1. Ministry of Maritime Economy and Inland Navigation, Regulation of the Minister of Maritime Economy and Inland Navigation from July 2019 on Substances Particularly Harmful to the Aquatic Environment and the Conditions to be Met When Discharging Sewage Into Waters or Ground, as Well as When Discharging Rainwater or Meltwater Into Waters or Into Devices Water, Official Gazette of the Republic of Poland, Poland, 2019.
  2. F. Hernández-del-Olmo, E. Gaudioso, R. Dormido, N. Duro, Energy and environmental efficiency for the
    N-ammonia removal process in wastewater treatment plants by means of reinforcement learning, Energies (Basel), 9 (2016) 755, doi: 10.3390/en9090755.
  3. J.-J. Zhu, L. Kang, P.R. Anderson, Predicting influent biochemical oxygen demand: balancing energy demand and risk management, Water Res., 128 (2018) 304–313.
  4. M.-J. Mehrani, J. Drewnowski, M. Majewska, G. Lagód, S. Kumari, F. Bux, B. Szęląg, Assessment of wastewater quality indicators for wastewater treatment influent using an advanced logistic regression model, Desal. Water Treat., 232 (2021) 421–432.
  5. B. Szeląg, L. Bartkiewicz, J. Studziński, K. Barbusiński, Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear, Arch. Environ. Prot., 43 (2017) 74–81.
  6. M. Henze, W. Gujer, T. Mino, M. van Loosedrecht, Activated Sludge Models ASM1, ASM2, ASM2D and ASM3, Water Intelligence Online, IAWPRC Scientific and Technical Reports No. 9, IAWPRC Publisher: IWA Publishing, ISBN: 9781780402369, 2000, doi: 10.2166/9781780402369.
  7. J. Drewnowski, J. Mąkinia, A. Szaja, G. Łagód, Ł. Kopeć, J.A. Aguilar, Comparative study of balancing SRT by using modified ASM2d in control and operation strategy at full-scale WWTP, Water (Basel), 11 (2019) 485, doi: 10.3390/w11030485.
  8. H. Hauduc, I. Takács, S. Smith, A. Szabo, S. Murthy, G.T. Daigger, M. Spérandio, A dynamic physicochemical model for chemical phosphorus removal, Water Res., 73 (2015) 157–170.
  9. B. Petersen, P.A. Vanrolleghem, K. Gernaey, M. Henze, Evaluation of an ASM1 model calibration procedure on a municipal–industrial wastewater treatment plant, J. Hydroinf., 4 (2002) 15–38.
  10. G. Mannina, A. Cosenza, P.A. Vanrolleghem, G. Viviani, A practical protocol for calibration of nutrient removal wastewater treatment models, J. Hydroinf., 13 (2011) 575–595.
  11. R. Vitanza, I. Colussi, A. Cortesi, V. Gallo, Implementing a respirometry-based model into BioWin software to simulate wastewater treatment plant operations, J. Water Process Eng., 9 (2015) 267–275.
  12. H. Haimi, M. Mulas, F. Corona, R. Vahala, Data-derived soft-sensors for biological wastewater treatment plants: an overview, Environ. Modell. Software, 47 (2013) 88–107.
  13. J. Fernandez de Canete, P. Del Saz-Orozco, R. Baratti, M. Mulas, A. Ruano, A. Garcia-Cerezo, Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network, Expert Syst. Appl., 63 (2016) 8–19.
  14. B. Szeląg, J. Drewnowski, G. Łagód, D. Majerek, E. Dacewicz, F. Fatone, Soft sensor application in identification of the activated sludge bulking considering the technological and economical aspects of smart systems functioning, Sensors, 20 (2020) 1941, doi: 10.3390/s20071941.
  15. J. Fernandez de Canete, P. del Saz-Orozco, J. Gómez-de-Gabriel, R. Baratti, A. Ruano, I. Rivas-Blanco, Control and soft sensing strategies for a wastewater treatment plant using a neurogenetic approach, Comput. Chem. Eng., 144 (2021) 107146, doi: 10.1016/j.compchemeng.2020.107146.
