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The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process

Author

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  • Hubert Byliński

    (Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, Narutowicza 11/12 Street, 80-233 Gdańsk, Poland)

  • Andrzej Sobecki

    (Department of Computer Architecture, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12 Street, 80-233 Gdańsk, Poland)

  • Jacek Gębicki

    (Department of Process Engineering and Chemical Technology, Faculty of Chemistry, Gdańsk University of Technology, Narutowicza 11/12 Street, 80-233 Gdańsk, Poland)

Abstract

This paper presents the application of artificial neural networks and decision trees for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant. The input parameters were concentrations of popular compounds present in the sludge, such as toluene, p-xylene, and p-cresol, and process parameters including the concentration of volatile fatty acids, pH, and alkalinity in the fermentation sludge. The analyses revealed that the implementation of artificial neural networks allowed the prediction of the values of odor intensity and the hedonic tone of the post-fermentation sludge at the level of 30% mean absolute percentage error. Application of the decision tree made it possible to determine what input parameters the fermentation feed should have in order to arrive at the post-fermentation sludge with an odor intensity <2 and hedonic tone >−1. It was shown that the aforementioned phenomenon was influenced by the following factors: concentration of p-xylene, pH, concentration of volatile fatty acids, and concentration of p-cresol.

Suggested Citation

  • Hubert Byliński & Andrzej Sobecki & Jacek Gębicki, 2019. "The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process," Sustainability, MDPI, vol. 11(16), pages 1-12, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4407-:d:257721
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    References listed on IDEAS

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    1. Wu, Di & Li, Lei & Zhao, Xiaofei & Peng, Yun & Yang, Pingjin & Peng, Xuya, 2019. "Anaerobic digestion: A review on process monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 1-12.
    2. Kor-Bicakci, Gokce & Eskicioglu, Cigdem, 2019. "Recent developments on thermal municipal sludge pretreatment technologies for enhanced anaerobic digestion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 423-443.
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    Cited by:

    1. Sławomir Francik & Adrian Knapczyk & Artur Knapczyk & Renata Francik, 2020. "Decision Support System for the Production of Miscanthus and Willow Briquettes," Energies, MDPI, vol. 13(6), pages 1-24, March.
    2. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    3. Marta Wiśniewska & Andrzej Kulig & Krystyna Lelicińska-Serafin, 2020. "The Impact of Technological Processes on Odorant Emissions at Municipal Waste Biogas Plants," Sustainability, MDPI, vol. 12(13), pages 1-18, July.
    4. Kowalczyk-Juśko, Alina & Pochwatka, Patrycja & Zaborowicz, Maciej & Czekała, Wojciech & Mazurkiewicz, Jakub & Mazur, Andrzej & Janczak, Damian & Marczuk, Andrzej & Dach, Jacek, 2020. "Energy value estimation of silages for substrate in biogas plants using an artificial neural network," Energy, Elsevier, vol. 202(C).
    5. Distefano, Veronica & Palma, Monica & De Iaco, Sandra, 2024. "Multi-class random forest model to classify wastewater treatment imbalanced data," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).

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