Effects of Nucleation and Crystal Growth Rates on Crystal Size Distribution for Seeded Batch Potash Alum Crystallization Process

https://doi.org/10.22146/ajche.74121

Siti Zubaidah Adnan(1), Noor Asma Fazli Abdul Samad(2*)

(1) Faculty of Chemical & Process Engineering Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Kuantan, Pahang, Malaysia
(2) Faculty of Chemical & Process Engineering Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Kuantan, Pahang, Malaysia
(*) Corresponding Author

Abstract


The driving force of the cooling crystallization process is supersaturation, where the supersaturation level during the crystallization process is crucial to grow the crystal sufficiently. Nucleation and crystal growth rates are two concurrent phenomena occurring during crystallization. Both are supersaturation functions that determine the growth of seed crystals and the formation of fine crystals. Trade-offs between nucleation and crystal growth are essential for achieving the large size of seed crystals with the minimum number of fine crystals. Thus, the objective of this study is to analyze the effects of nucleation and crystal growth rates on final product quality, which is crystal size distribution (CSD). Modeling of the crystallization process using a potash alum case study is highlighted and simulated using Matlab software. Then, the effects of nucleation rate, crystal growth rate, and both nucleation and crystal growth rates on CSD are evaluated using local sensitivity analysis based on the one-factor-at-a-time (OFAT) method. Based on simulation results for all strategies, a low combined rate delivers the best performance of the final CSD compared to others. Its primary peak has a mean crystal size of 455 µm with 0.0078 m3/m volume distribution. This means that the grown seed crystals are large with high volume distribution compared to the nominal strategy, which is at the mean crystal size of 415 µm and 0.00434 m3/m. Meanwhile, the secondary peak has the mean crystal size of 65 µm, 0.00028 m3/m in volume distribution. This corroborates the least number of fine crystals at the considerably small size compared to nominal’s (0.00151 m3/m, 35 µm). Overall, the low nucleation and crystal growth rates strategy provides useful insights into designing temperature profiles during the linear cooling crystallization process, whereby achievable supersaturation levels in obtaining large crystals with fewer crystal fines are provided via simulation.


Keywords


Crystallization, Crystal Growth Rate, Crystal Size Distribution, Local Sensitivity Analysis, Nucleation Rate, OFAT

Full Text:

PDF


References

Aamir, E., 2010. Population Balance Model-Based Optimal Control of Batch Crystallisation Processes for Systematic Crystal Size Distribution Design. PhD thesis. Loughborough University, Leicestershire, England

Acevedo, D., Yang, X., Liu, Y.C., O’Connor, T.F., Koswara, A., Nagy, Z.K., Madurawe, R., and Cruz, C.N., 2019. “Encrustation in continuous pharmaceutical crystallization processes - a review.” Org. Process Res. Dev., 23, 1134–1142.

Adnan, S.Z., Saleh, S., and Samad, N.A.F.A., 2019. “Evaluation of controlled cooling for seeded batch crystallization incorporating dissolution,” in: AIP Conference Proceedings. AIP Publishing, pp. 020042–1–020042–8.

Erdemir, D., Lee, A.Y., and Myerson, A.S., 2019. “Crystal Nucleation,” in: Handbook of Industrial Crystallization. Cambridge University Press, pp. 76–114.

Frey, D.D., Engelhardt, F., and Greitzer, E.M., 2003. “A role for ‘one-factor-at-a-time’ experimentation in parameter design.” Res. Eng. Des., 14, 65–74.

Fysikopoulos, D., Benyahia, B., Borsos, A., Nagy, Z.K., and Rielly, C.D., 2019. “A framework for model reliability and estimability analysis of crystallization processes with multi-impurity multi-dimensional population balance models.” Comput. Chem. Eng. 122, 275–292.

Fysikopoulos, D., Borsos, A., Li, W., Onyemelukwe, I., Benyahia, B., Nagy, Z.K., and Rielly, C.D., 2017. “Local vs global estimability analysis of population balance models for crystallization processes.” Comput. Aided Chem. Eng., 40, 55–60.

Hemalatha, K., Nagveni, P., Kumar, P.N., and Rani, K.Y., 2018. “Multiobjective optimization and experimental validation for batch cooling crystallization of citric acid anhydrate.” Comput. Chem. Eng., 112, 292–303.

Lee, A.Y., Erdemir, D., and Myerson, A.S., 2019. “Crystals and Crystal Growth,” in: Handbook of Industrial Crystallization. Cambridge University Press, pp. 32–75.

Morio, J., 2011. “Global and local sensitivity analysis methods for a physical system.” Eur. J. Phys., 32, 1577–1583.

Mullin, J.W., 2001a. “Nucleation,” in: Crystallization. Elsevier, pp. 181–215.

Mullin, J.W., 2001b. “Crystal growth,” in: Crystallization. Elsevier, pp. 216–288.

Nagy, Z.K., Fujiwara, M., and Braatz, R.D., 2019. “Monitoring and Advanced Control of Crystallization Processes,” in: Handbook of Industrial Crystallization. pp. 313–345.

Öner, M., Stocks, S.M., and Sin, G., 2020. “Comprehensive sensitivity analysis and process risk assessment of large scale pharmaceutical crystallization processes.” Comput. Chem. Eng., 135, 106746.

Penha, F.M., Zago, G.P., and Seckler, M.M., 2019. “Strategies to control product characteristics in simultaneous crystallization of NaCl and KCl from aqueous solution: Seeding with KCl.” Cryst. Growth Des.. 19, 1257–1267.

Rasmuson, Å.C., 2019. “Crystallization Process Analysis by Population Balance Modeling,” in: Handbook of Industrial Crystallization. pp. 172–196.

Rawlings, J.B., Miller, S.M., and Witkowski, W.R., 1993. “Model Identification and control of solution crystallization processes - a Review.” Ind. Eng. Chem. Res., 32, 1275–1296.

Seki, H., and Su, Y., 2015. “Robust optimal temperature swing operations for size control of seeded batch cooling crystallization.” Chem. Eng. Sci. 133, 16–23.

Trampuž, M., Teslić, D., and Likozar, B., 2021. “Crystal-size distribution-based dynamic process modelling, optimization, and scaling for seeded batch cooling crystallization of Active Pharmaceutical Ingredients (API).” Chem. Eng. Res. Des., 165, 254–269.

Trampuž, M., Teslić, D., and Likozar, B., 2020. “Process analytical technology-based (PAT) model simulations of a combined cooling, seeded and antisolvent crystallization of an active pharmaceutical ingredient (API).” Powder Technol., 366, 873–890.

Unno, J., and Hirasawa, I., 2020. “Partial seeding policy for controlling the crystal quality in batch cooling crystallization.” Chem. Eng. Technol., 43, 1065–1071.

Wang, L.G., Morrissey, J.P., Barrasso, D., Slade, D., Clifford, S., Reynolds, G., Ooi, J.Y., and Litster, J.D., 2021. “Model driven design for twin screw granulation using mechanistic-based population balance model.” Int. J. Pharm., 607, 120939.

Wölk, J., Strey, R., Heath, C.H., and Wyslouzil, B.E., 2002. “Empirical function for homogeneous water nucleation rates.” J. Chem. Phys., 117, 4954–4960



DOI: https://doi.org/10.22146/ajche.74121

Article Metrics

Abstract views : 819 | views : 506

Refbacks

  • There are currently no refbacks.


ASEAN Journal of Chemical Engineering  (print ISSN 1655-4418; online ISSN 2655-5409) is published by Chemical Engineering Department, Faculty of Engineering, Universitas Gadjah Mada.