Exploring Alternative Octane Specification Methods for Improved Gasoline Knock Resistance in Spark-Ignition Engines

Abdul-Manan, Amir F.N.; Kalghatgi, Gautam; Babiker, Hassan

doi:10.3389/fmech.2018.00020

Cited by 9 publications

(9 citation statements)

References 31 publications

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“…The framework utilizes recent methods from the field of generative graph-ML and GNNs to design molecules with high-knock resistance for modern SI engines. Specifically, we set out to maximize the sum of RON and OS, hence the OI, as high-efficiency SI engines require both a high RON and a high OS (Kalghatgi, 2005;Bell, 2010;Mittal and Heywood, 2008;Amer et al, 2012;Kalghatgi, 2014;Szybist and Splitter, 2017;Abdul-Manan et al, 2018). We show a high-level overview of our framework in Figure 1 and provide a detailed framework overview including our choices for algorithms and models in the three different modules in Figure 4.…”

Section: Graph-based Property Predictionmentioning

confidence: 99%

“…The weighted sum of RON and OS is referred to as the octane index (OI) (Kalghatgi, 2001). For modern SI engines, fuels with both high RON and high OS, hence high OI, are desired as they enable engine operation at conditions associated with particularly high efficiency (Kalghatgi, 2005;Bell, 2010;Mittal and Heywood, 2008;Amer et al, 2012;Kalghatgi, 2014;Szybist and Splitter, 2017;Abdul-Manan et al, 2018). To boost the OI of a fuel, chemical species with high RON and high OS such as ethanol and MTBE can be added (Demirbas et al, 2015;Badia et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Graph Machine Learning for Design of High-Octane Fuels

Rittig¹,

Ritzert²,

Schweidtmann³

et al. 2022

Preprint

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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 auto-ignition training data.

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Section: Graph-based Property Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Machine Learning for Design of High-Octane Fuels

Rittig¹,

Ritzert²,

Schweidtmann³

et al. 2022

Preprint

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“…However, many recent studies [2,3] have determined that modern spark ignition (SI) engines are better served by an antiknock rating called octane index (OI), given by…”

Section: Introductionmentioning

confidence: 99%

Evaluating causal‐based feature selection for fuel property prediction models

Nguyen

Whitmore

George

et al. 2021

Statistical Analysis

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In-silico screening of novel biofuel molecules based on chemical and fuel properties is a critical first step in the biofuel evaluation process due to the significant volumes of samples required for experimental testing, the destructive nature of engine tests, and the costs associated with bench-scale synthesis of novel fuels. Predictive models are limited by training sets of few existing measurements, often containing similar classes of molecules that represent just a subset of the potential molecular fuel space. Software tools can be used to generate every possible molecular descriptor for use as input features, but most of these features are largely irrelevant and training models on datasets with higher dimensionality than size tends to yield poor predictive performance. Feature selection has been shown to improve machine learning models, but correlation-based feature selection fails to provide scientific insight into the underlying mechanisms that determine structure-property relationships. The implementation of causal discovery in feature selection could potentially inform the biofuel design process while also improving model prediction accuracy and robustness to new data. In this study, we investigate the benefits causal-based feature selection might have on both model performance and identification of key molecular substructures. We found that causal-based feature selection performed on par with alternative filtration methods, and that a structural causal model provides valuable scientific insights into the relationships between molecular substructures and fuel properties.

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“…RON has been identified as more important for modern engine performance. − However, it is increasingly reported that the knock-limited performance of modern engines may be further enhanced by having lower MON for a given RON. The term octane sensitivity (OS = RON – MON) is commonly used to describe this attribute, with higher OS being considered advantageous. − The explanation for this benefit relates to the in-cylinder temperature and pressure trajectories under knock-limited operation in modern SI engines. , The chemical kinetics associated with the combustion of fuels with high OS yield longer ignition delays as the temperature decreases for a given pressure. − Consequently, high OS fuels exhibit greater knock resistance in modern SI engines and are, therefore, better suited for enabling efficiency gains relative to low OS fuels.…”

Section: Introductionmentioning

confidence: 99%

Refining Economics of Higher Octane Sensitivity, Research Octane Number and Ethanol Content for U.S. Gasoline

Hirshfeld¹,

Kolb²,

Anderson

et al. 2021

Energy Fuels

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Increasing the minimum octane ratings of the U.S. gasoline pool would enable higher engine efficiency in future light-duty vehicles (e.g., through higher compression ratio engines), facilitating greater vehicle fuel economy and lower greenhouse gas emissions. This study applied a linear programming model of the aggregate U.S. refining sector to estimate the technical and economic effects of producing a gasoline pool meeting alternative combinations of national standards for ethanol content, minimum research octane number (RON), and minimum octane sensitivity (OS = RON – MON, where MON is the motor octane number). The primary effects assessed included refining capacity additions and investments, incremental refining and fuel production costs, crude oil consumption, refinery CO2 emissions, and national average gasoline properties and composition. The analysis indicated that (i) with refining technology and gasoline blendstocks currently used in the U.S, the average RON and OS of the gasoline pool could be increased significantly using conventional refining processes and gasoline blendstocks, especially at higher levels of ethanol blending; (ii) a 102 RON standard could be met with E15, E20, and E25; (iii) the highest attainable OS standard would be 10 OS with E10, 12 OS with E15, 13 OS with E20, and 14 OS with E25. While incremental fuel production costs ($/gal) and refining investment requirements ($ billion) would increase with increasing RON and OS standards (for each level of assumed ethanol blending), increased RON for E10 gasoline might be attained at a cost of 3 and 10¢/gal for 95 and 98 RON, respectively. Adding large volumes of a high-RON, high-OS blendstock, such as ethanol (currently produced in large volume) or iso-octene (currently produced in only de minimis volumes), would extend the achievable RON and OS frontier, with high OS levels achievable only with iso-octene significantly increasing incremental refining cost. Additional ethanol use would offset some of the increase in incremental refining cost by reducing the required volume and RON of the hydrocarbon portion of the gasoline pool.

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Exploring Alternative Octane Specification Methods for Improved Gasoline Knock Resistance in Spark-Ignition Engines

Cited by 9 publications

References 31 publications

Graph Machine Learning for Design of High-Octane Fuels

Graph Machine Learning for Design of High-Octane Fuels

Evaluating causal‐based feature selection for fuel property prediction models

Refining Economics of Higher Octane Sensitivity, Research Octane Number and Ethanol Content for U.S. Gasoline

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