Dataset Information

Catalyst: Fast and flexible modeling of reaction networks.

ABSTRACT: We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst's broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

SUBMITTER: Loman TE

PROVIDER: S-EPMC10584191 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Catalyst: Fast and flexible modeling of reaction networks.

Loman Torkel E TE Ma Yingbo Y Ilin Vasily V Gowda Shashi S Korsbo Niklas N Yewale Nikhil N Rackauckas Chris C Isaacson Samuel A SA

PLoS computational biology 20231018 10

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude ...[more]

PMID: 37851697

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Project description:The propylene production processes currently used in the petrochemical industry (fluid catalytic cracking and steam cracking of naphtha and light diesel) are unable to meet the increase of propylene demand for industrial applications. For this reason, alternative processes for propylene production have been investigated, and among the others, the propane dehydrogenation (PDH) process, allowing the production of propylene as a main product, has been industrially implemented (e.g., Catofin and Oleflex processes). The main drawback of such processes is closely linked to the high temperature required to reach a sustainable propane conversion that affects catalyst stability due to coke formation on the catalyst surface. Accordingly, the periodic regeneration of the catalytic bed is required. In this work, the performance in the PDH reaction of different Sn-Pt catalysts, prepared starting by alumina- and hydrotalcite-based supports, is investigated in terms of propane conversion and selectivity to propylene in order to identify a more stable catalyst than the commercial ones. The experimental tests evidenced that the best performance was obtained using the catalyst prepared on commercial pellets of hydrotalcite PURALOX MG70. This catalyst has shown, under pressure conditions of 1 and 5 bar (in order to evaluate the potential future application in integrated membrane reactors), propane conversion values close to the thermodynamic equilibrium ones in all of the investigated temperature ranges (500-600 °C) and the selectivity was always higher than 95%. So, this catalyst was also tested in a stability run, performed at 500 °C and 5 bar: the results highlighted the loss of only 12% in the propane conversion with no changes in the selectivity to propylene. Properly designed experimental tests have also been performed in order to evaluate the kinetic parameters, and the developed mathematical model has been optimized to effectively describe the system behavior and the catalyst deactivation.

Dataset Information

Catalyst: Fast and flexible modeling of reaction networks.

Publications

Catalyst: Fast and flexible modeling of reaction networks.

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