The Measurement of Octane Numbers for Methanol and Reference Fuels Blends

Naegeli, D. W.; Yost, Douglas M.; Moulton, D.S.; Owens, E.C.; Chul, Granger K.

doi:10.4271/892038

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…As a database for our model, we take RON (MON) values for 335 (318) species from literature. 2,10,12,32,[60][61][62][63][64][65][66][67] For all reported data (RON, MON, DCN), we take average values whenever multiple values have been reported for a single species.…”

Section: Data Basis For Fuel Ignition Qualitymentioning

confidence: 99%

“…They are measured according to the ASTM D2699 and ASTM D2700 standards, respectively. As a database for our model, we take RON (MON) values for 335 (318) species from the literature. ,,,,− For all reported data (RON, MON, DCN), we take average values whenever multiple values have been reported for a single species.…”

Section: Databases For Fuel Ignition Qualitymentioning

confidence: 99%

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Graph Neural Networks for Prediction of Fuel Ignition Quality

et al. 2020

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Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn. File list (2) download file view on ChemRxiv Schweidtmann-et-al_Manuscript_ChemRxiv_20200511.... (627.65 KiB) download file view on ChemRxiv Schweidtmann-et-al_SI-Data-DCN-RON-MON_ChemR... (120.02 KiB)

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Section: Data Basis For Fuel Ignition Qualitymentioning

confidence: 99%

Section: Databases For Fuel Ignition Qualitymentioning

confidence: 99%

Graph Neural Networks for Prediction of Fuel Ignition Quality

et al. 2020

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“…In the last decades, because of increased public awareness to air pollution problems, TEL was practically banned from use in most of the industrialized nations. An alternative way to prevent knock without using TEL is to add a modest amount of methanol to gasoline , but it is not yet an economically viable alternative to gasoline . One of the main drawbacks of methanol is relatively high toxicity and corrosivity.…”

Section: Introductionmentioning

confidence: 99%

Energy analysis of ethanol steam reforming for hybrid electric vehicle

Tartakovsky

Mosyak

Zvirin

2011

Int. J. Energy Res.

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SUMMARY Thermodynamic equilibrium of ethanol steam reforming is studied using the Gibbs free energy minimization method. The reaction paths of ethanol steam reforming are simulated using Chem‐CAD software. Appropriate optimization of reactants ratio and reaction conditions is performed, to achieve the composition of ethanol steam reforming products, which will be favorable as an internal combustion engine (ICE) fuel. The effects of process variables, such as temperature and water : ethanol molar ratio are discussed. Numerical investigations are conducted to analyze energy performance of steam reforming of ethanol for ICE. Realization of ethanol steam reforming at high temperature leads to an increase in efficiency of the process. The optimal conditions are obtained as follows: 1100 K, water : ethanol molar ratio of 1.2. Copyright © 2011 John Wiley & Sons, Ltd.

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“…Such RON and OS predictions are generally in accordance with the literature values for representative molecules of these classes, cf. references 42,68,135,149,150.…”

Section: Resultsmentioning

confidence: 99%

Graph machine learning for design of high‐octane fuels

et al. 2022

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Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO 2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.

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The Measurement of Octane Numbers for Methanol and Reference Fuels Blends

Cited by 6 publications

References 8 publications

Graph Neural Networks for Prediction of Fuel Ignition Quality

Graph Neural Networks for Prediction of Fuel Ignition Quality

Energy analysis of ethanol steam reforming for hybrid electric vehicle

Graph machine learning for design of high‐octane fuels

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