Multivariable Control for Tracking Optimal Profiles in a Nonlinear Fed-Batch Bioprocess Integrated with State Estimation

Abstract

This paper aims to solve the problem of tracking optimal profiles for a nonlinear multivariable fed-batch bioprocess by a simple but efficient closed-loop control technique based on a linear algebra approach. In the proposed methodology, the control actions are obtained by solving a system of linear equations without the need for state transformations. The optimal profiles to follow are directly those corresponding to output desired variables, therefore, estimation of states for nonmeasurable variables is considered by employing a neural networks method. The efficiency of the proposed controller is tested through several simulations, including process disturbances and operation under parametric uncertainty. The optimal controller parameters are selected through the Montecarlo Randomized Algorithm. In addition, proof of convergence to zero of tracking errors is analyzed and included in this article.

Bioprocess Control: Current Progress and Future Perspectives

“…While open-loop control strategies have been successfully used for bioprocess control, significant limitations exist including requirement of pre-computed knowledge of profiles, difficulty in mathematical formulation of non-linear systems, challenges with respect to real-time application, and requirement of complex databases of the process [32][33][34]. Another major limitation is the incompetency to take corrective measures if random disturbances occur during process operations [39].…”

Optimización dinámica basada en Fourier. Aplicación al proceso de biodiesel

“…6-Luego de una serie de operaciones de selección, mutación y cruzamiento (repetición de los pasos 4 y 5), el algoritmo converge y se obtiene el conjunto de parámetros óptimos, por ende, el perfil óptimo de Temperatura con el que se logra la máxima concentración de biodiesel. Es importante resaltar que los perfiles óptimos son obtenidos en lazo abierto, por lo tanto, es necesario aplicar luego alguna estrategia control para el seguimiento de dichos perfiles óptimos en lazo cerrado, por ejemplo, empelando control de seguimiento basado en álgebra lineal , Cecilia Fernández et al, 2019, Fernández et al, 2019b, Fernández et al, 2019c, Pantano et al, 2018, Pantano et al, 2017…”

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

“…Although a specific sensor could be available, this kind of hardware is usually expensive. According to this, there are many studies for state estimation in chemical and biochemical processes (Bogaerts and Coutinho, 2014, Bogaerts and Wouwer, 2003, Dewasme et al, 2015, Hulhoven et al, 2006, Lara-Cisneros et al, 2016, Heidarinejad et al, 2012, Kravaris et al, 2007, Hulhoven et al, 2008, Oliveira-Esquerre et al, 2002, Pantano et al, 2017b.…”