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Machine Learning-Based Screening of Fuel Properties for Si and Ci Engines Using a Novel Group Extraction Method

https://doi.org/10.2139/ssrn.4677472
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inferredcites →https://doi.org/10.1002/aic.17971openalex-extracted
Graph machine learning for design of high‐octane fuels
inferredcites →https://doi.org/10.1002/aic.690401011openalex-extracted
New group contribution method for estimating properties of pure compounds
inferredcites →https://doi.org/10.1002/anie.201607257openalex-extracted
Advanced Biofuels and Beyond: Chemistry Solutions for Propulsion and Production
inferredcites →https://doi.org/10.1016/0009-2614(82)80009-2openalex-extracted
Highly discriminating distance-based topological index
inferredcites →https://doi.org/10.1016/j.applthermaleng.2018.04.017openalex-extracted
Comparative study on the pumping losses between continuous variable valve lift (CVVL) engine and variable valve timing (VVT) engine
inferredcites →https://doi.org/10.1016/j.cherd.2014.06.021openalex-extracted
Temperature dependent surface tension estimation using COSMO-RS sigma moments
inferredcites →https://doi.org/10.1016/j.combustflame.2019.12.011openalex-extracted
Investigating the impacts of thermochemical group additivity values on kinetic model predictions through sensitivity and uncertainty analyses
inferredcites →https://doi.org/10.1016/j.egyai.2021.100128openalex-extracted
Machine learning for combustion
inferredcites →https://doi.org/10.1016/j.enconman.2013.09.019openalex-extracted
Method for predicting the standard net heat of combustion for pure hydrocarbons from their molecular structure
inferredcites →https://doi.org/10.1016/j.enconman.2019.05.094openalex-extracted
Combustion, performance and emission analysis of a natural gas-hydrous ethanol dual-fuel spark ignition engine with internal exhaust gas recirculation
inferredcites →https://doi.org/10.1016/j.energy.2022.124823openalex-extracted
Prediction of research octane number loss and sulfur content in gasoline refining using machine learning
inferredcites →https://doi.org/10.1016/j.enpol.2009.06.026openalex-extracted
China's fuel economy standards for passenger vehicles: Rationale, policy process, and impacts
inferredcites →https://doi.org/10.1016/j.fluid.2011.12.024openalex-extracted
Determination of normal boiling vaporization enthalpy using a new molecular-based model
inferredcites →https://doi.org/10.1016/j.fluid.2012.02.010openalex-extracted
Group-contribution+ (GC+) based estimation of properties of pure components: Improved property estimation and uncertainty analysis
inferredcites →https://doi.org/10.1016/j.fluid.2013.06.052openalex-extracted
Modeling of pure compounds surface tension using QSPR
inferredcites →https://doi.org/10.1016/j.fuel.2016.08.040openalex-extracted
Methane/hydrogen fueling a spark-ignition engine for studying NO, CO and HC emissions with a research CFD code
inferredcites →https://doi.org/10.1016/j.fuel.2020.118589openalex-extracted
Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures
inferredcites →https://doi.org/10.1016/j.fuel.2020.118772openalex-extracted
Using machine learning with target-specific feature sets for structure-property relationship modeling of octane numbers and octane sensitivity
inferredcites →https://doi.org/10.1016/j.fuel.2022.123836openalex-extracted
A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties
inferredcites →https://doi.org/10.1016/j.fuel.2022.123941openalex-extracted
A machine learning model for predicting threshold sooting index (TSI) of fuels containing alcohols and ethers
inferredcites →https://doi.org/10.1016/j.ijhydene.2018.09.156openalex-extracted
A numerical study of the combustion and jet characteristics of a hydrogen fueled turbulent hot-jet ignition (THJI) chamber
inferredcites →https://doi.org/10.1016/j.jclepro.2016.06.021openalex-extracted
A cleaner and greener fuel: Biofuel blend formulation and emission assessment
inferredcites →https://doi.org/10.1016/j.jlp.2014.10.005openalex-extracted
Accurate prediction of the standard net heat of combustion from molecular structure
inferredcites →https://doi.org/10.1016/j.jmgm.2013.04.007openalex-extracted
Prediction of boiling points of organic compounds by QSPR tools
inferredcites →https://doi.org/10.1016/j.jmgm.2018.11.013openalex-extracted
QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods
inferredcites →https://doi.org/10.1016/j.proci.2020.07.009openalex-extracted
A comparison of computational models for predicting yield sooting index
inferredcites →https://doi.org/10.1016/s0378-3812(01)00431-9openalex-extracted
