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.
Highlights
To tackle climate change impacts, several international and national targets have been determined
In order to determine the optimum structure of the artificial neural network (ANN) model, we used historical data from 2009 to 2018 to train several structures with a different number of hidden layers, a different number of neurons in each layer (0 to 10), as well as various transfer functions
The results of this study indicate that the ANN model can forecast the wastewater treatment plant (WWTP) influent temperature based on historical data commonly recorded with an ignorable error
Summary
To tackle climate change impacts, several international and national targets have been determined. The main goal of the agreement is to strengthen the global response to the threat of climate change by keeping the global temperature increase at lower than 2 ◦C and to limit the rise even further to 1.5 ◦C, compared to pre-industrial levels [1]. The agreement states the need for all countries to set climate goals. To fulfil these ambitious goals, applicable evolution and arrangement of financial resources, innovative technology framework, and improved capacity-expansion are to be put in place [1]. The European Union has undertaken quantitative targets of decreasing greenhouse gas emissions by 40% compared to the level in 1990 until 2030, as well as a 27% increase in the share of renewable energy and 7% enhanced energy efficiency [3].
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