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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

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  • 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. 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.
    2. Kusiak, Andrew & Li, Mingyang & Zheng, Haiyang, 2010. "Virtual models of indoor-air-quality sensors," Applied Energy, Elsevier, vol. 87(6), pages 2087-2094, June.
    3. Kusiak, Andrew & Li, Mingyang & Tang, Fan, 2010. "Modeling and optimization of HVAC energy consumption," Applied Energy, Elsevier, vol. 87(10), pages 3092-3102, October.
    4. Ashok Kumar (ed.), 2010. "Air Quality," Books, IntechOpen, number 787.
    5. 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.
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    Cited by:

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    2. Halil Akbaş & Gültekin Özdemir, 2020. "An Integrated Prediction and Optimization Model of a Thermal Energy Production System in a Factory Producing Furniture Components," Energies, MDPI, vol. 13(22), pages 1-29, November.
    3. 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).
    4. Jamal Al Qundus & Kosai Dabbour & Shivam Gupta & Régis Meissonier & Adrian Paschke, 2022. "Wireless sensor network for AI-based flood disaster detection," Annals of Operations Research, Springer, vol. 319(1), pages 697-719, December.
    5. Asil Oztekin, 2018. "Creating a marketing strategy in healthcare industry: a holistic data analytic approach," Annals of Operations Research, Springer, vol. 270(1), pages 361-382, November.
    6. 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.
    7. 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.

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