Habit modelling and verification for glutaric acid

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Abstract
Crystal morphology is a major consideration in the design and optimization of industrial crystallization processes. Control of crystal morphology allows the optimization of particle properties such as filterability, flowability, and attrition resistance. Advances in morphology prediction are making it a useful tool, reducing the number of labour intensive experiments necessary to develop an optimum morphology. To be useful, however, experimentally verified morphology predictions must be made available quickly and efficiently. The authors report a unified approach to crystal habit prediction and verification. Up to now, crystal morphology prediction has been demonstrated only for idealized laboratory systems. They have applied this powerful technique to a model system, glutaric acid (CH2)3(COOH)2. Morphologies are predicted based on lattice geometry and lattice energy calculations and compared with actual morphologies of crystals grown batchwise in a 'microcrystallization' cell. Advanced digital image analysis is used to analyse video images from in situ optical microscopy. The approach is ideal for industrial application because modelling minimizes the experiments required. The experiments required for model verification are rapid, simple, and no complicated powder sampling is required.