[HTML][HTML] A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes
Process monitoring technologies play a key role in maintaining the steady state of industrial
processes. However, with the increasing complexity of modern industrial processes …
processes. However, with the increasing complexity of modern industrial processes …
[HTML][HTML] Deep learning in wastewater treatment: a critical review
Modelling wastewater processes supports tasks such as process prediction, soft sensing,
data analysis and computer assisted design of wastewater systems. Wastewater treatment …
data analysis and computer assisted design of wastewater systems. Wastewater treatment …
Enhancing nitrogen removal through directly integrating anammox into mainstream wastewater treatment: Advantageous, issues and future study
L Zhang, L Jiang, J Zhang, J Li, Y Peng - Bioresource Technology, 2022 - Elsevier
Anaerobic ammonium oxidation (anammox) has great potential to be applied to the process
of nitrogen removal from mainstream wastewater. However, directly applying complete …
of nitrogen removal from mainstream wastewater. However, directly applying complete …
Development of a wide-range soft sensor for predicting wastewater BOD5 using an eXtreme gradient boosting (XGBoost) machine
In wastewater monitoring, detecting extremely high pollutant concentrations is necessary to
properly calibrate the treatment process. However, existing hardware sensors have a limited …
properly calibrate the treatment process. However, existing hardware sensors have a limited …
Architecture for AI-Based Validation of Wastewater Management Using Open Data Exchange Technique
To processing plant-wide data, it is not uncommon for there to be issues with sensors. Some
examples of these issues include sensor failures, sensor calibration difficulties, sensor …
examples of these issues include sensor failures, sensor calibration difficulties, sensor …
Missing data imputation and sensor self-validation towards a sustainable operation of wastewater treatment plants via deep variational residual autoencoders
Missing data imputation and automatic fault detection of wastewater treatment plant (WWTP)
sensors are crucial for energy conservation and environmental protection. Given the …
sensors are crucial for energy conservation and environmental protection. Given the …
A hybrid extreme learning machine and deep belief network framework for sludge bulking monitoring in a dynamic wastewater treatment process
In biological wastewater treatment plants (WWTPs), sludge thickening is a common problem
with major economic and environmental effects. Monitoring the sludge volume index (SVI) is …
with major economic and environmental effects. Monitoring the sludge volume index (SVI) is …
Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and …
A membrane bioreactor (MBR) is a crucial wastewater treatment unit that requires
continuous and precise monitoring to ensure stable operation and avoid energy loss …
continuous and precise monitoring to ensure stable operation and avoid energy loss …
Simultaneous sensor fault diagnosis and reconstruction for intelligent monitoring in wastewater treatment plants: An explainable deep multi-task learning model
Sensor malfunctions in wastewater treatment plants (WWTPs) significantly disrupt process
control and energy usage, highlighting the critical need for effective sensor fault diagnosis …
control and energy usage, highlighting the critical need for effective sensor fault diagnosis …
A general knowledge-guided framework based on deep probabilistic network for enhancing industrial process modeling
J Wang, S Xie, Y Xie, X Chen - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
Deep learning models are increasingly being used as effective techniques for industrial
process modeling. However, decisions generated from deep learning models can hardly to …
process modeling. However, decisions generated from deep learning models can hardly to …