题目：Retrofit Self-Optimizing Control

时间：10月18日（周二）13：30

地点：延长校区Ⅳ403

简介：After 15 year development, it is still hard to find any real application of the self-optimizing control (SOC) strategy, although it can achieve optimal or near optimal operation in industrial processes without repetitive real-time optimization. This is partially because of the misunderstanding that the SOC requires to completely reconfigure the entire control system which is generally unacceptable for most process plants in operation, even though the current one may not be optimal. To alleviate this situation, this paper proposes a retrofit SOC methodology aiming to improve the optimality of operation without change of existing control systems. In the new retrofitted SOC systems, the controlled variables (CVs) selected are kept at constant by adjusting setpoints of existing control loops, which therefore constitutes a two layer control architecture. CVs made from measurement combinations are determined to minimise the global average losses. A subset measurement selection problem for the global SOC is solved though a branch and bound algorithm. The standard testbed Tennessee Eastman (TE) process is studied with the proposed retrofit SOC methodology. The optimality of the new retrofit SOC architecture is validated by comparing two state of art control systems by Ricker and Larsson et al., through steady state analysis as well as dynamic simulations.

报告人：英国Cranfield大学，曹毅教授

报告人简介：Yi Cao is a Reader in Control Systems Engineering, Cranfield University. He Obtained PhD in Control Engineering from the University of Exeter in 1996, MSc in Industrial Automation from Zhejiang University, China in 1985. His main research interest is in developing systematic approaches to solve various operational problems involved in industrial processes using both models and data. Dr Cao is the main inventor of the Inferential Slug Control technology to mitigate slugging of multiphase flow in offshore oil and gas production systems. A successful field trial has showed that the technology was able to increase oil production by 10%. This achievement received the Innovation Award from the East England Energy Group (EEEgr) in 2010. His recent research is focusing on data driven self-optimizing control methodology. By applying it to water flooding process for oil enhanced recovery, it can achieve near optimal operation in spite of the uncertainties of oil reservoirs. His research also covers data driven condition monitoring approaches for fault diagnosis an prognosis.