Model-Based Optimal Strategies for Controlling Particle Size in Antisolvent Crystallization Operations (original) (raw)
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Antisolvent crystallization: Model identification, experimental validation and dynamic simulation
Chemical Engineering Science, 2008
This paper is concerned with the development, simulation and experimental validation of a detailed antisolvent crystallization model. A population balance approach is adopted to describe the dynamic change of particle size in crystallization processes under the effect of antisolvent addition. Maximum likelihood method is used to identify the nucleation and growth kinetic models using data derived from controlled experiments. The model is then validated experimentally under a new solvent feedrate profile and showed to be in good agreement. The resulting model is directly exploited to understand antisolvent crystallization behavior under varying antisolvent feeding profiles. More significantly, the model is proposed for the subsequent step of model-based optimization to readily develop optimal antisolvent feeding recipes attractive for pharmaceutical and chemicals crystallization operations.
Chemical Engineering Science, 2018
This article focuses on the design and implementation of model-based control strategies for real time control of crystal size distribution (CSD) in semi batch crystallization processes. The objective of the feedback controller is to reach a desired particle mean size while the standard deviation is controlled in a feedforward fashion. Alternative model-based control strategies are formulated and implemented for a target mean size. An image processing technique based on wavelet-fractal and energy signature analysis is employed to determine online CSD status for controller corrective actions. To validate the proposed model-based control strategies, unseeded crystallization of sodium chloride in water using ethanol as antisolvent is performed in an experimental bench-scale semi-batch crystallizer.
Direct Design of Pharmaceutical Antisolvent Crystallization through Concentration Control
Crystal Growth & Design, 2006
Recent advances in in situ measurement technology and automation of batch crystallizers have enabled the development of batch crystallization recipes in which the desired supersaturation profile is followed by feedback control. This paper describes a new approach for following supersaturation setpoints for antisolvent crystallizations that is easy to implement for the tried crystallization. Simulations and application to a proprietary drug compound demonstrate how this combination of automation and in process measurements enables the rapid development of batch crystallization processes in the pharmaceutical industry.
This work aims a stochastic approach for the calculation of robust anti-solvent addition policies for controlling the mean crystal size (MCS) in fed-batch crystallization operations. The proposed strategy is based-on a non-structured population balance where uncertainties associated with the start-up condition and random fluctuations along the fed-batch operation can be taken into account in a very natural fashion. We include and quantify the effect of the uncertainties by embedding a deterministic crystal growth model into a Fokker-Planck equation (FPE) resulting in a stochastic model for the MCS dynamics. This approach uses the Generalized Logistic equation (GLE) that has an adequate mathematical structure that suits the dynamic characteristic of the crystal growth. Thus, the numerical solution of the FPE provides the most likely MCS evolution for a given anti-solvent flow-rate. The effect of the anti-solvent is incorporated into the parameters of the FPE. The parameters of the FPE are computed as linear piece-wise interpolating functions of the anti-solvent flow-rate. The strategy uses a PID-like regulator in closed-loop fashion with the FPE to compute the anti-solvent addition flow-rates for different set-point targets in the MCS. In order to validate the stochastic model and assess the merits of the proposed strategy, the crystallization of sodium chloride in water using ethanol as anti-solvent is performed in a bench-scale fed-batch crystallizer. The implementation of the calculated anti-solvent policies resulted in a good control of the MCS despite modelling mismatch and uncertainties present during the crystallization operation.
Chemical Engineering Science, 2003
A bi-dimensional population balance model was presented in the previous part of this series of papers to simulate the time variations of two characteristic sizes of hydroquinone particles during crystallization. The multidimensional population balance equations combined with kinetic models and mass balance equations were shown to allow the simulation of the solution crystallization of hydroquinone characterized by a rod-like habit. Semi-continuous isothermal operations were performed at the lab-scale in the presence of various additive concentrations. Both the experimental solute concentration trajectory and the ÿnal bi-dimensional crystals size distribution were correctly predicted by the model. The simulated elongation shape factor characterizing the crystal shape was therefore in agreement with the experimental one. Due to the use of tailor-made additive, inhibition e ects were observed to a ect both primary nucleation and growth kinetics in the length direction. For secondary nucleation, indirect e ects were assumed to occur which allowed satisfactory predictions of the ÿnal number of ÿne particles. The representation of the kinetics involved required the evaluation of a set of nine parameters. As a result it was observed that the elongation ratio characterizing the shape of the rod-like particles increases with the length in a nonlinear way. A major interest of the two-dimensional model lies in its ability to relate the time variations of the crystal habit: the particles lengthen in the ÿrst moments of their growth and then progressively get thicker until the end of the process. ?
