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Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis

Author

Listed:
  • Farzin Golzar

    (Division of Energy Systems, Department of Energy Technology, KTH-Royal Institute of Technology, 11428 Stockholm, Sweden)

  • David Nilsson

    (Water Centre, KTH Royal Institute of Technology, 11428 Stockholm, Sweden)

  • Viktoria Martin

    (Division of Energy Systems, Department of Energy Technology, KTH-Royal Institute of Technology, 11428 Stockholm, Sweden)

Abstract

Wastewater contains considerable amounts of thermal energy. Heat recovery from wastewater in buildings could supply cities with an additional source of renewable energy. However, variations in wastewater temperature influence the performance of the wastewater treatment plant. Thus, the treatment is negatively affected by heat recovery upstream of the plant. Therefore, it is necessary to develop more accurate models of the wastewater temperature variations. In this work, a computational model based on artificial neural network (ANN) is proposed to calculate wastewater treatment plant influent temperature concerning ambient temperature, building effluent temperature and flowrate, stormwater flowrate, infiltration flowrate, the hour of day, and the day of year. Historical data related to the Stockholm wastewater system are implemented in MATLAB software to drive the model. The comparison of calculated and observed data indicated a negligible error. The main advantage of this ANN model is that it only uses historical data commonly recorded, without any requirements of field measurements for intricate heat transfer models. Moreover, Monte Carlo sensitivity analysis determined the most influential parameters during different seasons of the year. Finally, it was shown that installing heat exchangers in 40% of buildings would reduce 203 GWh year −1 heat loss in the sewage network. However, heat demand in WWTP would be increased by 0.71 GWh year −1 , and the district heating company would recover 176 GWh year −1 less heat from treated water.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6386-:d:396180
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    References listed on IDEAS

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    Cited by:

    1. Huixian Shi & Zijing Wang & Haiyi Zhou & Kaiyan Lin & Shuping Li & Xinnan Zheng & Zheng Shen & Jiaoliao Chen & Lei Zhang & Yalei Zhang, 2022. "Using a Novel Algorithm Based on the Random Vector Functional Link Network and Multi-Verse Optimizer to Forecast Effluent Quality," Sustainability, MDPI, vol. 14(14), pages 1-14, July.
    2. Franz Huber & Georg Neugebauer & Thomas Ertl & Florian Kretschmer, 2020. "Suitability Pre-Assessment of in-Sewer Heat Recovery Sites Combining Energy and Wastewater Perspectives," Energies, MDPI, vol. 13(24), pages 1-32, December.
    3. Nilsson, David & Karpouzoglou, Timos & Wallin, Jörgen & Blomkvist, Pär & Golzar, Farzin & Martin, Viktoria, 2023. "Is on-property heat and greywater recovery a sustainable option? A quantitative and qualitative assessment up to 2050," Energy Policy, Elsevier, vol. 182(C).
    4. Sabina Kordana-Obuch & Mariusz Starzec & Daniel Słyś, 2021. "Assessment of the Feasibility of Implementing Shower Heat Exchangers in Residential Buildings Based on Users’ Energy Saving Preferences," Energies, MDPI, vol. 14(17), pages 1-30, September.
    5. Shivam Pandey & Bhekisipho Twala & Rajesh Singh & Anita Gehlot & Aman Singh & Elisabeth Caro Montero & Neeraj Priyadarshi, 2022. "Wastewater Treatment with Technical Intervention Inclination towards Smart Cities," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    6. Liu, Qipeng & Li, Ran & Dereli, Recep Kaan & Flynn, Damian & Casey, Eoin, 2022. "Water resource recovery facilities as potential energy generation units and their dynamic economic dispatch," Applied Energy, Elsevier, vol. 318(C).
    7. Jun Yuan & Jiang Zhu & Victor Nian, 2020. "Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures," Sustainability, MDPI, vol. 12(24), pages 1-14, December.
    8. Basma Souayeh & Suvanjan Bhattacharyya & Najib Hdhiri & Mir Waqas Alam, 2021. "Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape," Sustainability, MDPI, vol. 13(6), pages 1-24, March.
    9. Golzar, Farzin & Silveira, Semida, 2021. "Impact of wastewater heat recovery in buildings on the performance of centralized energy recovery – A case study of Stockholm," Applied Energy, Elsevier, vol. 297(C).

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