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Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

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  • ZhenHua Li
  • ZhiHong Zou
  • LiPing Wang

Abstract

Wastewater treatment plant (WWTP) is the energy-intensive industries. Energy is consumed at every stage of wastewater treatment. It is the main contributor to the costs of WWTP. Analysis and forecasting of energy consumption are critical to energy-saving. Many factors influence energy consumption. The relationship between energy consumption and wastewater is complex and challenging to identify. This article employed the fuzzy clustering method to categorize the sample data of WWTP and analyzed the relationship between energy consumption and the influence factors in different categories. The study found that energy efficiency in various categories was changed and the same influence factors in different types had different influence intensity. The Radial Basis Function (RBF) neural network was used to forecast energy consumption. The data from the complete set and categories was adopted to train and test the model. The results show that the RBF model using the date from the subset has better performance than the multivariable linear regression (MLR) model. The results of this study provided an essential theoretical basis for energy-saving in WWTP.

Suggested Citation

  • ZhenHua Li & ZhiHong Zou & LiPing Wang, 2019. "Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, July.
    • Handle: RePEc:hin:jnlmpe:8690898
    DOI: 10.1155/2019/8690898
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    Cited by:

    1. Nikolaos Tsalas & Spyridon K. Golfinopoulos & Stylianos Samios & Georgios Katsouras & Konstantinos Peroulis, 2024. "Optimization of Energy Consumption in a Wastewater Treatment Plant: An Overview," Energies, MDPI, vol. 17(12), pages 1-45, June.
    2. Bárbara de Matos & Rodrigo Salles & Jérôme Mendes & Joana R. Gouveia & António J. Baptista & Pedro Moura, 2022. "A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
    3. Maria Rosa di Cicco & Antonio Masiello & Antonio Spagnuolo & Carmela Vetromile & Laura Borea & Giuseppe Giannella & Manuela Iovinella & Carmine Lubritto, 2021. "Real-Time Monitoring and Static Data Analysis to Assess Energetic and Environmental Performances in the Wastewater Sector: A Case Study," Energies, MDPI, vol. 14(21), pages 1-16, October.
    4. Muhammad Tariq Khan & Riaz Ahmad & Gengyuan Liu & Lixiao Zhang & Remo Santagata & Massimiliano Lega & Marco Casazza, 2024. "Potential Environmental Impacts of a Hospital Wastewater Treatment Plant in a Developing Country," Sustainability, MDPI, vol. 16(6), pages 1-18, March.
    5. Ewelina Płuciennik-Koropczuk & Sylwia Myszograj & Mirosław Mąkowski, 2022. "Reducing CO 2 Emissions from Wastewater Treatment Plants by Utilising Renewable Energy Sources—Case Study," Energies, MDPI, vol. 15(22), pages 1-14, November.
    6. Catarina Silva & Maria João Rosa, 2021. "A Practical Methodology for Forecasting the Impact of Changes in Influent Loads and Discharge Consents on Average Energy Consumption and Sludge Production by Activated Sludge Wastewater Treatment," Sustainability, MDPI, vol. 13(21), pages 1-11, November.
    7. Jose M. Vindel & Estrella Trincado & Antonio Sánchez-Bayón, 2021. "European Union Green Deal and the Opportunity Cost of Wastewater Treatment Projects," Energies, MDPI, vol. 14(7), pages 1-18, April.
    8. Moazeni, Faegheh & Khazaei, Javad, 2021. "Co-optimization of wastewater treatment plants interconnected with smart grids," Applied Energy, Elsevier, vol. 298(C).
    9. Grzegorz Bartnicki & Piotr Ziembicki & Marcin Klimczak & Agnieszka Kalitka, 2022. "The Potential of Heat Recovery from Wastewater Considering the Protection of Wastewater Treatment Plant Technology," Energies, MDPI, vol. 16(1), pages 1-15, December.

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