Dynamic Modeling and Optimization of Batch Crystallization of Sugar Cane under Uncertainty

Bolaños-Reynoso, E.; Sánchez-Sánchez, Kelvyn B.; Urrea-García, Galo R.; Ricardez‐Sandoval, Luis A.

doi:10.1021/ie501800j

Cited by 16 publications

(21 citation statements)

References 40 publications

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“…Each batch occurred in two hours, and samples were taken every fifteen minutes through the equipment probe to evaluate both FCM and D (4,3) through a Nikon Eclipse E200-LED Binocular microscope. The mathematical modeling was adapted to a model of a process in pilot scale-batch crystallization with cooling (using evaporation and cooling the solution)-as developed in [10]. The pilot scale process starts with approximately 752 cm 3 of syrup, operating for 40 min at 60 °C and is then cooled for 50 min to 40 °C.…”

Section: Methodsmentioning

confidence: 99%

Fed-Batch Sucrose Crystallization Model for the B Massecuite Vacuum Pan, Solution by Deterministic and Heuristic Methods

et al. 2020

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Fed-batch crystallization is a crucial step for sugar production. In order to relate parameters that are difficult to measure (average diameter of the crystals and total mass formed) to other easier to measure parameters (volume, temperature, and concentration), a model was developed for a B massecuite vacuum pan composed of mass and energy balances together with empirical relations that describe the crystal development inside equipment. The generated system of ordinary differential equations (ODE) had eight parameters which were adjusted through minimization of relative differences between the model results and experimental data. It was solved through the function fmincon, available in MATLABTM, which is a deterministic and gradient-based optimization method. The objective of this paper is to improve the model obtained and, for this purpose, two metaheuristic functions were used: genetic algorithm and particle swarm. To compare the results, the convergence time of each algorithm was used as well as the resulting quadratic deviation. The particle swarm method was the best option among the three used, presenting a shorter execution time and lower quadratic relative deviation.

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Section: Methodsmentioning

confidence: 99%

Fed-Batch Sucrose Crystallization Model for the B Massecuite Vacuum Pan, Solution by Deterministic and Heuristic Methods

et al. 2020

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“…7 Generally, optimal trajectory batch process profiles strongly depend on the constraints. From a review of the solutions that were obtained in several papers [14][15][16][17][18][21][22][23][24][25] , it can be concluded that the solution touch a constraint or several constraints during operation. In terms of state controllability, it could be dangerous to be near a constraint, as in the case of exothermic polymerization, in which a temperature constraint exists to prevent the polymer gel effect; if the process is disturbed, it could reach dangerous temperatures and produce a gel effect problem 9 .…”

Section: Trajectory Design As An Optimization Problemmentioning

confidence: 99%

Reference Trajectory Design Using State Controllability for Batch Processes

Gómez-Pérez

Gómez

Álvarez

2015

Ind. Eng. Chem. Res.

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address: Carrera 80 #65-223, office: M3-100, Medellín, Colombia; zip code: 050041. Abstract:A proposal for the design of a reference trajectory for batch processes was developed, using set theory state controllability concepts. This proposal introduces the Available Control Action Set, which is an indicator of the state controllability and contains the largest amount of possible control actions that reach the desired final state. The volume of Available Control Action Sets can be maximized to improve the safety of batch processes while maintaining consistently high product quality. Thus a closed loop system has sufficient energy to reject disturbances, ensuring end-of-batch product quality. Case studies are presented to demonstrate the applicability of the approach to batch and semi-batch processes and to illustrate how the proposed method for the design of a reference trajectory for batch processes increases safe and robust operation.

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“…Rackemann, Broadfoot, and Stephens () conducted a two‐dimensional (2D) modeling of natural circulation vacuum pans, although the 2D model cannot reach many details of sugar circulation in this process. Bolaños‐Reynoso, Sánchez‐Sánchez, Urrea‐García, and Ricardez‐Sandoval () conducted a study on optimizing the impeller speed to get the best diameter of sugar crystals. Ensinas, Nebra, Lozano, and Serra () using a thermoeconomic method to optimize the steam cost of the sugar factory.…”

Section: Introductionmentioning

confidence: 99%

Optimization design of cane sugar evaporative crystallizer based on orthogonal test and computational fluid dynamics

Zhang

Meng

Wang

et al. 2020

J Food Process Engineering

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This article describes an optimization design of a sugar evaporative crystallizer using numerical simulation experiments. Computational fluid dynamics (CFD) is used to simulate the circulation flow of sugar juice in the crystallizer, and the numerical experiments are designed according to orthogonal experiment matrix. First, based on the specific working characteristics of sugar evaporative crystallizer, major factors influencing the cane sugar evaporative crystallization process were identified. Then CFD software was utilized to establish various three‐dimensional CFD models of the sugar evaporative crystallization with different factors and levels. Statistics software (IBM SPSS) is used to analyze the CFD simulation results. In order to measure circulation velocity under different conditions, physical experiments were conducted in the experiment platform in our laboratory. The circulation velocity measured under the optimal condition was increased by 128%, and the crystallization efficiency could be guaranteed. Practical Applications Computation fluid dynamics (CFD) enables studying the details within the crystallizer, making the invisible become visible. The orthogonal test replaces all tests with partial tests. The orthogonal test coupled CFD technique can hugely decrease the cost of optimization design. The application of this method will benefit the optimization design of food engineering.

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Dynamic Modeling and Optimization of Batch Crystallization of Sugar Cane under Uncertainty

Cited by 16 publications

References 40 publications

Fed-Batch Sucrose Crystallization Model for the B Massecuite Vacuum Pan, Solution by Deterministic and Heuristic Methods

Fed-Batch Sucrose Crystallization Model for the B Massecuite Vacuum Pan, Solution by Deterministic and Heuristic Methods

Reference Trajectory Design Using State Controllability for Batch Processes

Optimization design of cane sugar evaporative crystallizer based on orthogonal test and computational fluid dynamics

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