Application of machine learning in anaerobic digestion: Perspectives and challenges
Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with
simultaneous production of renewable energy and nutrient-rich digestate. AD process …
simultaneous production of renewable energy and nutrient-rich digestate. AD process …
Data to intelligence: The role of data-driven models in wastewater treatment
Increasing energy efficiency in wastewater treatment plants (WWTPs) is becoming more
important. An emerging approach to addressing this issue is to exploit development in data …
important. An emerging approach to addressing this issue is to exploit development in data …
Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms …
Wastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable
and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods …
and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods …
Urban water demand prediction for a city that suffers from climate change and population growth: Gauteng province case study
The proper management of a municipal water system is essential to sustain cities and
support the water security of societies. Urban water estimating has always been a …
support the water security of societies. Urban water estimating has always been a …
[HTML][HTML] Comparison of machine learning methods for ground settlement prediction with different tunneling datasets
L Tang, SH Na - Journal of Rock Mechanics and Geotechnical …, 2021 - Elsevier
This study integrates different machine learning (ML) methods and 5-fold cross-validation
(CV) method to estimate the ground maximal surface settlement (MSS) induced by …
(CV) method to estimate the ground maximal surface settlement (MSS) induced by …
Membrane fouling prediction and uncertainty analysis using machine learning: A wastewater treatment plant case study
Membrane bioreactors (MBRs) have proven to be an extremely effective wastewater
treatment process combining ultrafiltration with biological processes to produce high-quality …
treatment process combining ultrafiltration with biological processes to produce high-quality …
Water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system
X Wan, X Li, X Wang, X Yi, Y Zhao, X He, R Wu… - Environmental …, 2022 - Elsevier
Wastewater recycling is the measure with enormous potentiality to achieve carbon neutrality
in wastewater treatment plants. High-precision online monitoring can improve the stability of …
in wastewater treatment plants. High-precision online monitoring can improve the stability of …
Monitoring and detecting faults in wastewater treatment plants using deep learning
Wastewater treatment plants use many sensors to control energy consumption and
discharge quality. These sensors produce a vast amount of data which can be efficiently …
discharge quality. These sensors produce a vast amount of data which can be efficiently …
Industrial 6G-IoT and machine learning-supported intelligent sensing framework for indicator control strategy in sewage treatment process
In context of 6G mobile computing, the combination of Industrial Internet of Things (IoT) and
machine learning extends intelligent sensing ability to improve industrial operation …
machine learning extends intelligent sensing ability to improve industrial operation …
Robust unsupervised anomaly detection via multi-time scale DCGANs with forgetting mechanism for industrial multivariate time series
H Liang, L Song, J Wang, L Guo, X Li, J Liang - Neurocomputing, 2021 - Elsevier
Detecting anomalies in time series is a vital technique in a wide variety of industrial
application in which sensors monitor expensive machinery. The complexity of this task …
application in which sensors monitor expensive machinery. The complexity of this task …