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Prediction of methane production in wastewater treatment facility: a data-mining approach

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  • Andrew Kusiak
  • Xiupeng Wei

Abstract

A prediction model for methane production in a wastewater processing facility is presented. The model is built by data-mining algorithms based on industrial data collected on a daily basis. Because of many parameters available in this research, a subset of parameters is selected using importance analysis. Prediction results of methane production are presented in this paper. The model performance by different algorithms is measured with five metrics. Based on these metrics, a model built by the Adaptive Neuro-Fuzzy Inference System algorithm has provided most accurate predictions of methane production. Copyright Springer Science+Business Media, LLC 2014

Suggested Citation

  • Andrew Kusiak & Xiupeng Wei, 2014. "Prediction of methane production in wastewater treatment facility: a data-mining approach," Annals of Operations Research, Springer, vol. 216(1), pages 71-81, May.
    • Handle: RePEc:spr:annopr:v:216:y:2014:i:1:p:71-81:10.1007/s10479-011-1037-6
    DOI: 10.1007/s10479-011-1037-6
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    References listed on IDEAS

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    1. Kusiak, Andrew & Li, Mingyang & Tang, Fan, 2010. "Modeling and optimization of HVAC energy consumption," Applied Energy, Elsevier, vol. 87(10), pages 3092-3102, October.
    2. Tianshi Jiao & Jiming Peng & Tamás Terlaky, 2009. "A confidence voting process for ranking problems based on support vector machines," Annals of Operations Research, Springer, vol. 166(1), pages 23-38, February.
    3. Hyunchul Ahn & Kyoung-jae Kim, 2008. "Using genetic algorithms to optimize nearest neighbors for data mining," Annals of Operations Research, Springer, vol. 163(1), pages 5-18, October.
    4. Kusiak, Andrew & Li, Mingyang & Zheng, Haiyang, 2010. "Virtual models of indoor-air-quality sensors," Applied Energy, Elsevier, vol. 87(6), pages 2087-2094, June.
    5. Ashok Kumar (ed.), 2010. "Air Quality," Books, IntechOpen, number 787, January-J.
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    Cited by:

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    2. Chong, Daniel Jia Sheng & Chan, Yi Jing & Arumugasamy, Senthil Kumar & Yazdi, Sara Kazemi & Lim, Jun Wei, 2023. "Optimisation and performance evaluation of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the prediction of biogas production ," Energy, Elsevier, vol. 266(C).
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    5. Reza Salehi & Qiuyan Yuan & Sumate Chaiprapat, 2022. "Development of Data-Driven Models to Predict Biogas Production from Spent Mushroom Compost," Agriculture, MDPI, vol. 12(8), pages 1-20, July.
    6. Gnanasekaran, Sakthivel & Saravanan, N. & Ilangkumaran, M., 2016. "Influence of injection timing on performance, emission and combustion characteristics of a DI diesel engine running on fish oil biodiesel," Energy, Elsevier, vol. 116(P1), pages 1218-1229.
    7. Otilia Elena Dragomir & Florin Dragomir & Veronica Stefan & Eugenia Minca, 2015. "Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources," Energies, MDPI, vol. 8(11), pages 1-15, November.
    8. Farzin, Farzad & Moghaddam, Shabnam Sadri & Ehteshami, Majid, 2024. "Auto-tuning data-driven model for biogas yield prediction from anaerobic digestion of sewage sludge at the south-tehran wastewater treatment plant: Feature selection and hyperparameter population-base," Renewable Energy, Elsevier, vol. 227(C).

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