Title: Feedforward artificial neural network to improve model predictive control in biological processes

Authors: Senthil Kumar Arumugasamy; Zainal Ahmad

Addresses: School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia ' School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia

Abstract: Artificial neural networks (ANNs) offer the versatility of being able to model the dynamics of a biosystem without requiring a phenomenological model. In addition, model predictive control (MPC) is a member of advanced discrete-time process control algorithms. The recent developments in the biotechnology due to MPC utilising the capability of ANN make the practical application of non-linear process control strategies a reality. This paper reviews the recent enhancement and applications of MPC in various biochemical processes using feedforward artificial neural networks which is also known as neural predictive control. The capability of neural predictive control to handle the common problems associated with biochemical processes, namely optimisation of objective function, optimisation of dynamic behaviour of the system, control of ill-defined non-linear systems, improving the computational efficiency of the strategy, disturbance rejection ability and evaluating the control effectiveness are discussed. The review clearly indicates that enormous work has been carried out involving dynamic behaviour of the bioreactor system which is analysed and optimised revealing that feedforward neural network has evolved as a good bioreactor neuro-controller.

Keywords: artifical neural networks; ANNs; feedforward neural networks; process control; model-based control; advanced control; MPC; model predictive control; biotechnology; optimisation; modelling; biochemical processes; bioreactors.

DOI: 10.1504/IJAAC.2011.043623

International Journal of Automation and Control, 2011 Vol.5 No.4, pp.371 - 391

Received: 16 Aug 2011
Accepted: 23 Aug 2011
Published online: 17 Apr 2015 *

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