Multiobjective Optimization of an Unseeded Batch Cooling Crystallizer for Shape and Size Manipulation

Thermal Cracking Furnace Optimal Modeling Based on Enriched Kumar Model by Free-Radical Reactions

The Kumar model as a molecular model has achieved successful application. However, only 22 reactions limit its veracity and adaptability for feedstocks. A series of models with different degrees of integration of the free radical model and the molecular model has been proposed to enhance feedstock adaptability and simulation accuracy. An improved search engine algorithm, namely Improved PageRank (IPR), is provided and applied to calculate the importance of substances in Kumar model to screen the free-radical reaction network for efficient model selection. A methodology of optimal structure and model parameters chosen is applied to the target to improve the adaptability of the material and the accuracy of the model. Then, two cases with different feedstocks are demonstrated with industrial data to verify the correctness of the proposed approach and its wide feedstock adaptability. The proposed model demonstrates good performance: (1) The mean relative errors (MRE) of the K-R (Kumar and free-radical) model have reached an order of magnitude less than 0.1% compared with 5% in the Kumar model. Further, (2) the K-R model can be implemented to model some feedstocks which Kumar model can’t simulate successfully. The K-R model can be applied in simulation of extensive feedstocks with high accuracy.

Optimization of the Critical Parameters of the Spherical Agglomeration Crystallization Method by the Application of the Quality by Design Approach

This research work presents the use of the Quality by Design (QbD) concept for optimization of the spherical agglomeration crystallization method in the case of the active agent, ambroxol hydrochloride (AMB HCl). AMB HCl spherical crystals were formulated by the spherical agglomeration method, which was applied as an antisolvent technique. Spherical crystals have good flowing properties, which makes the direct compression tableting method applicable. This means that the amount of additives used can be reduced and smaller tablets can be formed. For the risk assessment, LeanQbD Software was used. According to its results, four independent variables (mixing type and time, dT (temperature difference between solvent and antisolvent), and composition (solvent/antisolvent volume ratio)) and three dependent variables (mean particle size, aspect ratio, and roundness) were selected. Based on these, a 2–3 mixed-level factorial design was constructed, crystallization was accomplished, and the results were evaluated using Statistica for Windows 13 program. Product assay was performed and it was revealed that improvements in the mean particle size (from ~13 to ~200 µm), roundness (from ~2.4 to ~1.5), aspect ratio (from ~1.7 to ~1.4), and flow properties were observed while polymorphic transitions were avoided.

Constructing regions of attainable sizes and achieving target size distribution in a batch cooling sonocrystallization process

The application of ultrasound to a crystallization process has several interesting benefits. The temperature of the crystallizer increases during ultrasonication and this makes it difficult for the temperature controller of the crystallizer to track a set temperature trajectory precisely. It is thus necessary to model this temperature rise and the temperature-trajectory tracking ability of the crystallizer controller to perform model-based dynamic optimization for a given cooling sonocrystallization set-up. In our previous study, we reported a mathematical model based on population balance framework for a batch cooling sonocrystallization of l-asparagine monohydrate (LAM). Here we extend the previous model by including energy balance equations and a Generic Model Control algorithm to simulate the temperature controller of the crystallizer that tracks a cooling profile during crystallization. The improved model yields very good closed-loop prediction and is conveniently used for studies related to particle engineering by optimization. First, the model is used to determine the regions of attainable particle sizes for LAM batch cooling sonocrystallization process by solving appropriate dynamic optimization problems. Then the model is used to determine optimal operating conditions for achieving a target crystal size distribution. The experimental evidence clearly demonstrates the efficiency of the particle engineering approach by optimization.

Application of feedback control and in situ milling to improve particle size and shape in the crystallization of a slow growing needle-like active pharmaceutical ingredient

Control of crystal size and shape is crucially important for crystallization process development in the pharmaceutical industries. In general crystals of large size and low aspect ratio are desired for improved downstream manufacturability. It can be extremely challenging to design crystallization processes that achieve these targets for active pharmaceutical ingredients (APIs) that have very slow growth kinetics and needle-like morphology. In this work, a batch cooling crystallization process for a GlaxoSmithKline patented API, which is characterized by very slow growth rate and needle morphology, was studied and improved using process analytical technology (PAT) based feedback control techniques and in situ immersion milling. Four specific approaches were investigated: Supersaturation control (SSC), direct nucleation control (DNC), sequential milling-DNC, and simultaneous milling-DNC. This is the first time that immersion wet milling is combined with feedback control in a batch crystallization process. All four approaches were found to improve crystal size and/or shape compared to simple unseeded or seeded linear cooling crystallizations. DNC provided higher quality crystals than SSC, and sequential and simultanesou milling-DNC approaches could reduce particle 2D aspect ratio without generating too much fines. In addition, an ultra-performance liquid chromatography (UPLC) system was used online as a novel PAT tool in the crystallization study.

Particle engineering by optimization for the unseeded batch cooling crystallization of l-asparagine monohydrate

A model-based optimization approach is proposed to obtain temperature profiles to achieve the target CSD in a batch cooling crystallization process.