This article deals with a complex simulation, including discrete and continuous events, to optimize production and logistics activities in a food plant. The application scope of this work refers to an industrial decaffeination process of coffee beans, based on a supercritical carbon dioxide extraction, executed by a semi-continuous flow of materials as well as a discrete units production. The proposed model considers the semi-continuous coffee beans flow rate representation and the secondary flow rates necessary to realize the process, as, for example, carbon dioxide and caffeine flow rates. Moreover, the process parameters, the flowing material, breakdowns and repairs, speed and accumulation, and waiting time were taken into account. The model was implemented using Arena® simulation software both for discrete and continuous processes, and Microsoft Excel for the project parameters settings and for the analysis of the outputs. The model was, then, validated, considering some plant parameters and the variation of the simulated parameters with respect to the “real case” ones; for example, we obtained an error of 1.76 % for the order fulfillment and 5.10 % for the extractors’ saturation.

Combined Semi-continuous and Discrete Simulation Model to Optimize a Decaffeination Process Based on Supercritical CO2

IANNONE, RAFFAELE;MIRANDA, Salvatore;RIEMMA, Stefano;DE MARCO, Iolanda
2015

Abstract

This article deals with a complex simulation, including discrete and continuous events, to optimize production and logistics activities in a food plant. The application scope of this work refers to an industrial decaffeination process of coffee beans, based on a supercritical carbon dioxide extraction, executed by a semi-continuous flow of materials as well as a discrete units production. The proposed model considers the semi-continuous coffee beans flow rate representation and the secondary flow rates necessary to realize the process, as, for example, carbon dioxide and caffeine flow rates. Moreover, the process parameters, the flowing material, breakdowns and repairs, speed and accumulation, and waiting time were taken into account. The model was implemented using Arena® simulation software both for discrete and continuous processes, and Microsoft Excel for the project parameters settings and for the analysis of the outputs. The model was, then, validated, considering some plant parameters and the variation of the simulated parameters with respect to the “real case” ones; for example, we obtained an error of 1.76 % for the order fulfillment and 5.10 % for the extractors’ saturation.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4644027
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