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

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.

Analysis of Cusp Catastrophic Model for Vertical Stability of Drilling Shaft Lining

“…e higher the height of the counterweight water was, the higher the critical depth was. e sensitivity of each influencing factor was also important to the result and was calculated, respectively [47][48][49][50]. For the reinforced concrete shaft lining, the sensitivity coefficients of the self-weight per unit length, elastic modulus, inner diameter, and height of the counterweight water were: 0.3117, 0.3113, 0.2777, 0.1018.…”

Assessment of the Feasibility of Implementing Shower Heat Exchangers in Residential Buildings Based on Users’ Energy Saving Preferences

“…One of the main ways to reduce the consumption of fossil fuels is the increase in the use of renewable energy sources, which have long played an important role in the energy systems of the European Union [23]. In addition to sources that are used to heat domestic hot water, such as solar energy [24] or ground energy [25], attention should be paid to a third generation renewable energy source, which is warm wastewater [26]. Especially since energy is not the only valuable resource that can be recovered from wastewater, its use may also enable sustainable consumption of drinking water and the production of fertilizers [27,28].…”

Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape

“…Ahmadi et al [62] employed neural networking for predicting the friction factor in a car radiator while using CuO-water nanofluid as a working agent. Golzar et al [63] utilized the machine learning-based technique of artificial neural networking and Monte-Carlo sensitivity analysis for predicting the temperature of wastewater. Koroleva et al [64] applied artificial neural networking for optimizing the rib roughness parameters in an internally roughened circular tube.…”