Seeding Effect on Product Crystal Size in Batch Crystallization.

Continuous processing gains importance in the fine chemical and pharmaceutical industries where crystallization is an important downstream operation. Seeded cooling crystallization of the l-alanine/water system was investigated under similar conditions, i.e., temperature interval, cooling rate, and seed material, both in a stirred batch vessel and in a continuous plug flow crystallizer in the coiled flow inverter (CFI) design with horizontal helical tube coils (ID = 4 mm) and frequent 90° bends of the coils. Short-cut calculations based on characteristic time scales and the Damköhler number allow for comparing the batch and continuous crystallization processes. The experimental results reveal crystal growth and growth rate dispersion to be dominating on the product crystal size distribution (CSD). However, at low flow rates of approximately 31 g min–1, a moving sediment flow of the slurry was present in the CFI crystallizer, resulting in further size dispersion effects. Elevated flow rates of approximately 40 g min–1 resulted in a more homogeneous suspension flow and a product CSD comparable to batch quality. Simulation studies based on a population balance equation model strengthen the hypothesis of the solid phase residence time distribution (RTDS) to be more spread in the moving sediment flow regime, leading to a wider product CSD.

In this paper, a data-driven modeling and optimization method is proposed for batch operation of the cooling crystallization process based on the design of experiments (DoE) in the operation ranges of cooling rate and initial solution supersaturation. The proposed model reflecting a mapping relationship between the manipulated variables of operating conditions and the product crystal size distribution (CSD) is established by two classes of basis functions, one is the wavelet basis function for reshaping the CSD and the other is the polynomial basis function for weighting the chosen wavelet basis functions to reflect the nonlinear relationship between the manipulated variables and the size distribution of product crystals. Meanwhile, a fractional factorial design of DoE is presented to reduce the number of experiments for the above modeling, in comparison with the classical full factorial design. The optimal operating conditions are designed by introducing a comprehensive objective function that combines the information entropy of the product CSD and the ratio of desired product yield with respect to the target crystal size. A particle swarm optimization algorithm with guaranteed convergence is given to solve the nonconvex optimization problem based on the established CSD prediction model. Simulation studies and experiments on the batch seeded β-form L-glutamic acid (LGA) crystallization process are conducted to demonstrate the effectiveness and advantage of the proposed method.

Characterization of the anti-solvent batch, plug flow and MSMPR crystallization of benzoic acid

Product engineering problems are considered that arise in the use of batch crystallization processes. Specifically forward and reverse process functions are identified that in the forward direction enable the calculation of product crystal size distributions (CSDs) from specified operating conditions, and in particular a specified temperature–time (T–t) profile. In the reverse direction the process function calculates the required processing conditions—the T–t profile—needed to make a specified product CSD. A series of example calculations is reported, based on the kinetics of the pentaerythritol–water system, that show that in the presence of nucleation and growth only, the relationship between desired, or target, CSD and required T–t profile is, in practice, unique. It is observed that some such profiles are either impractical or impossible, from which it is possible to conclude that the corresponding target CSD is itself impossible. A further example is considered in which the property function relating the product property (CSD) to the end‐use properties (cake porosity and filtration time) enables the specification of a target CSD and of the process conditions necessary for its production. © 2006 American Institute of Chemical Engineers AIChE J, 2006

Size distribution of product crystals from a continuous agitated crystallizer is an important source of information of the crystallization process. The shape of the distribution curve reveals the occurrence of subsidiary processes accompanying crystallization such as attrition, agglomeration, internal or external classification, fines dissolution etc. A proper analysis of the crystal size distribution based on the MSMPR concept can serve kinetic data on nucleation and crystal growth and simultaneously give quantitative description of some of the processes mentioned above. This review paper summarizes the steps of such an analysis of the product crystal size distribution.

