A Regression Model for Estimating Sugar Crystal Size in a Fed-batch Vacuum Evaporative Crystalliser (original) (raw)
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This paper reports on the comparison of three modeling approaches that were applied to a fed batch evaporative sugar crystallization process. They are termed white box, black box, and grey box modeling strategies, which reflects the level of physical transparency and understanding of the model. White box models represent the traditional modeling approach, based on modeling by first principles. The black box models rely on recorded process data and knowledge collected during the normal process operation. Among various tools in this group artificial neural networks (ANN) approach is adopted in this paper. The grey box model is obtained from a combination of first principles modeling, based on mass, energy and population balances, with an ANN to approximate three kinetic parameters ± crystal growth rate, nucleation rate and the agglomeration kernel. The results have shown that the hybrid modeling approach outperformed the other aforementioned modeling strategies. 1
Modeling of Sugar Crystallization through Knowledge Integration
Engineering in Life Sciences, 2003
This paper reports on the comparison of three modeling approaches that were applied to a fed batch evaporative sugar crystallization process. They are termed white box, black box, and grey box modeling strategies, which reflects the level of physical transparency and understanding of the model. White box models represent the traditional modeling approach, based on modeling by first principles. The black box models rely on recorded process data and knowledge collected during the normal process operation. Among various tools in this group artificial neural networks (ANN) approach is adopted in this paper. The grey box model is obtained from a combination of first principles modeling, based on mass, energy and population balances, with an ANN to approximate three kinetic parameters ± crystal growth rate, nucleation rate and the agglomeration kernel. The results have shown that the hybrid modeling approach outperformed the other aforementioned modeling strategies.
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This paper deals with the design of a model-based soft-sensor to improve the process monitoring and control in industrial sugar crystallization. This soft-sensor is based on an original model dedicated to the last stage of crystallization, avoiding the solving of the population balance. Additional information like the mass of crystals in the solution, the concentration of dissolved sucrose and the purity are relevant to improve the manufacturing process. As these physicochemical properties are not measurable on-line, a model based soft-sensor is developed. The effectiveness of the soft sensor is demonstrated using real plant data from an industrial crystallization process.