A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems
The optimal co-planning of the integrated energy system (IES) and machine learning (ML)
application on the multivariable prediction of IES parameters have mostly been carried out …
application on the multivariable prediction of IES parameters have mostly been carried out …
The role of machine learning to boost the bioenergy and biofuels conversion
The development and application of bioenergy and biofuels conversion technology can play
a significant role for the production of renewable and sustainable energy sources in the …
a significant role for the production of renewable and sustainable energy sources in the …
[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 …
Applications of artificial intelligence‐based modeling for bioenergy systems: A review
M Liao, Y Yao - GCB Bioenergy, 2021 - Wiley Online Library
Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐
scale applications of biomass‐based energy products are limited due to challenges related …
scale applications of biomass‐based energy products are limited due to challenges related …
Investigation on the ignition delay prediction model of multi-component surrogates based on back propagation (BP) neural network
The ignition delay prediction model of three-component surrogates was established based
on the back propagation (BP) neural network. The ambient temperature, ambient pressure …
on the back propagation (BP) neural network. The ambient temperature, ambient pressure …
Graph neural networks for prediction of fuel ignition quality
Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important
and challenging task for which quantitative structure–property relationship (QSPR) models …
and challenging task for which quantitative structure–property relationship (QSPR) models …
Influence of functional groups on low-temperature combustion chemistry of biofuels
B Rotavera, CA Taatjes - Progress in Energy and Combustion Science, 2021 - Elsevier
Ongoing progress in synthetic biology, metabolic engineering, and catalysis continues to
produce a diverse array of advanced biofuels with complex molecular structure and …
produce a diverse array of advanced biofuels with complex molecular structure and …
Optimal design, operational controls, and data-driven machine learning in sustainable borehole heat exchanger coupled heat pumps: Key implementation challenges …
The integration of technologies has made it possible to develop optimal operating conditions
at reduced costs, which results in a more sustainable energy transition away from fossil fuels …
at reduced costs, which results in a more sustainable energy transition away from fossil fuels …
A review on machine learning application in biodiesel production studies
Y Xing, Z Zheng, Y Sun… - International Journal of …, 2021 - Wiley Online Library
The consumption of fossil fuels has exponentially increased in recent decades, despite
significant air pollution, environmental deterioration challenges, health problems, and …
significant air pollution, environmental deterioration challenges, health problems, and …
[HTML][HTML] A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches
Biofuels from lignocellulosic biomass converted via thermochemical technologies can be
renewable and sustainable, which makes them promising as alternatives to conventional …
renewable and sustainable, which makes them promising as alternatives to conventional …