Challenges and opportunities for molecule-based management of chemical processes

Vijver, Ruben Van de; Devocht, Brigitte; Geem, Kevin Van; Thybaut, Joris; Marin, Guy

doi:10.1016/j.coche.2016.09.006

Cited by 23 publications

(28 citation statements)

References 62 publications

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“…Also, systematic merging of a parent mechanism and problem-specific, modular sub-mechanisms in terms of a fuel component library framework can approximate the reaction behavior of multi-component real fuels [332] . Systematically identified reaction classes and rate rules may form the basis for automatic mechanism generation [333] , [334] , [335] with the advantage of internal consistency in rate constant assignment [329] . Several prerequisites must be considered in automated mechanism construction, i.e.…”

Section: Selected Combustion Chemistry Advances – Overview and Recentmentioning

confidence: 99%

Combustion in the future: The importance of chemistry

Kohse‐Höinghaus

2021

Proceedings of the Combustion Institute

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Combustion involves chemical reactions that are often highly exothermic. Combustion systems utilize the energy of chemical compounds released during this reactive process for transportation, to generate electric power, or to provide heat for various applications. Chemistry and combustion are interlinked in several ways. The outcome of a combustion process in terms of its energy and material balance, regarding the delivery of useful work as well as the generation of harmful emissions, depends sensitively on the molecular nature of the respective fuel. The design of efficient, low-emission combustion processes in compliance with air quality and climate goals suggests a closer inspection of the molecular properties and reactions of conventional, bio-derived, and synthetic fuels. Information about flammability, reaction intensity, and potentially hazardous combustion by-products is important also for safety considerations. Moreover, some of the compounds that serve as fuels can assume important roles in chemical energy storage and conversion. Combustion processes can furthermore be used to synthesize materials with attractive properties. A systematic understanding of the combustion behavior thus demands chemical knowledge. Desirable information includes properties of the thermodynamic states before and after the combustion reactions and relevant details about the dynamic processes that occur during the reactive transformations from the fuel and oxidizer to the products under the given boundary conditions. Combustion systems can be described, tailored, and improved by taking chemical knowledge into account. Combining theory, experiment, model development, simulation, and a systematic analysis of uncertainties enables qualitative or even quantitative predictions for many combustion situations of practical relevance. This article can highlight only a few of the numerous investigations on chemical processes for combustion and combustion-related science and applications, with a main focus on gas-phase reaction systems. It attempts to provide a snapshot of recent progress and a guide to exciting opportunities that drive such research beyond fossil combustion.

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Section: Selected Combustion Chemistry Advances – Overview and Recentmentioning

confidence: 99%

Combustion in the future: The importance of chemistry

Kohse‐Höinghaus

2021

Proceedings of the Combustion Institute

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“…, number of elementary steps,…) or quantity of the information required for the application of the first-principles methods. 10 , 35 This complexity is particularly relevant at the microscale. For instance, the microkinetic model of a simple system such as partial oxidation of CH 4 on Rh is based on 41 reversible elementary steps between 13 adsorbed species.…”

Section: Escaping the Trap Of Complexitymentioning

confidence: 99%

Escaping the trap of complication and complexity in multiscale microkinetic modelling of heterogeneous catalytic processes

Maestri

2017

Chem. Commun.

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“…Owing to the large number of species and reactions, the generation of reaction mechanisms is generally performed using computer algorithms such as RMG, Genesys, RING, and EXGAS . The models provide insight into the chemistry underlying a chemical process, and the ultimate goal of the model lies in process optimization . Automatically generated kinetic networks are validated using experimental data.…”

Section: Introductionmentioning

confidence: 99%

“…5 The models provide insight into the chemistry underlying a chemical process, and the ultimate goal of the model lies in process optimization. 6 Automatically generated kinetic networks are validated using experimental data. However, finding suitable experimental datasets is often challenging.…”

Section: Introductionmentioning

confidence: 99%

“…The importance of adequate experimental data is shown in various fields such as biology, food processing, and preservation as well as engineering . ‐ Many authors indicate that confidence levels in the reported experimental data are as important as the value themselves, , , as the quality of the experimental data significantly determines the quality of the model parameters obtained regarding precision and accuracy . Cutler et al further concluded as early as 1988 that errors from experimental measurements have a more significant influence on errors in the kinetic rate expression than errors in the reactor model.…”

Section: Introductionmentioning

confidence: 99%

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QUANTIS: Data quality assessment tool by clustering analysis

Symoens

Aravindakshan

Vermeire

et al. 2019

Int J of Chemical Kinetics

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Automatically generated kinetic networks are ideally validated against a large set of accurate, reproducible, and easy‐to‐model experimental data. However, although this might seem simple, it proves to be quite challenging. QUANTIS, a publicly available Python package, is specifically developed to evaluate both the precision and accuracy of experimental data and to ensure a uniform, quick processing, and storage strategy that enables automated comparison of developed kinetic models. The precision is investigated with two clustering techniques, PCA and t‐SNE, whereas the accuracy is probed with checks for the conservation laws. First, the developed tool processes, evaluates, and stores experimental yield data automatically. All data belonging to a given experiment, both unprocessed and processed, are stored in the form of an HDF5 container. The demonstration of QUANTIS on three different pyrolysis cases showed that it can help in identifying and overcoming instabilities in experimental datasets, reduce mass and molar balance closure discrepancies, and, by evaluating the visualized correlation matrices, increase understanding in the underlying reaction pathways. Inclusion of all experimental data in the HDF5 file makes it possible to automate simulating the experiment with CHEMKIN. Because of the employed InChI string identifiers for molecules, it is possible to automate the comparison experiment/simulation. QUANTIS and the concepts demonstrated therein is a potentially useful tool for data quality assessment, kinetic model validation, and refinement.

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Challenges and opportunities for molecule-based management of chemical processes

Cited by 23 publications

References 62 publications

Combustion in the future: The importance of chemistry

Combustion in the future: The importance of chemistry

Escaping the trap of complication and complexity in multiscale microkinetic modelling of heterogeneous catalytic processes

QUANTIS: Data quality assessment tool by clustering analysis

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