Real‐time control of a semi‐industrial fed‐batch evaporative crystallizer using different direct optimization strategies

This article presents a model‐based control approach for optimal operation of a seeded fed‐batch evaporative crystallizer. Various direct optimization strategies, namely, single shooting, multiple shooting, and simultaneous strategies, are used to examine real‐time implementation of the control approach on a semi‐industrial crystallizer. The dynamic optimizer utilizes a nonlinear moment model for on‐line computation of the optimal operating policy. An extended Luenberger‐type observer is designed to enable closed‐loop implementation of the dynamic optimizer. In addition, the observer estimates the unmeasured process variable, namely, the solute concentration, which is essential for the intended control application. The model‐based control approach aims to maximize the batch productivity, as satisfying the product quality requirements. Optimal control of crystal growth rate is the key to fulfill this objective. This is due to the close relation of the crystal growth rate to product attributes and batch productivity. The experimental results suggest that real‐time application of the control approach leads to a substantial increase, i.e., up to 30%, in the batch productivity. The reproducibility of batch runs with respect to the product crystal size distribution is achieved by thorough seeding. The simulation and experimental results indicate that the direct optimization strategies perform similarly in terms of optimal process operation. However, the single shooting strategy is computationally more expensive. © 2010 American Institute of Chemical Engineers AIChE J, 57: 1557–1569, 2011     

Optimization and nonlinear control of a batch crystallization process

Crystallization process has been widely used for separation in many chemical industries due to its capability to provide high purity product. To obtain the desired quality of crystal product, an optimal cooling control strategy is studied in the present work. Within the proposed control strategy, a dynamic optimization is first preformed with the objective to obtain the optimal cooling temperature policy of a batch crystallizer, maximizing the total volume of seeded crystals. Two different optimization problems are formulated and solved by using a sequential optimization approach. Owing to the complex and nonlinear behavior of the batch crystallizer, the nonlinear control strategy which is based on a generic model control (GMC) algorithm is implemented to track the resulting optimal temperature profile. The optimization integrated with nonlinear control strategy is demonstrated on a seeded batch crystallizer for the production of potassium sulfate.     

Influence of Solvents on Solution‐Mediated Polymorphic Transformation of the Polymorphs of L‐Histidine

Solution‐mediated polymorphic transformation (SMPT) of the metastable polymorph B of L‐histidine (L‐his) into the stable polymorph A in water‐antisolvent solutions was performed. Methanol, acetone, and acetonitrile served as antisolvents. The SMPT was studied via nucleated batch antisolvent crystallization process by determining the change of the fraction of the stable polymorph A of L‐his in the crystal phase with time during the crystallization process. The fraction of the stable polymorph A of L‐his was assessed offline by Raman spectroscopy. The transformation time depended on the fractions of form A obtained at the initial stage of crystallization. The transformation rate of the metastable polymorph B into the stable polymorph A at lower antisolvent volume fraction was faster than at higher antisolvent volume fraction. The transformation time of polymorph B into polymorph A in water‐acetonitrile solution was the shortest compared to the other solutions.     

Optimal seeding in batch crystallization

This is the first comprehensive study on the optimization of seed distribution in a crystallization process. For a batch crystallizer, a dynamic programming formulation optimizes a property of the product crystals over the supersaturation profile and the seed characteristics, namely the mean size of the seed crystals, the seed mass, and the width of the seed distribution. Three optimization objectives are considered: (1) weight mean size, (2) coefficient of variation, and (3) the ratio of the nucleated crystal mass to seed crystal mass. Different objectives lead to substantially different optimal seed distributions. It is shown that optimizing over the seed distribution can have a larger effect on the product crystal size distribution than optimizing over the supersaturation profile.     

