ontrol of crystal size distribution (CSD) has been the subject of numerous research work. A survey in the literature reveals that C the majority of studies on the control of the CSD are theoretical. This is mainly due to the lack of reliable techniques for the on-line measurement of the CSD (Ashobi, 1995). Therefore, the researchers have been looking for an easily observable process variable that can serve as the measured variable (Rohani, 1986; Rohani et al., 1999a and b). Han (1967) used a feedforward control scheme based on the measurement of supersaturation to control the CSD. While it is acceptable that supersaturation level is the primary driving force in nucleation and growth of crystals, on-line measurement of this variable is extremely difficult, as the relative level of supersaturation is mostly in the order of one percent of concentration (Eek, 1995). Cupta and Timm (1 971) showed that measurement of quantities related to the fines trap such as fines total area might result in successful control of the CSD. Randolph et al. (1977) reported the theoretical studies of a complex crystallizer and proposed a proportional feedback control scheme to overcome the CSD instability observed in industrial scale crystallizers. Their algorithm was based on the on-line measurement of nuclei density and manipulation of fines removal rate. These authors showed that the algorithm might be employed to minimize CSD transients and instability.Rovang and Randolph (1 980) demonstrated that the on-line nuclei density can be estimated by integrating a zone-sensing particle counter in the fines loop of a crystallizer. This technique would be impractical on a large-scale crystallizer due to the frequent plugging of the orifice (Rohani, 1986). Randolph and Low (1 982) manipulated fines dissolution flow rate and dissolver temperature in response to changes in the estimated nuclei density to control the CSD. These investigators found that manipulation of fines flow rate disturbed the crystal cut size in the fines removal stream and did not show effective CSD control. To overcome this problem, Randolph et al. (1987) maintained the fines removal flow rate constant and manipulated the fraction of fines being passed through the fines dissolver.The methodology used by Randolph and his co-workers, to control the CSD was to measure and control nuclei density in the crystallizer. Rohani (1 986) argues that such measurements require an expensive particle size analyzer and the reliability of measurement is still restricted to low concentration slurries. In a simulation study, he demonstrated that the nuclei density in the crystallizer and solids suspension density inThe control of crystal size distribution (CSD) is investigated in a 1.5 L laboratory cooling KCI crystallizer using fines dissolution rate as the manipulated variable. The controlled variable was either the fines suspension density in the fines withdrawal loop, measured by an innovative double-sensor turbidity meter manufactured in-house, or the chord length distribution (CLD) me...

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...

Previous work on the modeling of potash crystallizers has been mainly limited to the estimation of crystal size distribution (CSD) in the presence of only one solid component (KCI). In the present study, an attempt has been made to develop a model that incorporates NaCl as a second component that may co-precipitate along with KCI under certain operating conditions. Addition of water to prevent co-saturation or as a means of internal fines dissolution is also taken into consideration. External fines dissolution using a heat exchanger is incorporated in the model. In addition to the CSD, the model is able to predict crystal impurity resulting from co-saturation with NaC1. The predictive capability of the model is tested using limited dynamic experimental data obtained from a 1 m3 pilot plant continuous evaporative crystallizer and the steady-state experimental data from a two-stage evaporative Swenson DTB industrial potash crystallizer circuit. In both cases, a good agreement between the model predictions and the experimental data was noticed.Les travaux anterieurs sur la modelisation des cristallisoirs de potasse se sont principalement limites a l'estimation de la distribution de taille des cristaux (CSD) en presence d'un seul composant solide (KCI). Dans la presente etude, on propose un modele qui introduit le NaCl comme second composant pouvant co-precipiter avec le KCI dans certaines conditions de fonctionnement. On prend egalement en consideration I'addition d'eau afin d'empkcher la co-saturation ou comme moyen de dissolution des fines internes. La dissolution des fines externes a I'aide d'un echangeur de chaleur est introduite dans le modele. Outre la CSD, le modele est capable de predire I'impurete des cristaux resultant de la cosaturation avec le NaCI. La capacite predictive du modele est verifiee a I'aide de donnees empiriques experimentales limitees provenant d'un cristallisoir a evaporation continu pilote de lm3 et de donnees experimentales en regime permanent provenant d'un cristallisoir de potasse industriel DTB de Swenson. Dans les deux cas, on a note un bon accord entre les predictions du modele et les donnees experimentales.Keywords: evaporative cooling crystallization, modeling, KCI-NaCI-H,O, CSD, crystal purity, co-precipitation.rystallization is an important separation process in C chemical industry. It is widely used in bulk production of fertilizers such as potassium chloride, ammonium sulfate and other chemicals like sucrose and sodium chloride. The process is also employed in the production of fine chemicals and pharmaceuticals. The crystal quality is assessed in terms of the crystal size distribution (CSD), crystal purity, and shape. Customers are usually interested in large crystals with a narrow size distribution. A wide CSD with a small mean size promotes caking of crystals and dust formation during storage and product handling. Crystals with a wide CSD may need additional processes such as compaction, dissolution and recrystallization; hence, increasing the cost of the final product.Crys...

A controller is developed by combining the extended linear quadratic matrix control (EQDMC) and neural network algorithms. The dynamic neural network scheme is used to identify the process and generate a nonlinear model. The control algorithm is applied to a multi-input multi-output (MIMO) evaporative cooling KCI-NaCI-Hz0 crystallizer. Closed loop responses of the system using the proposed algorithm and those of PID controllers are compared. It is shown that in all cases, the response of the proposed controller to step changes in setpoints is faster than the PID controllers