Non-linear control of continuous bioreactors

Abstract

Control of bioreactors has achieved importance in the recent years. This may be due to the fact that they are dif®cult to control which may be attributed to its nonlinear dynamic behavior. The model parameters of the bioreactor also vary in an unpredictable manner. The complexity of the biochemical processes inhibits the accurate modeling and also the lack of suitable sensors make the process state dif®cult to characterize. Considerable emphasis has been placed on the control of fed-batch fermentors because of their prevalence in industries. However, when production of biomass is to be optimized, continuous operation is desirable. Several procedures are available for the nonlinear control of processes, viz., differential geometric approach, internal model control approach, reference synthesis technique, predictive control design, etc., but the major disadvantage of these approaches is the computational time required to perform the prediction optimization. In this study, a nonlinear controller based on a polynomial discrete time model (NARMAX) is evaluated for its performance on a fermentor. It can be shown that a nonlinear self-tuning controller based on NARMAX model can be extended to the control of fermentors. The response is smooth for both load and setpoint changes even when process parameters are assumed to be zero and uncertainty in parameters are present and in the presence of controller constraints. The control action can be made more or less robust by changing the design parameters appropriately. Therefore, nonlinear self-tuning controller is suitable for control of industrial processes.

Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review

“…Radhakrishnan et al. 99 considered the nonlinear dynamic behavior of continuous bioreactors. A nonlinear self‐tuning controller using the nonlinear autorecursive moving average with exogenous input (NARMAX) model was built and the optimum cell concentration was evaluated for a continuous fermenter.…”

Nonlinear Systems Modeling Using LS-SVM with SMO-Based Pruning Methods

“…For simplicity, we consider the problem without a bias term, as did in [8]. The Lagrangian for problem (2) is…”

Identification of time-varying systems using multiresolution wavelet models

“…The noise variable , with maximum lag , accommodates the effects of measurement noise, modelling errors and unmeasured disturbances. A rigorous derivation of the NARMAX model and many applications have been proposed in the literature (see Leontaritis and Billings 1985, Tabrizi 1990, Cooper 1991, Noshiro et al 1993, Jang and Kim 1994, Aguirre and Billings 1995, Tabrizi 1998, Radhakrishnan et al 1999, Glass and Franchek 1999. …”