On the model-based design and comparison of crystallization-based deracemization techniques

Bodák, Brigitta; Breveglieri, Francesca; Mazzotti, Marco

doi:10.1016/j.ces.2022.117595

Cited by 7 publications

(4 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent survey on the numerical methods for the simulation of univariate PBE gives some advancements and issues with the numerical integration techniques available in the literature . MSMPR-based deracemization is performed, and the steady-state model is used to identify feasible operating conditions at different kinetics and temperatures . With a focus on producing crystals with low cost, experiments on continuous slug-flow tubular crystallization are carried out with spatial temperature variation to generate a variety of CSD.…”

Section: Introductionmentioning

confidence: 99%

“…12 MSMPR-based deracemization is performed, and the steady-state model is used to identify feasible operating conditions at different kinetics and temperatures. 13 With a focus on producing crystals with low cost, experiments on continuous slug-flow tubular crystallization are carried out with spatial temperature variation to generate a variety of CSD. Experimental data are fitted to a mathematical model, and a 20% prediction error is reported.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Inapakurthi

Naik

Mitra

2022

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Mixed-suspension mixed-product removal (MSMPR) crystallization process is critical for optimal separation and purification operations in pharmaceutical and fine chemical industries. To achieve this, detailed mathematical model-based optimization is the current practice, which is reported as an extremely time-consuming exercise, prohibiting their online implementation. To facilitate faster optimization, a novel datadriven modeling and optimization algorithm has been proposed in this work. Limited number of high-fidelity data from the computationally expensive model were utilized to build surrogates using support vector regression (SVR). Inputs of MSMPR are assigned different, instead of same, kernel parameters, and multiple kernels were explored to capture the complex dynamics of the crystallization process. Loaded with a sample size estimation algorithm to reduce the computational load on model execution time, the complete formulation is solved using an evolutionary algorithm enabling evolution of optimal SVR-based surrogate models. From the Pareto optimal set of several such models, two instances with differing prediction error were selected and the optimization of MSMPR was performed. The study indicates 3 orders of magnitude faster optimization with the surrogate models and 87.54% savings in the number of expensive function evaluations compared to the physics-based MSMPR model facilitating online optimization of such a process.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Inapakurthi

Naik

Mitra

2022

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…In this work, first, we present the process design approach, the metrics to evaluate the process performance, and the tools utilized for the analysis. Then, we investigate in modeling (section ) and experimental studies (section ) the effect of the process operating parameters on this technique, thus providing the basis for the design of an efficient and productive CID process, as well as for its comparison with the temperature cycle approach …”

Section: Introductionmentioning

confidence: 99%

“…Then, we investigate in modeling (section 4) and experimental studies (section 5) the effect of the process operating parameters on this technique, thus providing the basis for the design of an efficient and productive CID process, as well as for its comparison with the temperature cycle approach. 26…”

Section: Introductionmentioning

confidence: 99%

Crystallization-Induced Deracemization: Experiments and Modeling

Bodák

Breveglieri

Mazzotti

2022

Crystal Growth & Design

Self Cite

View full text Add to dashboard Cite

Inspired by deracemization via temperature cycles, which enables the collection of crystals of the desired enantiomer from an initially racemic mixture, we focus in this work on an alternative batch process, namely crystallization-induced deracemization. This process starts with a suspension of enantiomerically pure crystals, which undergoes a simple cooling crystallization, coupled with liquid-phase racemization. The experimental and model-based analysis of such a process, carried out here, revealed that: (i) deracemization via temperature cycles is a safe choice to operate with high enantiomeric purity, although its throughput is limited by the suspension density; (ii) if the distomer is less prone to nucleation, crystallization-induced deracemization is a simple process; however, its performance is strongly limited by the solubility; (iii) the purity achieved with crystallization-induced deracemization can be increased by utilizing large seed mass and by optimizing the cooling profile or catalyst concentration. Alternatively, the purity increases via partial dissolution of the seeds, which resembles the heating part of the deracemization process via temperature cycles.

show abstract

Data‐driven identification of crystallization kinetics

Nyande,

Nagy,

Lakerveld

2024

AIChE Journal

View full text Add to dashboard Cite

A novel data‐driven methodology is presented for developing mathematical models for crystallization processes. The data‐driven approach is based on the sparse identification of nonlinear dynamics (SINDy) method, which iterates between a partial least‐squares fit and a sparsity‐promoting step leading to the discovery of sparse interpretable models. The performance of the SINDy methodology is characterized for the identification of crystallization kinetics in a mixed tank operated in a continuous mode, the isothermal crystallization of lysozyme in a batch stirred tank and cooling crystallization of paracetamol. The SINDy method is robust against noise. Good agreement is obtained between the data‐driven model and the data obtained from crystallization experiments. The presented data‐driven approach can be attractive for modeling industrial crystallization processes where process analytical technology tools are available for the measurement of process variables but functional forms of kinetic expressions are unknown.

show abstract

On the model-based design and comparison of crystallization-based deracemization techniques

Cited by 7 publications

References 61 publications

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Crystallization-Induced Deracemization: Experiments and Modeling

Data‐driven identification of crystallization kinetics

Contact Info

Product

Resources

About