Robust nonlinear model predictive control of cascade of fermenters with recycle for efficient bioethanol production

Abstract

In this paper, dynamical behavior and control of the continuous ethanol fermentation process are studied. The process productivity is improved by using two fermenters connected in series with recycle of cells. First, dynamical behavior and productivity of the cascade system are studied by using numerical bifurcation analysis. Then, multi-stage nonlinear model predictive control (MS-NMPC), classical and offset-free NMPCs algorithms are designed to eliminate the undesired oscillations. To ensure acceptable efficiency of the cascade process, a minimal ethanol concentration is defined and included as additional constraint in the controller design. The unmeasured state variables are estimated by using the asymptotic observer. It is shown that for smaller sampling times the predictive controllers are very effective, even in presence of parameter uncertainties. It is also observed that offset-free NMPC algorithm is more sensitive to measurement noise, but MS-NMPC and classical NMPC are more sensitive to estimation errors for longer sampling times.