Design of neural controllers for various configurations of continuous bioreactor

Abstract

In the present study, the Neural network (NN) based controller design has been implemented for a non-linear continuous bioreactor process. Multilayer feed forward networks (FFNN) were used as direct inverse neural network (DINN) controllers as well as IMC based NN controllers. The training as well as testing database was created by perturbing the open loop process with pseudo random signals (PRS). For set point tracking; at an operating condition where the cell growth is substrate limited, the DINN controllers were designed for conventional turbidostat and nutristat configurations. DINN controllers performed effectively for set-point tracking. To address the disturbance rejection problems, which are very likely to be faced by the bioreactors, the IMC based neural control architecture was proposed with suitable choice of filter and disturbance transfer function. To assess the controllability of the various bioreactor configurations, like conventional turbidostat and nutristat & concentration turbidostat and nutristat, the offset or degree of disturbance rejection by the proposed IMC based NN controllers were utilized. The ‘concentration turbidostat’ using the feed substrate concentration as the manipulated variable was found to be the best control configuration among the continuous bioreactor configurations.