(223d) Automated Multi-Loop Direct Nucleation Control Using Focused Beam Reflectance Measurement in Multistage Continuous Mixed Suspension Mixed Product Removal Crystallizer

Batch crystallization is an economical and widely used separation and purification unit operation especially in pharmaceutical industry. It has been extensively studied in literature and very well understood due to the development of various process analytical technology (PAT) tools like focused beam reflectance measurement (FRBM), particle video microscope (PVM), UV/Vis spectroscopy, IR spectroscopy and Raman spectroscopy. In addition, some of these tools have been successfully applied in feedback control frameworks in order to control the batch crystallization process in real time. Direct nucleation control (DNC) is a methodology that uses the chord count information from in situ FBRM to actuate adaptive heating or cooling rate to have controlled dissolution in the batch cooling crystallization process. It has been proved being able to produce much larger and more uniform crystal as compared with simple temperature control (T-control) approach in batch process.^[1-2] Supersaturation control (SSC) and active polymorphic feedback control (APFC) apply UV/Vis or/and Raman spectroscopy in similar feedback control loops to maintain controlled supersaturation level or polymorphic transformation during batch cooling process.^[3-4] These model-free feedback control approaches are proved very robust to automatically reject disturbances or to adjust the process when the required target product quality is changed. They can provide automated fast recipe design and only require simple mathematical tools without any modeling effort or much system knowledge.^[5]

Continuous Crystallization is of great interest in recent decades due to consistent product quality, high process and equipment efficiency and reduced operation cost compared with batch crystallization. Mixed suspension mixed product removal (MSMPR) crystallizer is one of the most important continuous crystallization systems due to its simple operation and capability for long residence time. There are some published papers that investigate the recipe design for continuous MSMPR crystallization systems via modelling or experiment.^[6-9] However, according to the authors’ best knowledge, currently there are no published works that study the application of previously mentioned feedback control approaches (i.e. DNC, SSC, APFC) in any type of continuous crystallization systems.

In this work, a multi-loop DNC approach is experimentally investigated in a multistage MSMPR cooling crystallization process in order to automatically control the nucleation rate at each MSMPR stage. The feed solution is free of crystals. Each MSMPR stage is equipped with a heating circulator, a temperature sensor and a FBRM probe. A Labview based software Crystallization Monitoring and Control (CryMOCO) is installed on the supervisory computer to collect the chord count information from each individual FBRM probe. The information is then compared with the desired set-point of the corresponding MSMPR stage in CryMOCO, which remotely controls each temperature circulator and actuates a predefined adaptive heating or cooling rate in the corresponding MSMPR stage. In each DNC loop, the total chord count is targeted as the controlled variable and the set-point since it indicates information about nucleation, generation of fine particles as well as the average particle size. For example, in a certain stage the adaptive heating rate is actuated automatically once the measured FBRM total count is larger than the set-point of the corresponding stage, and vice versa. Paracetamol and ethanol are used as the model compound and solvent, respectively. The proposed multi-loop DNC approach is found being able to reach desired steady state and produce crystals of consistent desired size distribution after a few heating/cooling cycles with or without disturbances. The proposed approach has the following advantages: (1) It can automatically help avoid nucleation in the second stage and after when fine particles are not desired in the final product. This is achieved by designing a proper set-point for each MSMPR stage so that limited amount of nucleation or breakage is allowed. (2) It can automatically achieve optimal operating recipes to produce desired crystals of various sizes (i.e. either small or big). (3) It can automatically reject disturbances such as accidental seeding or nucleation. It is faster than simply washing out without any control. (4) It can reduce startup duration time and startup waste as compared with simple T-control that uses constant temperatures. Simulation work is also carried out in the group to validate and explain some of the experimental observations, and therefore to optimize the control performance.

References

[1] Saleemi, A.; Rielly, C.; Nagy, Z. K. Automated direct nucleation control for in situ dynamic fines removal in batch cooling crystallization. CrystEngComm 2012, 14(6), 2196-2203.

[2] Abu Bakar, M. R.; Nagy, Z. K.; Saleemi, A. N.; Rielly, C. D. The impact of direct nucleation control on crystal size distribution in pharmaceutical crystallization processes. Cryst. Growth Des. 2009, 9(3), 1378-1384.

[3] Saleemi, A. N.; Rielly, C. D.; Nagy, Z. K. Comparative investigation of supersaturation and automated direct nucleation control of crystal size distribution using ATR-UV/vis spectroscopy and FBRM. Cryst. Grwoth Des. 2012, 12(4), 1792-1807.

[4] Simone, E.; Saleemi, A. N.; Tonnon, N.; Nagy, Z. K. Active polymorphic feedback control of crystallization processes using a combined Raman and ATR-UV/Vis spectroscopy approach. Cryst. Growth Des. 2014, 14(4), 1839-1850.

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[6] Zhang, H.; Quon, J.; Alvarez, A. J.; Evans, J.; Myerson, A. S.; Trout, B. Development of continuous anti-solvent/cooling crystallization process using cascaded mixed suspension, mixed product removal crystallizers. Org. Process Res. Dev. 2012, 16, 915-924.

[7] Vetter, T.; Burcham, C. L.; Doherty, M. F. Regions of attainable particle size in continuous and batch crystallization processes. Chem. Eng. Sci. 2014, 106, 167-180.

[8] Yang, Y.; Nagy, Z. K. Advanced control approaches for combined cooling/antisolvent crystallization in continuous mixed suspension mixed product removal cascade crystallizers. Chem. Eng. Sci. 2015, 127, 362-373.

[9] Yang, Y.; Nagy, Z. K. Combined cooling and antisolvent crystallization in continuous mixed suspension mixed product removal cascade: steady state and startup optimization. Ind. Eng. Chem. Res. 2015, DOI: 10.1021/ie5034254.