Batch crystallization control

A critical control aspect common to all batch crystallization is controlling the initial population of crystals. Control action intended to produce large crystals may be insufficient to compensate for the massive generation of nuclei that results from spontaneous nucleation. Industrial experience supports the copious academic documentation that seeding with a defined mass and size range of crystals permits the growth of larger crystals with a narrower size distribution. 

The introduction of seed crystals to a solution that is saturated or within the lower portion of the metastable zone prevents spontaneous nucleation (Karpinski et al. 1980). In most industrial cases, seeding is a manual operation. The indication of when to seed is derived from an indication of the process temperature, typically provided by the control system, and knowledge of the product solubility and current solution concentration, whether measured on-line, off-line, or calculated from charge amounts. Obviously, accurately calibrated temperature sensors in the laboratory, where the solubility relationship was established, as well as in the crystallizer, where the solubility relationship will be utilized, are necessary. 

In addition to seeding, the CSD is controlled by influencing the supersaturation by manipulating temperature, pressure, the distribution of solids within the vessel, and the rate of introduction and dispersion of nonsolvents during the batch crystallization. Implementation of techniques and strategies based on the manipulation of these variables is illustrated with the following examples. 

Unbalanced Batch Temperature Control. The simplest and least expensive control configuration employs trapped steam heating for reaction and distillation with the steam flow rate moderated to maintain either the vessel contents temperature or a pressure drop across the column. Cooling can be accomplished by blocking the steam and controlling the flow of a coolant (water, brine, or glycol) to achieve the desired bulk temperature. Water is commonly used to reduce the crystallizing slurry temperature to 20-25 °C. 

The disadvantage of this strategy lies in the high steam usage near the maximum operating temperature and the loss of condensate from the boiler, which frequently contains expensive anticorrosive additives. Water consumption is also high. The temperature range may be insufficient for multi-function use. 

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Chang, C.-T. and Epstein, M.A.F., 1982. Identification of batch crystallization control strategies using characteristic curves. American Institute of Chemical Engineers Symposium Series, 78(215), 68-75. 

Ice Crystal Growth. In order to quantify these results for the production of large disc and spherical crystals, seversd batch experiments on 6% lactose solutions were undertaken. The experimental conditions and results are shown in Table II. In these experiments, nuclei were generated at -2.5 C (except for Run Sa at -4.0°C) and input to the batch crystallizer controlled at various refirigerant temperatures. As these crystals grew, the total crystal surface area was controlled manually in order to maintain a heat balance for a constant value of the refrigerant temperature. Slurry removal rate for these experiments... 

Crystallization batches range from 30,000 to 60,000 Hters for each pan. Continuous centrifugals are typically used for second, third, and affination steps continuous vacuum pans are less common but are used in the U.S. for intermediate strikes. Most horizontal batch crystallizers have been replaced by continuous units, and all are designed for controlled cooling of the massecuite to maintain supersaturation. 

In all such laboratory studies, plant conditions and compositions should be employed as far as possible. Agglomeration rates tend to increase with the level of supersaturation, suspension density and particle size (each of which will, of course, be related but the effects may exhibit maxima). Thus, agglomeration may often be reduced by operation at low levels of supersaturation e.g. by controlled operation of a batch crystallization or precipitation, and the prudent use of seeding. Agglomeration is generally more predominant in precipitation in which supersaturation levels are often very high rather than in crystallization in which the supersaturation levels are comparatively low. 

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. 

Bohlin, M. and Rasmuson, A.C., 1992. Application of controlled cooling and seeding in batch crystallization. Canadian Journal of Chemical Engineering, 70, 120-126. 

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. 

Heffels, S.K., de Jong, E.J. and Nienoord, M., 1994. Improved operation and control of batch crystallizers. In Particle design via crystallization, American Institute of Chemical Engineers Symposium Series, 87(284), 170-181. 

Mathews, H.B. and Rawlings, J.B., 1998. Batch crystallization of a photochemical Modelling, control and filtration. American Institution of Chemical Engineers Journal, 44(5), 1119-1127. 

Vega, A., Diez, F. and Alvarez, J.M., 1995. Programmed cooling control of a batch crystallizer. Computers and Chemical Engineering, 9, 471-476. 

Although cooling crystallization is the most common method of inducing supersaturation in batch crystallization processes, other methods can be used, as discussed in Chapter 10. For example, evaporation can be used, in which case the profile of the rate of evaporation through the batch can also be optimized7. Indeed, the profiles of both temperature and rate of evaporation can be controlled simultaneously to obtain greater control over the level of supersaturation as the batch proceeds7. However, it should be noted that there is often reluctance to use evaporation in the production of fine, specialty and pharmaceutical products, as evaporation can concentrate any impurities and increase the level of contamination of the final product. 

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. 

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. 

The key factors controlling the purity of L-Ile recovered from batch crystallizers are shown in the study to be the composition of the solution from which the crystals are recovered, agitation, and the rate at which supersaturation is generated. Also, the molecular form of the recovered amino acid determines which of the impurities investigated, L-Leu and L-Val, is the more plentiful impurity in the recovered crystals. 

Batch crystallizers are often used in situations in which production quantities are small or special handling of the chemicals is required. In the manufacture of speciality chemicals, for example, it is economically beneficial to perform the crystallization stage in some optimal manner. In order to design an optimal control strategy to maximize crystallizer performance, a dynamic model that can accurately simulate crystallizer behavior is required. Unfortunately, the precise details of crystallization growth and nucleation rates are unknown. This lack of fundamental knowledge suggests that a reliable method of model identification is needed. 

The laboratory batch crystallizer used in this study is shown in Figure 2. It consisted of a cylindricd vessel (ID = 155 mm, height = 250 mm) of three litre working capacity agitated by a variable speed 4-bladed (variable-pitch) impeUer. For temperature control, the crystallizer was immersed in a constant temperature water-bath. 

J.X. Shen, M.S. Chiu, Q.G. Wang, A comparative study of model-based control techniques for batch crystallization process, J. Chem. Eng. Jpn. 32 (4) (1999) 456 164. 

It may be easier to operate a continuous system so that it reproduces a particular crystal size distribution than it is do reproduce crystal characteristics from a batch unit. Moreover, the coupling of several transient variables and nucleation make it difficult to model and control the operation of a batch crystallizer. 

The rate of cooling, or evaporation, or addition of diluent required to maintain specified conditions in a batch crystallizer often can be determined from a population-balance model. Moments of the population density function are used in the development of equations relating the control variable to time. As defined earlier, the moments are... 

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. 

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