Seed recipe is known to directly impact crystal growth behavior and product performance in crystallization processes. However, effectively evaluating and reducing the uncertain influence of seed ...recipe on product consistency in batch crystallization is challenging. This paper utilizes uncertainty analyses based on the population balance model to evaluate the impact of seed recipe destabilization. Monte Carlo simulation (MCS) was employed as a direct and efficient method to quantify the risk of failing to achieve the desired product crystal size distribution (CSD). Sensitivity analysis was conducted by integrating the modified Morris method with the PAWN method to analyze the effects of individual parameters and all parameters in the seed recipe from local and global perspectives. To achieve the targeted product CSD in the presence of seed recipe uncertainties, a novel linear weighted multi-objective function was developed to establish the optimal cooling profile, which was optimized by the enhanced particle swarm optimization (PSO) algorithm. The results demonstrated that even under seed recipe uncertainties, the targeted product CSD can be achieved with minimal error through cooling profile optimization alone, without dissolution.
•A new perspective analyzes the impact of seed recipe uncertainty on product CSD of batch cooling crystallization.•Risk analysis and sensitivity analysis are combined to quantitatively evaluate the uncertainty of seed recipe.•The parameter μ in seed recipe has a serious impact on the quality of the desired product CSD.•A new objective function optimized by an improved PSO algorithm enhances the robustness of crystallization control.
•New supersaturation control in semi-batch implementation is proposed.•Additionally a combined batch/semi-batch control approach is also proposed.•Simulations show the benefits of the new approach ...compared to traditional supersaturation control.•Experimental proof-of-concept implementation of the new approach is provided.•The novel approach is excellent for polymorph control, heat sensitive systems and proteins.
A novel feedback control strategy by semi-batch implementation for cooling crystallization is proposed, in which the supersaturation is controlled by manipulating the feed flow rate of the concentrated solution rather than using the temperature. Different supersaturation control (SSC) approaches have been simulated and compared in three cooling crystallization operation modes: batch cooling crystallization using temperature to control supersaturation (T-SSC), semi-batch crystallization that uses the feed flow rate to control supersaturation (F-SSC) and combined batch cooling and semi-batch crystallization based on a combined mode of control (TF-SSC). A detailed comparison of these three scenarios is presented through numerical simulations, which enables the identification of the optimal operating mode of a cooling crystallizer for various types of crystallization systems. The potential advantages and prospects of the semi-batch SSC mode are discussed. By developing a suitable experimental system, proof-of-concept experimental demonstrations are also presented to support the simulation results.
Abstract The research on chemical process fault diagnosis has made significant progress, but there is still a big gap in its application to complex practical industrial processes. As for the fault ...diagnosis of batch crystallization processes, the recently‐proposed dynamic time warping–convolutional neural network (DTW‐CNN) model has achieved a great improvement in the fault diagnosis. However, its fault diagnosis rate (FDR) and timeliness of fault diagnosis are still low, and thus, it needs to improve further before being applied to the practical application. In this paper, a multiple pattern representation–convolutional neural network (MPR‐CNN) model is proposed and applied for the fault diagnosis of a semi‐batch crystallization process. The MPR‐CNN model enables the manual extraction of features with four pattern representation algorithms in the data pre‐processing stage, and generates a three‐dimensional matrix which is used as the training sample and input to the CNN for the formal feature extraction and weight learning. An excellent classification performance, with an average FDR of 97.5%, is achieved. This model is also applied for the fault diagnosis of process data within a shorter period of time after the occurrence of faults. The results indicate that the model could make timely fault diagnosis with a highly stable and accurate performance after the occurrence of a fault.
•Instead of crystallizer temperatures, temperature changes are used as a control vector for optimization.•Genetic algorithm (GA) and hybrid GA based solvers are used to obtain the temperature ...trajectories.•Multiobjective optimization is performed with different objective combinations.•A set of 12 Pareto-optimal points are inspected and analysed using the method of characteristics.
Batch cooling crystallization is a type of crystallization wherein supersaturation is brought about by reducing the temperature of the crystallization system with time. It is commonly used in the chemical and pharmaceutical industries to manufacture a wide variety of crystalline products. This work deals with multiobjective optimization of unseeded batch cooling crystallization of Aspirin. A novel method involving temperature changes rather than temperatures of the crystallization mixture over time has been discussed in this study. Optimization studies were carried out to minimize the coefficient of variation and maximize mean size. Optimization was carried out using the benchmark NSGA-II and NSGA-II hybrid optimizers available in MATLAB. A standard algorithm to select a trade-off point on the Pareto front is also discussed. Rigorous simulation studies were carried out to determine the best temperature trajectory by inspecting the crystal size distributions generated using the method of characteristics.
