[HTML][HTML] Machine-learning-aided thermochemical treatment of biomass: a review
H Li, J Chen, W Zhang, H Zhan, C He… - Biofuel Research …, 2023 - biofueljournal.com
Thermochemical treatment is a promising technique for biomass disposal and valorization.
Recently, machine learning (ML) has been extensively used to predict yields, compositions …
Recently, machine learning (ML) has been extensively used to predict yields, compositions …
Applications of machine learning in thermochemical conversion of biomass-A review
Thermochemical conversion of biomass has been considered a promising technique to
produce alternative renewable fuel sources for future energy supply. However, these …
produce alternative renewable fuel sources for future energy supply. However, these …
[HTML][HTML] Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
D Rangel-Martinez, KDP Nigam… - … Research and Design, 2021 - Elsevier
This study presents a broad view of the current state of the art of ML applications in the
manufacturing sectors that have a considerable impact on sustainability and the …
manufacturing sectors that have a considerable impact on sustainability and the …
Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review
H Guo, S Wu, Y Tian, J Zhang, H Liu - Bioresource technology, 2021 - Elsevier
Conventional treatment and recycling methods of organic solid waste contain inherent flaws,
such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past …
such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past …
Hydrogen-rich syngas production from the lignocellulosic biomass by catalytic gasification: A state of art review on advance technologies, economic challenges, and …
Global population growth, modernization, and industrialization have all significantly
increased energy consumption, which has worsened the climate and led to greenhouse gas …
increased energy consumption, which has worsened the climate and led to greenhouse gas …
[HTML][HTML] A survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
Innovation designs of industry 4.0 based solid waste management: Machinery and digital circular economy
Abstract The Industrial Revolution 4.0 (IR 4.0) holds the opportunity to improve the efficiency
of managing solid waste through digital and machinery applications, effectively eliminating …
of managing solid waste through digital and machinery applications, effectively eliminating …
Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening
Hydrogen production from wet organic wastes through supercritical water gasification
(SCWG) promotes sustainable development. However, it is always time-consuming and …
(SCWG) promotes sustainable development. However, it is always time-consuming and …
Utilizing support vector regression modeling to predict pyro product yields from microwave-assisted catalytic co-pyrolysis of biomass and waste plastics
The rise in plastic waste production has led to the development of co-pyrolysis of waste
plastics and biomass as a potential solution. This process converts waste into valuable …
plastics and biomass as a potential solution. This process converts waste into valuable …