Seed Recipe Design for Batch Cooling Crystallization with Application to l-Glutamic Acid

In this paper, a seed recipe design is proposed for batch cooling crystallization to obtain the desired product attributes including product yield and product size distribution, based on simulation studies and experiments on β-l-glutamic acid (β-LGA) crystallization. The impact of seed recipe on product attributes is investigated based on the population balance model (PBM) simulations with respect to the size-dependent growth of crystals. It is found that the product yield is primarily affected by the seed loading ratio (SLR) and the batch time, but less affected by the mean size and variance of seeds. Smaller seeds could improve the product yield, and in contrast, larger seeds facilitate the growth into larger crystals but require a larger SLR to ensure the product yield. By introducing an objective function for optimization with the above PBM, a seed recipe design is given for obtaining the desired product attributes as above-mentioned. In addition, it is found that washing seeds by the solvent is necessary to ensure seed quality for quantitative seed recipe design and implementation, by comparing three different seed preparation methods. Simulation tests and experiments well demonstrate the effectiveness of the proposed seed recipe design for seeded batch cooling crystallization

Experimental investigation on the applicability of FBRM in the control of batch cooling crystallization

Master'sMASTER OF ENGINEERIN

A Study of The Deep Learning-based Monitoring and Efficient Numerical Modeling Methodologies for Crystallization Processes

Driven by the increasing demands of producing consistent and high-quality crystals for high value-added products such as pharmaceutical ingredients, the operation and design of a crystallization process have phased from an empirical trial-and-error approach to the modern frameworks powered by the online process analytical technologies (PATs) and model-based process optimization techniques. The one-dimensional crystal size distribution (CSD) measured by the well-established PATs is inadequate due to the missing particle morphology information. A major contribution of this thesis is to develop an image analysis-based PAT powered by the deep learning image processing techniques, whose accuracy and functionality outperformed the traditional PATs and other image analysis techniques. The PAT was deployed to monitor and study the slurry mixture of glass beads and catalyst particles as well as a taurine-water batch crystallization process. The results confirmed the superb accuracy of two-dimensional size and shape characterization in a challengingly high solids concentration. The classification capability enabled unparalleled functionalities including quantification of agglomeration level and characterization of different polymorphs based on their distinct appearances. A computerized crystallization platform was built with the developed PAT, which could automate the time-consuming experiments for determining the metastable zone width (MSZW) and induction time of a crystallization system. The application of the PAT revealed the potential to simplify and speed up the research and development stage of a crystallization process. The rich two-dimensional crystal size and shape information provided by our PAT enabled more descriptive multi-dimensional modeling for the better prediction of the crystallization process. The novel population array (PA) solver developed in this thesis could solve the multi-dimensional crystallization population balance equation (PBE) more computationally efficient than the existing discretization-based numerical methods without compromising the accuracy. The PA solver could accurately model the complex phenomena including agglomeration, breakage, and size-dependent growth. The efficient computation enables solving the complex multi-dimensional PBE for crystal morphology modeling. The combination of the innovative PAT and modeling technique is a significant contribution to the crystallization field that enables better understanding and more effective control of a crystallization process

Model-based measurement and control of fluidised bed spray granulation processes

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2012von Andreas Büc