Dynamics and MPC of an Evaporative Continuous Crystallization Process

Moraes, Marcellus G.F. de; Souza, Maurício B. de; Secchi, Argimiro Resende

doi:10.1016/b978-0-444-64235-6.50175-3

Cited by 6 publications

(6 citation statements)

References 2 publications

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“…The results of the comparison of two approaches (PID and MPC) showed that the MPC is a more advanced control, which enables smooth operation, as the control structure in the PID case should be changed in the same cases (when a relatively large operating regime is required). Similar conclusions were also obtained in the study presented by de Moraes, in which the MPC method was proven to offer better controlling abilities. However, as the NMPC proved to be a more suitable approach, servo control and regulatory control were also performed using this approach.…”

Section: Model-based Predictive Control and Process Intensificationsupporting

confidence: 88%

Continuous Crystallization Processes in Pharmaceutical Manufacturing: A Review

Orehek

Teslić

Likozar

2020

Org. Process Res. Dev.

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This scientific paper presents an overview of continuous solution crystallization in the pharmaceutical industry. Since the specific knowledge spectrum of precipitation is very broad, topics covering the following were analyzed and assessed in detail: a comparison between batch and continuous crystallizers, their engineering advantages/disadvantages, the introduction of the solid form structuring to continuously oriented chemical production, and the usage of the online process analytical technology (PAT) methodologies in interacting functional systems. Furthermore, mechanistic multiscale modeling, whose understanding is of decisive importance for further control application development, was thoroughly discussed as well. The multiscale modeling section also includes the simulation of processes and the optimization of crystallizers. In the last section, model-based predictive control (MPC) is considered, which also covers the intensification approach methods in the field of crystal formation and the introduction of new, highly potential continuous systems, e.g. the coiled flow inverter (CFI) crystallizer, in industry and academia. In the mentioned main sections, individual theme-related articles are collected, discussed, and compared; therefore, a reader can get acquainted with pieces of research, their found results, which have been developed until now, and various emerging perspectives. With regard to the importance of crystallization in the pharmaceutical industry, a reader can already find literature on continuous crystallization. However, due to the growing tendency toward the synthesis of products with specific properties which cannot be achieved with conventional approaches, more attention has been paid in the present work to the establishment of continuous systems for the production of specific fine chemicals. In addition, continuous crystallization could also enable more efficient and "on-demand" production, modeling for optimization, MPC, and intensification approaches. A review of the literature classifying studies according to the choice of a continuous system and their description was omitted in this study, as such reviews are already available in the literature.

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Section: Model-based Predictive Control and Process Intensificationsupporting

confidence: 88%

Continuous Crystallization Processes in Pharmaceutical Manufacturing: A Review

Orehek

Teslić

Likozar

2020

Org. Process Res. Dev.

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“…It also promotes practical application at the industrial scale. It is worth emphasizing that a more advanced model-based approach, model predictive control, has been implemented in the crystallization process for solving the open-loop optimization repeatedly. , Moraes et al . applied the MPC approach to realize the control strategy of an MSMPR multivariate evaporative process.…”

Section: Process Control Of Crystalline Pharmaceutical Productsmentioning

confidence: 99%

“…It is worth emphasizing that a more advanced model-based approach, model predictive control, has been implemented in the crystallization process for solving the open-loop optimization repeatedly. 119,120 Moraes et al 121 applied the MPC approach to realize the control strategy of an MSMPR multivariate evaporative process. Nonlinear MPC was reported to handle high complexity and nonlinear systems and seems to be more robust than ordinary open-loop control strategies.…”

Section: Process Control Of Crystalline Pharmaceutical Productsmentioning

confidence: 99%

Recent Progress in Continuous Crystallization of Pharmaceutical Products: Precise Preparation and Control

Macaringue

et al. 2020

Org. Process Res. Dev.

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Crystallization, as a solid−liquid separation process, is employed to purify and isolate a great diversity of crystalline pharmaceutical products. In recent years, continuous crystallization has attracted increasing attention because of the product and process robustness as well as higher productivity. In this work, we review the use of novel continuous crystallizers or modified conventional continuous crystallizers for the preparation of polymorphs, chiral enantiomers, solvates/hydrates, cocrystals, and spherical crystals. In addition, the theoretical framework and verification of the model-based control approaches are demonstrated. The application of process analytical technology tools in classical feedback loop control strategies in continuous crystallization is also discussed. Despite all this, the application of continuous crystallization still remains challenging because of the existence of drawbacks such as fouling and blockages. Therefore, a systematic discussion should be done before continuous crystallization is more widely applied.

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“…62,63 de Moraes et al developed an MPC for controlling an MSMPR in the evaporative crystallization of potassium sulfate. 64 The regulatory control for common disturbances, namely, the feed solute concentration and the feed flow rate was achieved by manipulating the temperature and the steam outflow rate, presenting better performance than PI controllers.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Predictive Control of Cooling Crystallization of Potassium Sulfate by Dynamic Image Analysis: Exploring Phenomenological and Machine Learning Approaches

Moraes

Lima

Lage

et al. 2023

Ind. Eng. Chem. Res.

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Representative mathematical modeling is essential for understanding the batch cooling crystallization processes. Efficient process design and operation are relevant to achieving high-quality criteria and minimizing variation between batches. This work first presents the modeling of batch cooling crystallization based on online dynamic image analysis. A flow-through microscope was used to track the temporal evolution of the crystal population. A population balance modeling (PBM) approach, parameter estimation, and validation were obtained for the batch cooling crystallization of potassium sulfate in water. The performed experiments provided new experimental data, giving dynamic information about the crystal size throughout each run. The kinetic model parameters for crystal nucleation and growth were estimated using a hybrid optimization algorithm, followed by the confidence region construction using a more exploratory particle swarm algorithm. In the parameter estimation framework, in addition to solute concentration, the first fourth-order moments computed throughout all experiments were included in the objective function. A linear size-dependent growth rate was found to capture well the dynamics of the potassium sulfate crystal size distribution. The experimental results evidenced that the crystal shape of potassium sulfate is predominantly constant, allowing the adequacy of the developed model. The validated PBM was also employed as a digital twin of the crystallization process to develop a machine-learning-based control for the process. Then, a surrogate model based on a recurrent neural network, called an echo state network (ESN), was applied in a nonlinear model predictive controller approach (ESN-NMPC). The ESN model could predict the moments of the population balance model up to five steps (5 min) forward. The ESN-NMPC achieved the desired control scenarios for the crystal size and its coefficient of variation. Its performance was comparable to the controller that uses the PBM as the internal model (PB-NMPC).

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Dynamics and MPC of an Evaporative Continuous Crystallization Process

Cited by 6 publications

References 2 publications

Continuous Crystallization Processes in Pharmaceutical Manufacturing: A Review

Continuous Crystallization Processes in Pharmaceutical Manufacturing: A Review

Recent Progress in Continuous Crystallization of Pharmaceutical Products: Precise Preparation and Control

Modeling and Predictive Control of Cooling Crystallization of Potassium Sulfate by Dynamic Image Analysis: Exploring Phenomenological and Machine Learning Approaches

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