Hierarchical control for microalgae biomass production in photobiorreactors

Fernández, Ignacio; Berenguel, Manuel; Guzmán, José Luís; Acién, F.G.; Andrade, Gustavo A. de; Pagano, Daniel J.

doi:10.1016/j.conengprac.2016.06.007

Cited by 15 publications

(9 citation statements)

References 54 publications

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“…En este caso, todos los balances del sistema en las distintas partes del fotobioreactor fueron considerados de mezcla perfecta, donde la distribución espacial del lazo fue aproximada por un retardo variable que dependía de la velocidad del medio en el mismo. Este modelo fue de gran utilidad para el desarrollo de algoritmos de control jerárquicos, ya que gracias a esta simplificación el tiempo de cómputo se redujo sustancialmente (Fernández et al, 2016b).…”

Section: Otros Modelosunclassified

“…El objetivo fundamental, que es la maximización del rendimiento en la producción de microalgas, sólo se puede alcanzar en base a un detallado análisis de las principales variables del cultivo y su optimización a lo largo de las escalas de tiempo existentes. Por tanto, el diseño de estrategias de control avanzadas para conseguir llevar el sistema de producción de microalgas cerca de sus valores teóricos se ha convertido en una línea de investigación de candente actualidad (Fernández et al, 2016b;de Andrade et al, 2016a;Djema et al, 2020).…”

Section: Introductionunclassified

“…Por otro lado, otras contribuciones recientes han permitido mejorar la capacidad de transferencia de materia en el reactor mediante el ajuste del coeficiente de transferencia de materia de oxígeno disuelto (Barceló-Villalobos et al, 2018), lo cual ha permitido desarrollar estrategias de control capaces de regular el caudal de gas de formaóptima para conseguir un valor deseado de oxígeno disuelto en el sistema (Barceló-Villalobos et al, 2019b). En relación a otras variables del proceso, se están aplicando estrategias de control jerárquico y de optimización en tiempo real (Rodríguez-Blanco et al, 2018), orientadas a optimizar los procesos de dilución e inyección de medio en el reactor (de Andrade et al, 2016a;Malek et al, 2016;McGinn et al, 2017), así como a maximizar la producción de biomasa mediante estrategias de control multivariable y jerárquico (Ifrim et al, 2013;Fernández et al, 2016b), que como se ha indicado es el finúltimo de este tipo de instalaciones.…”

Section: Introductionunclassified

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Modelado y control de la producción de microalgas en fotobiorreactores industriales

Guzmán

Acién

Berenguel

2020

Rev. iberoam. autom. inform. ind.

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<p>Este artículo presenta una visión general sobre el proceso de producción de microalgas desde un punto de vista de modelado y control de procesos. En primer lugar se exponen las ventajas y el potencial de este tipo de microorganismos, así como los distintos tipos de reactores que se suelen utilizar para su producción. Posteriormente, se analiza el comportamiento dinámico de este tipo de procesos, el cual es muy complejo y cambiante debido a variaciones en las condiciones ambientales tanto diarias como anuales, y se presentan los distintos balances que permiten describir la evolución de las principales variables del sistema. Se exponen distintos tipos de modelos a nivel biológico y a nivel estructural que han sido validados a escala industrial. Tras analizar su comportamiento dinámico, se motivan los distintos problemas de control existentes en este tipo de sistemas y se resume una amplia batería de estrategias de control que han sido evaluadas con éxito en fotobiorreactores industriales. Finalmente, se concluye el trabajo con un balance de los aspectos más importantes expuestos a lo largo del mismo.</p>

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Section: Otros Modelosunclassified

Section: Introductionunclassified

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Modelado y control de la producción de microalgas en fotobiorreactores industriales

Guzmán

Acién

Berenguel

2020

Rev. iberoam. autom. inform. ind.

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“…Although there is not yet a definitive solution (reliable, easy to apply, with low computational effort and with a good balance between implementation costs and profitability attained) for overcoming such obstacles, many advances have been made in the last fifteen years in the development of sensors (real and soft-sensors) for predicting important biological variables. In addition, during the last three decades, several works in the literature [3][4][5][6][7][8][9][10][11][12] have reported the implementation of advanced control strategies to various biotechnological processes, such as biomass production, fermentations, anaerobic digestion, yeast and penicillin productions, and microalgae cultures. To improve the operation in the bioprocess industry, reliable sensors for on-line monitoring of important biological variables (i.e.…”

Section: Introductionmentioning

confidence: 99%

Real‐time optimization and control for polyhydroxybutyrate fed‐batch production at pilot plant scale

Ochoa

Alcaraz

2020

J of Chemical Tech & Biotech

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BACKGROUND: Polyhydroxyalkanoate-type polymers (PHAs) are claimed as an alternative for replacing conventional plastics; but the economic feasibility of its production should still be assured in order to accomplish such a massive goal. In a previous work on fed-batch production of PHAs, it was shown that the process yield and the properties of the biopolymer obtained highly depend on the type of feeding strategy applied. Such results evidenced the need for implementing optimization-based control strategies to control the process in real-time, rather than implementing pre-defined feeding policies. RESULTS: In this study, real-time optimization for the fed-batch production of PHAs at pilot plant scale from a low-cost substrate (vinasses-molasses mixture) is presented. Two optimization-based control strategies were developed to calculate the optimal feed flow rate. One strategy considered only the process productivity as an objective, whereas the other incorporated not only productivity but also a profitability-oriented objective. The latter resulted in a productivity of 0.364 g/L h and a maximal polymer concentration of 62.03% cell dry weight, versus 0.043 g/L h and 6.25% attained by applying a traditional feedback control law. CONCLUSION: Implementation of closed-loop based-optimal control policies in PHA production has led to higher yield and polymer concentration, confirming that an improved operation of fed-batch bioprocesses is reached when the feeding policy is updated in real-time, according to an optimization goal. Despite implementation costs, applying this approach might be the best alternative for simultaneously facing uncertainties and achieving optimal operation at an industrial scale, especially when living microorganisms are used as factories.

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“…The optimization of process operations based on the hierarchical structure has received a significant amount of attention in the chemical process industry over the past few years . The goal is to achieve the economic optimum in the presence of constraints, uncertainties about the plant behaviour, and disturbances.…”

Section: Introductionmentioning

confidence: 99%

Two‐layer model predictive control for chemical process model with integrating controlled variables

Yang

Ding

2019

Can J Chem Eng

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This paper proposes an overall solution to the two‐layer model predictive control (MPC) for the integrating controlled variables in the process model. The scheme includes three modules, that is, the open‐loop prediction module, the steady‐state target calculation (SSTC) module, and the dynamic control module. Based on the real‐time output measurements and past inputs, the open‐loop prediction module predicts the future outputs in the presence of disturbances. The economic optimization of SSTC is comprised of the feasibility stage and the economics stage, considering constraints of multi‐priority ranks. The dynamic control module receives the steady‐state targets from SSTC and calculates the control signals. The optimization problems of SSTC and dynamic control operate with the same frequency. This overall method guarantees the consistency of three modules with respect to the model, the constraints, and the targets. The simulation example illustrates that steady‐state targets are adjusted dynamically after the occurrence of disturbances, and offset‐free control is achieved.

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Hierarchical control for microalgae biomass production in photobiorreactors

Cited by 15 publications

References 54 publications

Modelado y control de la producción de microalgas en fotobiorreactores industriales

Modelado y control de la producción de microalgas en fotobiorreactores industriales

Real‐time optimization and control for polyhydroxybutyrate fed‐batch production at pilot plant scale

Two‐layer model predictive control for chemical process model with integrating controlled variables

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