[HTML][HTML] Pyrolysis of lignocellulosic, algal, plastic, and other biomass wastes for biofuel production and circular bioeconomy: a review of thermogravimetric analysis …
Fossil fuels are currently the most significant energy sources. They are expected to become
less available and more expensive, leading to a great demand for energy conservation and …
less available and more expensive, leading to a great demand for energy conservation and …
[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 …
Leveraging ai for superior efficiency in energy use and development of renewable resources such as solar energy, wind, and bioenergy
Energy efficiency and the development of renewable resources are crucial issues in
addressing the global energy crisis and climate change. This research explores the role of …
addressing the global energy crisis and climate change. This research explores the role of …
Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in …
As the global population and economy grow, so does the energy demand. Over-reliance on
non-renewable resources leads to depletion and price spikes, making renewable …
non-renewable resources leads to depletion and price spikes, making renewable …
Green and sustainable biomass supply chain for environmental, social and economic benefits
Bioenergy is a clean and renewable source of energy that can reduce global depency on
fossil fuel, and it is a sustainable, economically viable, and socially acceptable. Bioenergy …
fossil fuel, and it is a sustainable, economically viable, and socially acceptable. Bioenergy …
Advances in machine learning technology for sustainable biofuel production systems in lignocellulosic biorefineries
In view of the global climate change concerns, the society is approaching towards the
development of 'green'and renewable energies for sustainable future. The non-renewable …
development of 'green'and renewable energies for sustainable future. The non-renewable …
Machine learning technology in biohydrogen production from agriculture waste: recent advances and future perspectives
Agricultural waste biomass has shown great potential to deliver green energy produced by
biochemical and thermochemical conversion processes to mitigate future energy crises …
biochemical and thermochemical conversion processes to mitigate future energy crises …
State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery
Abstract Machine learning (ML) has emerged as a significant tool in the field of biorefinery,
offering the capability to analyze and predict complex processes with efficiency. This article …
offering the capability to analyze and predict complex processes with efficiency. This article …
Artificial intelligence for control and optimization of boilers' performance and emissions: A review
Burning fossil fuels is a major concern for global warming control. In Saudi Arabia, steam
power plants that relay on boilers to produce the steam accounted for around 50% of the …
power plants that relay on boilers to produce the steam accounted for around 50% of the …
Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
Biomass-derived materials (BDM) have broad applications in water and agricultural
systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to …
systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to …