學術報告1：Reliable iterative learning control for batch processes
Batch and semi-batch processes are of considerable importance in the industrial processes. A wide variety of specialty chemicals, plastic products and certain types of polymers are manufactured in batch operations. Batch processes are typically used when the production volumes are low, when isolation is required for reasons of sterility or safety, and when the materials involved are difficult to handle. With the recent trend in building small flexible plants that are close to the markets, there has been a renewed interest in batch processing.
As an important control method in batch processes, iterative learning control is based on the notion that the performance of a system that executes the same task multiple times can be improved by learning from previous executions. In this report, a reliable iteration control method is given. Taking the immediate previous batch as the reference batch, a linearized model relating the deviations in the control profiles with the deviations in the quality variable trajectories is presented. The linearized model is used in calculating the control policy updating for the current batch through solving an optimization problem. In order to cope with nonlinearities, the batch-wise linearized model is re-identified after each batch run.
學術報告2：Fault Localization in Batch Processes through Progressive Principal Component Analysis Modeling
Batch and semi-batch processes play a significant role in the processing of specialty chemical, semiconductor, food and biology industries for producing high-value-added products to meet today’s rapidly changing market. Hence safety and reliability of batch and semi-batch process is focused on and proper process monitoring and diagnosis method is of great importance.
In this talk, a technique for fault localization in batch processes using Progressive PCA modeling method is introduced. The proposed method uses progressive PCA modeling procedure with time series SPE plots to obtain enhanced fault diagnosis performance and establish the fault propagation paths. Progressive PCA modeling procedure makes all variables showing abnormal changes to be identified. Time series SPE plots are used to detect when the abnormal change is observed. On the basis of the abnormality detection times on the affected variables, fault propagation paths can be constructed in conjunction with the engineering knowledge for the monitored process.
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. His research interests include process control, computational intelligence, process monitoring and batch process optimization control. He has published over 190 papers in international journals, books, and conference proceedings. He is a Member of Editorial Board of Neuron-computing published by Elsevier, a Member of Editorial Board of Control Engineering of China, and a Senior Member of IEEE. He has severed as a referee for over 50 international journals.