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

This paper proposes a novel strategy for dynamic open-loop optimization of multivariable nonlinear systems.The methodology is based on the Fourier seriesand orthonormal polynomialsfor 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.Fil: Pantano, Maria Nadia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Fernández Puchol, María Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentin

Parameters optimization applying Monte Carlo methods and Evolutionary Algorithms. Enforcement to a trajectory tracking controller in non-linear systems.

[EN] In this work, a closed-loop control strategy is proposed. It allows tracking optimal profiles for a fed-batch bioprocess. The main advantage of this approach is that the control actions are computed from a linear equations system without linearizing the mathematical model, which allows working in any range. In addition, three techniques are developed to tune the controller. First, a completely probabilistic method, Monte Carlo. Second, a methodology based on Genetic Algorithms, an evolutionary optimization technique. Third, a Hybrid Algorithm, combining above algorithms advantages. Here, the objective function is to find the controller parameters that minimize the trajectory tracking total error. The controller performance is evaluated through simulations under normal operations conditions and parametric uncertainty, using the obtained controller parameters.[ES] En este trabajo se propone una estrategia de control en lazo cerrado para el seguimiento de perfiles óptimos previamente definidos para un bioproceso fed-batch. La mayor ventaja de este enfoque es que las acciones de control se calculan resolviendo un sistema de ecuaciones lineales, sin tener que linealizar el modelo matemático, lo que permite trabajar en cualquier rango. Además, se plantean tres técnicas para la sintonización de los parámetros del controlador diseñado. Primero se propone un método de Monte Carlo, el cual es un método probabilístico. En segundo lugar, se presenta una metodología basada en Algoritmos Genéticos, una técnica evolutiva de optimización. La tercera alternativa es el desarrollo de un Algoritmo Híbrido, diseñado a partir de la combinación de los dos métodos anteriores. En todos los casos, el objetivo es encontrar los parámetros del controlador que minimicen el error total de seguimiento de trayectorias. El desempeño del controlador se evalúa a través de simulaciones en condiciones normales de operación y frente a incertidumbre paramétrica, empleando los parámetros del controlador obtenidos.Este trabajo ha sido realizado con el apoyo del Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) y del Instituto de Ingeniería Química (IIQ) de la Universidad Nacional de San Juan. Se agradece la colaboración del Dr. Ing. Francisco Rossomando en la implementación del controlador PID neuronal.Fernández, C.; Pantano, N.; Godoy, S.; Serrano, E.; Scaglia, G. (2018). Optimización de Parámetros Utilizando los Métodos de Monte Carlo y Algoritmos Evolutivos. Aplicación a un Controlador de Seguimiento de Trayectoria en Sistemas no Lineales. Revista Iberoamericana de Automática e Informática. 16(1):89-99. doi:10.4995/riai.2018.8796SWORD899916

Process analytical technology in food biotechnology

Biotechnology is an area where precision and reproducibility are vital. This is due to the fact that products are often in form of food, pharmaceutical or cosmetic products and therefore very close to the human being. To avoid human error during the production or the evaluation of the quality of a product and to increase the optimal utilization of raw materials, a very high amount of automation is desired. Tools in the food and chemical industry that aim to reach this degree of higher automation are summarized in an initiative called Process Analytical Technology (PAT). Within the scope of the PAT, is to provide new measurement technologies for the purpose of closed loop control in biotechnological processes. These processes are the most demanding processes in regards of control issues due to their very often biological rate-determining component. Most important for an automation attempt is deep process knowledge, which can only be achieved via appropriate measurements. These measurements can either be carried out directly, measuring a crucial physical value, or if not accessible either due to the lack of technology or a complicated sample state, via a soft-sensor.Even after several years the ideal aim of the PAT initiative is not fully implemented in the industry and in many production processes. On the one hand a lot effort still needs to be put into the development of more general algorithms which are more easy to implement and especially more reliable. On the other hand, not all the available advances in this field are employed yet. The potential users seem to stick to approved methods and show certain reservations towards new technologies.Die Biotechnologie ist ein Wissenschaftsbereich, in dem hohe Genauigkeit und Wiederholbarkeit eine wichtige Rolle spielen. Dies ist der Tatsache geschuldet, dass die hergestellten Produkte sehr oft den Bereichen Nahrungsmitteln, Pharmazeutika oder Kosmetik angehöhren und daher besonders den Menschen beeinflussen. Um den menschlichen Fehler bei der Produktion zu vermeiden, die Qualität eines Produktes zu sichern und die optimale Verwertung der Rohmaterialen zu gewährleisten, wird ein besonders hohes Maß an Automation angestrebt. Die Werkzeuge, die in der Nahrungsmittel- und chemischen Industrie hierfür zum Einsatz kommen, werden in der Process Analytical Technology (PAT) Initiative zusammengefasst. Ziel der PAT ist die Entwicklung zuverlässiger neuer Methoden, um Prozesse zu beschreiben und eine automatische Regelungsstrategie zu realisieren. Biotechnologische Prozesse gehören hierbei zu den aufwändigsten Regelungsaufgaben, da in den meisten Fällen eine biologische Komponente der entscheidende Faktor ist. Entscheidend für eine erfolgreiche Regelungsstrategie ist ein hohes Maß an Prozessverständnis. Dieses kann entweder durch eine direkte Messung der entscheidenden physikalischen, chemischen oder biologischen Größen gewonnen werden oder durch einen SoftSensor. Zusammengefasst zeigt sich, dass das finale Ziel der PAT Initiative auch nach einigen Jahren des Propagierens weder komplett in der Industrie noch bei vielen Produktionsprozessen angekommen ist. Auf der einen Seite liegt dies mit Sicherheit an der Tatsache, dass noch viel Arbeit in die Generalisierung von Algorithmen gesteckt werden muss. Diese müsse einfacher zu implementieren und vor allem noch zuverlässiger in der Funktionsweise sein. Auf der anderen Seite wurden jedoch auch Algorithmen, Regelungsstrategien und eigne Ansätze für einen neuartigen Sensor sowie einen Soft-Sensors vorgestellt, die großes Potential zeigen. Nicht zuletzt müssen die möglichen Anwender neue Strategien einsetzen und Vorbehalte gegenüber unbekannten Technologien ablegen

