Multiobjective nonlinear model predictive control of pharmaceutical batch crystallizers

References

• Ward JD, Yu CC, Doherty MF. A new framework and a simpler method for the development of batch crystallization recipes. AIChE J. 2011;57(3):606–617.
   Web of Science ®Google Scholar
• Rawlings JB, Miller SM, Witkowski WR. Model identification and control of solution crystallization processes: a review. Ind Eng Chem Res. 1993;32(7):1275–1296.
   Web of Science ®Google Scholar
• Chang CT, Epstein MAF. Identification of batch crystallization control strategies using characteristic curves. In: Epstein MAF, editor. Nucleation, growth and impurity effects in crystallization process engineering. New York (NY): AIChE; 1982.
   Google Scholar
• Eaton JW, Rawlings JB. Feedback control of chemical processes using on-line optimization techniques. Comput Chem Eng. 1990;14(4–5):469–479.
   Web of Science ®Google Scholar
• Jones AG. Optimal operation of a batch cooling crystallizer. Chem Eng Sci. 1974;29(5):1075–1087.
   Web of Science ®Google Scholar
• Ma DL, Braatz RD. Robust identification and control of batch processes. Comput Chem Eng. 2003;27(8–9):1175–1184.
   Web of Science ®Google Scholar
• Paengjuntuek W, Kittisupakorn P, Arpornwichanop A. Optimization and nonlinear control of a batch crystallization process. J Chin Inst Chem Eng. 2008;39(3):249–256.
   Google Scholar
• Braatz RD, Nagy ZK, Chew JW, et al. Comparative performance of concentration and temperature controlled batch crystallizations. J Process Control. 2008;18(3–4):399–407.
   Web of Science ®Google Scholar
• Shi D, El-Farra NH, Li MH, et al. Predictive control of particle size distribution in particulate processes. Chem Eng Sci. 2006;61(1):268–281.
   Web of Science ®Google Scholar
• Yang AD, Montague G, Martin EB. Importance of heterogeneous energy dissipation in the modeling and optimization of batch cooling crystallization. Ind Eng Chem Res. 2007;46(22):7177–7187.
   Web of Science ®Google Scholar
• Shi D, Mhaskar P, El-Farra NH, et al. Predictive control of crystal size distribution in protein crystallization. Nanotechnology. 2005;16(7):S562–S574.
   PubMed Web of Science ®Google Scholar
• Zhang GP, Rohani S. On-line optimal control of a seeded batch cooling crystallizer. Chem Eng Sci. 2003;58(9):1887–1896.
   Web of Science ®Google Scholar
• Ge M, Wang QG, Chiu MS, et al. An effective technique for batch process optimization with application to crystallization. Chem Eng Res Des. 2000;78(1):99–106.
   Google Scholar
• Chung SH, Ma DL, Braatz RD. Optimal seeding in batch crystallization. Can J Chem Eng. 1999;77(3):590–596.
   Web of Science ®Google Scholar
• Sheikhzadeh M, Trifkovic M, Rohani S. Real-time optimal control of an anti-solvent isothermal semi-batch crystallization process. Chem Eng Sci. 2008;63(3):829–839.
   Web of Science ®Google Scholar
• Rohani S, Trifkovic M, Sheikhzadeh M. Kinetics estimation and single and multi-objective optimization of a seeded, anti-solvent, isothermal batch crystallizer. Ind Eng Chem Res. 2008;47(5):1586–1595.
   Web of Science ®Google Scholar
• Sarkar D, Rohani S, Jutan A. Multi-objective optimization of seeded batch crystallization processes. Chem Eng Sci. 2006;61(16):5282–5295.
   Web of Science ®Google Scholar
• Choong KL, Smith R. Novel strategies for optimization of batch, semi-batch and heating/cooling evaporative crystallization. Chem Eng Sci. 2004a;59(2):329–343.
   Web of Science ®Google Scholar
• Choong KL, Smith R. Optimization of batch cooling crystallization. Chem Eng Sci. 2004b;59(2):313–327.
   Web of Science ®Google Scholar
• Braatz RD, Nagy ZK. Robust nonlinear model predictive control of batch processes. AIChE J. 2003;49(7):1776–1786.
   Web of Science ®Google Scholar
• Nagy ZK, Braatz RD. Worst-case and distributional robustness analysis of finite-time control trajectories for nonlinear distributed parameter systems. IEEE Trans Contr Syst Technol. 2003;11(5):694–704.
   Web of Science ®Google Scholar
• Cavalcante P, Dondi M, Guarini G, et al. Colour performance of ceramic nano-pigments. Dyes Pigm. 2009;80(2):226–232.
   Web of Science ®Google Scholar
• Liu Y, Yin H, Yuan S, et al. Influence of particle characteristics and E-Z isomer ratio on the color of concentrated β-carotene dispersions. Int J Food Sci tech. 2010;45:1450.
   Web of Science ®Google Scholar
• Brazeau G, Sauberan SL, Gatlin L, et al. Effect of particle size of parental suspensions on in vitro muscle damage. Pharm Dev Technol. 2011;16:591.
   PubMed Web of Science ®Google Scholar
• Braatz RD, Ma DL, Chung SH. Worst-case performance analysis of optimal batch control trajectories. AIChE J. 1999;45(7):1469–1476.
   Web of Science ®Google Scholar
• Ackerson MR, Mysen BO, Tailby ND, et al. Watson Low-temperature crystallization of granites and the implications for crustal magmatism. Nature. 2018;559(7712):94.
