Abstract: This article analyzes the effect that the physicochemical parameters of the wastewater flowing into a treatment plant have on the activated sludge settleability. The statistical analysis shows that as far as the technological parameters are concerned, the activated sludge sedimentation capacity is mostly affected by the biomass concentration in the chamber, whereas as for the abiotic factors, settleability is significantly determined by the season of the year and thus the temperature. With regard to the wastewater quality-related parameters, biological oxygen demand has the greatest effect on settleability. The conducted analyzes involved the development of statistical models to predict the activated sludge sedimentation capacity on the basis of multiple linear regression and genetic programming
B I B L I O G R A F I A1. ANDRZEJCZAK O., LIWARSKA-BIZUKOJĆ E. The effect of the pollutant load on the actived sludge flocks morphology. Gas Water and Sanitary Engineering, 12, 480, 2014 [In Polish].
2. TRACZEWSKA T. M. Biotic and abiotic factors in active sludge bulking. Ochrona Środowiska, 2, 29, 1997 [In Polish].
3. ZHANG CH.H., HU H.D., CHEN J., ZHANG W.W. GOU Y.J., NING K. Influential Factors on Activated Sludge Deterioration in Anoxic-Oxic (A/O) Biological Treatment Plant of Coking Wastewater. Pol. J. Environ. Stud., 22 (6) 1877, 2013.
4. CORTÉS U., MARTINEZ M., COMAS J., SÁNCHEZ-MARRÉ M., RODRIDUEZ-RODA I.A. conceptual model to facilitate knowledge sharing for bulking solving in wastewater treatment plant. AI Communications, 16, 279, 2003.
5. RŐSSLE W.H., PRETORIUS W.A. Batch and automated SVI measurement based on the short–term temperature variations. Water SA, 34 (2), 237, 2008.
6. BAGHERI M., MIRBAGHERI S.A., BAGHERI Z., KAMARKHANI A.M. Modeling and optimization for a real wastewater treatment plant using hybrid artificial neural networks – genetic algorithm approach. Process Saf Environ. 95, 12, 2015.
7. HAN H., QIAO J. Hierarchical Neural Network Modeling Approach to Predict Sludge Volume Index of Wastewater Treatment Process. Control Systems Technology, IEEE Transactions on 21, 2423, 2013.
8. SHAHZAD M., KHAN J.S., PAUL P. Influence of the temperature on the performance of a full-scale activated sludge process operated at varying solids retention time whilst treating municipal sewage. Water, 7, 855, 2015.
9. LUO I., ZHAO Y. Sludge Bulking Prediction Using Principle Component Regression and Artificial Neural Network. Math. Prob. Eng. 2012, 1, 2012.
10. SZELĄG B., GAWDZIK J. 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. J. Environ. Stud., 26 (1) 315, 2016.
11. SZELĄG B., SIWICKI P. Application of the selected classification models to the analysis of the settling capacity of the activated sludge – case study. E3S Web of Conference, 17, 00089, 2017.
12. BEZAK-MAZUR E., STOIŃSKA R., SZELĄG B. Evaluation of the impact of operational parameters and particular filamentous bacteria on activated sludge volume index – case study. Annual Set The Environment Protection. 18, 487, 2016 [In Polish].
13. DHRYMES-PHOEBUS J. Mathematic for Econometrics, Springer, New York, 2013.
14. KOZA J.R. Genetic Programming: On the Programming of Computers by Natural Selection, MIT Press, Cambridge, 1992.
15. LIU Y., GUO J., WANG Q., HUANG D. Prediction of filamentous sludge bulking using a state – based Gaussian process regression model. Scientific Reports. 6, 31303, 2016. 
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