A stochastic formulation for the description of the crystal size distribution in antisolvent crystallization processes

A stochastic approach to describe the crystal size distribution dynamics in antisolvent based crystal growth processes is here introduced. Fluctuations in the process dynamics are taken into account by embedding a deterministic model into a Fokker‐Planck equation, which describes the evolution in time of the particle size distribution. The deterministic model used in this application is based on the logistic model, which shows to be adequate to suit the dynamics characteristic of the growth process. Validations against experimental data are presented for the NaCl–water–ethanol antisolvent crystallization system in a bench‐scale fed‐batch crystallization unit. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Online polymer molecular weight and conversion monitoring via calorimetric measurements in RAFT emulsion polymerization

BACKGROUND: Online measurements of key emulsion polymerization attributes, such as conversion and molar mass distribution, are unavailable. Costly offline measurements at low sampling frequencies with time delays usually lead to insurmountable challenges in real‐time product quality monitoring and process/product control. RESULTS: We developed an online calorimetric method monitoring the evolution of conversion and molecular weight in complex polymerization reactors. Our experiments were carried out in a 1 L reactor to produce polystyrene homopolymer. Monomer conversion was obtained in real time from polymerization rate, which was estimated from temperature measurements using platinum thermal transducers. The calorimetric model was validated offline for batch and semi‐batch emulsion polymerization of styrene with and without transfer agents. The conversion was validated using offline gravimetry. The molecular weights measured offline via size exclusion chromatography with multiple detectors compared well with those estimated online using the calorimetric method. CONCLUSION: We found that a semi‐batch emulsion polymerization process can be controlled online to approach living polymerization involving transfer agents. Thus our model is suitable as a ‘soft‐sensor’ for real‐time control applications. Copyright © 2009 Society of Chemical Industry     

DYNAMIC OPTIMAL CONTROL OF BATCH CRYSTALLIZATION PROCESSES

This work applies an on-line optimal control strategy developed by Zhang (2001) to two cooling batch crystallization processes. The algorithm initially finds the optimal crystallizer temperature and subsequently uses a feedback control system in order to achieve the desired final product quality of the crystals expressed in terms of the final crystal size distribution. In both batch processes, it is shown that the on-line optimal control approach provides better final product quality as compared with a simplified optimal cooling policy. The improvement is especially noticeable in the presence of plant/model mismatch or errors in the initial conditions.     

A study on crystallization kinetics of pentaerythritol in a batch cooling crystallizer

The crystallization kinetics of pentaerythritol (PeE) in aqueous solution in the presence of impurity or not in a batch cooling crystallizer was explored. Also, the solubility and the nucleation and crystal growth kinetics of PeE in aqueous solution were investigated. A second-order dependence of PeE growth rate on supersaturation is observed in pure PeE-water system. The crystal growth rate of PeE-water system in the presence of impurity is proportional to the supersaturation to the 3.5 power. The nucleation and crystal growth behaviors for PeE-water system in a batch cooling crystallizer were grasped according to Mersmann's criteria. The nucleation in this crystallizer was found to act with heterogeneous nucleation. In this system, it suggests that the crystal growth is controlled by a complex mechanism behavior of surface integrated and diffusion limited. Simplified relation was derived for calculation of mean crystal size of product crystals from the batch cooling crystallizer. The obtained relation was verified by a set of experiments.     

Mathematical modelling and experimental validation of a novel periodic flow crystallization using MSMPR crystallizers

The challenges of insufficient residence time for crystal growing and transfer line blockage in conventional continuous mixed‐suspension mixed‐product removal (MSMPR) operations are still not well addressed. Periodic flow crystallization is a novel method whereby controlled periodic disruptions are applied to the inlet and outlet flows of an MSMPR crystallizer to increase its residence time. A dynamic model of residence time distribution in an MSMPR crystallizer was first developed to demonstrate the periodic flow operation. Besides, process models of periodic flow crystallizations were developed with an aim to provide a better understanding and improve the performance of the periodic flow operation, wherein the crystallization mechanisms and kinetics of the glycine‐water system were estimated from batch cooling crystallization experiments. Experiments of periodic flow crystallizations were also conducted in single‐/three‐stage MSMPR crystallizers to validate the process models and demonstrate the advantages of using periodic flow operation in MSMPR stages. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1313–1327, 2017     

A simplified approach to the operation of a batch crystallizer

A general model for a seeded cooling batch crystallizer based on first principles is developed, incorporating either size-dependent or size-independent growth rates. A simple approach is proposed to obtain temperature-time trajectories at constant supersaturation or nucleation rate, without resorting to optimization techniques. Cooling curves at constant supersaturation, which lead to a substantial improvement (a smaller coefficient of variation and a larger mean size) of the terminal crystal size distribution, can be determined even in the absence of precise nucleation and growth kinetics, whereas properties related to the crystal size distribution are sensitive to such kinetics. Experimental results for the potassium sulfate-water system, potash alum-water system, and hexamethylenetetramine in ethanol, methanol, and 2-propanol/water are predicted reasonably well by the model. Extension to any type of batch crystallization with super-saturation induced by means other than cooling, such as reactive precipitation and salting out, is briefly discussed.     

