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Control of dissolved oxygen concentration using neural network in a batch bioreactor

Artificial neural networks (ANN) have been utilized for many chemical applications because of their ability to learn system features. This paper presents the use of feedforward neural networks for dynamic modelling and dissolved oxygen (DO) control of a batch yeast fermentation. The ARMAX model of this nonlinear process is also presented. Model verification is tested by using experimental data. Different ANNs are trained using the backpropagation learning algorithm. The resulting ANNs are introduced in a Model Predictive Control (MPC) scheme to test the control performance of the structure. At system, output variable that is DO concentration and adjusting variable that is air flow rate are chosen. The robustness of this control structure is studied in the case of setpoint changes. Results obtained with NN‐MPC is also presented and compared with those obtained with Nonlinear Auto Regressive Moving Average (NARMA‐L2) control strategy. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 619–628, 2012