlined before concluding the paper. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
MODEL-PREDICTIVE CONTROL
INTELLIGENT CONTROL
OPTIMIZING CONTROL
OPTIMIZATION
SYSTEM
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.7
论文数:
830
被引数:
5.9K
机构
引用论文
NCO tracking and self-optimizing control in the context of real-time optimization实时优化背景下的NCO跟踪和自优化控制
Demand reduction in building energy systems based on economic model predictive control基于经济模型预测控制的建筑能源系统需求削减

