Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with
simultaneous production of renewable energy and nutrient-rich digestate. AD process …

Machine learning (ML) models have been widely used in the modeling, design and
prediction in energy systems. During the past two decades, there has been a dramatic …

Artificial neural networks (ANNs) have recently attracted significant attention in
environmental areas because of their great self-learning capability and good accuracy in …

Nowadays, learning-based modeling system is adopted to establish an accurate prediction
model for renewable energy resources. Computational Intelligence (CI) methods have …

This study is an attempt to quantitatively test and compare novel advanced-machine
learning algorithms in terms of their performance in achieving the goal of predicting flood …

Biorefinery systems are playing pivotal roles in the technological support of resource
efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential …

Anaerobic co-digestion (AcoD) offers several merits such as better digestibility and process
stability while enhancing methane yield due to synergistic effects. Operation of an efficient …

Studies on develo** strategies to predict the stability and performance of the composting
process have increased in recent years. Machine learning (ML) has focused on process …

The circular economy (CE) aims to decouple the growth of the economy from the
consumption of finite resources through strategies, such as eliminating waste, circulating …

Agricultural wastes such as lignocellulosic residues are renewable resources can be used
for mushroom cultivation. Spent mushroom substrate (SMS) is defined as leftover of biomass …