Computationally efficient process control with neural network-based predictive models

  • Authors:

  • Luis Alberto Paz Suárez;Petia Georgieva;Sebastião Feyo de Azevedo

  • Affiliations:

  • Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal;Department of Electronics Telecommunications and Informatics and Institute of Electrical Engineering and Telematics, University of Aveiro, Portugal;Department of Chemical Engineering, Faculty of Engineering, University of Porto, Portugal

  • Venue:
  • IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
  • Year:
  • 2009

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Abstract

The present work reports our study on the benefits of integrating the Artificial Neural Network (ANN) technique as a time series predictor, with the concept of Model-based Predictive Control (MPC) in order to build an efficient process control. The combination of ANN and MPC usually leads to computationally very demanding procedure, that finally makes this approach less popular or even impossible to apply for real time industrial applications. The main contribution of this paper is the introduction of an error tolerance in the MPC optimization algorithm that reduces considerably the computational costs. Besides, the new ANNMPC framework proved to bring substantial improvements compared with traditional Proportional-Integral (PI) control with respect to macro process performance measures as less energy consumption and higher productivity.