Abstract:Control of supersaturation in a 1-L continuous cooling KCI crystallizer was investigated. The supersaturation was determined from on-line measurements of the density and temperature of clear liquor samples. A cascade control scheme was used to control the supersaturation through the manipulation of the co-saturated feed temperature set-point while maintaining the crystallizer temperature at 303.2 K. Experimental results showed that due to the suppression of spontaneous nuclearion, a decrease in the measured su… Show more
“…Figure shows the closed-loop block diagram of such a feedback control system. The feedback controller can be a simple PID (proportional-integral-derivative) algorithm that, except for the tuning of its parameters, does not require an accurate model of the process, or the feedback controller can be a sophisticated model predictive controller or a geometric nonlinear controller. − 1 Block diagram of a simple feedback controller for the direct/inferential control of product quality.…”
Section: The External Controlmentioning
confidence: 99%
“…The feedback controller can be a simple PID (proportionalintegral-derivative) algorithm that, except for the tuning of its parameters, does not require an accurate model of the process, or the feedback controller can be a sophisticated model predictive controller or a geometric nonlinear controller. [20][21][22][23][24] A limited number of experimental studies have been reported in the literature for the actual feedback control of crystal quality. To control the CSD, Rohani and his group [16][17][18]49,50 used the fines suspension density sensor (FSDS) to measure the fines concentration in a crystallizer.…”
Section: Ii2 Parameters Estimation For Nucleation and Growthmentioning
confidence: 99%
“…A model predictive controller and a nonlinear geometric controller were also used as the feedback controller. − The direct or inferential feedback control of other properties such as supersaturation was also attempted. Redman et al used an online density meter to measure and control supersaturation. This method is, however, restricted to two-component or three-component systems and requires a careful calibration of the density meter.…”
Section: Introductionmentioning
confidence: 99%
“…Redman et al 24 used an online density meter to measure and control supersaturation. This method is, however, restricted to two-component or three-component systems and requires a careful calibration of the density meter.…”
We use the term "external control" to refer to two process control configurations: First, the "direct or inferential feedback control" of a given product quality index such as the crystal size distribution. Second, the "optimal control" of a process variable such as the control of the cooling policy (temperature) or the reactants addition rate to optimize an objective function defined in terms of the product quality. The intent of this twopart contribution is to discuss the various approaches used for the control of crystal quality. In Part 1, the design of the crystallization process and the selection of the process variables affecting the product quality were presented. In this part the implementation of an "external controller" will be presented. Initially, state-of-the-art instrumentation in crystallization research and technology is discussed. Mathematical models involving the population density and moments equation are presented and evaluated. Feedback control of crystal size distribution and supersaturation is presented in some detail. Polymorphic outcome of pharmaceuticals is dictated by the choice of solvent, presence of impurities, and the operating variables such as the temperature and the degree of supersaturation that are controlled by the implementation of an "external controller". A new algorithm for the solution of the integrohyperbolic partial differential equation representing the population balance is discussed, and its potential use in the design of real-time optimal control policies for the control of batch crystallizers is highlighted. Open-loop and real-time optimal control policies in cooling batch crystallizers are presented, and their efficacy is evaluated.
“…Figure shows the closed-loop block diagram of such a feedback control system. The feedback controller can be a simple PID (proportional-integral-derivative) algorithm that, except for the tuning of its parameters, does not require an accurate model of the process, or the feedback controller can be a sophisticated model predictive controller or a geometric nonlinear controller. − 1 Block diagram of a simple feedback controller for the direct/inferential control of product quality.…”
Section: The External Controlmentioning
confidence: 99%
“…The feedback controller can be a simple PID (proportionalintegral-derivative) algorithm that, except for the tuning of its parameters, does not require an accurate model of the process, or the feedback controller can be a sophisticated model predictive controller or a geometric nonlinear controller. [20][21][22][23][24] A limited number of experimental studies have been reported in the literature for the actual feedback control of crystal quality. To control the CSD, Rohani and his group [16][17][18]49,50 used the fines suspension density sensor (FSDS) to measure the fines concentration in a crystallizer.…”
Section: Ii2 Parameters Estimation For Nucleation and Growthmentioning
confidence: 99%
“…A model predictive controller and a nonlinear geometric controller were also used as the feedback controller. − The direct or inferential feedback control of other properties such as supersaturation was also attempted. Redman et al used an online density meter to measure and control supersaturation. This method is, however, restricted to two-component or three-component systems and requires a careful calibration of the density meter.…”
Section: Introductionmentioning
confidence: 99%
“…Redman et al 24 used an online density meter to measure and control supersaturation. This method is, however, restricted to two-component or three-component systems and requires a careful calibration of the density meter.…”
We use the term "external control" to refer to two process control configurations: First, the "direct or inferential feedback control" of a given product quality index such as the crystal size distribution. Second, the "optimal control" of a process variable such as the control of the cooling policy (temperature) or the reactants addition rate to optimize an objective function defined in terms of the product quality. The intent of this twopart contribution is to discuss the various approaches used for the control of crystal quality. In Part 1, the design of the crystallization process and the selection of the process variables affecting the product quality were presented. In this part the implementation of an "external controller" will be presented. Initially, state-of-the-art instrumentation in crystallization research and technology is discussed. Mathematical models involving the population density and moments equation are presented and evaluated. Feedback control of crystal size distribution and supersaturation is presented in some detail. Polymorphic outcome of pharmaceuticals is dictated by the choice of solvent, presence of impurities, and the operating variables such as the temperature and the degree of supersaturation that are controlled by the implementation of an "external controller". A new algorithm for the solution of the integrohyperbolic partial differential equation representing the population balance is discussed, and its potential use in the design of real-time optimal control policies for the control of batch crystallizers is highlighted. Open-loop and real-time optimal control policies in cooling batch crystallizers are presented, and their efficacy is evaluated.
