Using data mining to predict sludge and filamentous microorganism sedimentation

• Autorzy: Chmielowski K. , Cyekanksi A. , Lesnianska A.
• Języki publikacji: EN

Abstrakty

EN

This study attempted to develop statistical regression models for predicting the settleability of activated sludge based on the quality of incoming sewage and on the identified dominant filamentous species. As part of the analyses conducted for the purpose, classification models are presented that enable identification of the respective filamentous microorganisms, based on the working parameters of the bioreactor and the quality of the influent. The study calculations demonstrated that the modeling methods based on artificial neural networks, random forests, and boost trees can be applied for the identification of filamentous microorganisms Microthrix parvicella, Nostocoida sp., and Thiotrix sp. in activated sludge chambers in the STP located in Sitkówka-Nowiny. The best predictive capacity, covering identification of the above-mentioned filamentous bacterial species in activated sludge chambers, was observed for statistical models obtained by the random forest method.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2019
• Tom: 28
• Numer: 5
• Opis fizyczny: p.3105-3113,fig.,ref.

Twórcy

autor

Chmielowski K.

• Department of Sanitary Engineering and Water Management, University of Agriculture in Krakow, Krakow, Poland

autor

Cyekanksi A.

• Sanockie Przedsiebiorstwo Gospodarki Komunalnej Sp. z o.o., Sanok, Poland

autor

Lesnianska A.

• EkoWodrol Sp. z o.o., Koszalin, Poland

Bibliografia

• 1. FLORES-ALSINAA X., ARNELLA M., AMERLINCKD Y., COROMINAS L., GERNAEY K., GUOF L., LINDBLOMA E., NOPENS I., PORROD J., SHAWI A., SNIP L., VANROLLEGHEM P., JEPPSSON U. Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs. Science of the Total Environment, 466, 2014.
• 2. COROMINASA L., LARSEN H., FLORES-ALSINAA X., VANROLLEGHEM P. Including Life Cycle Assessment for decision-making in controlling wastewater nutrient removal systems. Journal of Environmental Management, 128, 759, 2013.
• 3. KICZKO A., SZELĄG B., KOZIOŁ A., KRUKOWSKI M., KUBRAK E., KUBRAK J., ROMANOWICZ R. Optimal Capacity of a Stormwater Reservoir for Flood Peak Reduction.J. Hydrol. Eng., 23 (4), 2018.
• 4. FLORES-ALSINAA X., RODRÍGUEZ-RODAA I., SINB G., GERNAEY K. Multi-criteria evaluation of wastewater treatment plant control strategies under uncertainty. Water Research, 42, 4485, 2008.
• 5. 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. Vol. 26 (1), 315, 2017.
• 6. 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 Conferences 17, 00089. 2017.
• 7. BAGHERII 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.
• 8. HENZE, M., HARREMOES, P., COUR JANSEN, J. LA, ARVIN, E. Wastewater Treatment Biological and Chemical Processes, 2002.
• 9. BARTOSZEWSKI K., BICZ W., DYMACZEWSKI BARTOSZEWSKI, K., BICZ, W., DYMACZEWSKI, Z., JAROSZYŃSKI, T., KUJAWA, K., LEMAŃSKI, J., ŁOMOTOWSKI, J., NALBERCZYŃSKI, A., NIEDZIELSKI, W., OLESZKIEWICZ, J., SAWICKI, M., SOZAŃSKI, M., URBANIAK, A., WASILEWSKI M. Guide to the operator of the sewage treatment plant, Polish Association of Sanitary Engineers and Technicians, Poznań, 2011.
• 10. CORTÉS U., MARTINEZ J., COMAS M., SÁNCHEZ–MARRÉA M., RODRIDUEZ I.A conceptual model to facilitate knowledge sharing for bulking solving in wastewater treatment plant. AI Communications, 16, 279, 2003.
• 11. 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.
• 12. SZELĄG B., GAWDZIK A., GAWDZIK A. Application of selected methods of black box for modelling the settleability process in wastewater treatment plant. ECOL CHEM ENG. 24 (1), 119, 2017.
• 13. GATNAR E. A multi-model approach in issues of discrimination and regression. PWN Publisher, Warsaw, 2012.
• 14. EIKELBOOM D.H., VAN BUIJSEN H.J.J. Manual of microscopic examination of activated sludge. Edition: "Seidel - Przywecki", Szczecin, 1999.
• 15. RUTKOWSKI L. Metody i techniki sztucznej inteligencji. PWN, Warszawa 2006.
• 16. LAI K., LIM S., THE P., YEAP K. An Artificial Neural Network Approach to Predicting Electrostatic Separation Performance for Food Waste Recovery. PJOES 26 (4), 1921, 2017.
• 17. WĄSIK E., CHMIELOWSKI K., KACZOR G., CUPAK A. Stability Monitoring of the Nitrification Process: Multivariate Statistical Analysis. PJOES 27 (5), 1, 2018.
• 18. BREIMAN L. Random forest. Journal Machine Learning. 45 (1), 5, 2000.
• 19. FRIEDMAN J. Stochastic gradient boosting. Computational Statis-tics and Data Analysis, 38 (4), 367, 2002.
• 20. SZELĄG B., STUDZIŃSKI J. A data mining approach to the prediction of food-to-mass ratio and mixed liquor suspended solids. Pol. J. Environ. Stud. 26 (5), 2231, 2017.
• 21. COMAS J., DZEROSKI S., GIBERT K., RODA I., SÀNCHEZ–MARRÈ M. Knowledge discovery by means of inductive methods in wastewater treatment plant. AI Communication 14, 45, 2001.
• 22. DEEPNARAIN N., KUMARI S., RAMJITH J., MAHOMEDF., TANDOI V., PILLAY K., BUX F. A logistic model for the remediation of filamentous bulking in a biological nutrient removal wastewater treatment plant.Water Science and Technology. 72 (3), 391, 2015.
• 23. BELANCHE L., VALDE J., COMAS J., RODA I., POCH M. Prediction of the bulking phenomenon in wastewater treatment plants. ArtificialIntelligence in Engineering 14, 307, 2000.
• 24. BEZAK-MAZUR E., STOIŃSKA R., SZELĄG B. Evaluation of the impact of operational parameters and particular filamentous bacteria on activated sludge volume index - a case study. Rocznik Ochrona Środowiska 18, 480, 2016.

Identyfikatory

DOI

10.15244/pjoes/94050