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