Estimation of Kinetic Parameters and Simulation of Methylacetylene and Propadiene Liquid-Phase Selective Hydrogenation Reactor Considering the Catalyst Deactivation

Ghasemigoudarzi, Pouria; Hojjati, Mahmoud Reza; Omidkhah, Mohammadreza; Tavallali, M. Sadegh

doi:10.1021/acs.iecr.1c01593

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…Note that the discussion of the first three types mostly ignored the effect of transport phenomena on reaction kinetics modeling. To incorporate the transport effect into kinetics modeling, one can collect plant data from largescale reactors 302 but the economic cost is usually very high. Alternatively, one can also combine ML/DL with the CFD or the classic simplified reactor model.…”

Section: Simultaneous Determination Of Pathways and Kinetic Parametersmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

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Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

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Section: Simultaneous Determination Of Pathways and Kinetic Parametersmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

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Modeling and simulation of trickle bed reactors for the purification of 1-butene

Alves

Colli

Martı́nez

et al. 2023

International Journal of Chemical Reactor Engineering

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In this contribution, a mathematical model of an industrial trickle-bed reactor employed in the purification of a C4 cut by selective hydrogenation of acetylenic or dienes compounds to obtain high purity 1-butene is presented. A reaction network of ten reactions is included in the model, with kinetics expressions and parameter estimation obtained from previous experimental studies on a commercial catalyst. Internal mass transfer resistances in the catalyst particles are significant; therefore the reaction-diffusion equations must be solved. External mass transfer resistances in the liquid phase were retained, while those in the vapor phase were negligible. The model was employed to analyze the reactor behavior by varying the inlet molar flow rate of hydrogen, the operating pressure, inlet temperature and the level of activity of the catalyst, taking into account its deactivation. It was demonstrated that the mass transfer resistances, inside and outside the catalyst particles, have a significant impact on the selectivity, but a careful operation of the reactor can improve the selectivity and extent the catalyst life. On the other hand, an alternative system was proposed, with two beds and a distributed input of H2, which led to a significant improvement in the selectivity.

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Estimation of Kinetic Parameters and Simulation of Methylacetylene and Propadiene Liquid-Phase Selective Hydrogenation Reactor Considering the Catalyst Deactivation

Cited by 2 publications

References 47 publications

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Modeling and simulation of trickle bed reactors for the purification of 1-butene

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