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A stochastic formulation for the description of cooling-antisolvent mediated crystal growth processes based on the Fokker-Planck equation is discussed. Previous results are further extended to include not only the additional degree of freedom (temperature) in the approach, but also to formulate the model parameters dependencies with the input manipulated variables (antisolvent flow rate and temperature) toward a global model to be used within all possible operating regimes. The obtained global models are used to define, for the first time, an operating map of the crystallization process, where asymptotic isomean and isovariance curves are reported in an antisolvent flow-rate-temperature plane. Input multiplicities are identified and validated both numerically and experimentally for the NaCl-water-ethanol nonisothermal antisolvent crystallization system. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2843-2851, 2013 [ABSTRACT FROM AUTHOR]

A new formulation of the recent stochastic approach for the description of the particle-size distribution (PSD) time evolution in antisolvent crystal-growth processes is presented. In this new approach, the crystals size is modeled as a random variable driven by a Gompertz growth term and the corresponding Fokker-Planck equation is carried out. This proposed formulation, allows an analytical solution to describe the time evolution of the PSD as a function of the model parameters. The analytical solution is obtained by exploiting the typical properties of linear partial differential equations with linear coefficients, and using the analogy with Kalman filter, in terms of the first two stochastic moments: mean and variance of the PSD. Furthermore, an alternative way for the parameters estimation based on the maximum likelihood estimation is also introduced. Validations against experimental data are provided for the NaCl-water-ethanol antisolvent crystallization system. © 2012 American Institute of Chemical Engineers AIChE J, 2012 [ABSTRACT FROM AUTHOR]

Abstract: This study investigates issues relating to the modeling of the batch cooling crystallization process. A systems model is presented where at the microscale, a population balance model is used and solved numerically with the aim of predicting end-product properties under the effect of different cooling conditions affected by mesoscale fluid heat transfer and macroscale flow/temperature control. Model validation is carried out experimentally. Technical issues arising from these implementations including the aspects of the numerical solution, generalization of the crystal size domain and connectivity with an advanced distributed control system are discussed and results presented. The model is proposed for applications as a soft-sensor for the prediction of the crystal size and in the design of the model-based control scheme. This paper presents an environment for advanced simulation and operation of crystallization processes. This environment supports the ultimate challenge of crystallization which is control towards efficient separations and purifications. [Copyright &y& Elsevier]

• Convolutional Neural Network is used to measure the size distribution of crystals. • Reinforcement Learning is used to learn an optimal control policy. • Removes the need to solve optimization problems at run time. • The system is tested on non-isothermal anti-solvent crystallization. In this work, a combination of a Convolutional Neural Network (CNN) based measurement sensor and a reinforcement learning (RL) framework that speeds up the control loop is presented. The objective of the controller is to reach a target mean size and to reduce the variability of the crystal sizes. The CNN based sensor improves the quality of crystal size measurement and reduces the time to process images while the RL framework learns to navigate the crystallization model optimally even in the face of disturbances. The proposed data driven strategy is validated against an unseeded crystallization of sodium chloride in water using ethanol as antisolvent in an experimental bench-scale semi-batch crystallizer. We find that the RL-based controller can be trained offline to optimize multiple target conditions while the CNN provides accurate feedback for the controller to recompute the optimal actions in the face of disturbances and guide the system towards the target. [ABSTRACT FROM AUTHOR]

Theeffect of the temperature on the Crystal Size Distribution(CSD) in antisolvent crystallization operation, for systems wherethe solubility is weakly dependent on the temperature, is analyzed.The hydrogen bonding properties of the solvents used that influencethe supersaturation of the solution and consequently the growth andthe nucleation dynamic can explain this effect on the CSD. To verifyand quantify these effects, experiments were conducted and the Fokker–Planckmodeling equations were used to obtain the quantifying parameters(growth velocity, the asymptotic mean size, and the diffusivity).Results are provided through investigations into the nonisothermalantisolvent crystallization of sodium chloride (NaCl), in which thesolubility is practically independent of temperature for the rangeof operating conditions considered. [ABSTRACT FROM AUTHOR]

