Hybrid modeling supported development of an industrial small-molecule flow chemistry process

Polak, Jakub; Stosch, Moritz von; Sokolov, Michael; Piccioni, Lorenzo; Streit, Alexander; Schenkel, Berthold; Guélat, Bertrand

doi:10.1016/j.compchemeng.2022.108127

Cited by 4 publications

(6 citation statements)

References 35 publications

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“…Although PINNs and further kinetic ML approaches were previously introduced, − ,,− it should be noted that the influence of external parameter variation was not taken into detailed account. However, reaction optimization requires a sufficient sampling of the parameter space, such that the benefits of the previous models for process design concepts are limited .…”

Section: Discussionmentioning

confidence: 99%

“…40 order to study the kinetic properties of time-evolving systems. 41,42 For such models, neural networks or further supervised ML approaches are used for the determination of time-dependent kinetic rates from experimental data. 42 In addition to hybrid models, pure data-driven methods like recurrent neural networks were also introduced for the study and prediction of complex kinetic behavior and concentration profiles.…”

Section: Introductionmentioning

confidence: 99%

“…A recent perspective article addresses the use of ML-based methods for the prediction of chemical reaction profiles and kinetics . As few further rare examples from ML, so-called hybrid models were introduced in order to study the kinetic properties of time-evolving systems. , For such models, neural networks or further supervised ML approaches are used for the determination of time-dependent kinetic rates from experimental data . In addition to hybrid models, pure data-driven methods like recurrent neural networks were also introduced for the study and prediction of complex kinetic behavior and concentration profiles .…”

Section: Introductionmentioning

confidence: 99%

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Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions

Adebar,

Keupp,

Emenike

et al. 2024

J. Phys. Chem. A

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Emerging concepts from scientific deep machine learning such as physics-informed neural networks (PINNs) enable a data-driven approach for the study of complex kinetic problems. We present an extended framework that combines the advantages of PINNs with the detailed consideration of experimental parameter variations for the simulation and prediction of chemical reaction kinetics. The approach is based on truncated Taylor series expansions for the underlying fundamental equations, whereby the external variations can be interpreted as perturbations of the kinetic parameters. Accordingly, our method allows for an efficient consideration of experimental parameter settings and their influence on the concentration profiles and reaction kinetics. A particular advantage of our approach, in addition to the consideration of univariate and multivariate parameter variations, is the robust model-based exploration of the parameter space to determine optimal reaction conditions in combination with advanced reaction insights. The benefits of this concept are demonstrated for higher-order chemical reactions including catalytic and oscillatory systems in combination with small amounts of training data. All predicted values show a high level of accuracy, demonstrating the broad applicability and flexibility of our approach.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions

Adebar,

Keupp,

Emenike

et al. 2024

J. Phys. Chem. A

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“…Our previous study described the detailed methodology. [ 21 ] In short, similar experiments are clustered together once the experiments have been transformed using a Principle Component Analysis (PCA). Each cluster is sampled randomly to obtain a representative training set of the desired size.…”

Section: Methodsmentioning

confidence: 99%

“…When predicting the process evolution from a generic state s, the following expression is then used: Our previous study described the detailed methodology. [21] In…”

Section: Propagation Model (Pm): Discrete Hybrid Model (Dhm)mentioning

confidence: 99%

An innovative hybrid modeling approach for simultaneous prediction of cell culture process dynamics and product quality

Polak,

Huang,

Sokolov

et al. 2024

Biotechnology Journal

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The use of hybrid models is extensively described in the literature to predict the process evolution in cell cultures. These models combine mechanistic and machine learning methods, allowing the prediction of complex process behavior, in the presence of many process variables, without the need to collect a large amount of data. Hybrid models cannot be directly used to predict final product critical quality attributes, or CQAs, because they are usually measured only at the end of the process, and more mechanistic knowledge is needed for many classes of CQAs. The historical models can instead predict the CQAs better; however, they cannot directly relate manipulated process parameters to final CQAs, as they require knowledge of the process evolution. In this work, we propose an innovative modeling approach based on combining a hybrid propagation model with a historical data‐driven model, that is, the combined hybrid model, for simultaneous prediction of full process dynamics and CQAs. The performance of the combined hybrid model was evaluated on an industrial dataset and compared to classical black‐box models, which directly relate manipulated process parameters to CQAs. The proposed combined hybrid model outperforms the black‐box model by 33% on average in predicting the CQAs while requiring only around half of the data for model training to match performance. Thus, in terms of model accuracy and experimental costs, the combined hybrid model in this study provides a promising platform for process optimization applications.

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Assessing process systems models for pharmaceutical development

Geremia

Diab

Christodoulou

et al. 2023

Computer Aided Chemical Engineering

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Hybrid modeling supported development of an industrial small-molecule flow chemistry process

Cited by 4 publications

References 35 publications

Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions

Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions

An innovative hybrid modeling approach for simultaneous prediction of cell culture process dynamics and product quality

Assessing process systems models for pharmaceutical development

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