References

  1. S.J. Cañote, R.M. Barros, E.E.S. Lora, O.A. del Olmo, I.F.S. dos Santos, J.A.V. Piñas, H.L. Castro e Silva, Energy and economic evaluation of the production of biogas from anaerobic and aerobic sludge in Brazil, Waste Biomass Valorization, 12 (2021) 947–969.
  2. J.P. Brans, Y. De Smet, PROMETHEE Methods, In: Multiple Criteria Decision Analysis, Springer, New York, 2016, pp. 187–219.
  3. European Commission, A New Circular Economy Action Plan: For a Cleaner and More Competitive Europe, European Commission: Brussels, Belgium, 2020. Available at https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri =CELEX:52020DC0098&from=EN (Accessed on 15 October 2022).
  4. D. Fytili, A. Zabaniotou, Utilization of sewage sludge in EU application of old and new methods—a review, Renewable Sustainable Energy Rev., 12 (2008) 116–140.
  5. European Commission, Eurostat, Sewage Sludge Production and Disposal, 2022. Available at https://ec.europa.eu/eurostat/databrowser/view/env_ww_spd/default/table?lang=en (Accessed on 15 October 2022).
  6. Statistics Poland, Environment, Warsaw 2021. Available at https://stat.gov.pl/en/topics/environment-energy/environment/environment-2021,1,13.html (Accessed on 17 October 2022).
  7. Y. Lorenzo-Toja, I. Vázquez-Rowe, M.J. Amores, M. Termes-Rifé, D. Marín-Navarro, M.T. Moreira, G. Feijoo, Benchmarking wastewater treatment plants under an eco-efficiency perspective, Sci. Total Environ., 566 (2016) 468–479.
  8. M. Abbasi, M.A. Abduli, B. Omidvar, A. Baghvand, Results uncertainty of support vector machine and hybrid of wavelet transform-support vector machine models for solid waste generation forecasting, Environ. Prog. Sustainable Energy, 33 (2014) 220–228.
  9. K.P. Wai, M.Y. Chia, C.H. Koo, Y.F. Huang, W.C. Chong, Applications of deep learning in water quality management: a state-of-the-art review, J. Hydrol., 613 (2022) 128332, doi: 10.1016/j.jhydrol.2022.128332.
  10. S. Ahmad, I.H. Khan, B. Parida, Performance of stochastic approaches for forecasting river water quality, Water Resour., 35 (2001) 4261–4266.
  11. A. Taheri Tizro, M. Ghashghaie, P. Georgiou, K. Voudouris, Time series analysis of water quality parameters, J. Appl. Res. Water Wastewater, 1 (2014) 40–50.
  12. A. Katimon, S. Shahid, M. Mohsenipour, Modeling water quality and hydrological variables using ARIMA: a case study of Johor River, Malaysia, Sustainable Water Resour. Manage., 4 (2018) 991–998.
  13. D.O. Faruk, A hybrid neural network and ARIMA model for water quality time series prediction, Eng. Appl. Artif. Intell., 23 (2010) 586–594.
  14. A. Sentas, A. Psilovikos, T. Psilovikos, N. Matzafleri, Comparison of the performance of stochastic models in forecasting daily dissolved oxygen data in dam-Lake Thesaurus, Desal. Water Treat., 57 (2016) 11660–11674.
  15. K.P. Oliveira-Esquerre, D.E. Seborg, M. Mori, R.E. Bruns, Application of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill: Part II. Non-linear approaches, Chem. Eng. J., 105 (2004) 61–69.
  16. E. Dogan, A. Ates, E.C. Yilmaz, B. Eren, Application of artificial neural networks to estimate wastewater treatment plant inlet biochemical oxygen demand, Environ. Prog., 27 (2008) 439–446.
  17. T.Y. Pai, Y.P. Tsai, H.M. Lo, C.H. Tsai, C.Y. Lin, Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent, Comput. Chem. Eng., 31 (2007) 1272–1281.
  18. M.M. Hamed, M.G. Khalafallah, E.A. Hassanien, Prediction of wastewater treatment plant performance using artificial neural networks, Environ. Modell. Software, 19 (2004) 919–928.
  19. F. Tufaner, Y. Demirci, Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and non-linear regressions models, Clean Technol. Environ. Policy, 22 (2020) 713–724.
