Robust controller design for a heat exchanger

Vasičkaninová, Anna; Bakošová, Monika

doi:10.1109/pc.2015.7169947

Cited by 12 publications

(11 citation statements)

References 26 publications

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“…varying the manipulated variable flow rate from 150 L/h up to 450 L/h, in a response recording the output temperature of cold water. Finally, based on input-output data acquired from the experimental test, the system model in equation 1and (2) has been obtained using system identification. As shown in Fig.…”

Section: Experimental System Modelingmentioning

confidence: 99%

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PID Temperature Controller Design for Shell and Tube Heat Exchanger

Olana¹,

Abose²

2021

IJEM

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Heat exchangers are one of the most important thermal devices. Shell and tube heat exchangers are the common types of heat exchangers and sustained a wide range of operating temperature and pressure. Modeling and controlling heat exchanger system is a difficult assignment because of its nonlinearity. As the flow rates changes, the gain, time delay and time constant varies, hence causing system nonlinearity. The solution for such problems is finding acceptable mathematical model and design a controller which provides better performance indices. In this paper mathematical model (experimental or empirical based) to represent the real system and design suitable controller which remove the offset and settle fast with minimum steady state error has been proposed. To this end, system model design the Proportional-Integral-Derivative controller for shell and tube heat exchanger using Ziegler Nichols method, Cohen-coon method and Chein et al. method. Since two opposing dynamic effects are existing in the system and has a problem of dynamics of inverse response and large overshoot. Therefore, Chein et al. tuning method have better performance than that of the others. In case of Chein et al. the overshoot of 2.577 % and settling time of 63.1 s.

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Section: Experimental System Modelingmentioning

confidence: 99%

“…In many engineering processes heat exchangers are important thermal devices, and used to change the temperature distribution of two materials either direct or indirect contacts [1,2,3]. There are different types of heat exchangers that can be used in industries but the most commonly used ones are shell and tube heat exchangers.…”

Section: Introductionmentioning

confidence: 99%

PID Temperature Controller Design for Shell and Tube Heat Exchanger

Olana¹,

Abose²

2021

IJEM

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“…Recently, first principle modelling of a heat exchanger for a high temperature milk pasteurization unit was enumerated using log mean temperature difference approach [39]. In our research, a nonlinear physical model of a fluid-fluid HE process is detailed in this section, adopted from [40]. Here, we consider the outlet temperature of the process fluid po T as the controlled variable and flow rate c F of the heating fluid as the manipulated variable.…”

Section: Example 3: Heat Exchangermentioning

confidence: 99%

Nonlinear System Identification in coherence with Nonlinearity measure for Dynamic Physical systems- Case Studies

Xavier

Panda

Patnaik

2021

Preprint

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With the recent success of using the time series to vast applications, one would expect its boundless adaptation to problems like nonlinear control and nonlinearity quantification. Though there exist many system identification methods, finding suitable method for identifying a given process is still cryptic. Moreover, to this notch, research on their usage to nonlinear system identification and classification of nonlinearity remains limited. This article hovers around the central idea of developing a ‘kSINDYc’ (key term based Sparse Identification of Nonlinear Dynamics with control) algorithm to capture the nonlinear dynamics of typical physical systems. Furthermore, existing two reliable identification methods namely NL2SQ (Nonlinear least square method) and N3ARX (Neural network based nonlinear auto regressive exogenous input scheme) are also considered for all the physical process-case studies. The primary spotlight of present research is to encapsulate the nonlinear dynamics identified for any process with its nonlinearity level through a mathematical measurement tool. The nonlinear metric Convergence Area based Nonlinear Measure (CANM) calculates the process nonlinearity in the dynamic physical systems and classifies them under mild, medium and highly nonlinear categories. Simulation studies are carried-out on five industrial systems with divergent nonlinear dynamics. The user can make a flawless choice of specific identification methods suitable for given process by finding the nonlinear metric (Δ0). Finally, parametric sensitivity on the measurement has been studied on CSTR and Bioreactor to evaluate the efficacy of kSINDYc on system identification.

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“…Sin embargo, el controlador H∞ es un controlador de alto orden difícil de implementar y requiere un gran conocimiento del sistema para selección de las funciones de peso. En [45] se presenta dos controladores robustos diseñados por el criterio de frontera de estabilidad y el teorema de pequeña ganancia.…”

Section: Algoritmos De Control Y Sintoníaunclassified

“…Por lo tanto, φ no será nulo, pero cuanto más pequeño sea su valor mejor será el comportamiento del sistema. (3)Los índices de desempeño basado en criterios de integrales "optimas" encontrados en la literatura son: IAE (Integral of the Absolute Value of the error)[8],[13],[23],[45],[48],[50], este criterio es sensible a pequeños errores, es un indicador de un pobre amortiguamiento del lazo cerrado, la desventaja de este criterio es que su evaluación requiere una gran cantidad de cálculo o simulación del proceso; ISE (Integral of the Square of the error)[8],[23],[36],[48],[50], es relativamente insensible a pequeños errores, pero los grandes errores contribuyen fuertemente al valor de la integral. ITAE (Integral of the time-Weighted Absolute of the Error)[26],[36], es insensible a errores iniciales y penaliza fuertemente los errores que permanecen a lo largo del tiempo, indicador de tiempo de respuesta cortos y sobrepasos; por último, ITSE (Integral of…”

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Control de temperatura en intercambiadores de calor tipo coraza-tubo: una revisión realizada a la industria

2020

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Esta revisión sobre lazos de temperatura en intercambiadores de calor tipo coraza-tubo, parte de un estudio realizado a la industria de la región del Valle del Cauca donde se identifican en la ingeniería de control de los intercambiadores de calor las siguientes deficiencias: desconocimiento del modelo, inexperiencia en la selección de las estrategias de control, pobre sintonía de los algoritmos de control y desconocimiento de criterios de evaluación de desempeño. La literatura consultada muestra una gran cantidad de métodos de identificaciones de modelos y estrategias de controles y sintonías aplicados a intercambiadores, sin embargo, en su mayoría son implementados a nivel de simulación, despreciando las no linealidades e interacciones del intercambiador en el proceso industrial. La revisión presenta diferentes métodos y conceptos usados para la obtención de modelos, estrategias de control, sintonía y evaluación de lazos de control que se encuentran en la literatura desde el 2010.

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Robust controller design for a heat exchanger

Cited by 12 publications

References 26 publications

PID Temperature Controller Design for Shell and Tube Heat Exchanger

PID Temperature Controller Design for Shell and Tube Heat Exchanger

Nonlinear System Identification in coherence with Nonlinearity measure for Dynamic Physical systems- Case Studies

Control de temperatura en intercambiadores de calor tipo coraza-tubo: una revisión realizada a la industria

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