Model-based versus model-free control designs for improving microalgae growth in a closed photobioreactor: Some preliminary comparisons

Controlling microalgae cultivation, i.e., a crucial industrial topic today, is a challenging task since the corresponding modeling is complex, highly uncertain and time-varying. A model-free control setting is therefore introduced in order to ensure a high growth of microalgae in a continuous closed photobioreactor. Computer simulations are displayed in order to compare this design to an input-output feedback linearizing control strategy, which is widely used in the academic literature on photobioreactors. They assess the superiority of the model-free standpoint both in terms of performances and implementation simplicity.Comment: The 24th Mediterranean Conference on Control and Automation (MED'16), Athens, Greece (June 21-24, 2016

Batch-to-batch iterative learning control of a fed-batch fermentation process

PhD ThesisRecently, iterative learning control (ILC) has been used in the run-to-run control of batch processes to directly update the control trajectory. The basic idea of ILC is to update the control trajectory for a new batch run using the information from previous batch runs so that the output trajectory converges asymptotically to the desired reference trajectory. The control policy updating is calculated using linearised models around the nominal reference process input and output trajectories. The linearised models are typically identified using multiple linear regression (MLR), partial least squares (PLS) regression, or principal component regression (PCR). ILC has been shown to be a promising method to address model-plant mismatches and unknown disturbances. This work presents several improvements of batch to batch ILC strategy with applications to a simulated fed-batch fermentation process. In order to enhance the reliability of ILC, model prediction confidence is incorporated in the ILC optimization objective function. As a result of the incorporation, wide model prediction confidence bounds are penalized in order to avoid unreliable control policy updating. This method has been proven to be very effective for selected model prediction confidence bounds penalty factors. In the attempt to further improve the performance of ILC, averaged reference trajectories and sliding window techniques were introduced. To reduce the influence of measurement noise, control policy is updated on the average input and output trajectories of the past a few batches instead of just the immediate previous batch. The linearised models are re-identified using a sliding window of past batches in that the earliest batch is removed with the newest batch added to the model identification data set. The effects of various parameters were investigated for MLR, PCR and PLS method. The technique significantly improves the control performance. In model based ILC the weighting matrices, Q and R, in the objective function have a significant impact on the control performance. Therefore, in the quest to exploit the potential of objective function, adaptive weighting parameters were attempted to study the performance of batch to batch ILC with updated models. Significant improvements in the stability of the performance for all the three methods were noticed. All the three techniques suggested have established improvements either in stability, reliability and/or convergence speed. To further investigate the versatility of ILC, the above mentioned techniques were combined and the results are discussed in this thesis

