Development of Batch Crystallizations

Batch crystallizations are used throughout the chemical industry to isolate a substance from the reaction broth and obtain particles with desired properties. At the same time, batch crystallization is a very versatile technique that can be adapted to the specific properties of the moiety and to the needs of product properties. 

The underlying principles of the batch process are discussed that will lead to a stable and robust process. 

Before any crystallization process is designed, the goals of the process and desired particle properties have to be defined. Table 10.1 lists some of these properties. One can distinguish goals that are directly influenced by the crystallization process such as yield, particle size, and/or particle size distribution (PSD) and properties that can be derived from these properties such as the bulk density. It is not always easy to predict the influence of primary particle properties on the derived ones flowability, for example, is a very complex property that can depend on many primary particle properties such as shape, particle size, and particle size distribution or roughness. Some of these parameters can be influenced by the crystallization process, for example, particle size and particle size distribution, some are not that easy to control, for example, amorphous content and roughness, and others are more or less intrinsic and cannot be influenced by the process, for example, hardness and plasticity of the crystals. 

Especially during scale-up, one has to focus on the most critical property for the foreseen downstream process such as the high purity of large crystals achieved by slow crystallization and only for a limited yield and neglect the others such as the particle size distribution that may be negatively affected by long stirring. 

It is of utmost importance to have a close contact with the customers of the crystalline material to understand their needs, to define reasonable specifications, and on the one hand to meet their expectations and on the other hand to allow 

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The crystallization literature is replete with theoretical developments of batch crystallizer models and techniques to estimate their parameters. However, most of the schemes are constrained to specific crystallizer configurations and model formulations. 

Using piecewise constant control profiles and orthogonal collocation on finite elements, this approach was further developed by Renfro (Renfro, 1986 Renfro et al, 1987) to deal with much larger problems. More recent simultaneous applications that involve SQP, orthogonal collocation, and piecewise constant control profiles have been presented by Patwardhan et al (1988) for online control, and by Eaton and Rawlings (1988) for optimization of batch crystallizers. These studies have shown that simultaneous approaches can be applied successfully to small-scale applications with complex constraints. 

A population balance can be used to follow the development of a crystal size distribution in batch crystallizer, but both the mathematics and physical phenomena being modeled are more complex than for continuous systems at steady state. The balance often utilizes the population density defined in terms of the total crystallizer volume, rather than on a specific basis n = nVj. Accordingly, the general population balance given by Eq. (51) can be modified for a batch crystallizer to give ... 

It is clear that stringent control of batch crystallizers is critical to obtaining a desired crystal size distribution. It is also obvious that the development of a strategy for generating supersaturation can be aided by the types of modeling illustrated above. However, the initial conditions in the models were based on properties of seed crystals added to the crystallizer. In operations without seeding, initial conditions are determined from a model of primary nucleation. 

Sensors for particle size characterization used for crystallization include ultrasound attenuation measurement/ " laser diffraction/ and laser backscatteiing/ commercially called focused beam reflectance measurement (FBRM). Ultrasonic attenuation spectroscopy has been used to monitor the crystallization process parameters such as the crystal size distribution, concentration, and the onset of nucleation during batch crystallization of L-glutamic acid/ Off-line laser diffraction has been used to measure the crystal size distribution in the development of the crystallization process for a pharmaceutical intermediate/ ... 

For APIs, limits are set on chemical purity, mean particle size, PSD, and other appropriate physical attributes by the biobatch model for clinical evaluation. The term biobatch refers to the regulatory requirement of identifying a particular batch, normally a pilot scale batch used in clinical trials, as the defining standard for physical and chemical attributes that must be reproduced at the manufacturing scale to be acceptable for sale. The critical process attributes (CPAs), once established, must be met on scale-up to the manufacturing facility. In addition, the process must be operated within the ranges established as critical process parameters (CPPs). Development of a crystallization process must include determination of realistic and reproducible ranges for both the CPPs and the CPAs. 