Extended kalman filter based nonlinear geometric control of a seeded batch cooling crystallizer

A nonlinear dynamic model of a seeded potash alum batch cooling crystallizer is presented. The model of the batch crystallizer is based on the conservation principles of mass, energy and population. In order to maintain constant supersaturation, a nonlinear geometric feedback controller is implemented. It is shown that compared to a natural and a simplified optimal cooling policies, the nonlinear geometric control (NCC) leads to a substantial improvement of the final crystal quality. An extended Kalman filter (EKF) is used as a closed loop observer for this nonlinear system to predict the non‐measurable state variables. It is found that the EKF is capable of effectively predicting the first four leading moments of the population density function. The effectiveness of the EKF based nonlinear geometric controller in the presence of plant/model mismatch is also studied. Simulation results show that the EKF based nonlinear geometric controller is reasonably robust in the presence of modeling error.     

Entropic analysis of a batch-crystallization process

The analysis of crystallization processes considering an entropic perspective is the primary purpose of this paper. Although the modeling of crystallization processes is well established in the literature, the entropic representation of these processes still needs to be studied. The modeling considering the second law of thermodynamics was investigated, which resulted in a model that represents the entropy production rate of a batch-crystallization process. The results indicate that the entropy production of the batch cooling crystallization is related to the variability of the crystal size distribution. The model of the entropy production rate could be used as a restriction criterion for multi-objective optimization of the cooling temperature profile of a batch crystallizer to improve the quality of the crystal size distribution of the final product.     

Product quality improvement of batch crystallizers by a batch-to-batch optimization and nonlinear control approach

Batch crystallization is one of the widely used processes for separation and purification in many chemical industries. Dynamic optimization of such a process has recently shown the improvement of final product quality in term of a crystal size distribution (CSD) by determining an optimal operating policy. However, under the presence of unknown or uncertain model parameters, the desired product quality may not be achieved when the calculated optimal control profile is implemented. In this study, a batch-to-batch optimization strategy is proposed for the estimation of uncertain kinetic parameters in the batch crystallization process, choosing the seeded batch crystallizer of potassium sulfate as a case study. The information of the CSD obtained at the end of batch run is employed in such an optimization-based estimation. The updated kinetic parameters are used to modify an optimal operating temperature policy of a crystallizer for a subsequent operation. This optimal temperature policy is then employed as new reference for a temperature controller which is based on a generic model control algorithm to control the crystallizer in a new batch run.     

Influence of Impeller Diameter on Crystal Growth Kinetics of Borax in a Mixed Dual‐Impeller Batch‐Cooling Crystallizer

The influence of impeller diameter on crystal growth kinetics of borax decahydrate in a batch‐cooling crystallizer of non‐standard aspect ratio was evaluated. The dual‐impeller configuration consisted of a pitched‐blade turbine which was mounted below a straight‐blade turbine on a single shaft. Three different impeller‐to‐tank diameter ratios were investigated. In all experiments, mixing was conducted at just‐suspended impeller speed. To examine hydrodynamic conditions, mixing times were measured. The fluid flow pattern and velocity distribution were determined by computational fluid dynamics. Results showed that the smallest but also more regularly shaped crystals were produced in a system with standard diameter impellers. Product yield and power consumption were highest in this case.     

DYNAMIC MODELING AND OPERATION OF A SEEDED BATCH COOLING CRYSTALLIZER

In this study, a dynamic model of a batch cooling crystallizer is developed. The seeded crystallization of potash alum from aqueous solutions. Four different cooling policies namely natural cooling, linear cooling, optimal cooling, and controlled cooling (nonlinear geometric control (NGC) cooling) are presented. The simulation results indicate that both optimal and controlled cooling improve the weight mean size of the final product significantly. The influence of seed loading policy on the end product quality is also studied for the NGC cooling. It is found that the appropriate seed loading is important to achieve a good final CSD, especially for a fixed batch time.     

