Generic Model Control of Biomethanation Process

Abstract

This paper focuses on the design of Generic Model Control (GMC) algorithm for the control of the acetate concentration in a biomethanation process with production of methane gas. A nonlinear dynamic model for the biomethanation process is used in the derivation of the control law of Generic Model Control scheme. The controller parameters are estimated using the Trial and Error method. The effectiveness and performance of the proposed control strategy is illustrated by a MATLAB program under various operation conditions and disturbances. The simulation results obtained confirmed the good quality of the control. Keyword: Generic model control; process control; biomethanation process I. Introduction The control of biotechnological processes has been an important problem attracting wide attention. The main engineering motivation in applying advanced control methods to such processes is to improve operational stability and production efficiency. But, the use of modern control for these bioprocesses is still low. The nonlinearity of the bioprocesses and the uncertainty of kinetics impose the advanced control strategy as a suitable approach. So, the difficulties encountered in the measurement of the state variables of the bioprocesses impose the use of the so-called sensors. Note that these software sensors are used not only for the estimation of the concentrations of some components but also for the estimation of the kinetic parameters or even kinetic reactions. The dynamics of this biotechnological process are described by a set of nonlinear differential equations obtained from the reaction scheme and the unknown reaction rates are estimated. For the estimation of unknown parameters of the process, the distribution approach is used. The parameter identification of deterministic nonlinear continuous-time systems (NCTS), modeled by polynomial type differential equation has been considered by numerous authors (1), (2). The generic model control scheme used in this paper is based on a control law that uses the nonlinear dynamic model of the process to calculate the effect of sequences of control steps on the controlled variables.