Optimization-based simultaneous modelling of stoichiometries and kinetics in complex organic reaction system

Xing, Yafeng; Dong, Yachao; Zhou, Wenjing; Du, Jian; Meng, Qingwei

doi:10.1016/j.ces.2023.118758

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…By complex model, we mean a system with multiphases and several reactions. To develop kinetic models for such a system, one needs to know the concentration evolution of species. − Using adiabatic conditions in reaction calorimeters requires no withdrawal of samples to avoid interference with temperature, limiting the kinetic description of all reaction steps. On the other hand, one could imagine combining some experiments in isothermal conditions, including offline analysis, and some in adiabatic conditions, including online reaction temperature.…”

Section: Introductionmentioning

confidence: 99%

Combining Isothermal and Adiabatic Mode Experiments for Kinetic Constant Estimation: Application to the Hydrogenation of 5-(Hydroxymethyl)furfural (5-HMF)

Leveneur

2024

Ind. Eng. Chem. Res.

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In the vast majority, kinetic modeling is done under isothermal conditions. The benefit of the adiabatic mode is using temperature as an online observable. Nevertheless, in the adiabatic mode, one can only get the initial/inlet and final/outlet concentrations, making the development of kinetic models difficult in the presence of several reaction steps. Obtaining kinetic models tested in isothermal and adiabatic modes could change our methodology in kinetic modeling because such models can be used in optimization, thermal risk assessment, and pinch analysis. Besides, for chemical systems with several reaction steps, one needs to spend much time analyzing the different samples to get concentration profiles. Thus, developing kinetic models combining isothermal and adiabatic experimental runs could reduce the experimental stage. We tested this methodology in the hydrogenation of 5-(hydroxymethyl)furfural (5-HMF) by using different combinations of adiabatic and isothermal noised synthetic runs. We compared the estimated kinetic constants obtained from these noised synthetic runs with the hypothetical true ones and the sum of squared residuals. The hypothetical true kinetic constants were created. Global sensitivity analysis using the hypothetical true kinetic constants in isothermal and adiabatic modes was carried out to measure the influence of these constants on the estimated concentration and temperature. We found that implementing some adiabatic runs in a set of experimental runs could be beneficial from a parameter estimation standpoint.

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Section: Introductionmentioning

confidence: 99%

Combining Isothermal and Adiabatic Mode Experiments for Kinetic Constant Estimation: Application to the Hydrogenation of 5-(Hydroxymethyl)furfural (5-HMF)

Leveneur

2024

Ind. Eng. Chem. Res.

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A new prediction method for sodium aluminate solution evaporation integrating process knowledge and data-driven spatial-temporal adaptive model

Xie,

Hua,

Lou

et al. 2024

ISA Transactions

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Data-Driven Modeling Methods and Techniques for Pharmaceutical Processes

et al. 2023

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As one of the most influential industries in public health and the global economy, the pharmaceutical industry is facing multiple challenges in drug research, development and manufacturing. With recent developments in artificial intelligence and machine learning, data-driven modeling methods and techniques have enabled fast and accurate modeling for drug molecular design, retrosynthetic analysis, chemical reaction outcome prediction, manufacturing process optimization, and many other aspects in the pharmaceutical industry. This article provides a review of data-driven methods applied in pharmaceutical processes, based on the mathematical and algorithmic principles behind the modeling methods. Different statistical tools, such as multivariate tools, Bayesian inferences, and machine learning approaches, i.e., unsupervised learning, supervised learning (including deep learning) and reinforcement learning, are presented. Various applications in the pharmaceutical processes, as well as the connections from statistics and machine learning methods, are discussed in the narrative procedures of introducing different types of data-driven models. Afterwards, two case studies, including dynamic reaction data modeling and catalyst-kinetics prediction of cross-coupling reactions, are presented to illustrate the power and advantages of different data-driven models. We also discussed current challenges and future perspectives of data-driven modeling methods, emphasizing the integration of data-driven and mechanistic models, as well as multi-scale modeling.

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Optimization-based simultaneous modelling of stoichiometries and kinetics in complex organic reaction system

Cited by 3 publications

References 32 publications

Combining Isothermal and Adiabatic Mode Experiments for Kinetic Constant Estimation: Application to the Hydrogenation of 5-(Hydroxymethyl)furfural (5-HMF)

Combining Isothermal and Adiabatic Mode Experiments for Kinetic Constant Estimation: Application to the Hydrogenation of 5-(Hydroxymethyl)furfural (5-HMF)

A new prediction method for sodium aluminate solution evaporation integrating process knowledge and data-driven spatial-temporal adaptive model

Data-Driven Modeling Methods and Techniques for Pharmaceutical Processes

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