Model predictive controller for cryogenic tunnel freezers

Shaikh, Nazrul I.; Prabhu, Vittaldas V.

doi:10.1016/j.jfoodeng.2006.04.064

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…This function can be estimated by an artificial neural network [30]. Considering (7)(8)(9)(10)(11) and the concept of dead-time…”

Section: Predictive Approach For Feedback Control and Final Control Smentioning

confidence: 99%

“…In other cases, the reference or setpoint is the input signal to the feedforward controller; this area of feedforward-feedback control system design lacks a general well-established methodology and is still subject to emerging ideas. In this area, the most popular feedforward control laws are the inverse models of the systems [2,6,[9][10][11]14], which are not always available, and artificial neural networks have also been used to generate the feedforward control command [5,17]. In 2009, the authors of this paper introduced the concepts of control equilibrium point and a steady state control law used as the feedforward controller [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Feedback‐feedforward control systems have many applications including in power systems , medical engineering , aircraft/helicopter control , vibration and noise control , manufacturing and temperature control of different processes . A variety of feedback controllers have been used in these systems such as PID , PD , state vector feedback , adaptive , iterative learning , and predictive , as well as nonlinear controllers designed by feedback linearization , optimal tracking control , and fuzzy logic .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neuro‐Predictive Control of an Infrared Dryer with a Feedforward‐Feedback Approach

Mohammadzaheri¹,

Chen²,

Mirsepahi³

et al. 2014

Asian Journal of Control

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In this research, a hybrid control system is proposed to address the temperature control of an infrared dryer. The control system includes a feedback-predictive controller and a neural network steady state control law. The feedback-predictive controller outputs the amplified value of the predicted error as the transient control command. The predictive model was employed to suppress the undesirable effect of the dead-time of the system. A multilayer perceptron was designed and tested based on a control equilibrium point and steady state control to be used as a feedforward controller. The stability of the control system in a continuous domain was proved with no limit on the amplification gain of the predictive-feedback controller. In other words, there is no concern about losing stability with accelerating convergence towards the reference. The entire control system was constructed in Simulink and compiled to a C code and applied on the experimental setup. Experimental results are outstanding in comparison with the results of an interactively tuned IMC-based PID controller.

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“…This function can be estimated by an artificial neural network [30]. Considering (7)(8)(9)(10)(11) and the concept of dead-time…”

Section: Predictive Approach For Feedback Control and Final Control Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neuro‐Predictive Control of an Infrared Dryer with a Feedforward‐Feedback Approach

Mohammadzaheri¹,

Chen²,

Mirsepahi³

et al. 2014

Asian Journal of Control

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“…However, in the enterprise production of frozen food, spiral tunnel freezing method (SF) has become a traditional freezing method because of its high efficiency, large freezing space, and more suitable for industrialized mass production, which has been widely promoted by the industry. [8,9] In addition, among the emerging freezing methods, liquid nitrogen spraying freezing method (LF) has gradually attracted extensive attention of experts due to its low weight loss, fast freezing speed, simple equipment structure, small land space, and convenient operation. [10,11] Importantly, there are few reports on the effects of these three freezing methods on food quality and its internal components.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Freezing Methods on Water Distribution, Retrogradation, and Digestion Properties of Liangpi (Starch Gel Food)

Zheng

Yang

et al. 2022

Starch Stärke

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In this work, the effects of refrigerator freezing method (RF), spiral tunnel freezing method (SF), and liquid nitrogen spray freezing method (LF) on water distribution, microstructure, retrogradation, and digestion properties of Liangpi are investigated. Results from SEM and water distribution show the starch gel structure of Liangpi after LF treatment is more continuous, and its A 21 content is higher, which are closer to fresh Liangpi, followed by SF and RF. XRD and FT-IR data indicate that as the freezing rate increases, the relative crystallinity and R 1047/1022 value of the samples decrease. This is consistent with the data obtained from texture properties. Texture properties of samples treat by LF and SF are closer to fresh Liangpi, especially the hardness index. The in vitro digestibility of Liangpi also shows the same trend. Therefore, the freezing method with a faster rate could inhibit the starch gel retrogradation, improve the texture properties of Liangpi, and guarantee the quality of Liangpi. However, considering the economics of enterprise production, the spiral tunnel freezing method is more suitable for the actual production of frozen Liangpi. The data obtained provide a theoretical guidance for the actual production of frozen Liangpi.

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“…Por tanto, es preciso un buen mecanismo de control que minimice las pérdidas debidas a sobreenfriamiento o subenfriamiento y al mismo tiempo mantenga el rendimiento requerido. En ese sentido, se han propuesto esquemas de control que buscan dar respuesta a la mencionada problemática, como los descritos en [6][7][8][9]. En el presente trabajo se diseñó una estrategia de control mediante la combinación de técnicas de control clásico y lógica difusa, para llevar a cabo el acoplamiento de los lazos de control de flujo y temperatura de un proceso de congelación de pescado (churrascos de atún) que utiliza un equipo de congelación criogénico de aspersión con inyección de nitrógeno líquido dentro de un túnel de alimentación continua.…”

Section: Introductionunclassified

Diseño de una estrategia de control difuso aplicada al proceso de ultracongelación de alimentos

García

Torres

Pinto

et al. 2017

Ingeniare. Rev. chil. ing.

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RESUMENEl control de la temperatura de los alimentos mediante la utilización de un equipo de congelación criogénico se torna complicado cuando se emplean técnicas de control clásico tipo PID (Proporcional + Integral + Derivativo), debido al fuerte acoplamiento entre las variables y las no linealidades propias del sistema. Actualmente, en la mayoría de las plantas procesadoras de alimentos que utilizan túneles de congelación criogénico, el control es llevado a cabo por medio del ajuste manual de los valores de consigna o setpoint de los lazos de control de velocidad y temperatura del túnel de congelación, lo que trae como consecuencia desviaciones en la salida del proceso, derivadas principalmente de la diferencia de criterios existentes entre los operadores y de la velocidad de respuesta de estos últimos ante variaciones en las condiciones del proceso. En este trabajo se tratan separadamente los lazos de control de velocidad y temperatura, se reconfiguran y entonan los lazos existentes, para luego desarrollar un controlador difuso tipo Takagi-Sugeno que permite ajustar el valor de consigna de la temperatura del túnel de congelación en función al producto y las condiciones de entrada del alimento al proceso. El comportamiento del sistema se comprueba mediante la simulación del mismo, verificándose que se mantiene la temperatura del producto en -18 ºC, pese a que fue sometido a cambios en la masa de producto a enfriar (de 200 kg a 220 kg) y en el flujo másico del producto (de 650 kg/h a 700 kg/h).Palabras clave: Congelación de alimentos, controlador PID, identificación de sistemas, control difuso, controlador Takagi-Sugeno. ABSTRACT

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Model predictive controller for cryogenic tunnel freezers

Cited by 14 publications

References 9 publications

Neuro‐Predictive Control of an Infrared Dryer with a Feedforward‐Feedback Approach

Neuro‐Predictive Control of an Infrared Dryer with a Feedforward‐Feedback Approach

A Comparative Study of Different Freezing Methods on Water Distribution, Retrogradation, and Digestion Properties of Liangpi (Starch Gel Food)

Diseño de una estrategia de control difuso aplicada al proceso de ultracongelación de alimentos

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