Intelligent Predictive Control - Application to Scheduled Crystallization Processes

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Luis Alberto Paz Suárez (Author) FEUP; Petia Georgieva (Author) FEUP; Sebastião Feyo de Azevedo (Author) FEUP

Conference proceedings International

Title: 2009 International Conference on Adaptive and Intelligent Systems

Pages: 160-165

International Conference on Adaptive and Intelligent Systems

Klagenfurt, Austria, 24-26 September 2009

Indexing

Scopus

Scientific classification

FOS: Engineering and technology > Chemical engineering

CORDIS: Technological sciences > Engineering > Chemical engineering

Other information

Abstract (EN): The purpose of this paper is twofold. On one hand, we propose a modification of the general Model Predictive Control (MPC) approach where a prespecified tracking error is tolerated. The introduction of error tolerance (ET) in the MPC optimization algorithm reduces considerably the average duration of each optimization step and makes the MPC computationally more efficient and attractive for industrial applications. On the other hand a challenging scheduled crystallization process serves as a case study to show the practical relevance of the new intelligent predictive control. Comparative tests with different control policies are performed: i) Classical MPC with analytical or Artificial Neural Network (ANN) process model; ii) ET MPC with analytical or ANN process model; iii) Proportional-Integral (PI) control. Besides the computational benefits of ET MPC, the integration of ANN into the ET MPC brings substantial improvements of the final process performance measures and further relaxes the computational demands.

Language: English

Type (Professor's evaluation): Scientific