A review on applications of artificial intelligence in wastewater treatment
In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve
practical problems, has attracted much attention and has been widely used in various areas …
practical problems, has attracted much attention and has been widely used in various areas …
Artificial neural network modeling of photocatalytic degradation of pollutants: a review of photocatalyst, optimum parameters and model topology
In the modern world, wastewater treatment is a critical responsibility for both residential and
commercial processes. This article compiled and discussed the photocatalytic degradation …
commercial processes. This article compiled and discussed the photocatalytic degradation …
Plant-scale biogas production prediction based on multiple hybrid machine learning technique
The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting
in low prediction accuracy when using traditional machine learning algorithms. In this study …
in low prediction accuracy when using traditional machine learning algorithms. In this study …
Optimization and prediction of biogas yield from pretreated Ulva Intestinalis Linnaeus applying statistical-based regression approach and machine learning algorithms
A statistical-based regression approach and machine learning (ML) algorithms (response
surface methodology (RSM), feed-forward backpropagation artificial neural network (ANN) …
surface methodology (RSM), feed-forward backpropagation artificial neural network (ANN) …
An integrated online dynamic modeling scheme for organic Rankine cycle (ORC): Adaptive self-organizing mechanism and convergence evaluation
X Ping, F Yang, H Zhang, C Xing, H Yang… - Applied Thermal …, 2023 - Elsevier
The reasonable construction of organic Rankine cycle (ORC) data-driven model is the basis
of analysis, prediction and optimization. The validity of data, the rationality of input variables …
of analysis, prediction and optimization. The validity of data, the rationality of input variables …
Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator
J Li, Z Chen, X Li, X Yi, Y Zhao, X He, Z Huang… - … Science & Engineering, 2023 - Springer
Anaerobic process is regarded as a green and sustainable process due to low carbon
emission and minimal energy consumption in wastewater treatment plants (WWTPs) …
emission and minimal energy consumption in wastewater treatment plants (WWTPs) …
Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review
Biogas plant operators often face huge challenges in the monitoring, controlling and
optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding …
optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding …
Evaluation of artificial neural network models for predictive monitoring of biogas production from cassava wastewater: A training algorithms approach
IA Cruz, VRS Nascimento, RJA Felisardo… - Biomass and …, 2023 - Elsevier
Anaerobic digestion (AD) is an established technology for resource recovery and renewable
energy production. However, its performance is dictated by diverse variables including …
energy production. However, its performance is dictated by diverse variables including …
Evolutionary optimization of biogas production from food, fruit, and vegetable (FFV) waste
OO Olatunji, PA Adedeji, N Madushele… - Biomass Conversion …, 2024 - Springer
The success of anaerobic digestion (AD) process for biogas production is contingent upon
complex mix of operating factors, process conditions, and feedstock types, which could be …
complex mix of operating factors, process conditions, and feedstock types, which could be …
A systematic review of machine-learning solutions in anaerobic digestion
The use of machine learning (ML) in anaerobic digestion (AD) is growing in popularity and
improves the interpretation of complex system parameters for better operation and …
improves the interpretation of complex system parameters for better operation and …