1. L.A.H.M. Verheijen, D. Weirsema, L.W. Hwshoffpol, J. Dewit, Live Stock and the Environment: Finding a Balance Management of Waste from Animal Product Processing, International Agriculture Centre, Wageningen,
    The Netherlands, 1996.
  2. R. Ganesh, G. Balaji, R.A. Ramanujam, Biodegradation of tannery wastewater using sequencing batch reactor—respirometric assessment, Bioresour. Technol., 97 (2006) 1815–1821.
  3. A. Malviya, D. Jaspal, Artificial intelligence as an upcoming technology in wastewater treatment:
    a comprehensive review, Environ. Technol. Rev., 10 (2021) 177–187.
  4. M. Bagheri, S.A. Mirbhageri, M. Ehteshami, Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks, Process. Saf. Environ., 93 (2015) 111–123.
  5. P. Bajpai, A. Mehna, P.K. Bajpai, Decolorization of Kraft bleach plant effluent with the white rot fungus Trametes versicolor, Process Biochem., 28 (1993) 377–384.
  6. M. Bongards, Improving the efficiency of a wastewater treatment plant by fuzzy control and neural networks, Water Sci. Technol., 43 (2001) 189–196.
  7. L. Govindarajan, Optimal Design of Reactors, Ph.D. Dissertation, Annamalai University, India, 2005.
  8. M. Hack, M. Kohne, Estimation of wastewater process parameters using neural networks, Water Sci. Technol., 33 (1996) 101–115.
  9. Y. Hamamoto, S. Tabata, Y. Okubo, Development of the intermittent cyclic process for simultaneous nitrogen and phosphorus removal, Water Sci. Technol., 35 (1999) 145–152.
  10. M. Huggi, S.R. Mise, Ann model of wastewater treatment process, Int. J. Adv. Res. Eng. Technol., 10 (2019) 1–10.
  11. D.S. Manu, A.K. Thalla, Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl nitrogen from wastewater, Appl. Water Sci., 7 (2017) 3783–3791.
  12. K. Mehrotra, C.K. Mohan, S. Ranka, Elements of Artificial Neural Networks Complex Adaptive Systems, MIT Press, USA, 1997.
  13. E. Molga, R. Ski. Cherba, L. Szpyrkowicz, Modeling of an industrial full scale plant for biological treatment of textile wastewaters: application of neural networks, Ind. Eng. Chem. Res., 45 (2006) 1039–1046.
  14. Y. Mustafa, A.I. Alwared, G. Majeed, The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP, Environ. Sci. Pollut. Res., 21 (2014) 7530–7537.
  15. V. Nourani, G. Elkiran, SI. Abba, Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach, Water Sci. Technol., 78 (2018) 2064–2076.
  16. K.P. Oliveira-Esquerre, M. Mori, R.E. Bruns, Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis, Braz. J. Chem. Eng., 19 (2002) 365–370.
  17. A.R. Picos-Benítez, B.L. Martínez-Vargas, S.M. Duron-Torres, The use of artificial intelligence models in the prediction of optimum operational conditions for the treatment of dye wastewaters with similar structural characteristics, Process Saf. Environ., 143 (2020) 36–44.
  18. M. Ghaedi, A. Ansari, F. Bahari, A.M. Ghaedi, A. Vafaei, A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon, Spectrochim. Acta, Part A, 137 (2015) 1004–1015.
  19. D. Reena, J. Sureshkumar, AI based control approach for membrane bioreactor in sewage water treatment, Int. J. Res. Eng. Technol., 3 (2014) 354–359.
  20. J.P. Steyer, C. Pelayo-Ortiz, V. Gonzalez-Alvarez, Neural network modelling of a depollution process, Bioprocess Eng., 23 (2000) 727–730.
  21. H.A. Zaqoot, M. Hamada, Application of artificial neural networks for the prediction of Gaza wastewater treatment plant performance-Gaza strip, J. Appl. Res. Water Wastewater, 5 (2018) 399–406.
  22. P. Das, A. Debnath, Reactive orange 12 dye adsorption onto magnetically separable CaFe2O4 nanoparticles synthesized by simple chemical route: kinetic, isotherm and neural network modeling, Water Pract. Technol., 16 (2021) 1141–1158.
  23. K. Murugan, S.A. Al-Sohaibani, Biocompatible removal of tannin and associated color from tannery effluent using the biomass and tannin acyl hydrolase (E.C. enzymes of mango industry solid waste isolate Aspergillus candidus MTTC 9628, Res. J. Microbiol., 5 (2010) 262–271.
  24. M. Bhowmik, K. Deb, A. Debnath, B. Saha, Mixed phase
    Fe2O3/Mn3O4 magnetic nanocomposite for enhanced adsorption of methyl orange dye: neural network modeling and response surface methodology optimization, Appl. Organomet. Chem., 32 (2017) e4186.
  25. R. Mohammadi, H. Eskandarloo, M. Mohammadi, Application of artificial neural network (ANN) for modeling of dyes decolorization by Sn/Zn–TiO2 nanoparticles, Desal. Water Treat., 55 (2014) 1922–1933.
  26. A.C. Elekli, S.S. Birecikligil, F. Geyik, H. Bozkurt, Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model, Bioresour. Technol., 103 (2012) 64–70.
  27. A. Debnath, K. Deb, K. Kumar Chattopadhyay, B. Saha, Methyl orange adsorption onto simple chemical route synthesized crystalline α-Fe2O3 nanoparticles: kinetic, equilibrium isotherm, and neural network modeling, Desal. Water Treat., 57 (2016) 13549–13560.
  28. N.H. Singh, K. Kezo, A. Debnath, B. Saha, Enhanced adsorption performance of a novel Fe‐Mn‐Zr metal oxide nanocomposite adsorbent for anionic dyes from binary dye mix: Response surface optimization and neural network modeling, Appl. Organomet. Chem., 32 (2018) e4165.