  20. N.E. Mougari, J.F. Largeau, N. Himrane, M. Hachemi, M. Tazerout, Application of artificial neural network and kinetic modeling for the prediction of biogas and methane production in anaerobic digestion of several organic wastes, Int. J. Green Energy, 18 (2021) 1584–1596.
  21. H. Şenol, Methane yield prediction of ultrasonic pretreated sewage sludge by means of an artificial neural network, Energy, 215 (2021) 119173, doi: 10.1016/j.energy.2020.119173.
  22. X. Wei, A. Kusiak, Optimization of Biogas Production Process in a Wastewater Treatment Plant, Proceedings of the 62nd IIE Annual Conference and Expo, 2012, pp. 1432–1440.
  23. P. Sakiewicz, K. Piotrowski, J. Ober, J. Karwot, Innovative artificial neural network approach for integrated biogas – wastewater treatment system modelling: effect of plant operating parameters on process intensification, Renewable Sustainable Energy Rev., 124 (2020) 109784, doi: 10.1016/j.rser.2020.109784.
  24. J. Gonçalves Neto, L. Vidal Ozorio, T.C. Campos de Abreu, B. Ferreira dos Santos, F. Pradelle, Modeling of biogas production from food, fruits and vegetables wastes using artificial neural network (ANN), Fuel, 285 (2021) 119081, doi: 10.1016/j.fuel.2020.119081.
  25. F. Almomani, Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network, Fuel, 280 (2020) 118573, doi: 10.1016/j.fuel.2020.118573.
  26. I.S. Baruch, P. Georgieva, J. Barrera-Cortes, S. Feyo de Azevedo, Adaptive recurrent neural network control of biological wastewater treatment, Int. J. Intell. Syst., Special Issue: Soft Computing for Modeling, Simulation, and Control of Non-linear Dynamical Systems, 20 (2005) 173–193.
  27. H. Han, S. Zhu, J. Qiao, M. Guo, Data-driven intelligent monitoring system for key variables in wastewater treatment process, Chin. J. Chem. Eng., 26 (2018) 2093–2101.
  28. J.F. Qiao, G.T. Han, H.G. Han, C.L. Yang, W. Li, Decoupling control for wastewater treatment process based on recurrent fuzzy neural network, Asian J. Control, 21 (2019) 1270–1280.
  29. Z. Wang, Y. Man, Y. Hu, J. Li, M. Hong, P. Cui, A deep learning based dynamic COD prediction model for urban sewage, Environ. Sci. Water Res. Technol., 5 (2019) 2210–2218.
  30. I. Groenen, Representing Seasonal Patterns in Gated Recurrent Neural Networks for Multivariate Time Series Forecasting (Doctoral Dissertation, Master Thesis), 2018.
  31. G.P. Zhang, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50 (2003) 159–175.
  32. E. Rahnamaa, O. Bazrafshana, G. Asadollahfardib, S.Y. Samadi, Comparison of Box–Jenkin time series and radial basis function for sodium adsorption rate forecasting; a case study Aras, Sefidrud, Karun, and Mond Rivers, Desal. Water Treat., 218 (2021) 193–209.
  33. G.E.P. Box, G. Jenkins, Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco, CA, 1970.
  34. N. Farhi, E. Kohen, H. Mamane, Y. Shavitt, Prediction of wastewater treatment quality using LSTM neural network, Environ. Technol. Innovation, 23 (2021) 101632, doi: 10.1016/j.eti.2021.101632.
  35. L.R. de Araújo Morais, G.S. da Silva Gomes, Forecasting daily Covid-19 cases in the world with a hybrid ARIMA and neural network model, Appl. Soft Comput., 126 (2022) 109315, doi: 10.1016/j.asoc.2022.109315.
  36. I.S. Markham, T.R. Rakes, The effect of sample size and variability of data on the comparative performance of artificial neural networks and regression, Comput. Oper. Res., 25 (1998) 251–263.
  37. J. Friedman, T. Hastie, R. Tibshirani, The Elements of Statistical Learning, Springer Series in Statistics, Springer, New York, NY, 2001
  38. F. Chollet, Keras: The Python Deep Learning Library, Astrophysics Source Code Library, ASCL-1806, Astrophysics Data System - About ADS (harvard.edu), 2018.