Modeling and Testing of an Ice Bank for Milk Cooling After Milking

Jordan, Rodrigo Aparecido; Cortez, Luı́s Augusto Barbosa; Silveira, Vivaldo; Mata, M. E. R. M. C.; Oliveira, Fellipe de

doi:10.1590/1809-4430-eng.agric.v38n4p510-517/2018

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…The milk temperature comes in at 36˚C and the temperature reaches 4˚C with a cooling time of 120 minutes. Since the gain is 1 while the time constant is 42.3548 minutes, the process will reach 2 ̊ C at about more than 4 times the time constant [21]. The time constant is the time required for the first-order process to reach 63.2% to the new steady state value when a step disturbance is given to the input variable [19], [37].…”

Section: A Dynamic Study Of Batch Processmentioning

confidence: 99%

“…The waste heat can be utilized to heat the water which was used to clean the milk processing equipment. Another modeling work that focuses on an ice bank for milk cooling after milking has also been performed [21]. Agustriyanto et al [22], [23] provided a transfer function model for continuous milk cooling, while this paper also derived a batch cooling system.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Study and PI Control of Milk Cooling Process

Agustriyanto¹,

Mochni²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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The background of this research is to understand the operation process, which is the main goal of developing the process model. This model is often used for operator training, process design, safety system analysis, or control system design. The dynamic model of the milk cooling process from 36˚C to 4˚C using chilled water available at 2˚C was performed. Chilled water was maintained at a constant temperature by using a refrigerant unit. The process being investigated was a Packo brand milk cooling tank belonging to KUD SAE Pujon (Malang -Indonesia). A fundamental heat balance method was used to derive the model, leading to a first-order transfer function process. For a 2-hr cooling process, the gain and time constant values are 1.00 and 42.3548 mins, respectively. Heat balance was then extended to continuous processes so that its transfer function could also be obtained. This study simulated and investigated the behavior of batch and continuous processes. Process Identification via input-output data was also introduced for continuous process. The process model obtained from the system identification toolbox was very useful in control, such as for determining tuning parameters via the Ziegler-Nichol method for Proportional-Integral control. However, a small delay was required to be introduced to the system as the first order process without time Ziegler Nichol method cannot be implemented. Further research may include other system identification methods, such as ARX, ARMAX, Output-Error, Box Jenkins etc., or implementing advanced process control for milk cooling.

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Section: A Dynamic Study Of Batch Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Study and PI Control of Milk Cooling Process

Agustriyanto¹,

Mochni²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The waste heat can be utilized to heat the water which was used to clean the milk processing equipment. Another modelling work but focuses on an ice bank for milk cooling after milking has also been performed [9]. Agustriyanto et.al [10,11] provided transfer function model for continuous milk cooling, while in this paper for batch cooling system.…”

Section: Introductionmentioning

confidence: 99%

Dynamic study of batch milk cooling process at KUD SAE Pujon

Agustriyanto¹,

Setyopratomo²,

Srihari³

2022

INTERNATIONAL CONFERENCE ON INFORMATICS, TECHNOLOGY, AND ENGINEERING 2021 (InCITE 2021): Leveraging Smart Engineering

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The background of this research is to understand the operation process which is the overall main goal for developing the process model. This model is often used for operator training, process design, safety system analysis or t temperature by using refrigerant unit. The process being investigated was a Packo brand milk cooling tank belong to KUD SAE Pujon (Malang). A fundamental method using heat balance were used to derive the model, leading to a first order transfer function process in Laplace Transform. Transient simulation then could be done in order to investigate the dynamic behavior of the cooling process. The process is first order. For 2 hr cooling process (to achieve the goal) then the gain and time constant values are 1.00

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“…Un método para reducir la demanda eléctrica máxima es incorporar almacenamiento de energía térmica de refrigeración [14], el mérito radica en transferir el consumo de energía de las horas pico a las horas de bajo consumo [7,10,15]. La energía térmica se puede almacenar utilizando calor sensible o latente [16,17], es decir, agua fría o hielo, siendo este último ventajoso ya que requiere volúmenes de almacenamiento más pequeños [18,19].…”

Section: Introductionunclassified

Estudio paramétrico para optimización de un generador de hielo tubular de laboratorio

Guapulema

Hidalgo²

2019

ings

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En Ecuador existe un gran consumo energético por climatización y refrigeración en los sectores industrial, comercial y residencial. Un método para reducir la demanda eléctrica máxima es incorporar un sistema óptimo de almacenamiento de energía térmica de refrigeración. Este trabajo tiene por objetivo realizar el estudio paramétrico de un generador de hielo tubular de laboratorio para su posterior optimización. Se estudiaron los principales parámetros que intervienen en la formación de hielo como temperatura del agua en el reservorio, temperaturas del refrigerante en el evaporador y condensador, temperatura de subenfriamiento del hielo y velocidad de formación de hielo. Los parámetros destacados que intervinieron en el proceso fueron las condiciones ambientales del lugar y la temperatura del agua utilizada. Al disminuir la temperatura ambiental, disminuye la carga térmica y mejora la eficiencia del condensador, el cual influye directamente sobre la eficiencia del equipo. La inestabilidad observada en la primera hora de prueba intervino en la temperatura final del agua, la misma que varió en el rango de 1,1 °C a –0,4 °C en 3 horas.

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Modeling and Testing of an Ice Bank for Milk Cooling After Milking

Cited by 9 publications

References 10 publications

Dynamic Study and PI Control of Milk Cooling Process

Dynamic Study and PI Control of Milk Cooling Process

Dynamic study of batch milk cooling process at KUD SAE Pujon

Estudio paramétrico para optimización de un generador de hielo tubular de laboratorio

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