Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.

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Bioprocess Control: Current Progress and Future Perspectives.

The problem of optimal profiles tracking control under uncertainties for a nonlinear fed-batch bioprocess is addressed in this paper. Based on the results reported by Pantano et al. [ Ind. Eng. Chem. Res. 2017, 56, 6043], this work aims to improve the control system response against parametric uncertainty and process disturbances. The methodology is simple and easy to implement, but with excellent results. The design parameters are optimized by a randomized Monte Carlo algorithm. Besides, demonstration of the tracking error convergence to zero when the system is subjected to uncertainties is included in the article. The control system performance is tested through simulations, showing the improvement achieved.

Tracking Control of Optimal Profiles in a Nonlinear Fed-Batch Bioprocess under Parametric Uncertainty and Process Disturbances

Fil: Sardella, Maria Fabiana. Universidad Nacional de San Juan. Facultad de Ingenieria. Instituto de Ingenieria Quimica; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - San Juan; Argentina

Design and Application of a Linear Algebra Based Controller from a Reduced-Order Model for Regulation and Tracking of Chemical Processes under Uncertainties

Online deep neural network-based feedback control of a Lutein bioprocess

In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.

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State estimation and trajectory tracking control for a nonlinear and multivariable bioethanol production system

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.

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

El objetivo de este trabajo es desarrollar una tecnica de control simple pero eficiente, basada en un enfoque del algebra lineal para el seguimiento de perfiles optimos de un bioproceso fed-batch, multivariable y no lineal. La metodologia propuesta permite, conociendo los estados deseados, encontrar las acciones de control adecuadas mediante la resolucion de un sistema de ecuaciones lineales. La principal ventaja es que el error de seguimiento tiende a cero. La eficiencia del controlador propuesto es verificada a traves de varias simulaciones. Los parametros optimos del controlador se seleccionan mediante un algoritmo de Montecarlo bajo la condicion de minimizar un cierto indice de costo.

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Trajectory Tracking Controller for a Nonlinear Fed-batch Bioprocess

In this paper, a controller based on linear algebra for a fed-batch ethanol production process is proposed. It involves finding feed rate profiles (control actions obtained as a linear equations system solution) in order to make the system follow predefined concentration profiles. A Monte Carlo experiment is used for controller tuning. Moreover, several tests (adding parametric uncertainty, perturbations in the control action and in the initial conditions) are carried out so as to evaluate the controller performance. A comparison with other controller is made and the demonstration of the error convergence is included.

Nonlinear multivariable tracking control: application to an ethanol process

This paper proposes a novel strategy for dynamic open-loop optimization of multivariable nonlinear systems. The methodology is based on the Fourier series and orthonormal polynomials for the control vector parameterization in a sequential direct solution approach. The advantages of this technique are that a few number of parameters is required for optimization and a smooth control profile is obtained. The proposed strategy is evaluated in the case study of recombinant protein production, that is a nonlinear system with two control actions, the substrate and inhibitor feed flow rate. The algorithms are tested through simulations and the results are compared with those published in the bibliography.

Maria Nadia Pantano

Papers

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

Tracking Control of Optimal Profiles in a Nonlinear Fed-Batch Bioprocess under Parametric Uncertainty and Process Disturbances

Trajectory Tracking Controller for a Nonlinear Fed-batch Bioprocess

Nonlinear multivariable tracking control: application to an ethanol process

Open-Loop Dynamic Optimization for Nonlinear Multi-Input Systems. Application to Recombinant Protein Production