Combustion machine learning: Principles, progress and prospects
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 …
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
The REACH regulation (recent EU regulation for “Registration, Evaluation, Authorization,
and Restriction of Chemicals”) entered its product-recording phase after December 2008. 1 …
and Restriction of Chemicals”) entered its product-recording phase after December 2008. 1 …
[HTML][HTML] Machine learning for combustion
Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions 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 …
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 …
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 …
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
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 …
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 …
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 …
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 …
Since contaminants are commonly present as mixtures, numerous interactions may occur …