Combustion machine learning: Principles, progress and prospects

M Ihme, WT Chung, AA Mishra - Progress in Energy and Combustion …, 2022 - Elsevier
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

A general guidebook for the theoretical prediction of physicochemical properties of chemicals for regulatory purposes

C Nieto-Draghi, G Fayet, B Creton, X Rozanska… - Chemical …, 2015 - ACS Publications
The REACH regulation (recent EU regulation for “Registration, Evaluation, Authorization,
and Restriction of Chemicals”) entered its product-recording phase after December 2008. 1 …

[HTML][HTML] Machine learning for combustion

L Zhou, Y Song, W Ji, H Wei - Energy and AI, 2022 - Elsevier
Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …

Machine learning to predict biochar and bio-oil yields from co-pyrolysis of biomass and plastics

A Alabdrabalnabi, R Gautam, SM Sarathy - Fuel, 2022 - Elsevier
Because of high oxygen content, pH and viscosity, pyrolysis bio-oil is of low quality.
Upgrading bio-oil can be achieved by co-pyrolysis of biomass with waste plastics, and it is …

Chemical kinetic and combustion characteristics of transportation fuels

FL Dryer - Proceedings of the Combustion Institute, 2015 - Elsevier
Internal combustion engines running on liquid fuels will remain the dominant prime movers
for road and air transportation for decades, probably for most of this century. The world's …

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

R Li, JM Herreros, A Tsolakis, W Yang - Fuel, 2021 - Elsevier
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 …

A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties

AE Comesana, TT Huntington, CD Scown… - Fuel, 2022 - Elsevier
Abstract Machine learning has proven to be a powerful tool for accelerating biofuel
development. Although numerous models are available to predict a range of properties …

[PDF][PDF] Progress in the application of machine learning in combustion studies

Z Zheng, X Lin, M Yang, Z He, E Bao… - ES Energy & …, 2020 - espublisher.com
Combustion is the main source of energy and environmental pollution. The objective of the
combustion study is to improve combustion efficiency and reduce pollution emissions. In the …

Machine learning to predict standard enthalpy of formation of hydrocarbons

KK Yalamanchi, VCO Van Oudenhoven… - The Journal of …, 2019 - ACS Publications
Thermodynamic properites of molecules are used widely in the study of reactive processes.
Such properties are typically measured via experiments or calculated by a variety of …

Modeling the toxicity of pollutants mixtures for risk assessment: a review

M Sigurnjak Bureš, M Cvetnić, M Miloloža… - Environmental chemistry …, 2021 - Springer
The occurrence of contaminants in natural waters is a potential threat to the environment.
Since contaminants are commonly present as mixtures, numerous interactions may occur …