Comparative evaluations of bulk seeded protein crystallization in batch versus continuous slug flow crystallizers

Continuous reaction crystallization of struvite from water solutions containing phosphate(V) (1.0 mass%) and calcium ions (from 0.01 to 0.20 mass%) was investigated. Process was carried out in temperature 298 K in continuous DT MSMPR type crystallizer with internal circulation of suspension. Influence of pH (from 9 to 11) and mean residence time of suspension in crystallizer (from 900 to 3600 s) on product crystal size distribution, mean size, population homogeneity and shape of crystals, as well as chemical composition of solid phase was tested. Within the investigated process parameter ranges struvite crystals of mean size from 18 to ca. 50 μm were produced. With the increase in calcium ions concentration in a feed mean crystal size decreased from 34.2 to 18.4 μm (pH 9, τ 900 s). Coexistence of struvite and hydroxyapatite crystals in the solid product was confirmed analytically (Ca content in solid product from 0.3 to 8.4 mass%). Presence of calcium ions favoured crystallization of struvite in a form of tubular crystals, characterized by lengthwise cracks and irregular edges. Co–precipitated hydroxyapatite particles showed relatively small sizes, even below 1 μm, forming agglomerates on the surface of larger struvite crystals and individual agglomerates. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Crystal size distribution (CSD) is an essential criterion for determining the production of high-quality crystals since it influences the efficiency of the crystallization process. Producing specified CSD in the crystallization process represents a main challenge as it depends on temperature control, which indirectly regulates the solution’s concentration and affects the crystal’s evolution. Different temperature profiles may influence the distribution of crystal products, and a suitable optimization algorithm is required to produce an optimum temperature trajectory that produces the desired CSD. Thus, this study aims to maximize the CSD of the grown seed crystals while minimizing the nucleus-grown crystals by employing the best optimization algorithm for the potash alum crystallization process. The crystallization process was developed and simulated in Matlab software using a potash alum in the water system. Four optimization algorithms were proposed with different objective functions, such as maximizing mean crystal size (I), minimizing coefficient of variation (II), minimizing nucleus-grown crystals (III), and maximizing CSD (IV). Based on the simulation results, optimization IV, which maximizes CSD, performs best with a large mean crystal size of 490 µm. Furthermore, the number of fine crystals was among the lowest at a volume distribution of 0.00071 m3/m compared to the linear profile at 0.00191 m3/m. Optimization IV employs a dissolution strategy, which manipulates two quality specifications in one algorithm (size of crystals and number of fines), which is considered the best optimal cooling profile for seeded batch crystallization by maximizing CSD and minimizing the generation of nucleus-grown crystals.

The ideal requirements for highly efficient evaporative crystallization in batch or semi-batch type operations are a high crystal growth rate, a high density of suspended crystalline particles in a slurry and a sharp crystal size distribution. On the other hand, the initial seed crystal conditions are also very important because they affect the crystal size and distribution during the entire period of operation. In the present study, the influence of seeding conditions (average diameter and mass) and heating rate on both the crystal growth rate of sodium chloride (NaCl) and the crystal size distribution were investigated in an optimal design draft-tube stirred vessel containing high suspension density slurries of sodium chloride particles up to 30 vol%. In order to determine the optimal seeding conditions to simultaneously achieve a high crystal growth rate and narrow size distribution, an optimal operation index Iop has been defined based on a comparison of the experimental data and a model calculation with the assumption of ideal crystal growth. The results show that Iop correlate with the particle number of seed crystals and have a maximum value. With Iop correlation, the appropriate seeding conditions for achieving a high crystal growth rate and sharp size distribution have been determined.