Dynamic optimization based on Fourier. Application to the biodiesel process

[EN] This work presents a novel methodology for the dynamic optimization of the biodiesel production process from vegetable oils in discontinuous mode. The proposed methodology has the particularity of using the Fourier series for the parameterization of the control action, and evolutionary algorithms for the optimization of parameters. The main advantages of this strategy are, on the one hand, that the profiles obtained are smooth, that is, continuous and differentiable, therefore they can be directly implemented in real systems, without the need to filter or soften the control signal; on the other hand, a minimum amount of parameters is required for optimization, avoiding over-parameterization, which can decrease the quality of the response. The proposed algorithms have been evaluated through simulations, obtaining very satisfactory results compared to those published in the literature.[ES] Este trabajo presenta una novedosa metodología para la optimización dinámica del proceso de producción de biodiesel a partir de aceites vegetales en modo discontinuo. La metodología propuesta tiene la particularidad de emplear la serie de Fourier para la parametrización de la acción de control, y algoritmos evolutivos para la optimización de parámetros. Las ventajas principales de esta estrategia son, por un lado, que los perfiles obtenidos son suaves, es decir, continuos y diferenciables, por lo tanto pueden implementarse directamente en sistemas reales, sin necesidad de filtrar o suavizar la señal de control; por otro lado, se requiere una mínima cantidad de parámetros para la optimización, evitando la sobre-parametrización, la cual puede disminuir la calidad de la respuesta. Los algoritmos propuestos han sido evaluados a través de simulaciones, obteniendo resultados muy satisfactorios comparados con los existentes en bibliografía.Agradecemos al Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET) por financiar este proyecto, y al Instituto de Ingeniería Química (IIQ) de la Universidad Nacional de San Juan (UNSJ) por su continua colaboración.Pantano, MN.; Fernández, MC.; Rodríguez, L.; Scaglia, GJ. (2020). Optimización dinámica basada en Fourier. Aplicación al proceso de biodiesel. Revista Iberoamericana de Automática e Informática industrial. 18(1):32-38. https://doi.org/10.4995/riai.2020.12920OJS3238181Benavides, P. T. & Diwekar, U., 2012a. Optimal control of biodiesel production in a batch reactor: Part I: Deterministic control. Fuel,94, 211- 217. https://doi.org/10.1016/j.fuel.2011.08.035Benavides, P. T. & Diwekar, U., 2012b. Optimal control of biodiesel production in a batch reactor: Part II: Stochastic control. Fuel,94, 218-226. https://doi.org/10.1016/j.fuel.2011.08.033Brásio, A. S., Romanenko, A., Leal, J., Santos, L. O. & Fernandes, N. C., 2013. Nonlinear model predictive control of biodiesel production via transesterification of used vegetable oils. Journal of Process Control,10,23, 1471-1479. https://doi.org/10.1016/j.jprocont.2013.09.023Cantrell, D. G., Gillie, L. J., Lee, A. F. & Wilson, K., 2005. Structure-reactivity correlations in MgAl hydrotalcite catalysts for biodiesel synthesis. Applied Catalysis A: General,2,287, 183-190. https://doi.org/10.1016/j.apcata.2005.03.027Fernández, Cecilia, M., Nadia Pantano, M., Rossomando, F. G., Alberto Ortiz, O. & Scaglia, G. J., 2019. State estimation and trajectory tracking control for a nonlinear and multivariable bioethanol production system. Brazilian Journal of Chemical Engineering,1,36, 421-437. https://doi.org/10.1590/0104-6632.20190361s20170379Fernández, C., Pantano, N., Godoy, S., Serrano, E. & Scaglia, G., 2019a. Optimización de Parámetros Utilizando los Métodos de Monte Carlo y Algoritmos Evolutivos. Aplicación a un Controlador de Seguimiento de Trayectoria en Sistemas no Lineales. Revista Iberoamericana de Automática e Informática industrial,1,16, 89-99. https://doi.org/10.4995/riai.2018.8796Fernández M. C., P. M. N., Rodriguez L., Scaglia G., 2020. State Estimation and Nonlinear Tracking Control Simulation Approach. Application to a Bioethanol Production System. Bioprocess and Biosystems Engineering,In press.Fernández, M. C., Pantano, M. N., Machado, R. A. F., Ortiz, O. A. & Scaglia, G. J., 2019b. Nonlinear multivariable tracking control: application to an ethanol process. International Journal of Automation and Control,4,13, 440-468. https://doi.org/10.1504/IJAAC.2019.10020240Fernández, M. C., Pantano, M. N., Rómoli, S., Patiño, H. D., Ortiz, O. A. & Scaglia, G. J., 2019c. An algebra approach for nonlinear multivariable fedbatch bioprocess control. International Journal of Industrial and Systems Engineering,1,33, 