Perhaps the most troublesome aspect of batch crystallizers is the difficulty associate ciystal size distributions in going from one batch to the next. This may be overcome and control of mixing conditions. In general, however, the development of methods for design and analysis of batch crystallizers lags those for cortinuous systems. 

The analysis of batch crystallization processes is generally more difficult than that of continuous crystallization processes. This is mainly due to the complexity of problems encountered in the batch systems the mass and surface area of the crystals increase during the run, and the supersaturation varies in a complex way as a function of time. Thus, in the development of a descriptive model, one needs to consider the time-dependent batch conservation... 

Tavare, N.S., Garside, J. and Chivate, M.R. (1980) Analysis of batch crystallizers. Industrial and Engineering Chemistry Process Design and Development, 19, 653-665. 

For preparative purposes batch fractionation is often employed. Although fractional crystallization may be included in a list of batch fractionation methods, we shall consider only those methods based on the phase separation of polymer solutions fractional precipitation and coacervate extraction. The general principles for these methods were presented in the last section. In this section we shall develop these ideas more fully with the objective of obtaining a more narrow distribution of molecular weights from a polydisperse system. Note that the final product of fractionation still contains a distribution of chain lengths however, the ratio M /M is smaller than for the unfractionated sample. 

Tailoring of the particle size of the crystals from industrial crystallizers is of significant importance for both product quality and downstream processing performance. The scientific design and operation of industrial crystallizers depends on a combination of thermodynamics - which determines whether crystals will form, particle formation kinetics - which determines how fast particle size distributions develop, and residence time distribution, which determines the capacity of the equipment used. Each of these aspects has been presented in Chapters 2, 3, 5 and 6. This chapter will show how they can be combined for application to the design and performance prediction of both batch and continuous crystallization. 

Mathews and Rawlings (1998) successfully applied model-based control using solids hold-up and liquid density measurements to control the filtrability of a photochemical product. Togkalidou etal. (2001) report results of a factorial design approach to investigate relative effects of operating conditions on the filtration resistance of slurry produced in a semi-continuous batch crystallizer using various empirical chemometric methods. This method is proposed as an alternative approach to the development of first principle mathematical models of crystallization for application to non-ideal crystals shapes such as needles found in many pharmaceutical crystals. 

Farrell, R.J. and Yen-Cheng Tsai, 1994. Nonlinear controller for batch crystallization Development and experimental demonstration. In American Institute of Chemical Engineers National meeting. Atlanta, Paper 89e. 

The design and operation of industrial crystallizers is where developments in the laboratory are confirmed and their practical significance determined. In recent years, crystallization processes involving specialty chemicals and pharmaceuticals have increased. This has led increased interest in batch crystallization operation, optimization and desigrt At the same time, the advent of powerful computers and their routine avaUabilily has stimulated interest in the area of on-line control of crystallization process (both batch and continuous). Progress in batch crystallization is surrunarized in a number of recent papers and reviews 173-801. In this section I will discuss two areas which I think will have an impact in the next decade. 

Tavare and Garside ( ) developed a method to employ the time evolution of the CSD in a seeded isothermal batch crystallizer to estimate both growth and nucleation kinetics. In this method, a distinction is made between the seed (S) crystals and those which have nucleated (N crystals). The moment transformation of the population balance model is used to represent the N crystals. A supersaturation balance is written in terms of both the N and S crystals. Experimental size distribution data is used along with a parameter estimation technique to obtain the kinetic constants. The parameter estimation involves a Laplace transform of the experimentally determined size distribution data followed a linear least square analysis. Depending on the form of the nucleation equation employed four, six or eight parameters will be estimated. A nonlinear method of parameter estimation employing desupersaturation curve data has been developed by Witkowki et al (S5). 

The dynamic model used in predicting the transient behavior of isothermal batch crystallizers is well developed. Randolph and Larson (5) and Hulburt and Katz (6) offer a complete discussion of the theoretical development of the population balance approach. A summary of the set of equations used in this analysis is given below. 

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