“…The open-loop response of the reactor temperature is identified by the Identification tool of the MATLAB and 99% best fit is obtained for measured and simulated data for 4th order of the state-space model. The bioreactor model parameters, steady-state operating parameters, and nomenclature are given in Appendix A, Appendix B, and Appendix C [1] and its open loop responses are shown in Fig. 2.Fig.…”
Section: Modeling Of a Continuous Bioreactormentioning
HighlightsIdentification tool used for process model by simulating bioreactor’s mathematical models.IMC-PID controller designed for stable and unstable second order time delayed process.Proposed method is able to control the reactor temperature.Performance of controller is evaluated in the terms of IAE, ISE, ITAE and settling time.
“…The open-loop response of the reactor temperature is identified by the Identification tool of the MATLAB and 99% best fit is obtained for measured and simulated data for 4th order of the state-space model. The bioreactor model parameters, steady-state operating parameters, and nomenclature are given in Appendix A, Appendix B, and Appendix C [1] and its open loop responses are shown in Fig. 2.Fig.…”
Section: Modeling Of a Continuous Bioreactormentioning
HighlightsIdentification tool used for process model by simulating bioreactor’s mathematical models.IMC-PID controller designed for stable and unstable second order time delayed process.Proposed method is able to control the reactor temperature.Performance of controller is evaluated in the terms of IAE, ISE, ITAE and settling time.
“…Sin embargo, los controladores PID clásicos no responden adecuadamente frente a sistemas con largos retardos, salidas oscilatorias y subprocesos inestables, como es el caso de los bioprocesos. Es por ello que se desarrollaron estrategias que combinan la simplicidad de un PID con otras técnicas, y así poder aplicarlos en sistemas de esta índole (Imtiaz et al, 2014, Imtiaz et al, 2013, Pachauri et al, 2017.…”
Section: Desempeño Del Controlador Frente a Un Pid Tradicional Y Un Punclassified
<p>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.</p>
“…Pachauri N et. al proposed modified fractional order IMC based PID controller for nonlinear Bioreactor temperature control application, where the controller tuning parameter such as proportional gain and fractional order filter are tuned using water cycle optimization algorithm [24]. Heydarpoor, S. and Tabatabaei, M., presented the practical applicability of fractional order PI and fractional order IMC based controller for DC motor velocity control application [25].…”
In this paper, an Internal model Controller (IMC) based PID with fractional filter for a first order plus time delay process is proposed. The structure of the controller has two parts, one is integer PID controller part cascaded with fractional filter. The proposed controller has two tuning factors λ, filter time constant and a, fractional order of the filter. In this work, the two factors are decided in order to obtain low Integral Time Absolute Error (ITAE). The effectiveness of the proposed controller is studied by considering a non linear (hopper tank) process. The experimental set up is fabricated in the laboratory and then data driven model is developed from the experimental data. The non linear process model is linearised using piecewise linearization and two linear regions are obtained. At each operating point, linear first order plus dead time model is obtained and the controller is designed for the same. To show the practical applicability, the proposed controller is implemented for the proposed experimental laboratory prototype.
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