A model-based approach for controlling particle size distribution in combined cooling-antisolvent crystallization processes (original) (raw)
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Chemical engineering transactions, 2017
This contribution deals with the formulation and implementation of different nonlinear model-based controllers for controlling the crystal size distribution (CSD) in non-isothermal antisolvent crystallization processes. Three different control algorithms are developed and tested experimentally. First, knowing the exact transfer function of the crystal mean size and CSD variance as a function of antisolvent feed rate and temperature, an internal model based controller is defined to achieve the desired CSD characteristics. Subsequently, by exploiting the analytical solution of the CSD mean size, the geometric linearizing controller is developed to track the system to the target. An alternative configuration of the geometric linearizing controller is also tested using an observer based PI controller, which is more convenient for experimental implementation. Experimental validation of the strategies is carried out for the ternary system of water-ethanol-sodium chloride.
Feedback control of crystal size distribution in a continuous cooling crystallizer
The Canadian Journal of Chemical Engineering, 1991
Control of crystal size distribution (CSD) in a 21.8 L continuous cooling KCI crystallizer was attempted. Feed saturated at 54°C with potash, nearly saturated with NaCl and containing 0.75 g MgS04/100 g of H,O was cooled to the crystallizer temperature at 40°C. The control scheme consisted of a proportional-integral controller with the rate of fines dissolution/removal as the input variable and the fines suspension density (crystals smaller than 150 pm) as the output variable. The measuredicontrolled variable was a temperature difference, AT, corresponding to the temperature of a slurry sample containing representative fines, before and after the fines were dissolved by heating. An increase in the product weight-mean crystal size and a decrease in the coefficient of variation of product were observed in the controlled runs.
Modelling and control of crystallization process
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.
2010
A generic model-based framework has been developed for crystallization processes, with applications aiming at the control of process operations and the monitoring of product quality. This generic model-based framework allows the systematic development of a wide range of crystallization models for different operational scenarios. This enables the design and control engineers to analyze various crystallization operations and conditions, thus facilitating the development of process control and monitoring systems (PAT systems) for crystallization processes. The generic framework has been implemented in the ICAS-PAT software which allows the user to design and validate PAT systems through a systematic computer-aided framework. The application of the framework is highlighted for batch cooling crystallization of paracetamol where the framework was applied for design of a process monitoring and control system to obtain a desired crystal size distribution (CSD).
CONTROL OF CRYSTALLIZATION PROCESSES
In this chapter we take a fairly broad look at industrial crystallizer control. Since the people working on these problems may come from an instrumentation and control background or a crystallization process background, we begin with a review of the basics of crystallizer design and operation as well as a discussion of some of the measurements and manipulated variables available for use in the simple control schemes outHned in Section 9.2. Section 9.3 presents the state of the art in practiced crystallizer control. As such, this section is probably of primary importance to the audience responsible for actual crystallizer design, instrumentation, and operation. Finally, Sections 9.4 and 9.5 review the progress being made in improving crystal quality control for both continuous and batch crystallizers. It is hoped that this article will serve as a useful reference for industrial practitioners as well as pointing out fruitful opportunities for academic researchers.
Control of crystal size distribution in a batch cooling crystallizer
Canadian Journal of Chemical Engineering, 1990
A control scheme for crystal size distribution (CSD) in a batch crystallizer, based on indirect measurement of fines suspension density and manipulation of fines dissolving rate, is proposed and implemented on a 27 L laboratory batch cooling crystallizer using the potash alum-water system. The measured variable was a temperature difference related to the fines suspension density detected by a new fines sampling/suspension density measuring device proposed by Rohani and Paine (1987). Servo-control of the fines suspension density was achieved using a conventional PI control mode. Two different cooling policies, namely, linear cooling and isothermal operation were examined and improvement in the final CSD was observed in both cases. The weight-mean crystal size and the coefficient of variation of the end product showed a maximum improvement (larger mean size and smaller coefficient of variation) of 80% and 31% over the uncontrolled experiments, respectively. The weight fraction of fines (smaller than 150 μm) in the end product was decreased by a maximum of 99% over the uncontrolled run. Higher overall rates of fines dissolution led to a more uniform product with a larger weight-mean crystal size at the expense of a small reduction in the rate of solids make.Un schéma de contrôle de la distribution de taille des cristaux dans un cristalliseur discontinu, basé sur la mesure indirecte de la densité de suspension des fines et la manipulation du taux de dissolution des fines, est proposé et utilisé sur un cristalliseur à refroidissement discontinu de laboratoire d'une capacité de 27 L avec un système alun de potasse-eau. La variable mesurée est une différence de température reliée à la densité de suspension des fines détectée par un nouveau dispositif de mesure d'échantillons de fines et de densité de suspension proposé par Rohani et Paine (1987). On a effectué la servorégulation de la densité de suspension des fines en utilisant un mode de contrôle PI classique. Deux procédures de refroidissement différentes ont été étudiées, à savoir le refroidissement linéaire et le fonctionnement isotherme, et on a observé une amélioration de la distribution finale de taille des cristaux dans les deux cas. La taille des cristaux de poids moyen et le coefficient de variation du produit final montrent une amélioration maximale (taille moyenne plus grande et coefficient de variation plus petit) de 80 et 31% pour les expériences non régulées, respectivement. La fraction de poids des fines (inférieure à 150 μm) dans le produit final est réduite jusqu'à 99% lors du cycle non régulé. Des vitesses globales supérieures de dissolution des fines donnent un produit plus uniforme avec une taille des cristaux de poids moyen plus grande au détriment d'une légère réduction de la vitesse de formation des solides.
Multiscale modeling, simulation and validation of batch cooling crystallization
Separation and Purification Technology, 2007
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.
Crystal Growth & Design, 2008
In this paper, a model-based optimal strategy is presented for the control of particle size in antisolvent crystallization. Size is controlled on demand by dynamic optimization using a population balance based model. Knowledge of the ternary solutesolvent-antisolvent equilibrium and crystallization kinetics is crucial in this strategy and are both experimentally identified and incorporated in the model. The optimization is capable of determining the optimal antisolvent feed profile that achieves a desired particle size. Experimental validation of the strategy is carried out and presented herein. Such a strategy stands as a key solution to antisolvent operations ubiquitous in the pharmaceutical and fine chemicals industries.