  16. T.Y. Pai, P.Y. Yang, S.C. Wang, M.H. Lo, C.F. Chiang, J.L. Kuo, H.H. Chu, H.C. Su, L.F. Yu, H.C. Hu, Y.H. Chang, Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality, Appl. Math. Modell., 35 (2011) 3674–3684.
  17. H. Guo, K. Jeong, J. Lim, J. Jo, Y.M. Kim, J. Park, J.H. Kim, K.H. Cho, Prediction of effluent concentration in a wastewater treatment plant using machine learning models, J. Environ. Sci., 32 (2015) 90–101.
  18. B. Szeląg, K. Barbusiński, J. Studziński, Application of the model of sludge volume index forecasting to assess reliability and improvement of wastewater treatment plant operating conditions, Desal. Water Treat., 140 (2019) 143–154.
  19. Y. Zhang, C. Li, H. Duan, K. Yan, J. Wang, W. Wang, Deep learning based data-driven model for detecting time-delay water quality indicators of wastewater treatment plant influent, Chem. Eng. J., 467 (2023) 143483, doi: 10.1016/j.cej.2023.143483.
  20. American Public Health Association, Standard Methods for the Examination of Water and Wastewater, 21st ed., Washington D.C., 2005.
  21. T. Hastie, R. Tibshirani, J. Friedman, Random Forests, In: The Elements of Statistical Learning, Springer Series in Statistics, Springer, New York, NY, 2009. doi: 10.1007/978-0-387-84858-7_15
  22. T. Chen, C. Guestrin, XGBoost: A Scalable Tree Boosting System, KDD’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785–794. doi: 10.1145/2939672.2939785
  23. B. Ráduly, K.V. Gernaey, A.G. Capodaglio, P.S. Mikkelsen, M. Henze, Artificial neural networks for rapid WWTP performance evaluation: methodology and case study, Environ. Modell. Software, 22 (2007) 1208–1216.
  24. D.S. Lee, M.W. Lee, S.H. Woo, Y.J. Kim, J.M. Park, Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant, Process Biochem., 41 (2006) 2050–2057.
  25. F. Luo, R.H. Yu, Y.G. Xu, Y. Li, Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks, 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009, 2009, pp. 47–51. doi: 10.1109/FSKD.2009.494
  26. H.W. Lee, M.W. Lee, J.M. Park, Multi-scale extension of PLS algorithm for advanced on-line process monitoring, Chemom. Intell. Lab. Syst., 98 (2009) 201–212.
  27. H. Guo, K. Jeong, J. Lim, J. Jo, Y.M. Kim, J. pyo Park, J.H. Kim, K.H. Cho, Prediction of effluent concentration in a wastewater treatment plant using machine learning models, J. Environ. Sci. (China), 32 (2015) 90–101.
  28. M. Yaqub, H. Asif, S. Kim, W. Lee, Modeling of a full-scale sewage treatment plant to predict the nutrient removal efficiency using a long short-term memory (LSTM) neural network, J. Water Process Eng., 37 (2020), doi: 10.1016/j.jwpe. 2020.101388.
  29. B. Szeląg, K. Barbusiński, J. Studziński, Activated sludge process modelling using selected machine learning techniques, Desal. Water Treat., 117 (2018) 78–87.
  30. N. Hvala, J. Kocijan, Design of a hybrid mechanistic/Gaussian process model to predict full-scale wastewater treatment plant effluent, Comput. Chem. Eng., 140 (2020), doi: 10.1016/j.compchemeng.2020.106934.
  31. F. Bagherzadeh, M.-J. Mehrani, M. Basirifard, J. Roostaei, Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance, J. Water Process Eng., 41 (2021) 102033, doi: 10.1016/j.jwpe. 2021.102033.