References
- K. Barbusiński, H. Kościelniak, Influence of substrate loading
intensity on floc size in activated sludge process, Water Res., 29
(1995) 1703–1710.
- H. Chua, P.H. Yu, S.N. Sin, K.N. Tan, Effect of food:
microorganism ratio in activated sludge foam control, Appl.
Biochem. Biotechnol., 84–86 (2000) 1127–1135.
- M. Henze, P. Harremoes, E. Arvin, J. Lacour, Wastewater
Treatment, Biological and Chemical Processes, Springer-Verlag,
Berlin, 2002.
- K.-U. Do, R.J. Banu, D.-H. Son, I.-T. Yeom, Influence of ferrous
sulphate on thermochemical sludge disintegration and on
performance of wastewater treatment in an anoxic/oxic MBR,
Biochem. Eng. J., 66 (2012) 20–26.
- K.-U. Do, I.-T. Yeom, P. Arulazhagan, J.R. Banu, Effects
of sludge pretreatment on sludge reduction in a lab-scale
anaerobic/anoxic/oxic system treating domestic wastewater,
Int. J. Environ. Sci. Technol., 10 (2013) 495–502.
- S.A. Dellana, D. West, Predictive modeling for wastewater
applications: linear and nonlinear approaches, Environ. Modell.
Software, 24 (2009) 96–106.
- H. Liu, M. Huang, C.K. Yoo, A fuzzy neural network-based
soft sensor for modeling nutrient removal mechanism in fullscale
wastewater treatment system, Desal. Wat. Treat., 51 (2013)
5184–5193.
- H. Boztopak, Y. Őzbay, D. Güçlü, M. Küçükhemek, Prediction
of sludge volume index bulking using image analysis and
neural network at full-scale activated sludge plant, Desal. Wat.
Treat., 57 (2016) 17195–17205.
- B. Szeląg, P. Siwicki, Application of the Selected Classification
Models to the Analysis of the Settling Capacity of the Activated
Sludge – Case Study, B. Kaźmierczak, M. Kutyłowska, K.
Piekarska, A. Trusz-Zdybek, E3S Web of Conferences, Vol. 17,
Boguszów-Gorce, Poland, 2017, pp. 1–7.
- Y.S.T. Hong, R. Bhamidimarri, Evolutionary self-organising
modelling of a municipal wastewater treatment plant, Water
Res., 37 (2003) 1199–1212.
- D. Güçlü, Ş. Dursun, Artificial neural network modelling of a
large-scale wastewater treatment plant operation, Bioprocess
Biosyst. Eng., 33 (2010) 1051–1058.
- D. Ribeiro, A. Sanfins, O. Belo, ICDM’13 Proceedings of the
13th International Conference on Advance in Data Mining:
Applications and Theoretical Aspects, Wastewater Treatment
Plant Performance Prediction with Support Vector Machines,
New York, 2013, pp. 99–111.
- K.-U. Do, R.J. Banu, S. Kaliappan, Y. Tae, Influence of the
thermochemical sludge pretreatment on nitrification of A/O
reactor removing phosphorus simultaneous precipitation,
Biotechnol. Bioprocess Eng., 18 (2013) 313–320.
- K. Yetilmezsoy, Modeling Studies for the Determination of
Completely Mixed Activated Sludge Reactor Volume: Steady-
State, Empirical and ANN Applications, Q. Ashton, Advance
in Machine Learning Research and Application, Atlanta, 2012,
pp. 559–589.
- B. Szeląg, L. Bartkiewicz, J. Studziński, Black-box forecasting
of selected indicator values for influent wastewater quality in
municipal treatment plant, Environ. Prot., 38 (2016) 39–46 (in
Polish).
- L. Jurik, T. Kaletova, M. Sedmakova, P. Balazova,
A. Cervenanska, Comparison of service characteristics of two
town’s WWTP, J. Ecol. Eng., 18 (2017) 61–67.
- D. Rousseau, F. Verdanck, D. Moerman, R. Carrette, C. Thoeye,
J. Meirlaen, P.A. Venrolleghem, Development of a risk
assessment based technique for design/retroffing WWTP,
Water Sci. Technol., 43 (2001) 287–294.
- A. Verma, X. Wei, A. Kusiak, Predicting the total suspended
solids in wastewater: a data-mining approach, Eng. Appl. Artif.
Intell., 26 (2013) 1366–1372.
- A. Kusiak, H. Zheng, Z. Zhang, Virtual wind speed sensor for
wind turbines, J. Energy Eng., 37 (2011) 59–69.
- B. Szeląg, J. Gawdzik, Assessment of the effect of wastewater
quantity and quality, and sludge parameters on predictive
abilities of non-linear models for activated sludge settleability
predictions, Pol. Environ. Stud., 26 (2017) 315–322.
- L. Bartkiewicz, B. Szeląg, J. Studziński, Impact assessment
of input variables and ANN model structure on forecasting
wastewater inflow into sewage treatment plants, Environ. Prot.,
38 (2016) 29–36 (in Polish).
- L. Rutkowski, Artificial Intelligence Methods and Techniques:
Computational Intelligence, PWN, Warszawa, 2006 (in Polish).
- S. Ossowski, Neural Networks for Information Processing,
Publishing House of the Warsaw University of Technology,
Warszawa, 2013.
- H.R. Maier, A. Jain, G.C. Dandy, K.P. Sudheer, Methods used
for the development of neural networks for the prediction of
water resource variables in river systems: current status and
future directions, Environ. Modell. Software, 25 (2010) 891–909.
- I. Lou, Y. Zhao, Sludge bulking prediction using principle
component regression and artificial neural network, Math.
Prob. Eng., 2012 (2012) 1–17.
- G. Capizzi, G.L. Sciuto, P. Monforte, C. Napoli, Cascade
feed forward neural network-based model for air pollutants
evaluation of single monitoring stations in urban areas, Int.
J. Electron. Telecommun., 61 (2015) 327–332.
- M.S. Al-batah, M.S. Alkhasawneh, L.T. Tay, U.K. Ngah, H.H.
Lateh, N.A.M. Isa, Landslide occurrence prediction using
trainable cascade forward network and multilayer perceptron,
Math. Prob. Eng., 2015 (2015) 1–9.
- V. Vapnik, Statistical Learning Theory, John Wiley and Sons,
New York, 1998.
- C. Burges, A Tutorial on Support Vector Machines for Pattern
Recognition, U. Fayyad, Knowledge Discovery and Data
Mining, Kluwer, 1998, pp. 1–43.
- J.H. Friedman, Stochastic gradient boosted, Comput. Stat. Data
Anal., 38 (2002) 367–378.
- H.Z. Abyaneh, Evaluation of multivariate linear regression
and artificial neural networks in prediction of water quality
parameters, J. Environ. Health Sci., 12 (2014) 1–8.
- K. Minsoo, K. Yejin, K. Hyosoo, P. Wenhua, K. Changwon,
Evaluation of the k-nearest neighbour method for forecasting
the influent characteristics of wastewater treatment plant,
Front. Environ. Sci. Eng., 10 (2016) 299–310.
- 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.
- R. Rustum, Modelling Activated Sludge Wastewater Treatment
Plants Using Artificial Intelligence Techniques (Fuzzy Logic
and Neural Networks), Doctor of Philosophy, Heriot, 2009.