“…• No explicit plant model (for PID or selftuning controller design), see e.g. (Redman et al, 1995), (Randolph et al, 1987). • finite-dimensional model obtained by system identification, see (Eek, 1995), (Rohani et al, 1999) .…”
Robustly stabilizing control of an open loop oscillatory crystallization process is considered. The crystallizer is described by a population balance model. From this distributed parameter model an irrational transfer function is obtained which has infinitely many poles and thus represents the infinite-dimensional nature of the system. An infinite-dimensional Hoc controller synthesis method is applied to solve the weighted mixed sensitivity problem for this transfer function. This procedure results in an irrational controller. For practical implementation, the controller needs to be approximated by a rational transfer function. The effectiveness of the controller is demonstrated in simulations.
ontinuous crystallizers are widely used to produce bulk commodity materials such as potassium chloride, ammonium sulfate, and C sodium chloride. Due to the economical significance of the process, control of crystallizes has been the subject of many research work.Important properties of crystals are crystal size distribution (CSD), purity, and shape. From manufacturer's point of view, the crystallizer needs to be operated at high productivity. Crystals with small mean size and wide size distribution can result in caking of dried product leading to problems in storage and handling. Poorly shaped crystals often need compaction and recrystallization. In many applications, purity is more important than the CSD. Low purity product negatively affects the marketability of the product.Majority of investigations in the area of crystallization control are focused on the CSD and its oscillatory behaviour that may be observed in industrial continuous crystallizers. A theoretical study of a complex crystallizer is carried out by Randolph et al., (1977) to identify the CSD instability observed in industrial crystallizers. A feedback control scheme to overcome the problem is proposed by Randolph and Low (1 982) and Randolph et al., (1987). A feedforward algorithm to control the CSD is also used by Han (1967). Rohani (1986) proposes the magma density in the fines loop as the measured variable rather than the crystallizer nuclei density used by Randolph et al., (1 977). The controllers in these studies are single input-single output (SISO) and the main issue is to select an appropriate measured variable to control the CSD. In the 1990s, with the availability of faster computers, advanced controllers and estimation algorithms are employed for the multi-input multi-output (MIMO) control of crystallizers. Eek et al. (1995) report a dynamic model for a suspension crystallizer using pilot plant data. A closed loop identification algorithm and a model predictive controller (MPC) are developed by Eek (1995). Rohani et al. (1999a, b) conduct a theoretical study of a nonlinear MPC to control the size distribution, crystal purity, and productivity in a KCI crystallizer. Tadayyon and Rohani (2000) develop a MIMO nonlinear QDMC to control a pilot plant KCI crystallizer. It is assumed that the online measurements of the CSD and impurity are available. However, these assumptions are not realistic as the online and robust measurement of crystal size distribution is still a formidable task. A study of various currently available CSD sensors indicates that the majority of these instruments still impose some limitations. A back light scattering technique, patented by Laser Sensor Technology Inc. (Redmond, WA, USA), makes the online monitoring of chord length distribution (CLD) possible.An extended Kalman filter (EKF)-based nonlinear quadratic dynamic matrix control (EQDMC) for an evaporative cooling draft-tube baffled (DTB) KCI crystallizer is developed. The controller is used to maintain the productivity, crystal mean size and impurity of crys...
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