Group-contribution based estimation of pure component properties
inferredcites →https://doi.org/10.1021/acs.chemrev.5b00215openalex-extracted
A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes
inferredcites →https://doi.org/10.1021/acs.energyfuels.5b02674openalex-extracted
Model-Based Design of Tailor-Made Biofuels
inferredcites →https://doi.org/10.1021/acs.energyfuels.6b01690openalex-extracted
Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression
inferredcites →https://doi.org/10.1021/acs.energyfuels.7b00616openalex-extracted
A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure
inferredcites →https://doi.org/10.1021/acs.energyfuels.7b04075openalex-extracted
Quantitative Structure–Property Relationship Model for Hydrocarbon Liquid Viscosity Prediction
inferredcites →https://doi.org/10.1021/acs.energyfuels.8b00722openalex-extracted
Compositional Effect of Gasoline on Fuel Economy and Emissions
inferredcites →https://doi.org/10.1021/acs.energyfuels.8b02349openalex-extracted
Target-Oriented Fuel Design for the Homogeneous Charge Autoignition Combustion Mode: A Case Study of a n-Heptane–PODE3–Ethanol Mixture. 1. A Pathway To Increase the Combustion Efficiency and Reduce Pollutant Emissions
inferredcites →https://doi.org/10.1021/acs.energyfuels.8b02761openalex-extracted
Target-Oriented Fuel Design for the Homogeneous Charge Autoignition Combustion Mode: A Case Study of a n-Heptane–PODE3–Ethanol Mixture. 2. Identification of a Functional Configuration of Fuel Components
inferredcites →https://doi.org/10.1021/acs.iecr.2c04661openalex-extracted
Predicting the Physicochemical Properties of Molecules in Petroleum Based on Structural Increments
inferredcites →https://doi.org/10.1021/acs.iecr.7b02753openalex-extracted
Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds
inferredcites →https://doi.org/10.1021/ci000139topenalex-extracted
Prediction of Surface Tension, Viscosity, and Thermal Conductivity for Common Organic Solvents Using Quantitative Structure−Property Relationships
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QSPR Correlation of Melting Point for Drug Compounds Based on Different Sources of Molecular Descriptors
inferredcites →https://doi.org/10.1021/cr900238dopenalex-extracted
Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction
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Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure Property Relationship (QSPR) Methods
inferredcites →https://doi.org/10.1021/ie0501881openalex-extracted
Automatic Creation of Missing Groups through Connectivity Index for Pure-Component Property Prediction
inferredcites →https://doi.org/10.1021/jp953224qopenalex-extracted
Correlation of Boiling Points with Molecular Structure. 1. A Training Set of 298 Diverse Organics and a Test Set of 9 Simple Inorganics
inferredcites →https://doi.org/10.1080/00986448708960487openalex-extracted
ESTIMATION OF PURE-COMPONENT PROPERTIES FROM GROUP-CONTRIBUTIONS
inferredcites →https://doi.org/10.1080/1062936x.2013.766634openalex-extracted
On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods
inferredcites →https://doi.org/10.1080/17460441.2020.1770223openalex-extracted
What place does molecular topology have in today’s drug discovery?
inferredcites →https://doi.org/10.1155/2014/520652openalex-extracted
QSPR Models for Octane Number Prediction
inferredcites →https://doi.org/10.1201/9781315380476openalex-extracted
CRC Handbook of Chemistry and Physics
inferredcites →https://doi.org/10.1520/stp225-ebopenalex-extracted
Knocking Characteristics of Pure Hydrocarbons
inferredcites →https://doi.org/10.2139/ssrn.3990072openalex-extracted
inferredcites →https://doi.org/10.2172/1291241openalex-extracted
A Group Contribution Method for Estimating Cetane and Octane Numbers
inferredcites →https://doi.org/10.2172/1345058openalex-extracted
Compendium of Experimental Cetane Numbers
inferredcites →https://doi.org/10.2172/1806564openalex-extracted
Top 13 Blendstocks Derived from Biomass for Mixing-Controlled Compression-Ignition (Diesel) Engines: Bioblendstocks with Potential for Decreased Emissions and Improved Operability
inferredcites →https://doi.org/10.4271/2016-01-2166openalex-extracted
Compositional Effects of Gasoline Fuels on Combustion, Performance and Emissions in Engine
inferredcites →https://doi.org/10.4271/2016-01-9073openalex-extracted
MMT Effects on Gasoline Vehicles: A Literature Review
inferredcites →https://doi.org/10.4271/2017-36-0259openalex-extracted
The Use of Split-Injection Technique and Ethanol Lean Combustion on a SIDI Engine Operation for Reducing the Fuel Consumption and Pollutant Emissions
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