Application of selected methods of black box for modelling the settleability process in wastewater treatment plant

• Autorzy: Szeląg B. , Gawdzik A. , Gawdzik A.

Identyfikatory

DOI

10.1515/eces-2017-0009

Warianty tytułu

PL

Zastosowanie wybranych metod czarnej skrzynki do modelowania opadalności w oczyszczalni ścieków

• Języki publikacji: EN

Abstrakty

EN

The paper described how the results of measurements of inflow wastewater temperature in the chamber, a degree of external and internal recirculation in the biological-mechanical wastewater treatment plant (WWTP) in Cedzyna near Kielce, Poland, were used to make predictions of settleability of activated sludge. Three methods, namely: multivariate adaptive regression splines (MARS), random forests (RF) and modified random forests (RF + SOM) were employed to compute activated sludge settleability. The results of analysis indicate that modified random forests demonstrate the best predictive abilities.

Słowa kluczowe

EN

multivariate adaptive regression splines; random forests; modified random forests; sludge settleability

PL

wielowymiarowe sprężyny regresji adaptacyjnej; MARS; metoda lasów losowych (RF); opadalność osadu

• Wydawca: Towarzystwo Chemii i Inżynierii Ekologicznej
• Czasopismo: Ecological Chemistry and Engineering. S
• Rocznik: 2017
• Tom: Vol. 24, nr 1
• Strony: 119--127
• Opis fizyczny: Bibliogr. 19 poz., wykr., tab.

Twórcy

autor

Szeląg B.

• Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland

autor

Gawdzik A.

• Department of Process Engineering, University of Opole, ul. R. Dmowskiego 7/9, 45-365 Opole, Poland, phone [phone]

autor

Gawdzik A.

• Department of Process Engineering, University of Opole, ul. R. Dmowskiego 7/9, 45-365 Opole, Poland, phone [phone]

Bibliografia

• [1] Giokas DL, Daigger GT, Sperling M, Kim Y, Paraskevas PA. Comparison and evaluation of empirical zone settling velocity parameters based on sludge volume index using a unified settling characteristics database. Water Res. 2006;37(16):3821-3836. DOI: 10.1016/s0043-1354(03)00298-7.
• [2] Dellana SA, West D. Predictive modeling for wastewater applications: Linear and nonlinear approaches. Environ Modell Software. 2009;24(1):96-106. DOI: 10.1016/j.envsoft.2008.06.002.
• [3] Zhang R, Hu X. Effluent quality prediction of wastewater treatment system based on small-world. Ann J Computers. 2012;7(9):2136-2143. DOI: 10.4304/jcp.7.9.2136-2143.
• [4] Lou I, Zhao Y. Sludge bulking prediction using principle component regression and artificial neural network. Mathemat Probl in Eng. 2012;2012(2012): DOI: 10.1155/2012/237693.
• [5] Poutiainen H, Niska H, Heinonen-Tanski H, Kolehmainen M. Use of sewer on-line total solids data in wastewater treatment plant modelling. Water Sci Technol. 2010;62(4):743-750. DOI: 10.2166/wst.2010.317.
• [6] Verma A, Wei X, Kusiak A. Predicting the total suspended solids in wastewater: A data-mining approach. Engineering Applications of Artificial Intelligence. 2013;26(4):1366-1372. DOI: 10.1016/j.engappai.2012.08.015.
• [7] Kusiak A, Wei X. A data-driven model for maximization of methane production in a wastewater treatment plant. Water Sci Technol. 2012;65(6):1116-1122. DOI: 10.2166/wst.2012.953.
• [8] Grieu S, Thiéry F, Traoré A, Nguyen TP, Barreau M, Polit M. KSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge proces. Chem Eng J. 2006;1116(1):1-11. DOI: 10.1016/j/cej.2005/10.004.
• [9] Andres JD, Lorca P, de Cos Juez FJ, Sánchez-Lasheras F. Bankruptcy forecasting: a hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS). Expert Systems Appl. 2010;38:1866-1875. DOI: 10.1016/j.eswa.2010.07.117.
• [10] Han HG, Qiao JF. Prediction of actived sludge bulking based on a self-organizing RBF neural network. J Process Control. 2012;22(6):1103-1112. DOI: 10.1016/j.jprocont.2012.04.002.
• [11] Mwale FD, Adeloye AJ, Rustum R. Application of self-organising maps and multi-layer perceptron-artificial neural networks for streamflow and water level forecasting in data-poor catchments: The case of the Lower Shire floodplain, Malawi. Nordic Hydrol. 2014;45(6):838-854. DOI: 10.2166/nh.2014.168.
• [12] Rustum R, Adeloye A. Improved modelling of wastewater treatment primary clarifier using hybrid anns. Int J Computer Sci Artificial Intelligen. 2012;2(4):14-22. DOI: 10.5963/IJCSA/0204002. ]
• [13] Belanche L, Valdes J, Comas J, Roda I, Poch M. Prediction of the bulking phenomenon in wastewater treatment plants. Artificial Intellige in Eng. 2000;14(4);307-317. DOI: 10.1016/S0954-1810(00)00012-1.
• [14] Friedman J. Multivariate adaptive regression splines. Annals Statistics. 1991;19:1-141. http://projecteuclid.org/download/pdf_1/euclid.aos/1176347963.
• [15] Breiman L. Random forests. J Machine Learning. 2000;45(1):5-32. DOI: 10.1023/A:1010933404324.
• [16] Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybernetics. 1982;43:59-69. DOI: 10.1007/BF00337288.
• [17] Han HG, Chen QL, Qiao JF. An efficient self-organizing RBF neural network for water quality prediction. Neural Network. 2011;24(7):717-725. DOI: 10.1016/j.neunet.2011.04.006.
• [18] Han HG, Ying L, Guo YN, Qiao JF. A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network. J Appl Soft Computing. 2016;38(C):477-486. DOI: 10.1016/j.asoc.2015.09.051.
• [19] Martins AMP, Heijnen JJ, van Loosdrecht MCM. Bulking sludge in biological nutrient removal systems. Biotechnol Bioeng. 2004;86(2):125-135. DOI: 10.1002/bit.20029.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).