Prediction Modeling of Biogas Production with Classification and Regression Tree at Wastewater Treatment Plants

Halil Akbas; Gultekin Ozdemır

Research Article

Year 2018, Issue: 4, 212 - 217, 04.12.2018

Halil Akbas Gultekin Ozdemır

Abstract

References

Akbas, H., Bilgen, B, & Turhan, A.M. (2015). An integrated prediction and optimization model of biogas production system at a wastewater treatment facility. Bioresource Technology, 196, 566-576. Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (Eds.). (1984). Classification and regression trees. Florida, FL: Chapman & Hall/CRC Press. Cakmakci, M. (2007). Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge. Bioprocess Biosystem Engineering, 30, 349-357. Holubar, P., Zani, L., Hager, M., Froschl, W., Zorana, R., & Braun, R. (2002). Advanced controlling of anaerobic digestion by means of hierarchical neural networks. Water Research, 36, 2582-2588. Kusiak, A, & Wei, X. (2011). Prediction of methane production in wastewater treatment facility: A data-mining approach. Annals of Operations Research, 216, 71-81. Kusiak, A., & Smith, M. (2007). Data mining in design of products and production systems. Annual Reviews in Control, 31, 147-156. Kusiak, A., Zheng, H. Y., & Song, Z. (2009). Wind farm power prediction: a data-mining approach. Wind Energy, 12, 275–293. Kusiak, A., Li, M. Y., & Tang, F. (2010). Modeling and optimization of HVAC energy consumption. Applied Energy, 87, 3092–3102. Loh, W. Y. (2002). Regression trees with unbiased variable selection and interaction detection. Statistica Sinica, 12, 361-386. Shah, S., Kusiak, A., & O’Donnell, M. (2006). Patient-recognition data-mining model for BCG-plus interferon immunotherapy bladder cancer treatment. Computers in Biology and Medicine, 36, 634–655. Strik, D., Domnanovich, A.M., Zani, L., Braun, R., & Holubar, P. (2005). Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB neural network toolbox. Environmental Modelling & Software, 20, 803-810. Takada, T., Sanou, K., & Fukumara, S. (1995). A neural network system for solving an assortment problem in the steel industry. Annals of Operations Research, 57, 265–281. Tay, J.H., & Zhang, X. (1999). Neural fuzzy modeling of anaerobic biological wastewater treatment systems. Journal of Environmental Engineering, 125, 1149-1159. Wang, Q., Sun, X., Golden, B. L., & Jia, J. (1995). Using artificial neural networks to solve the problem. Annals of Operations Research, 61, 111–120. Witten, I.H., Frank, E., & Hall, M.A. (3rd Eds.). (2011). Data mining practical machine learning tools and techniques. Burlington, MA: Morgan Kaufmann Publishers. Zhang, Z., Zeng, Y., & Kusiak A., (2012). Minimizing pump energy in a wastewater processing plant. Energy, 47, 505-514.

Prediction Modeling of Biogas Production with Classification and Regression Tree at Wastewater Treatment Plants

Year 2018, Issue: 4, 212 - 217, 04.12.2018

Halil Akbas Gultekin Ozdemır

Abstract

Predicting biogas production is important for energy
management in wastewater treatment plants (WWTPs). Biogas production quantity
depends on its production system variables, such as, influent flow rate,
process temperature, alkalinity, volatile fatty acid, sludge retention time,
total suspended solid, etc. WWTPs keep the records of wastewater treatment process
values with supervisory control and data acquisition (SCADA) system on a
regular basis. The relationship between the biogas production and its
production system variables, which are measured continuously with SCADA system,
can be identified with classification and regression tree (CART) algorithm by
using the existing data. In this paper, CART approach is presented for the
prediction of biogas production at WWTPs. Standard CART algorithm is used to
select split predictor. Curvature and interaction tests are also applied in the
model to search for reducing split predictor selection bias and improving the
detection of important interactions among each predictor and response and among
each pair of predictors and response in turn.

Keywords

Prediction , Classification and regression tree , Biogas production , Wastewater treatment plant

References

Akbas, H., Bilgen, B, & Turhan, A.M. (2015). An integrated prediction and optimization model of biogas production system at a wastewater treatment facility. Bioresource Technology, 196, 566-576. Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (Eds.). (1984). Classification and regression trees. Florida, FL: Chapman & Hall/CRC Press. Cakmakci, M. (2007). Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge. Bioprocess Biosystem Engineering, 30, 349-357. Holubar, P., Zani, L., Hager, M., Froschl, W., Zorana, R., & Braun, R. (2002). Advanced controlling of anaerobic digestion by means of hierarchical neural networks. Water Research, 36, 2582-2588. Kusiak, A, & Wei, X. (2011). Prediction of methane production in wastewater treatment facility: A data-mining approach. Annals of Operations Research, 216, 71-81. Kusiak, A., & Smith, M. (2007). Data mining in design of products and production systems. Annual Reviews in Control, 31, 147-156. Kusiak, A., Zheng, H. Y., & Song, Z. (2009). Wind farm power prediction: a data-mining approach. Wind Energy, 12, 275–293. Kusiak, A., Li, M. Y., & Tang, F. (2010). Modeling and optimization of HVAC energy consumption. Applied Energy, 87, 3092–3102. Loh, W. Y. (2002). Regression trees with unbiased variable selection and interaction detection. Statistica Sinica, 12, 361-386. Shah, S., Kusiak, A., & O’Donnell, M. (2006). Patient-recognition data-mining model for BCG-plus interferon immunotherapy bladder cancer treatment. Computers in Biology and Medicine, 36, 634–655. Strik, D., Domnanovich, A.M., Zani, L., Braun, R., & Holubar, P. (2005). Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB neural network toolbox. Environmental Modelling & Software, 20, 803-810. Takada, T., Sanou, K., & Fukumara, S. (1995). A neural network system for solving an assortment problem in the steel industry. Annals of Operations Research, 57, 265–281. Tay, J.H., & Zhang, X. (1999). Neural fuzzy modeling of anaerobic biological wastewater treatment systems. Journal of Environmental Engineering, 125, 1149-1159. Wang, Q., Sun, X., Golden, B. L., & Jia, J. (1995). Using artificial neural networks to solve the problem. Annals of Operations Research, 61, 111–120. Witten, I.H., Frank, E., & Hall, M.A. (3rd Eds.). (2011). Data mining practical machine learning tools and techniques. Burlington, MA: Morgan Kaufmann Publishers. Zhang, Z., Zeng, Y., & Kusiak A., (2012). Minimizing pump energy in a wastewater processing plant. Energy, 47, 505-514.

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Details

Primary Language	English
Subjects	Engineering
Journal Section	Articles
Authors	Halil Akbas Gultekin Ozdemır
Publication Date	December 4, 2018
Published in Issue	Year 2018 Issue: 4

Cite

APA	Akbas, H., & Ozdemır, G. (2018). Prediction Modeling of Biogas Production with Classification and Regression Tree at Wastewater Treatment Plants. The Eurasia Proceedings of Science Technology Engineering and Mathematics(4), 212-217.