2010
During recent years crystallisation has found applications in many chemical industries, such as pharmaceutical, petrochemical, micro-electronics and food industries. Crystallisation is a basic step for purification or separation for a large variety of organic, inorganic and pharmaceutical compounds. Most of the product qualities are directly related to the shape of the crystal size distribution (CSD). The main difficulty in batch crystallisation processes is to accomplish a uniform and reproducible CSD. On-line control during the process allows for improved crystalline product quality, shorter process times and reduction or elimination of compromised batches. The actual prediction and estimation of the shape of the distribution at the end of the batch can provide useful information for monitoring or designing the operating curve for the supersaturation controller. Model-based approaches provide consistency of the CSD, can be used for better control and also for product design by rev...
IFAC-PapersOnLine, 2021
An optimal control framework was employed to obtain optimal supersaturation/temperature policies for controlling the crystal mass, size, and shape that meet target product specifications. It uses a bivariate population balance model that includes crystal nucleation, growth, dissolution, and disappearance. The optimal control scheme, solving a dynamic optimization problem, was applied to the batch cooling crystallization of potassium dihydrogen phosphate. The population balance model was evaluated in open-loop experiments, showing good prediction for the mean characteristic lengths and number of particles, both for the supersaturation and undersaturation zones. The deterministic optimal control simulations demonstrated the application of the control action policies to produce crystals of desired mass and average shape for different control targets.
Organic Process Research & Development, 2015
In the pharmaceutical industries, the requirements of rapid process development and scalable design have made the tubular crystallizer a promising platform for continuous manufacturing and crystallization processes, capable of replacing conventional capital-and labor-intensive batch operations. This paper takes a process systems engineering (PSE) approach to the optimal design of a continuous anti-solvent addition crystallizer to deliver the most promising product qualities, such as the crystal size distribution. A multi-segment multi-addition plug-flow crystallizer (MSMA-PFC) is considered as an example of a continuous anti-solvent crystallization processes, in which the total number, location, and distribution of anti-solvent additions are to be optimized. First principles dynamic and steady-state mathematical models for the MSMA-PFC are presented, based on example kinetic models for nucleation and growth of paracetamol crystallizing in acetone, with water as the anti-solvent. The performances of different crystallizer configurations operated under optimal design conditions are then compared. The configuration in which anti-solvent could be added at a variety of different locations along the tube length and at optimal flow rates was able to outperform previous designs in the literature which considered equally-spaced anti-solvent additions. The use of dynamic models to detect problems during startup of an MSMA-PFC was also highlighted.
7th IFAC International Symposium on Advanced Control of Chemical Processes (2009), 2009
This work aims a stochastic approach for the calculation of robust anti-solvent addition policies for controlling the mean crystal size (MCS) in fed-batch crystallization operations. The proposed strategy is based-on a non-structured population balance where uncertainties associated with the start-up condition and random fluctuations along the fed-batch operation can be taken into account in a very natural fashion. We include and quantify the effect of the uncertainties by embedding a deterministic crystal growth model into a Fokker-Planck equation (FPE) resulting in a stochastic model for the MCS dynamics. This approach uses the Generalized Logistic equation (GLE) that has an adequate mathematical structure that suits the dynamic characteristic of the crystal growth. Thus, the numerical solution of the FPE provides the most likely MCS evolution for a given anti-solvent flow-rate. The effect of the anti-solvent is incorporated into the parameters of the FPE. The parameters of the FPE are computed as linear piece-wise interpolating functions of the anti-solvent flow-rate. The strategy uses a PID-like regulator in closed-loop fashion with the FPE to compute the anti-solvent addition flow-rates for different set-point targets in the MCS. In order to validate the stochastic model and assess the merits of the proposed strategy, the crystallization of sodium chloride in water using ethanol as anti-solvent is performed in a bench-scale fed-batch crystallizer. The implementation of the calculated anti-solvent policies resulted in a good control of the MCS despite modelling mismatch and uncertainties present during the crystallization operation.