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Predictive Model for Post-Seeding Super-Saturation of Sugar Massecuite in a Fed-Batch Evaporative Crystalliser

Aniediong M. Umo and Sunday B. Alabi
Department of Chemical and Petroleum Engineering, University of Uyo, Uyo, Nigeria
Abstract—The conflicting reports on the performances of the online probes for super-saturation of sugar massecuite necessitate the application of soft-sensor to complement or replace them. Unfortunately, the available sugar crystallisation models which are theoretical and semi-empirical in nature are not in the form which can be directly utilised as soft sensor for real time estimation of the massecuite super-saturation. Therefore, in this study, easy-to-measure online variables that can be correlated with the super-saturation were identified and used to develop a regression model for online estimation of the super-saturation value of sugar massecuite after seeding. The post-seeding regression model gave coefficient of determination and maximum relative error of 0.994 and 4.7%, respectively. It is therefore concluded that the resulting model has the potential of being used for real time estimation of post-seeding super-saturation of sugar massecuite, as opposed to the existing complex fundamental and semi-empirical sugar crystallisation models. 
 
Index Terms—predictive model, super-saturation, post-seeding, sugar crystallization

Cite: Aniediong M. Umo and Sunday B. Alabi, "Predictive Model for Post-Seeding Super-Saturation of Sugar Massecuite in a Fed-Batch Evaporative Crystalliser," International Journal of Food Engineering, Vol. 2, No. 2, pp. 119-123, December 2016. doi: 10.18178/ijfe.2.2.119-123
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