A mathematical model of a Vacuum Pan Sugar Crystallizer is developed. This mathematical model is based on the fundamentals of the physics of two-phase flow. Because of the complexity of the model, the time dependent solution is accomplished using numerical methods designed to be executed by a digital computer. The model generates velocity, pressure, temperature, and supesaturation profiles for the Pan, in response to the operating conditions and the design of the Pan. The motivation for the development of this model is to give an analytical tool to study the effects of Pan design, control systems, and operating conditions on the product crystal size distribution. The emphasis of this research is on the development of the numerical methods that are consistent with conservative and transportive principles reflected in the equations of motion. Important features of the numerical code are that it employs a dynamic grid system designed for free surface flows, and is compatible with viscous two-phase flows exhibited in the Pan. The modeling equations are two-dimensional for the volume above the Calandria, but in other areas where two-dimensional equations are an extravagance, one-dimensional and lumped equations are used. The velocity and growth rate profiles provides the information needed for a distributed population balance equation needed to analyze the effect of these profiles on the crystal size distribution. The development of such a population balance is discussed but is not part of the present numerical code. Nonetheless, the model provides valuable information concerning the effects of Pan design, control systems, and operating conditions in a qualitative sense, as demonstrated in the results. Sensitivity studies varying Pan design and operating conditions demonstrate that of the cases studied, the volumn fraction crystals, pan level, heat load, pan pressure, and whether or not the pan major diameter is flared, had the most significant effects on the pan performance. These variations of Pan design and operating conditions were judged significant based on their effect on the velocity and growth profiles, maximum and minimum supersaturation, and average supersaturation. A summary of the results is presented in Chapter 7 and discussions concerning particular cases along with raw data are presented in Chapter 5.

The experimental results concerning continuous mass crystallization process in L(+)‐ascorbic acid – water system are presented and discussed. Influence of L(+)‐ascorbic acid concentration in a feeding solution and mean residence time of suspension in laboratory DT MSMPR crystallizer on product crystal size distribution as well as nucleation and growth kinetics were determined. Kinetic parameter values were evaluated on the basis of size–independent growth (SIG) kinetic model (McCabe's ΔL law). It was observed, that within the examined range of crystallizer productivity (120–1600 kg LAA crystals m–3h–1) crystal product population of mean size Lm from 0.2 to 0.3 mm and CV from 66.6 to 49% is withdrawn. Linear growth rate values present decreasing trend (from ca. 7 · 10–8 to ca. 6 · 10–8 m s–1) with the productivity increase (assuming constant mean residence time of suspension τ = 900 s). Occurrence of secondary nucleation within the circulated and mixed suspension, resulting from crystal attrition and breakage, was observed. The parameter values in design equation connecting linear growth rate and suspension density with nucleation rate were determined. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Continuous Reaction Crystallization of Struvite from Diluted Aqueous Solutions of Phosphate (V) Ions in DT MSMPR Crystallizer

New perspectives for the on-line monitoring of pharmaceutical crystallization processes using in situ infrared spectroscopy

Batch crystallizers are predominantly used in chemical industries like pharmaceuticals, food industries and specialty chemicals. The nonlinear nature of the batch process leads to difficulties when the objective is to obtain a uniform Crystal Size Distribution (CSD). In this study, a linear PI controller is designed using classical controller tuning methods for controlling the crystallizer outlet temperature by manipulating the inlet jacket temperature; however, the response is not satisfactory. A simple PID controller cannot guarantee a satisfactory response that is why an optimal controller is designed to keep the concentration and temperature in a range that suits our needs. Any typical process operation has constraints on states, inputs and outputs. So, a nonlinear process needs to be operated satisfying the constraints. Hence, a nonlinear controller like Generic Model Controller (GMC) which is similar in structure to the PI controller is implemented. It minimizes the derivative of the squared error, thus improving the output response of the process. Minimization of crystal size variation is considered as an objective function in this study. Model predictive control is also designed that uses advanced optimization algorithm to minimize the error while linearizing the process. Constraints are fed into the MPC toolbox in MATLAB and Prediction, Control horizons and Performance weights are tuned using Sridhar and Cooper Method. Performances of all the three controllers (PID, GMC and MPC) are compared and it is found that MPC is the most superior one in terms of settling time and percentage overshoot.

There has been increasing recognition of the phenomenon of growth rate dispersion in the analysis and characterization of the crystal growth process. The concept originated from early work on the crystallization of sugar (White and Wright, 1971). Since then, several researchers have shown that for many crystallization systems—when a group of crystals, all of which have the same initial size, has been grown under globally identical external conditions of supersaturation, temperature, and hydrodynamics in a batch crystallizer under conditions of negligible nucleation—a stochastic variation in crystal growth rates exists, resulting in a broadening of the product crystal size distribution.