Machine learning-quantitative structure property relationship (ML-QSPR) method for fuel physicochemical properties prediction of multiple fuel types

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Abstract

A machine learning-quantitative structure property relationship (ML-QSPR) method is proposed to predict 15 fuel physicochemical properties of 23 fuel types. QSPR-UOB 3.0 functional group classification system is developed to extract and digitalize the molecular structure feature. ML algorithms are used to map the molecular structure feature and fuel properties as well as model parameter tuning. UOB Fuel Property Database (1797 pure compounds and 465 mixtures) is established to provide massive properties data for model training. Cross-validation is chosen to examine predictive precision, avoid overfitting and estimate inter/extrapolation capacity. ML-QSPR method has 4 distinct advantages compared to published statistical methods: (1) It applies to 15 properties of CN, RON, MON, T m, T b, ΔH vap, surface tension γ, LHV, liquid density ρ, YSI, IT, FP, VP, LFL, UFL. (2) It applies to 23 fuel types of alkanes, cycloalkanes, alkenes, cyclic alkenes, alkadienes, alkynes, alcohols, cycloalcohols, aldehydes, ketones, cyclic ketone, saturated esters, unsaturated esters, acyclic ethers, furans, other cyclic ethers, aromatics, carbonate ester, carboxylic anhydride, peroxide, hydroperoxide, polyfunctionals, carboxylic acids. (3) High predictive accuracy is achieved and the average R2 of 15 fuel properties reaches 0.9816. (4) The regression models display reasonable interpolation and extrapolation capacity to test new molecules. The success is attributed to 2 key factors: (1) QSPR-UOB 3.0 system accounts for the contribution of structural features, functional group interaction and fuel reactivity. (2) ML algorithms accurately capture the dependence of fuel properties on chemical structures.

Original languageEnglish
Article number121437
Number of pages20
JournalFuel
Volume304
Early online date20 Jul 2021
DOIs
Publication statusPublished - 15 Nov 2021

Bibliographical note

Funding Information:
This work is supported by Innovate UK (The Technology Strategy Board, TSB, No. 400176/149) and Engineering & Physical Sciences Research Council (EPSRC, No. EP/P03117X/1). Runzhao Li also thanks to University of Birmingham for the award of a Ph.D. research scholarship (No. 1871018). This work is conducted in Future Engines & Fuels Lab, University of Birmingham. The authors also thank Shenzhen Gas Corporation Ltd. for providing us the technical guidance. The authors are indebted to the reviewers of this article for their invaluable suggestions.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Fuel molecular structure
  • Machine learning
  • Multiple fuel properties
  • Multiple fuel types
  • Quantitative structure property relationship

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Organic Chemistry

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