Handling sensor malfunctions in control of particulate processes

This work focuses on feedback control of particulate processes in the presence of sensor data losses. Two typical particulate process examples, a continuous crystallizer and a batch protein crystallizer, modeled by population balance models (PBMs), are considered. In the case of the continuous crystallizer, a Lyapunov-based nonlinear output feedback controller is first designed on the basis of an approximate moment model and is shown to stabilize an open-loop unstable steady-state of the PBM in the presence of input constraints. Then, the problem of modeling sensor data losses is investigated and the robustness of the nonlinear controller with respect to data losses is extensively investigated through simulations. In the case of the batch crystallizer, a predictive controller is first designed to obtain a desired crystal size distribution at the end of the batch while satisfying state and input constraints. Subsequently, we point out how the constraints in the predictive controller can be modified as a means of achieving constraint satisfaction in the closed-loop system in the presence of sensor data losses.     

Effect of initial dextrose concentration,seeding and cooling profile on the crystallization of dextrose monohydrate

A batch seeded cooling crystallizer was used to study dextrose monohydrate crystallization. Experiments were conducted to investigate how a 2% increase in the initial dextrose concentration (from 65.5 to 67.5%) would influence final crystal yield and size. The crystallizations were performed for three different seed masses and cooling profiles, consequently the influence of these parameters was also investigated. The parameters were varied in accordance with an industrial scale process. An in-line focused beam reflectance measurement probe and an in-line process refractometer were used to continuously monitor the crystallizations. The experimental results showed that the 2% increase in initial dextrose concentration had a major influence on the rate of crystallization and yield over a 24 h crystallization period, and only a minor influence on the median crystal size.     

Preferential crystallization: Multi‐objective optimization framework

A four objective optimization framework for preferential crystallization of D‐L threonine solution is presented. The objectives are maximization of average crystal size and productivity, and minimization of batch time and the coefficient of variation at the desired purity while respecting design and operating constraints. The cooling rate, enantiomeric excess of the preferred enantiomer, and the mass of seeds are used as the decision variables. The optimization problem is solved by using adaptation of the nondominated sorting genetic algorithm. The results obtained clearly distinguish different regimes of interest during preferential crystallization. The multi‐objective analysis presented in this study is generic and gives a simplified picture in terms of three zones of operations obtained because of relative importance of nucleation and growth. Such analysis is of great importance in providing better insight for design and decision making, and improving the performance of the preferential crystallization that is considered as a promising future alternative to chromatographic separation of enantiomers. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Programmed cooling crystallization of potassium sulphate solutions

The crystal size distribution from a batch cooling crystallizer is predicted by the numerical solution of a mathematical model which uses empirical kinetics of nucleation and crystal growth. The predictions clearly point out the potential advantages of controlled cooling at a constant nucleation rate for improving the product crystal size over that obtained by either natural or linear cooling.Experimental runs following programmed cooling curves for seeded potassium sulphate solutions showed reasonable agreement with the theoretical predictions. A size dispersion of the crystals was observed which contributes to a slight deviation from theory. Nevertheless, controlled cooling significantly reduced the quantity of nuclei formed and improved the product crystal size distribution.     

Predictive control of particle size distribution in particulate processes

In this work, we focus on the development and application of predictive-based strategies for control of particle size distribution (PSD) in continuous and batch particulate processes described by population balance models (PBMs). The control algorithms are designed on the basis of reduced-order models, utilize measurements of principle moments of the PSD, and are tailored to address different control objectives for the continuous and batch processes. For continuous particulate processes, we develop a hybrid predictive control strategy to stabilize a continuous crystallizer at an open-loop unstable steady-state. The hybrid predictive control strategy employs logic-based switching between model predictive control (MPC) and a fall-back bounded controller with a well-defined stability region. The strategy is shown to provide a safety net for the implementation of MPC algorithms with guaranteed stability closed-loop region. For batch particulate processes, the control objective is to achieve a final PSD with desired characteristics subject to both manipulated input and product quality constraints. An optimization-based predictive control strategy that incorporates these constraints explicitly in the controller design is formulated and applied to a seeded batch crystallizer. The strategy is shown to be able to reduce the total volume of the fines by 13.4% compared to a linear cooling strategy, and is shown to be robust with respect to modeling errors.