Early Warning and Management of Excessive Discharge of Water Pollutants in Municipal Wastewater Treatment Plants Based on Fluctuation Coefficients DOI
Yong Ma, Yan Liu,

Kaixuan Liang

et al.

Published: Jan. 1, 2024

Language: Английский

Machine learning enhanced grey box soft sensor for melt viscosity prediction in polymer extrusion processes DOI Creative Commons
Yasith S. Perera, Jie Li, Chamil Abeykoon

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 15, 2025

Melt viscosity is regarded as a key quality indicator of the polymer melt in extrusion processes. However, limitations such disturbances to flow and measurement delays existing in-line side-stream rheometers prevent monitoring controlling this parameter real time. Soft sensors can be employed monitor physical parameters that are difficult measure using hardware sensing instruments. This study presents grey-box soft solution predict time, which combines physics-based knowledge with machine learning. A fine-tuned mathematical model used make predictions, deep neural network compensate for its prediction errors. The proposed sensor reported normalised root mean square error 2.2[Formula: see text]10-3 (0.22%), outperforming fully data-driven models based on multilayer perceptron long short-term memory networks. Furthermore, it exhibited an improvement approximately 95% terms predictive performance, compared radial basis function previous study. changes caused by operating conditions but not suitable detecting due material properties. findings aid enhancing process control

Language: Английский

Citations

1
Frontiers in machine learning strategies for dye removal in water treatment DOI
Guanfeng Zheng, Peng Fu, Xinglin Li

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 71, P. 107251 - 107251

Published: Feb. 15, 2025

Language: Английский

Citations

1
Early Warning and Management of Excessive Discharge of Water Pollutants in Municipal Wastewater Treatment Plants Based on Fluctuation Coefficients DOI
Yong Ma, Yan Liu,

Kaixuan Liang

et al.

Environmental Research, Journal Year: 2025, Volume and Issue: unknown, P. 121127 - 121127

Published: Feb. 1, 2025

Language: Английский

Citations

0
A Multivariable Probability Density-Based Auto-Reconstruction Bi-LSTM Soft Sensor for Predicting Effluent BOD in Wastewater Treatment Plants DOI Creative Commons
Wenting Li,

Yonggang Li,

Dong Li

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7508 - 7508

Published: Nov. 25, 2024

The precise detection of effluent biological oxygen demand (BOD) is crucial for the stable operation wastewater treatment plants (WWTPs). However, existing methods struggle to meet evolving drainage standards and management requirements. To address this issue, paper proposed a multivariable probability density-based auto-reconstruction bidirectional long short-term memory (MPDAR-Bi-LSTM) soft sensor predicting BOD, enhancing prediction accuracy efficiency. Firstly, selection appropriate auxiliary variables soft-sensor modeling determined through calculation k-nearest-neighbor mutual information (KNN-MI) values between global process BOD. Subsequently, considering existence strong interactions among different reaction tanks, Bi-LSTM neural network model constructed with historical data. Then, multivariate (MPDAR) strategy developed adaptive updating model, thereby its robustness. Finally, effectiveness demonstrated experiments using dataset from Benchmark Simulation Model No.1 (BSM1). experimental results indicate that not only outperforms some traditional models in terms performance but also excels avoiding ineffective reconstructions scenarios involving complex dynamic conditions.

Language: Английский

Citations

1
Early Warning and Management of Excessive Discharge of Water Pollutants in Municipal Wastewater Treatment Plants Based on Fluctuation Coefficients DOI
Yong Ma, Yan Liu,

Kaixuan Liang

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0