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Abstract 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.
13On-line dynamic optimization integrated with generic model control of a batch crystallizer
On-line dynamic optimization integrated with generic model control of a batch crystallizer
1Estimation of crystal size distribution based on two dimensional characteristics: an exploration using artificial images
Image analysis based crystal size distribution (CSD) estimation can be very useful in the monitoring and control of crystallization process. Estimation of CSD from particle vision and measurement images is based on the characteristic length of the crystal particle alone. The estimation of the crystal width along with the length can improve the identification of the size and shape of crystals. The current work is an extended version of our multi-objective optimization based image analysis methodology, which measured only the characteristic length. In this work, we include the estimation of the crystal width as well. Also, the previous work was based on the assumption that the crystal particles align parallel to the surface of the solution due to the shear force, which will hold good only in ideal situations. Therefore, in this work, the effect of particle orientation along the depth of the solution on CSD estimation is studied. A modification factor has been proposed for particle orientation less than 10° so that a more accurate CSD can be estimated.
Read more2Optimal Seeding Conditions for Semi-Batch Type Evaporative Crystallization of a High Suspension Density Sodium Chloride Slurry in a Draft-Tube Stirred Vessel
The ideal requirements for highly efficient evaporative crystallization in batch or semi-batch type operations are a high crystal growth rate, a high density of suspended crystalline particles in a slurry and a sharp crystal size distribution. On the other hand, the initial seed crystal conditions are also very important because they affect the crystal size and distribution during the entire period of operation. In the present study, the influence of seeding conditions (average diameter and mass) and heating rate on both the crystal growth rate of sodium chloride (NaCl) and the crystal size distribution were investigated in an optimal design draft-tube stirred vessel containing high suspension density slurries of sodium chloride particles up to 30 vol%. In order to determine the optimal seeding conditions to simultaneously achieve a high crystal growth rate and narrow size distribution, an optimal operation index Iop has been defined based on a comparison of the experimental data and a model calculation with the assumption of ideal crystal growth. The results show that Iop correlate with the particle number of seed crystals and have a maximum value. With Iop correlation, the appropriate seeding conditions for achieving a high crystal growth rate and sharp size distribution have been determined.
Read more12Comparative evaluations of bulk seeded protein crystallization in batch versus continuous slug flow crystallizers
Comparative evaluations of bulk seeded protein crystallization in batch versus continuous slug flow crystallizers
Optimizing Crystal Size Distribution Based on Different Cooling Strategies in Batch Crystallization Process
Crystal size distribution (CSD) is an essential criterion for determining the production of high-quality crystals since it influences the efficiency of the crystallization process. Producing specified CSD in the crystallization process represents a main challenge as it depends on temperature control, which indirectly regulates the solution’s concentration and affects the crystal’s evolution. Different temperature profiles may influence the distribution of crystal products, and a suitable optimization algorithm is required to produce an optimum temperature trajectory that produces the desired CSD. Thus, this study aims to maximize the CSD of the grown seed crystals while minimizing the nucleus-grown crystals by employing the best optimization algorithm for the potash alum crystallization process. The crystallization process was developed and simulated in Matlab software using a potash alum in the water system. Four optimization algorithms were proposed with different objective functions, such as maximizing mean crystal size (I), minimizing coefficient of variation (II), minimizing nucleus-grown crystals (III), and maximizing CSD (IV). Based on the simulation results, optimization IV, which maximizes CSD, performs best with a large mean crystal size of 490 µm. Furthermore, the number of fine crystals was among the lowest at a volume distribution of 0.00071 m3/m compared to the linear profile at 0.00191 m3/m. Optimization IV employs a dissolution strategy, which manipulates two quality specifications in one algorithm (size of crystals and number of fines), which is considered the best optimal cooling profile for seeded batch crystallization by maximizing CSD and minimizing the generation of nucleus-grown crystals.
