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Progress of Pharmaceutical continuous crystallization

Engineering 2017年第3期3卷 354-364页

作者： Dejiang Zhang Shijie Xu Shichao Du Jingkang Wang Junbo Gong School of Chemical Engineering and Technology State Key Laboratory of Chemical Engineering Tianjin University Tianjin 300072. China Collaborative Innovation Center of Chemical Science and Chemical Engineering Tianjin University Tianjin 300072 China

crystallization is an important unit operation in the pharmaceutical industry. At present, most pharmaceutical crystallization processes are performed in batches. However, due to product variability from batch to batch and to the low productivity of batch crystallization, continuous crystallization is gaining increasing attention. In the past few years, progress has been made to allow the products of continuous crystallization to meet different requirements. This review summarizes the progress in pharmaceutical continuous crystallization from a product engineering perspective. The advantages and disadvantages of different types of continuous crystallization are compared, with the main difference between the two main types of crystallizers being their difference in residence time distribution. Approaches that use continuous crystallization to meet different quality requirements are summarized. continuous crystallization has advantages in terms of size and morphology control. However, it also has the problem of a process yield that may be lower than that of a batch process, especially in the production of chirality crystals. Finally, different control strategies are compared.

关键词： continuous crystallization Pharmaceutical MSMPR Tubular crystallizer Control strategy

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continuous crystallization process control with deep Galerkin method

Continuous crystallization process control with deep Galerki...

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第40届中国控制会议

作者： Hao Xia Weijie Wang Yan Pang School of Control Science and Engineering Dalian University of Technology School of School of Aeronautics and Astronautics Dalian University of Technology

The aim of the continuous crystallization process control is to produce final product with desirable crystal size distribution（CSD）.The evolution of CSD during the process can be obtained by solving the so-called population balance equation（PBE） which governs the dynamic behavior of crystallization *** a model,PBE is essentially a nonlinear partial integro-differential *** numerical solution of such PBE is a remarkably *** the advances of deep learning,a new type of neural networks called physics informed neural network（PINN）,are trained for supervised learning tasks while respecting physics described by general nonlinear partial differential equations has been *** this paper,we use a specific PINN called deep Galerkin method（DGM） to train the neural networks to approximate the solution of the PBE for continuous crystallization *** the PBE solved by the DGM,we are able to optimize the temperature profile of the process to produce products matching the desired *** experiments are used to prove the feasibility of the proposed method.

关键词： continuous crystallization Population balance equation Deep Galerking method

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Deep learning-based on-line image analysis for continuous industrial crystallization processes

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Particuology 2023年第3期74卷 173-183页

作者： Shiliang Zong Guangzheng Zhou Meng Li Xuezhong Wang Beijing City Key Laboratory of Enze Biomass and Fine Chemicals College of New Materials and Chemical EngineeringBeijing Institute of Petrochemical TechnologyBeijing102617China

In situ microscopic imaging is a useful tool in monitoring crystallization processes,including crystal nucleation,growth,aggregation and breakage,as well as possible polymorphic *** convert the qualitative information to be quantitative for the purpose of process optimization and control,accurate analysis of crystal images is ***,the accuracy of image segmentation with traditional methods is largely affected by many factors,including solid concentration and image *** this study,the deep learning technique using mask region-based convolutional neural network(Mask R-CNN)is investigated for the analysis of on-line images from an industrial crystallizer of 10 m^(3) operated in continuous mode with high solid concentration and overlapped *** detailed label points for each crystal and transfer learning technique,two models trained with 70,908 and 7,709 crystals respectively are compared for the effect of training data *** former model effectively segments the aggregated and overlapped crystals even at high solid ***,it performs much better than the latter one and traditional multi-scale method both in terms of precision and recall,revealing the importance of large number of crystals in deep *** geometrical characteristics of segmented crystals are also analyzed,involving equivalent diameter,circularity,and aspect ratio.

关键词： continuous crystallization Crystal shape Image analysis Deep learning Instance segmentation Process analytical technology

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Modeling and Optimization of continuous crystallization Using a continuous Oscillatory Baffled Crystallizer

Modeling and Optimization of Continuous Crystallization Usin...

