Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning
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Le résumé fourni par la source
High Resolution Image Download MS PowerPoint Slide Co-design of alternative fuels and future spark-ignition (SI) engines allows very high engine efficiencies to be achieved. To tailor the fuel’s molecular structure to the needs of SI engines with very high compression ratios, computer-aided molecular design (CAMD) of renewable fuels has received considerable attention over the past decade. To date, CAMD for fuels is typically performed by computationally screening the physicochemical properties of single molecules against property targets. However, achievable SI engine efficiency is the result of the combined effect of various fuel properties, and molecules should not be discarded because of individual unfavorable properties that can be compensated for. Therefore, we present an optimization-based fuel design method directly targeting SI engine efficiency as the objective function. Specifically, we employ an empirical model to assess the achievable relative engine efficiency increase compared to conventional RON95 gasoline for each candidate fuel as a function of fuel properties. For this purpose, we integrate the automated prediction of various fuel properties into the fuel design method: Thermodynamic properties are calculated by COSMO-RS; combustion properties, indicators for environment, health and safety, and synthesizability are predicted using machine learning models. The method is applied to design pure-component fuels and binary ethanol-containing fuel blends. The optimal pure-component fuel tert -butyl formate is predicted to yield a relative efficiency increase of approximately 8% and the optimal fuel blend with ethanol and 3,4-dimethyl-3-propan-2-yl-1-pentene of 19%.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning
- Date Crossref
- 24/01/2023
- Éditeur
- American Chemical Society (ACS)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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ETH Zurich pays non établi dans la noticeUniversité ou école supérieure
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RWTH Aachen University Institute for Technical Thermodynamics pays non établi dans la noticeUniversité ou école supérieure
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Delft University of Technology Department of Chemical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Forschungszentrum Jülich pays non établi dans la noticeStructure de recherche
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Jülich Aachen Research Alliance pays non établi dans la noticeStructure de recherche
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Energy & Process Systems Engineering pays non établi dans la noticeInstitution
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Institute of Energy and Climate Research (IEK-10) pays non établi dans la noticeStructure de recherche
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JARA-ENERGY pays non établi dans la noticeInstitution
ETH Zurich, Institute for Technical Thermodynamics — RWTH Aachen University et Department of Chemical Engineering — Delft University of Technology, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.