38-57. https://doi.org/10.1504/IJISE.2019.10023564Fernández, M. C., Pantano, M. N., Serrano, E. & Scaglia, G., 2020. Multivariable Tracking Control of a Bioethanol Process under Uncertainties. Mathematical Problems in Engineering,2020. https://doi.org/10.1155/2020/8263690Ho, Y., Mjalli, F. & Yeoh, H., 2010. Multivariable adaptive predictive model based control of a biodiesel transesterification reactor. Journal of Applied Sciences,12,10, 1019-1027. https://doi.org/10.3923/jas.2010.1019.1027Ignat, R. M. & Kiss, A. A., 2013. Optimal design, dynamics and control of a reactive DWC for biodiesel production. Chemical Engineering Research and Design,9,91, 1760-1767. https://doi.org/10.1016/j.cherd.2013.02.009Kreyszig, E. 1978. Introductory functional analysis with applications, Wiley New York.Mjalli, F. S., Kim San, L., Chai Yin, K. & Azlan Hussain, M., 2009. Dynamics and control of a biodiesel transesterification reactor. Chemical Engineering & Technology,1,32, 13-26. https://doi.org/10.1002/ceat.200800243Nagle, R. K., Saff, E. B. & Snider, A. D. 2001. Ecuaciones diferenciales y problemas con valores en la frontera, Pearson Educación.Nasir, N., Daud, W. R. W., Kamarudin, S. & Yaakob, Z., 2013. Process system engineering in biodiesel production: A review. Renewable and Sustainable Energy Reviews,22, 631-639. https://doi.org/10.1016/j.rser.2013.01.036Nearing, J., 2006. Mathematical tools for physics.Pantano, M. N., Fernández, M. C., Serrano, M. E., Ortiz, O. A. & Scaglia, G. J. E., 2018. Tracking Control of Optimal Profiles in a Nonlinear Fed-Batch Bioprocess under Parametric Uncertainty and Process Disturbances. Industrial & Engineering Chemistry Research,32,57, 11130-11140.https://doi.org/10.1021/acs.iecr.8b01791Pantano, M. N., Serrano, M. E., Fernández, M. C., Rossomando, F. G., Ortiz, O. A. & Scaglia, G. J., 2017. Multivariable Control for Tracking Optimal Profiles in a Nonlinear Fed-Batch Bioprocess Integrated with State Estimation. Industrial & Engineering Chemistry Research,20,56, 6043- 6056. https://doi.org/10.1021/acs.iecr.7b00831Rajarathinam, K., Gomm, J. B., Yu, D.-L. & Abdelhadi, A. S., 2016. PID controller tuning for a multivariable glass furnace process by genetic algorithm. International Journal of Automation and Computing,1,13, 64- 72. https://doi.org/10.1007/s11633-015-0910-1Salvi, B. & Panwar, N., 2012. Biodiesel resources and production technologies-A review. Renewable and Sustainable Energy Reviews,6,16, 3680-3689. https://doi.org/10.1016/j.rser.2012.03.050Santori, G., Di Nicola, G., Moglie, M. & Polonara, F., 2012. A review analyzing the industrial biodiesel production practice starting from vegetable oil refining. Applied energy,92, 109-132. https://doi.org/10.1016/j.apenergy.2011.10.031Tempo, R. & Ishii, H., 2007. Monte Carlo and Las Vegas Randomized Algorithms for Systems and Control: An Introduction. European Journal of Control,2-3,13, 189-203. https://doi.org/10.3166/ejc.13.189-203Wali, W., Al-Shamma'a, A., Hassan, K. H. & Cullen, J., 2012. Online geneticANFIS temperature control for advanced microwave biodiesel reactor. Journal of Process Control,7,22, 1256-1272. https://doi.org/10.1016/j.jprocont.2012.05.013Wali, W., Hassan, K., Cullen, J., Shaw, A. & Al-Shamma'a, A., 2013. Real time monitoring and intelligent control for novel advanced microwave biodiesel reactor. Measurement,1,46, 823-839. https://doi.org/10.1016/j.measurement.2012.10.004Zhang, M., Gao, Z., Zheng, T., Ma, Y., Wang, Q., Gao, M. & Sun, X., 2016. A bibliometric analysis of biodiesel research during 1991-2015. Journal of Material Cycles and Waste Management, 1-9. https://doi.org/10.1007/s10163-016-0575-zZhang, Y., Dube, M., McLean, D. & Kates, M., 2003. Biodiesel production from waste cooking oil: 1. Process design and technological assessment. Bioresource technology,1,89, 1-16. https://doi.org/10.1016/S0960-8524(03)00040-

Robust Control of Continuous Bioprocesses

This paper deals with robust control of continuous bioprocesses. According to the material balance equations of continuous bioprocesses, a uniform framework for mathematical modeling of this class of processes is first presented. Then a robust controller is designed by using the H∞ mixed sensitivity method for the biotechnology processes. The corresponding control objective is described as the development of a robust reference-tracking control structure with the best possible disturbance compensation, able to cope with variations in key process parameters. Finally, the proposed robust control strategy is applied to bio-dissimilation process of glycerol to 1, 3-propanediol. Simulation results are given which show that the designed robust controller makes the system have a favourable robust tracking performance