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第30届中国控制与决策会议

作者： Hao Xia Zhifen Li School of Control Science and Engineering Dalian University of Technology

In this paper,the continuous crystallization process with a continuous oscillatory baffled crystallizer（COBC）has been *** control objective is to obtain the desired crystal size distribution（CSD） from this process.A population balance equation（PBE） is derived by combining the crystallization mechanism and the COBC structure *** solution of the PBE can be used to characterize the CSD of any given position within the *** supersaturation control has been chosen to control the crystallization process and the optimal supersaturation value is obtained by minimizing the error between the desired and the calculated CSDs,which leads to the desired cooling temperature profile along the *** inlet temperature of the cooling water of the jacketed glass tubes is calculated based on energy balance which ensures that the solution temperature along the crystallizer follows the desired cooling temperature curve.A set of simulations has been provided to demonstrate the effectiveness of proposed method.

关键词： continuous crystallization Population balance equation Constant supersaturation control Optimal cooling temperature curve

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crystallization Simulation and Optimization in continuous Oscillatory Baffled Crystallizer

Crystallization Simulation and Optimization in Continuous Os...

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第37届中国控制会议

作者： Hao Xia Zhifen Li School of Control Science and Engineering Dalian University of Technology

In this paper, the cooling crystallization process in a continuous oscillatory baffled crystallizer（COBC） has been studied. The control objective is to obtain the desired crystal size distribution（CSD） from this process. A population balance equation（PBE） is derived by combining the crystallization mechanism and the COBC’s structure characteristics. The simulation of crystallization has been done by combining the PBE with the residence time distribution of the flow, where the residence time distribution is approximated by the tanks-in-series model. In order to get the optimal temperature profile, an optimization formulation is defined. By solving the optimization problem, we can get the optimal temperature profile. The inlet temperature of the cooling water of the jacketed glass tubes is calculated based on energy balance which ensures that the solution temperature along the crystallizer follows the desired cooling temperature profile. A set of simulations has been provided to demonstrate the effectiveness of proposed methods.

关键词： continuous crystallization Population balance equation Residence time distribution Optimal temperature profile

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Controlling Nonlinear Dynamics in continuous Crystallizers

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Journal of Mathematics and System Science 2012年第1期2卷 45-52页

作者： Victoria Gamez-Garcia Eusebio Bolanos-Reynoso Oscar Velazquez-Camilo Hector Puebla Universidad Autonoma Metropolitana-Azcapotzalco. Av. San Pablo No. 180 Reynosa-Tamaulipas Azcapotzalco D.F. 02200 Mexico Facultad de Ciencias Quimicas Instituto Tecnologico de Orizaba Orizaba Veracruz 94340 Mexico Facultad de Ingenieria Universidad Veracruzana Region Boca de Rio Veracruz 94340 Mexico

crystallization is used to produce vast quantities of materials. For several applications, continuous crystallization is often the best operation mode because it is able to reproduce better crystal size distributions than other operation modes. Nonlinear oscillation in continuous industrial crystallization processes is a well-known phenomenon leading to practical difficulties such that control actions are necessary. Nonlinear oscillation is a consequence of the highly nonlinear kinetics, different feedbacks between the variables and elementary processes taking place in crystallizers units, and the non-equilibrium thermodynamic operation. In this paper the control of a continuous crystallizer model that displays oscillatory behavior is addressed via two practical robust control approaches：（i） modeling error compensation, and （ii） integral high order sliding mode control. The controller designs are based on the reduced-order model representation of the population balance equations resulting after the application of the method of moments. Numerical simulations show good closed-loop performance and robustness properties

关键词： crystallization continuous crystallization population balances nonlinear dynamics robust model-based control

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Parameter Identification of Multi-dimensional PBE Growth Model Based on PINN.

Parameter Identification of Multi-dimensional PBE Growth Mod...

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第43届中国控制会议

作者： Shengyuan Wu Hao Xia School of Control Science and Engineering Dalian University of Technology

This study focuses on the identification of multi-dimensional growth model parameters in continuous oscillatory baffle crystallizers using physics-informed neural networks(PINNs). Due to the fact that crystal growth parameters are varying with changing operational conditions, it is crucial to identify these parameters for better control results. The paper first employs a mechanistic model, the morphological and multi-dimensional Population Balance Model(PBM), to describe multi-dimensional crystallization processes. Subsequently, high-precision numerical solution algorithms are used to obtain crystal size distributions. A uniform design scheme is implemented to set crystal growth model parameters and generate training data for ***, the mechanistic model are incorporated into the loss function as a regularization term for network training. This approach guides the training process towards physically consistent solutions rather than purely data correlations. Finally, the PINN model is applied to the identification of growth model parameters in glutamic acid experiments, demonstrating its capability for parameter identification.

关键词： continuous crystallization Morphological population balance equation PINN Parameter identification

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