Process alternatives for continuous crystallization, i.e., cascades of mixed suspension, mixed product removal crystallizers (MSMPRCs) and plug flow crystallizers (PFCs), as well as batch ...crystallizers are discussed and modeled using population balance equations. The attainable region approach that has previously been used in the design of chemical reactor networks and separation systems is applied to the above-mentioned alternatives for crystallization processes in order to identify attainable regions in a diagram of mean product particle size vs. total process residence time. It is demonstrated that the boundaries of these attainable regions can be found numerically by solving appropriate optimization problems and that the region enclosed by these boundaries is fully accessible. Knowing the attainable region of particle sizes, it is possible to generate feasible process alternatives that allow specific particle sizes to be obtained in a given process configuration. The attainable regions presented in this paper are useful to determine whether a desired mean particle size can be achieved in a specific crystallizer type. The concept of the attainable region is illustrated on three case studies: the cooling crystallization of paracetamol grown from ethanol, the anti-solvent crystallization of l-asparagine monohydrate from water using isopropanol as the anti-solvent and the combined cooling/anti-solvent crystallization of aspirin from ethanol using water as the anti-solvent.
•Continuous and batch crystallization processes are modeled using PBEs.•Attainable regions (AR) in a diagram of process time vs. particle size are obtained.•The influence of additional processing constraints on the ARs is investigated.•The ARs show if fulfilling a set of specifications is possible in given process equipment.
One of the most important challenges in the pharmaceutical industry is to produce crystals with desired size and shape distributions, to enhance the critical quality attributes of the drug product, ...such as efficacy, and to improve manufacturability during downstream processing, such as filtration, drying and granulation. The paper provides a framework for effective crystal shape and size tuning, based on a systematic exploration of standard techniques, such as the linear cooling and supersaturation control (SSC), and novel methods based on the systematic combination of several techniques, namely direct nucleation control (DNC), wet milling, SSC and shape modification additives. The crystallization of lovastatin, which is notorious for its challenging needle-shaped crystals, with an extremely high aspect ratio, was used as a case study, and polypropylene glycol (PPG-4000), at different concentrations, was used as an effective shape modifier from small-scale tests studied previously. The proposed techniques were implemented in the case of seeded and unseeded systems. It was demonstrated that the combination of temperature cycling and polymer additive enhances greatly the control over the aspect ratio and crystal size distribution, compared to conventional linear cooling and SSC strategies. The implementation of wet milling at the beginning of the process, or the introduction of seeds, enhances even further the control of the critical quality attributes of the crystalline product.
Display omitted
•Developed and validated a new framework for systematic crystal shape and size tuning.•Standard cooling crystallization with a polymer additive was insufficient to improve the aspect ratio of lovastatin.•Crystal shape successfully modified using additives and Direct Nucleation Control (DNC).•The additive caused inhibition along the longest axis which resulted in an effective control of the aspect ratio.•The lowest aspect ratio was achieved using a combination of seeding, milling, additives and DNC.
•A deep convolutional neural network model based on dynamic time warping was proposed for the fault diagnosis.•Supersaturation control based on temperature and flow was applied.•Dynamic time warping ...makes the data from semi-batch process steady.•Compared to the traditional model, the new model has an outstanding performance in the fault diagnosis.•An average fault diagnosis rate of 88.6% is achieved.
The abnormal conditions of the crystallization process seriously affect the crystal quality and the smooth operation of the process. Compared to the continuous steady process, it is a big challenge to realize the fault detection and diagnosis (FDD) in a batch or semi-batch crystallization process which is unsteady and nonlinear. In this paper, a coupled method combining convolutional neural network (CNN) with dynamic time warping (DTW) is proposed for FDD in semi-batch crystallization process based on temperature and flow supersaturation control (TF-SSC). DTW solves the problem that the data is unsteady in a semi-batch process. Different fault data produced by introducing disturbances are calculated through DTW to obtain the similarity which is steady. Then, the similarity of different operating states is preprocessed and classified by the CNN. Compared to the traditional CNN, Resnet18 and Inception10, DTW-CNN method has an outstanding performance in FDD, especially under a small number of samples.
Abstract
The possibility of spherical agglomeration was investigated for two active pharmaceutical ingredients from different classes of the Biopharmaceutics Classification System. The effects of ...different batch crystallization solvent systems on the granulometric properties and structures of dronedarone hydrochloride and ceritinib were investigated. Light and stereomicroscopy were used to determine the size and shape of the agglomerates, while X‐ray powder diffraction was applied to assess the changes in polymorphic form. Since the change in the solvent system had no effect on the crystal structure but did alter the size and shape of the crystals, dissolution experiments were carried out to determine drug release profiles.
Serial crystallography, at both synchrotron and X‐ray free‐electron laser light sources, is becoming increasingly popular. However, the tools in the majority of crystallization laboratories are ...focused on producing large single crystals by vapour diffusion that fit the cryo‐cooled paradigm of modern synchrotron crystallography. This paper presents several case studies and some ideas and strategies on how to perform the conversion from a single crystal grown by vapour diffusion to the many thousands of micro‐crystals required for modern serial crystallography grown by batch crystallization. These case studies aim to show (i) how vapour diffusion conditions can be converted into batch by optimizing the length of time crystals take to appear; (ii) how an understanding of the crystallization phase diagram can act as a guide when designing batch crystallization protocols; and (iii) an accessible methodology when attempting to scale batch conditions to larger volumes. These methods are needed to minimize the sample preparation gap between standard rotation crystallography and dedicated serial laboratories, ultimately making serial crystallography more accessible to all crystallographers.
Some ideas and methods on how to produce high‐quality samples for successful serial crystallography experiments are presented. The methods here described are aimed at experimenters trying to convert their vapour diffusion crystallization conditions into large‐scale batch micro‐crystallization.
PDF