Predictive solubility models can be of great use for crystallization modeling and optimization, and can decrease the amount of experimental effort needed to create a robust crystallization model. In this study, predictive solubility models such as MOSCED, UNIFAC, NRTL–SAC, and the Jouyban–Acree model are compared against an empirical model for predicted solubility accuracy. The best models are subsequently compared against the empirical model for the antisolvent crystallization of acetaminophen in acetone, using water as the antisolvent. The effects of these solubility models on the predicted relative supersaturation, volume mean size, volume-percent crystal size distribution (CSD), and generated optimal antisolvent feed profiles are investigated. It was found that, for this system, only the NRTL–SAC and Jouyban–Acree solubility models were accurate enough to predict crystallization mean size and crystal size distributions. The Jouyban–Acree and NRTL–SAC solubility models respectively predicted end-volume mean-size differences up to 13% and 29% from the empirical model. When used to create optimal antisolvent feed profiles, the Jouyban–Acree and NRTL–SAC profiles produced results that varied up to 32% and 60%, respectively, from the desired objective. None of the predictive solubility models was accurate enough for the creation of optimal antisolvent feed profiles. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: In this solution crystallization study, a population balance model that predicts the crystal size distribution (CSD) is used for targeting, by optimization, the product mean size and the coefficient of variation of the final CSD. The model is robust over a wide range of conditions and predicts the effects of heating during a batch since the kinetics of dissolution have been identified and incorporated into the model. The dynamic temperature profile and the initial seed size distribution are optimized while the initial seed induction time is conferred through knowledge of saturation conditions. This is quite a highly developed method that provides a systematic approach towards optimal operation. Results from optimizations under different objective functions are presented and validated experimentally. [Copyright &y& Elsevier]

Abstract: Recently, a novel stochastic formulation based on the Fokker–Planck equation (FPE) for the description of anti-solvent mediated crystal growth process was proposed. Here, we further expand these results by analyzing the asymptotic (end of the batch) solution of the FPE for the CSD. In this regard, the analytical solution of the stationary FPE is exploited for predicting the end of the batch CSD as function of the model parameters. Furthermore, the availability of such analytical solution is used to simplify and diminish the computational burden of the parameter estimation problem. Two alternative approaches for parameter estimation are discussed based on the use of the analytical solution of the FPE and of the dynamic of the logistic equation (deterministic component of the FPE approach). Validations against experimental data for the NaCl–water–ethanol anti-solvent crystallization system are presented. [Copyright &y& Elsevier]

Abstract: Fine sodium chloride particles are of pharmaceutical interest in aerosol delivery for diagnosis of asthma and bronchial related disorders. In this paper, stable NaCl particles with size ranging from approximately to sub-micron range were achieved by sonocrystallisation. The effects of sonocrystallisation temperature, ultrasonic power output and salt concentration were investigated in the ranges of , 22–60W and 8–34g/100g water, respectively, under a fixed ultrasonic frequency of 20kHz. Size and morphology characterisation of particles were undertaken using laser diffraction and scanning electron microscopy. Optimally prepared particles were spray dried followed by in vitro impaction test for inhalation efficiency determination using an . Optimal conditions of temperature ), ultrasonic power output and salt concentration water) exist that reduced the size of the crystals to the sub-micron scale. Spray drying increased the size of the prepared particles and altered the cubic shape to more rounded morphology. The fine particle fraction (based on recovery) was improved from approximately 12 to 34% when dispersion air flow rate was increased from 60 to 120L/min. Sonocrystallisation represents an effective method for the preparation of NaCl particles suitable for pharmaceutical inhalation. The main advantage of this method is that it is rapid, simple and produces small crystal NaCl particles suitable for inhalation. [Copyright &y& Elsevier]