Assessment of the effect of wastewater quantity and quality, and sludge parameters on predictive abilities of non-linear models for activated sludge settleability predictions

• Autorzy: Szelag B. , Gawdzik J.
• Języki publikacji: EN

Abstrakty

EN

This paper discusses the possibility of applying the three “black-box” methods to sludge settleability predictions. Additionally, the impact of the load of biogenic compounds and parameters of activated sludge on predictive abilities of the devised mathematical models is analysed in the paper. To conduct analyses we relied on the results of measurements of wastewater quantity and quality, and of the bioreactor operational parameters, taken on continuous basis at the Sitkówka-Nowiny treatment plant in 2012-16. The analyses conducted for the study indicate that the lowest values of errors in activated sludge settleability predictions for the wastewater treatment plant of concern were obtained for input data on the load of biogenic compounds at the inflow, microorganism culture environment, and activated sludge concentration.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2017
• Tom: 26
• Numer: 1
• Opis fizyczny: p.315-322,fig.,ref.

Twórcy

autor

Szelag B.

• Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, Tysiaclecia Panstwa Polskiego 7, 25-314 Kielce, Poland

autor

Gawdzik J.

• Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, Tysiaclecia Panstwa Polskiego 7, 25-314 Kielce, Poland

Bibliografia

• 1. ROMANOWICZ R.J., KICZKO A., NAPIÓRKOWSKI J.J. Stochastic Transfer Function Simulator of a 1-D Flow Routing. Publs. Inst. Geophys. Pol. Acad. Sc., E-10 (406), 151, 2008.
• 2. KICZKO A., ROMANOWICZ R., OSUCH M., KARAMUZ E. Maximising the usefulness of flood risk assessment for the River Vistula in Warsaw. Natural Hazards and Earth System Sciences, 13, 3443, 2013.
• 3. KUSIAK A., ZENG Y., ZHANG Z. Modeling and analysis of pumps in a wastewater treatment plant: A data-mining approach. Engineering Applications of Artificial Intelligence 26 (4), 1643, 2013.
• 4. VERMA A., WEI X., KUSIAK A. Predicting the total suspended solids in wastewater: A data-mining approach. Engineering Applications of Artificial Intelligence, 26 (4), 1366, 2013.
• 5. WEI X., KUSIAK A. Optimization of Biogas Production Process in a Wastewater Treatment Plant. Proceedings of the 2012 Industrial and Systems Engineering Research Conference 2012.
• 6. HAN H., LI Y., GUO Y., QIAO J. A soft computing method to predict sludge volume index based on a recurrent self organizing neural network. Applied Soft Computing. 38, 477, 2016.
• 7. LOU I., ZHAO Y. Sludge Bulking Prediction Using Principle Component Regression and Artificial Neural Network. Mathematical Problems in Engineering. 2012, 1, 2012.
• 8. GIOKAS D.L., DAIGGER G.T., SPERLING M., KIM Y., PARASKEVAS P.A. Comparison and evaluation of empirical zone settling velocity parameters based on sludge volume index using a unified settling characteristics database. Water Research. 37 (16), 3821, 2006.
• 9. RUTKOWSKI L. Artificial intelligence methods and techniques Warszawa, PWN, 2006 [In Polish].
• 10. VAPNIK V. Statistical Learning Theory. John Wiley and Sons. New York, 1998.
• 11. BURGES C., A tutorial on support vector machines for pattern recognition (Knowledge discovery and data mining, Usama Fayyad). Kluwer Academic Publishers, Boston. Manufactured in The Netherlands, 1, 2000.
• 12. PIOTROWSKI A., NAPIORKOWSKI J., ROWIŃSKI P. Flash-flood forecasting by means of neural networks and nearest neighbour approach – a comparative study. Nonlinear Processes Geophysics, 13, 443, 2006.
• 13. ZHANG W., GOH A.T.C. Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geoscience Frontiers. 7 (1), 45, 2016.
• 14. SHARDA V.N., PRASHER S.O., PATEL R.M., OJASVI P.R., PRAKASH C. Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan microwatersheds with limited data. Hydrological Sciences Journal des Sciences Hydrologiques, 53 (6), 1165, 2008.
• 15. DE VEAUX R.D., PSICHOGIOS D.C., UNGAR L.H. A Comparison of two nonparametric estimation schemes: MARS and neural networks. Computers & Chemical Engineering 17 (8), 819, 1993.
• 16. GUTIÉRREZ G., SCHNABEL Á., S., CONTADOR J. F. L. Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecological Modelling 220 (24), 3630, 2009.
• 17. FRIEDMAN J. Multivariate Adaptive Regression Splines, Annals of Statistics, 19, 1, 1991.

Identyfikatory

DOI

10.15244/pjoes/64810