• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
U.S. Environmental Protection Agency, "Energy Efficiency in Water and Wastewater Facilities: A Guide to Developing and Implementing Greenhouse Gas Reduction Programs," Local Government Climate and Energy Strategy Series, U.S. EPA, 2013. Google Search
2 
W. Shen, X. Chen, J. Pons, J. P. Corriou, "Model predictive control for wastewater treatment process with feedforward compensation," Chemical Engineering Journal, vol. 155, no. 1-2, pp. 161-174, 2009. DOI
3 
S. Hochreiter, J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997. DOI
4 
S. Bai, J. Z. Kolter, V. Koltun, "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling," arXiv Preprint, 2018. arXiv:1803.01271 DOI
5 
M. Henze, W. Gujer, T. Mino, M. van Loosdrecht, "Activated Sludge Models ASM1, ASM2, ASM2d and ASM3," IWA Scientific and Technical Report no. 9, IWA Publishing, London, UK, 2000. DOI
6 
Y. Xie, Y. Chen, Q. Wei, H. Yin, "A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant," Water Research, vol. 250, 2024. DOI
7 
A. Kendall, Y. Gal, R. Cipolla, "Multi-task learning using uncertainty to weigh losses for scene geometry and semantics," pp. 7482-7491, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. DOI
8 
R. Li, K. Feng, T. An, P. Cheng, L. Wei, Z. Zhao, L. Zhu, "Enhanced insights into effluent prediction in wastewater treatment plants: Comprehensive deep learning model explanation based on SHAP," ACS ES&T Water, vol. 4, no. 4, pp. 1904-1915, 2024. DOI
9 
A. Bernardelli, S. Marsili-Libelli, A. Manzini, S. Stancari, G. Tardini, D. Montanari, G. Anceschi, P. Gelli, S. Venier, "Real-time model predictive control of a wastewater treatment plant based on machine learning," Water Science and Technology, vol. 81, no. 11, pp. 2391-2400, 2020. DOI
10 
O. Icke, D. M. van Es, M. F. de Koning, J. J. G. Wuister, J. Ng, K. M. Phua, Y. K. K. Koh, W. J. Chan, G. Tao, "Performance improvement of wastewater treatment processes by application of machine learning," Water Science and Technology, vol. 82, no. 12, 2020. DOI
11 
I. Kalogeropoulos, A. Alexandridis, H. Sarimveis, "Economic oriented dynamic matrix control of wastewater treatment plants," Journal of Process Control, vol. 118, pp. 202-217, 2022. DOI
12 
T. Protoulis, I. Kordatos, I. Kalogeropoulos, H. Sarimveis, A. Alexandridis, "Control of wastewater treatment plants using economic-oriented MPC and attention-based RNN disturbance prediction models," Computers & Chemical Engineering, vol. 196, 2025. DOI