Read more71Analysis of Crystal Size Dispersion Effects in a Continuous Coiled Tubular Crystallizer: Experiments and Modeling
Continuous processing gains importance in the fine chemical and pharmaceutical industries where crystallization is an important downstream operation. Seeded cooling crystallization of the l-alanine/water system was investigated under similar conditions, i.e., temperature interval, cooling rate, and seed material, both in a stirred batch vessel and in a continuous plug flow crystallizer in the coiled flow inverter (CFI) design with horizontal helical tube coils (ID = 4 mm) and frequent 90° bends of the coils. Short-cut calculations based on characteristic time scales and the Damköhler number allow for comparing the batch and continuous crystallization processes. The experimental results reveal crystal growth and growth rate dispersion to be dominating on the product crystal size distribution (CSD). However, at low flow rates of approximately 31 g min–1, a moving sediment flow of the slurry was present in the CFI crystallizer, resulting in further size dispersion effects. Elevated flow rates of approximately 40 g min–1 resulted in a more homogeneous suspension flow and a product CSD comparable to batch quality. Simulation studies based on a population balance equation model strengthen the hypothesis of the solid phase residence time distribution (RTDS) to be more spread in the moving sediment flow regime, leading to a wider product CSD.
Read more24Improvement of batch crystallization control under uncertain kinetic parameters by model predictive control
Improvement of batch crystallization control under uncertain kinetic parameters by model predictive control
98Characterization of the anti-solvent batch, plug flow and MSMPR crystallization of benzoic acid
Characterization of the anti-solvent batch, plug flow and MSMPR crystallization of benzoic acid
6Predictive time optimal algorithm for a third-order dynamical system with delay
AnnotationThere are given the results for the analytical synthesis of optimal control algorithm in a closed circuit for the third-order dynamical system, describing by two different groups of state variables. For improvement the quality of time optimal control system is offered to use the predictive output value, expected through the object’s dead-time. A comparative analysis of quality and sensitivity control systems with the time optimal and linear PID or PI controllers is done. Calculations were carried out on the example of the automatic control system of superheated steam temperature of strait-flow boiler TPP-210.
Read more64Seeding Effect on Product Crystal Size in Batch Crystallization.
This paper reviews research work concerning the size distribution of product crystals in batch crystallization of well-soluble materials. The discussion was made with particular emphasis on seeding effect on product crystal size distribution and crystallizer performance. First, it is shown by a brief survey of the history of batch crystallization research that the importance of seeding in the performance of a batch crystallizer has been ignored while the importance of cooling profile (or supersaturation profile) had been overestimated. Then, the extensive research work carried out by the group of the present authors on seeding effect is reviewed, where the effectiveness of seeding in controlling product crystal size is clearly shown with experimental evidence. Specifically, it is shown that seed crystals added at the beginning of a batch can be grown with no generation of fine crystals if seed crystals are added enough over a critical seed loading ratio. Finally, recent research work in literature on batch crystallization is briefly surveyed.
Read moreChapter 19 - Optimal state estimator based control of an exothermic batch chemical reactor
Chapter 19 - Optimal state estimator based control of an exothermic batch chemical reactor
94New perspectives for the on-line monitoring of pharmaceutical crystallization processes using in situ infrared spectroscopy
New perspectives for the on-line monitoring of pharmaceutical crystallization processes using in situ infrared spectroscopy
Read more47Experimental implementation of a Quality-by-Control (QbC) framework using a mechanistic PBM-based nonlinear model predictive control involving chord length distribution measurement for the batch cooling crystallization of l-ascorbic acid
Experimental implementation of a Quality-by-Control (QbC) framework using a mechanistic PBM-based nonlinear model predictive control involving chord length distribution measurement for the batch cooling crystallization of l-ascorbic acid
Read more38Fixed-Time Constrained Model Predictive Sliding Mode Control of Spacecraft Simulator
In this article, two dual-loop control methods are proposed for attitude control and reaction wheels momentum management of the spacecraft simulator by assimilating the advantages of sliding mode and model predictive control (MPC). In the first method, by utilizing the high accuracy and constrained features of the MPC, an optimal controller is designed in the inner loop. MPC drives the angular velocity to the desired value while keeping the reaction wheel velocity to a small region near the origin, considering the constraints of the actuators. Furthermore, based on the fixed-time terminal sliding mode (TSM) the proper angular velocity is produced in the outer layer of this composite controller for the MPC part. In the second method, contrary to the first one, in the exterior loop MPC and in the interior loop finite-time TSM have been designed. The incorporated twofold nonlinear controllers’ policy insures a good dynamic and robustness against external disturbances. The high yielding of the control algorithms has been analyzed by simulation and experimental validation utilizing MATLAB software with real-time connection based on LabVIEW.
Read more9Feedback control of an experimental electrically-heated steam methane reformer
Feedback control of an experimental electrically-heated steam methane reformer
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