Application of feed forward neural networks in modeling and control of a fed-batch crystallization process

• Type: Article in International Scientific Journal
• Date: 2006

Authors

Petia Georgieva (Author) FEUP; Sebastião Feyo de Azevedo (Author) FEUP

Journal

Title: Transactions on Engineering, Computing and Technology

Pages: 65-70

ISSN: 1305-5313

Scientific classification

FOS: Engineering and technology > Other engineering and technologies

CORDIS: Technological sciences

Other information

Abstract (EN): This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.

Language: English

Type (Professor's evaluation): Scientific