Simple Gain-Scheduled Control System for Dissolved Oxygen Control in Bioreactors

Mantas Butkus,Donatas Levišauskas,Vytautas Galvanauskas

doi:10.3390/pr9091493

Abstract

An adaptive control system for the set-point control and disturbance rejection of biotechnological-process parameters is presented. The gain scheduling of PID (PI) controller parameters is based on only controller input/output signals and does not require additional measurement of process variables for controller-parameter adaptation. Realization of the proposed system does not depend on the instrumentation-level of the bioreactor and is, therefore, attractive for practical application. A simple gain-scheduling algorithm is developed, using tendency models of the controlled process. Dissolved oxygen concentration was controlled using the developed control system. The biotechnological process was simulated in fed-batch operating mode, under extreme operating conditions (the oxygen uptake-rate’s rapidly and widely varying, feeding and aeration rate disturbances). In the simulation experiments, the gain-scheduled controller demonstrated robust behavior and outperformed the compared conventional PI controller with fixed parameters.

Highlights

The biomass specific growth rate depends on the actual substrate concentration and dissolved oxygen concentration (DOC) level (Equation (17), Figure 2e)
Controller is based on the controller input/output signals only and, does ance rejection of dissolved oxygen concentration is proposed, in which gain scheduling of not require online measurements of process variables for development of gain scheduling
PID (PI) controller is based on the controller input/output signals only and, does algorithms

Summary

Introduction

Control-system design is greatly influenced by the number of nonlinearities present within the process. Classical controllers, such as proportional–integral–derivative (PID) or proportional–integral (PI) are adequate if the nonlinearity encountered is very mild. In presence of significant number of nonlinearities, such linear models are ineffective, since even small disturbances can force the process away from the operating point [3]. Control quality is influenced by the controller’s ability to provide a stable performance while dealing with process variability and disturbances [1,2,4]. Temperature, pH, dissolved oxygen concentration, and other basic process variables are usually controlled in these systems [6]

Methods

Results

Conclusion