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

Farzin Golzar, David Nilsson and Viktoria Martin
Additional contact information
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

Sustainability, 2020, vol. 12, issue 16, 1-17

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.

Keywords: heat recovery; artificial neural network technique; wastewater temperature; sewer; Monte Carlo simulation; Stockholm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/16/6386/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/16/6386/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:16:p:6386-:d:396180

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().