Development of a novel approach to modelling of continuous stirred-tank crystallizers, subject to withdrawal Classification

Continuous crystallization has been identified as a means of reducing costs and improving product consistency in the pharmaceutical industry. Withdrawal classification has been identified as a complex topic with broad relevance to the use of continuous stirred-tank crystallizers in the industry and in the laboratory.This thesis begins by summarising recent research into various forms of continuous crystallization, contextualising the main body of work and identifying relevant gaps in the literature. Withdrawal classification constitutes one such gap, with little research data available. The review is used as justification for the research following.Preliminary experiments are detailed, the purpose of which was to compare and assess operational strategies. This work also confirms that controllable classified withdrawal is possible with the experimental setup decided upon. The findings were applied to the main body of work following and used to optimise experimental methods.Two main sets of experiments are detailed in the principal experimental portion of the thesis. The first focuses on withdrawal velocity as a means of directly controlling classified behaviour. This was found to be a significant predictor of certain key system characteristics. The second experimental set focuses on the influence of mixing, by changing agitation rate and withdrawal location. These were also found to significantly affect classification behaviour.Finally, the experimental data was combined to produce a set of empirical equations which allow for qualitative modelling of the system.Two separate approaches to modelling classification behaviour were developed. Experimental data was used to fit kinetics to the models, allowing for simple simulation and comparison. A method for meaningful comparison of model outputs was developed and simulated results compared.Finally, more sophisticated simulation was performed by combining the models with the empirical data gathered. It was demonstrated that particle size data can be predicted based only on system settings and characteristics using this method. Crystallizer responses to independent variables were assessed. The work concludes with a discussion of the possible mechanisms of classification, the applicability of the different models to these, and the relative strengths and weaknesses of the models.