Abstract
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.
Highlights
Crystallization is the main process in the sugar industry for the separation and obtaining of sucrose in its commercial form, sugar, and is one of the crucial steps to increase the yield from the plant [1]
The industrial sucrose crystallization process consists of three basic equipment: vacuum pan, crystallizer, and centrifuge
The instrumentation of massecuite vacuum pans is a challenge for automation companies due to reduce these costs, two models were developed to relate the mean diameter of the crystals D
Summary
Crystallization is the main process in the sugar industry for the separation and obtaining of sucrose in its commercial form, sugar, and is one of the crucial steps to increase the yield from the plant [1]. The crystallization of sucrose occurs only in vacuum pans, making its study the eliminates the need to reheat the massecuite to feed the centrifuges The instrumentation of massecuite vacuum pans is a challenge for automation companies due to reduce these costs, two models were developed to relate the mean diameter of the crystals D to the high values of the sensors and large amount of equipment to be monitored [5,6]. To improve the model parameters and a thermodynamic data), two metaheuristic methods were used—genetic algorithm obtained for B massecuite vacuum pan, which has eight parameters for adjustment (seven kinetic (GA) and particle swarm. The convergence time of each algorithm was used as as the relative resulting quadratic deviation
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