Modeling of the process of boiling sugar massecuite of the first product

Chochowski, A.; Ladanyuk, Anatoliy; Reshetiuk, Volodymyr; Smityuch, Yaroslav; Сокол, Ростислав Михайлович; Yeshchenko, Oxana

doi:10.22630/aafe.2018.71.9

Cited by 2 publications

(2 citation statements)

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“…Usually, the operating parameters of the crystallization system are set at a fixed value and as stable as possible in the process, including steam inlet flow, vacuum in the tank, temperature inside the tank, and air drum temperature. Therefore, nine variables are parameterized and the range of design parameters are shown in Table 2 by the requirements for production and related research (Chochowski et al., 2018; Dogbe et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

Multi‐objective optimization of cane sugar continuous crystallization system design based on computational fluid dynamics

Meng

Chen

Liu

et al. 2021

J. Food Process. Preserv.

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This work focused on improving circulation and mixing of the massecuite and reducing the energy loss in the cane sugar continuous crystallization system. The developed Computational Fluid Dynamics (CFD) model is based on the continuous crystallization system of a sugarcane mill in Guangxi, China and verified with the actual operation data. The calculated entropy production and pressure drop of the system are used as indices for assessing the performance of the system. Next, 350 CFD simulations are conducted in the parametric experiment platform with 9 parameters and produces a collection of data. Then based on the CFD data set, the data‐driven model is employed for regression of the relations between key parameters and indices. The implemented data‐driven model is used for Non‐dominated Sorting Genetic Algorithm (NSGA‐II) to obtain the optimal result of Pareto frontier. CFD simulation with the optimized parameters reduces entropy by 9.76% and reduces pressure drop by 11.52%. Novelty impact statement Multi‐objective optimization of cane sugar continuous crystallization system is performed. The entropy is reduced by 9.76% and the pressure drop is reduced up to 11.52%. The proposed method show the good applicability.

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Section: Methodsmentioning

confidence: 99%

Multi‐objective optimization of cane sugar continuous crystallization system design based on computational fluid dynamics

Meng

Chen

Liu

et al. 2021

J. Food Process. Preserv.

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“…Динамическая модель, использующая программное обеспечение (ПО) SysCAD, разработана для имитации работы вакуум-аппарата периодического действия и отображает зависимость пересыщения маточного раствора от скорости испарения воды при обогреве аппарата паром низкого давления [21]. Имитационная модель процесса уваривания сахарного утфеля, в которой установлена взаимосвязь между основными технологическими параметрами, описана в [22]. В работе B. J. C. de Castro и др.…”

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Specific Mass Growth Rate of Sugar Crystals: Probabilistic Modeling

Арапов

Kuritsyn

Petrov

et al. 2021

Food Processing: Techniques and Technology

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Introduction. The present research featured industrial sugar crystallization. The article introduces a generalized mathematical model of specific growth rate of sugar crystals depending on temperature, solids, and the purity of solution, as well as on the concentration and average size of crystals. The model includes the probabilistic component of growth rate of monocrystals and the reduced adjustment of the constrained crystal growth depending on the abovementioned as-pects. Study objects and methods. The research focused on mass crystallization of sucrose, including the growth rate of monocrystals and the number of crystals in the fill mass. The obtained experimental data were processed using nonlinear programming. Results and discussion. 421 experiments made it possible to develop a probabilistic mathematical model of specific mass growth rate of sugar monocrystals and its dependence on the solution temperature, purity, and solids content. Model error: ± 11.3%. The model covers the temperature range, concentration of solids, and purity of the solution. The proximity of crystals was calculated according to the dependence of the growth rate on their concentration and the average size (error: ± 1.3%). The adjustment range: concentration of crystals = 5–60%, average size = 0.25–1.50 mm. Conclusion. The present generalized mathematical model of crystallization considered the temperature, as well as the purity and solids content in the fill mass, the concentration of sucrose crystals and their average size. The research compared the effect of linear size and concentration of sugar monocrystals on the calculated and experimental sizes of specific mass growth rate and the dimensionless adjustment of growth rate. The calculated sizes proved to be close to the ex-perimental data, which showed adequacy to the developed crystallization model. The research results can be used to optimize the process of mass sugar crystallization.

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Modeling of the process of boiling sugar massecuite of the first product

Cited by 2 publications

References 0 publications

Multi‐objective optimization of cane sugar continuous crystallization system design based on computational fluid dynamics

Multi‐objective optimization of cane sugar continuous crystallization system design based on computational fluid dynamics

Specific Mass Growth Rate of Sugar Crystals: Probabilistic Modeling

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