Multi-objective optimization of seeded batch crystallization processes

Sarkar, Debasis; Rohani, Sohrab; Jutan, Arthur

doi:10.1016/j.ces.2006.03.055

Cited by 107 publications

(115 citation statements)

References 35 publications

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“…However, a simple power law expression gives a good approximation and has been used widely. 30,32,35 In this study, we use the growth rate model proposed by Angelov et al 32 This model describes the growth rate as a function of supersaturation as follows: …”

Section: Preferential Crystallization: Modelingmentioning

confidence: 99%

“…A significant contribution in the area of the multi-objective optimization in the field of chemical engineering is due to Gupta and coworkers 26 through various adaptation of NSGA-II to different complex systems such as the polymerization reactor, fluidized bed catalytic cracker, 27 membrane separation, 28 heat integration, 29 etc. As far as multi-objective optimization in crystallization is concerned, the first and the latest effort has been by Sarkar et al 30 for optimization of seeded batch cooling crystallization using NSGA-II for problems involving two and three objectives. Amanullah and Mazzotti 31 have also reported a genetic-algorithm-based two-objective optimization for hybrid chromatography-crystallization process for the separation of Troger's base enantiomers.…”

Section: Introductionmentioning

confidence: 99%

“…However, the most widely used method for solving the population balance equation for use in optimization and control is a scheme based on the first few moments of the crystal size distribution 32,35 although there are a few studies involving use of a high resolution models for optimization. 15,30 The scheme based on moments is, however, faster, gives fairly good representation of the process, and is used in this study. In fact, Angelov et al 32 have used the moment based model for optimization of the same preferential crystallization system in their latest single objective optimization framework and this study is the extension of their work to a more comprehensive four objective optimization framework.…”

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confidence: 99%

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Preferential crystallization: Multi‐objective optimization framework

Bhat¹,

Huang²

2009

AIChE Journal

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in Wiley InterScience (www.interscience.wiley.com).A four objective optimization framework for preferential crystallization of D-L threonine solution is presented. The objectives are maximization of average crystal size and productivity, and minimization of batch time and the coefficient of variation at the desired purity while respecting design and operating constraints. The cooling rate, enantiomeric excess of the preferred enantiomer, and the mass of seeds are used as the decision variables. The optimization problem is solved by using adaptation of the nondominated sorting genetic algorithm. The results obtained clearly distinguish different regimes of interest during preferential crystallization. The multi-objective analysis presented in this study is generic and gives a simplified picture in terms of three zones of operations obtained because of relative importance of nucleation and growth. Such analysis is of great importance in providing better insight for design and decision making, and improving the performance of the preferential crystallization that is considered as a promising future alternative to chromatographic separation of enantiomers.

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Section: Preferential Crystallization: Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Preferential crystallization: Multi‐objective optimization framework

Bhat¹,

Huang²

2009

AIChE Journal

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“…Mazzotti et al [166,167] used model-based optimization techniques to control CSD in batch cooling crystallisation of paracetamol in ethanol and also the combined cooling/antisolvent crystallisation of acetylsalicylic acid in ethanol-water mixtures. Rohani and co-workers (see for example [168][169][170][171][172][173][174]) minimized the optimization objective function with respect to a parameter vector temperature input, subject to the mass balance dynamics as well as the PB equation to obtain optimal cooling policy for product quality control. They also developed real-time model-based optimal control of anti-solvent semi-batch crystallisation processes and seeded batch crystallisation processes using real-time single and multi-objective optimization, rigid logic and fuzzy logic control methods [171][172][173][174][175].…”

Section: Optimisation and Control Of Crystal Size Distribution (Csd)mentioning

confidence: 99%

“…Rohani and co-workers (see for example [168][169][170][171][172][173][174]) minimized the optimization objective function with respect to a parameter vector temperature input, subject to the mass balance dynamics as well as the PB equation to obtain optimal cooling policy for product quality control. They also developed real-time model-based optimal control of anti-solvent semi-batch crystallisation processes and seeded batch crystallisation processes using real-time single and multi-objective optimization, rigid logic and fuzzy logic control methods [171][172][173][174][175]. Kramer and co-workers [158,176] applied dynamic optimization for throughput maximization of an industrial semi-batch crystallisation process with a control strategy based on a non-linear moment model and the resulting problem being solved by a non-linear programming algorithm.…”

Section: Optimisation and Control Of Crystal Size Distribution (Csd)mentioning

confidence: 99%

Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives

2016

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Crystal morphology is known to be of great importance to the end-use properties of the crystal product and affect the down-stream processing such as in filtration and drying, but was previously regarded as too challenging to achieve automatic closed-loop control. As a consequence, previous work has focused on control the crystal size distribution (CSD) where the size of a crystal is often defined as the diameter of a sphere that has the same volume of the crystal. This paper reviews very promising new advances made in recent years in morphological population balance models for modelling and simulation of crystal shape distribution (CShD), measurement and estimation of crystal facet growth kinetics, as well as in 2D and 3D imaging for on-line characterisation of crystal morphology and CShD. A framework is presented integrating various components in order to achieve the ultimate objective of model-based closed-loop control of CShD. The knowledge gaps and challenges that require further research are also identified.

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Continuous Crystallisation

Brown

McGlone

Florence

2017

Continuous Manufacturing of Pharmaceuticals

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Mixing 2) Nucleation 3) Growth 4) Filter/wash/dry Required to:  Produce consistent composition  Control crystallisation driving force  Influence flow rate, shear-kinetic effects  Consistent suspension of seed and product Controlled via:  Supersaturation  High shear  External fields, e.g. ultrasound, nonphotochemical laser induced nucleation  Seed. Seeds produced via dry milling, wet media milling, precipitation, recycle, dissolution, spray drying or secondary nucleation Controlled to avoid:  Fouling  Agglomeration  Dissolution  Crystalline form transformation(s) Controlled via:  Supersaturation (typically low) via cooling, anti-solvent, evaporation  Effective heat/mass transfer Ensures:  Efficient desupersaturation Controlled to avoid:  Fouling  Agglomeration  Attrition (particle breakage)  Dissolution  Crystalline form transformation(s) Required to:  Create filter cake  Remove mother liquor and impurities  Maintain particle attributes Controlled to avoid:  Agglomeration  Attrition (particle breakage)Continuous crystallisation has been implemented for many decades in other manufacturing sectors, typically for large volume commodity chemicals, and offers a range of potential advantages [16,17] including lower operating and capital costs, reduced down time and more efficient use of energy and materials. Cooling and anti-solvent crystallisation are commonly used in industrial applications and can be readily implemented in continuous. The aim is to follow a controlled trajectory through the solubility phase diagram (section 1.2).

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Multi-objective optimization of seeded batch crystallization processes

Cited by 107 publications

References 35 publications

Preferential crystallization: Multi‐objective optimization framework

Preferential crystallization: Multi‐objective optimization framework

Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives

Continuous Crystallisation

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