大会报告

********欢迎来到第26届中国过程控制会议********

本次会议将邀请6位国内外知名学者(美国工程院院士、IEEE Fellow、中国科学院院士等)作大会报告和主旨报告,同时将从录用论文中择优选取3篇作主旨报告。

1.  Model-based control and optimization with imperfect models

Sebastian Engell, Prof. Dr.-Ing.

Process Dynamics and Operations Group, Dept. of Biochemical and Chemical Engineering

TU Dortmund, Germany

ABSTRACT: The economics and energy and resource efficiency of chemical production processes can be significantly improved by employing model-based optimizing control and model-based real-time optimization. However, plant models never are perfect, and the effort to develop very accurate models may be prohibitively high. The talk will discuss several approaches to implement optimizing control and real-time optimization using models which describe the plant behaviour approximately, but not accurately.

We will first discuss the online optimization of the operating points of chemical plants using a combination of models and measured data from previous operating points. The measured data is used to iteratively correct the optimization model by means of the so-called modifier adaptation. In this context, we propose a novel way of estimating the modifiers – the empirical plant gradients- which is based on ideas from derivative-free optimization.

Then we show how to implement robust optimizing control solutions for polymerization processes, either by an optimizing control scheme that is realized in a simple feedback structure or by employing state and parameter estimation to adapt simplified models to the true behaviour of the plant.

Finally, we discuss multi-stage optimizing control which is a systematic way to achieve optimal performance and robust constraint satisfaction in the presence of model uncertainties. In contrast to other approaches, the presence of feedback in the control loop which enables corrections of the predicted control moves after new information has been obtained is taken into account by computing so-called recourse actions.

 

2. Economic plantwide control

Professor Sigurd Skogestad

Chemical Engineering

Norwegian University of Science and Technology (NTNU)

ABSTRACT: A chemical plant may have thousands of measurements and control loops. By the term plantwide control it is not meant the tuning and behavior of each of these loops, but rather the control philosophy of the overall plant with emphasis on the structural decisions. In practice, the control system is usually divided into several layers, separated by time scale: scheduling (weeks) , site-wide optimization (day), local optimization (hour), supervisory (predictive, advanced) control (minutes) and regulatory control (seconds). Such a hiearchical (cascade) decomposition with layers operating on different time scale is used in the control of all real (complex) systems including  biological systems and airplanes, so the issues in this section are not limited to process control. In the talk the most important issues are discussed, especially related to the choice of variables that provide the link the control layers and the location of throughput manipulator (TPM). Some simple rules for plantwide control will also be presented, and demonstrated on case studies.

Bio: Sigurd Skogestad received his Ph.D. degree from the California Institute of Technology, Pasadena, USA in 1987. He has been a full professor at Norwegian University of Science and Technology (NTNU), Trondheim, Norway since 1987 and he was Head of the Department of Chemical Engineering from 1999 to 2009. He is the principal author, together with Prof. Ian Postlethwaite, of the book "Multivariable feedback control" published by Wiley in 1996 (first edition) and 2005 (second edition). He received the Ted Peterson Award from AIChE in 1989, the George S. Axelby Outstanding Paper Award from IEEE in 1990, the O. Hugo Schuck Best Paper Award from the American Automatic Control Council in 1992, and the Best Paper Award 2004 from Computers and Chemical Engineering. He was an Editor of Automatica during the period 1996-2002. His research interests include the use of feedback as a tool to make the system well-behaved (including self-optimizing control), limitations on performance in linear systems, control structure design and plantwide control, interactions between process design and control, and distillation column design, control and dynamics.  He is a Fellow of AIChE and IFAC.

-------------------------------------------------------------------------------

Keynote Talk 1:Data-driven modelling, optimisation, and control of batch processes


Dr Jie Zhang

School of Chemical Engineering and Advanced Materials

Newcastle University, Newcastle upon Tyne NE1 7RU, UK


Abstract: Batch processes are suitable for the agile manufacturing of high value added products, such as specialty polymers, pharmaceuticals, and bio-products. In contrast to continuous processes, batch processes have strong nonlinear behaviour and always operated in transient states. A further difficulty in batch process control is that product quality variables usually cannot be measured on-line and can only be obtained through laboratory analysis after a batch has finished. The main objective in batch process control is to produce a maximum amount of high quality product while under safe process operations. This talk presents several robust neural network based data driven modelling, inferential estimation, reliable optimal control, and iterative learning control methods for batch processes. Bootstrap aggregated neural networks have enhanced model generalisation capability and can also provide model prediction confidence bounds. One of the most important issues of empirical model based batch process optimal control is that the calculated optimal control profile can degrade very significantly when applied to the actual process due to model plant mismatches. “Optimal on the model” can be quite different from “optimal on the process”. In order to address this issue, the optimisation objective function can be augmented by an additional term (or an additional objective in multi-objective optimisation) to penalise wide model prediction confidence bound at the end-point of a batch. By such a means, the calculated optimal control profile is much reliable in the sense that, when being applied to the actual process, the degradation in control performance is limited. Utilising the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch to batch optimal control strategy based on the linearization of bootstrap aggregated neural network model is developed to overcome the influence of unknown disturbances.


Dr Jie Zhang received his PhD in Control Engineering from City University, London, in 1991. He has been with the School of Chemical Engineering and Advanced Materials, Newcastle University, UK, since 1991 and is currently a Senior Lecturer and Degree Programme Director for MSc in Applied Process Control. His research interests are in the general areas of process system engineering including process modelling, batch process control, process monitoring, and computational intelligence. He has published over 250 papers in international journals, books, and conference proceedings (H-index of 26 based on Web of Science). He is on the Editorial Boards of a number of journals including Neurocomputing published by Elsevier and Control Engineering of China. 

 

 

 

 

谢谢您对第26届中国过程控制会议的支持!

--------------------------------------

会议秘书处电话:0791-87046270; 87046252; 87046284

E-mailscpcc2015@163.com ; cpcc2015@sohu.com

会议官网:http://2015.cn-tcpc.org