   PubMed Web of Science ®Google Scholar
• Bruno AE, Charbonneau P, Newman J, et al. Classification of crystallization outcomes using deep convolutional neural networks. PLoS One. 2018;13(6):e0198883.
   PubMed Web of Science ®Google Scholar
• Poudel P, Chandran S, Majumder S, et al. Sivasurender Chandran Sumit Majumder Günter Reiter controlling polymer crystallization kinetics by sample history. Macromol Chem Phys. 2018;219(3):1700315.
   Google Scholar
• De Souza B, Cogoni G, Tyrrell R, et al. Evidence of crystal nuclei breeding in laboratory scale seeded batch isothermal crystallization experiments. Cryst. Growth Des. 2016;16(6):3443–3453.
   Web of Science ®Google Scholar
• Steendam RRE, Keshavarz L, Blijlevens MAR, et al. Effects of scale-up on the mechanism and kinetics of crystal nucleation. Cryst Growth Des. 2018;18:9.
   Web of Science ®Google Scholar
• Hsu C, Ward JD. The best objective function for seeded batch crystallization. AIChE J. 2013;59(2):390–398.
   Web of Science ®Google Scholar
• Randolph AD, Larson MA. Theory of particulate processes: analysis and techniques of continuous crystallization. 2nd ed. New York (NY): Academic Press; 1988.
   Google Scholar
• S Lawton S, Steele G, Shering P, et al. Continuous crystallization of pharmaceuticals using a continuous oscillatory baffled crystallizer. Org Process Res Dev. 2009;13(6):1357–1363.
   Web of Science ®Google Scholar
• Chen J, Sarma B, Evans JM, et al. Pharmaceuticalcrystallization. Cryst Growth Des. 2011;11(4):887–895.
   Web of Science ®Google Scholar
• Genck W. Optimizingcrystallizerscaleup. Chem Eng Prog. 2003;99:36–44.
   Google Scholar
• Larsen PA, Patience DB, Rawlings JB. Industrialcrystallizationprocess control. IEEEControlSyst Mag. 2006;26:70–80.
   Google Scholar
• Lindenberg C, KräTtli M, Cornel J, et al. Design and optimization of a combined cooling/antisolvent crystallization process. Cryst Growth Des. 2009;9(2):1124–1136.
   Web of Science ®Google Scholar
• Mullin JW, Nyvlt J. Programmed cooling of batch crystallizers. Chem Eng Sci. 1971;26(3):369–377.
   Web of Science ®Google Scholar
• Mayrhofer B, Nývlt J. Programmed cooling of batch crystallizers. Chem Eng Process. 1988;24(4):217–220.
   Web of Science ®Google Scholar
• Rohani S, Bourne JR. A simplified approach to the operation of a batch crys- tallizer. Can J Chem Eng. 1990;68(5):799–806.
   Web of Science ®Google Scholar
• Xie W, Rohani S, Phoenlx A. Dynamic modeling and operation of a seeded batch cooling crystallizer. Chem Eng Comm. 2001;187(1):229–249.
   Web of Science ®Google Scholar
• Bohlin M, Rasmuson AC. Application of controlled cooling and seeding in batch crystallization. Can J Chem Eng. 1992;70(1):120–126.
   Web of Science ®Google Scholar
• Hemalatha K, Nagveni P, Naveen Kumar P, et al. Multiobjective optimization and experimental validation for batch cooling crystallization of citric acid anhydrate. Comput Chem Eng. 2018;112:292–303.
   Web of Science ®Google Scholar
• Hemalatha K, Yamuna Rani K. Multiobjective optimization of unseeded and seeded batch cooling crystallization processes. Ind Eng Chem Res. 2017;56(20):6012–6021.
   Web of Science ®Google Scholar
• Miettinen, K. Nonlinear multiobjective optimization. In: Kluwers international series. Norwell (MA): Springer Science & Business Media.
   Google Scholar
• Flores-Tlacuahuac A, Morales P, Rivera-Toledo M. Pilar Morales and Martin Riveral Toledo; Multiobjective Nonlinear model predictive control of a class of chemical reactors. Ind Eng Chem Res. 2012;51(17):5891–5899.
   Web of Science ®Google Scholar
• Sridhar LN. Multiobjective optimization and nonlinear model predictive control of the continuous fermentation process involving Saccharomyces cerevisiae. Biofuels. 2019:1–7277.
   Google Scholar
• Nagy ZK, Fujiwara M, Braatz RD, et al. Determination of kinetic parameters for batch pharmaceutical crystallization using metastable zone experiments. Ind Eng Chem Res. 2008;47(4):1245–1252.
   Web of Science ®Google Scholar
• Hulburt HM, Katz S. Some problems in particle technology. Chem Eng Sci. 1964;19(8):555–574.
   Web of Science ®Google Scholar
• Hart WE, Laird CD, Watson J-P, et al. Pyomo – optimization modeling in Python. 2nd ed. Vol. 67. Cham Switzerland: Springer; 2017.
   Google Scholar
• Tawarmalani M, Sahinidis NV. A polyhedral branch-and-cut approach to global optimization. Math Program. 2005;103(2):225–249.
   Web of Science ®Google Scholar
• Bussieck MR, Meeraus A. General Algebraic Modeling System (GAMS). In: Kallrath J, editor. Modeling languages in mathematical optimization. Applied optimization. Vol. 88. Boston (MA): Springer; 2004.
   Google Scholar