Modelling heat recovery potential from household wastewater

Wärff, Christoffer; Arnell, Magnus; Sehlén, Robert; Jeppsson, Ulf

doi:10.2166/wst.2020.103

Cited by 16 publications

(10 citation statements)

References 3 publications

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“…Wastewater temperature drop in the heat exchangers depends on various parameters such as the type of heat exchanger, the flowrates and temperatures of wastewater as well as cold water, which vary during the day. Considering previous studies [48,64,74], 5 • C on average is assumed as the wastewater temperature drop in heat exchangers. A decrease of 5 • C in the temperature of discharged wastewater from 40% of buildings would lead to 2 • C decrease in the total building effluent temperature.…”

Section: Model Applicationmentioning

confidence: 99%

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

2020

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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.

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Section: Model Applicationmentioning

confidence: 99%

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

2020

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“…As resource recovery from wastewater (WW) is widely applied to improve sustainability of wastewater treatment (WWT) systems (Verstraete et al 2009), wastewater heat recovery (WWHR) is gaining wider interest (Culha et al 2015;Cecconet et al 2020;Wärff et al 2020). The energy in the WW largely comes from domestic hot water.…”

Section: Introductionmentioning

confidence: 99%

Plant-wide modelling and analysis of WWTP temperature dynamics for sustainable heat recovery from wastewater

Arnell

Ahlström

Wärff

et al. 2021

Water Science and Technology

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Wastewater heat recovery up-stream of wastewater treatment plants (WWTPs) poses a risk to treatment performance, i.e. the biological processes. In order to perform a sustainability analysis, a detailed prediction of the temperature dynamics over the WWTP is needed. A comprehensive set of heat balance equations were included in a plant-wide process model and validated for the WWTP in Linköping, Sweden, to predict temperature variations over the whole year in a temperate climate. A detailed model for the excess heat generation of biological processes was developed. The annual average temperature change from influent to effluent was 0.78 °C with clear seasonal variations. 45% of the temperature change arises from processes other than the activated sludge unit. Hence, plant-wide energy modelling was necessary to predict in-tank temperature in the biological treatment steps. The energy processes with the largest energy gains were solar radiation and biological processes, while the largest losses were from conduction, convection and atmospheric radiation. Tanks with large surface areas have significant impact on the heat balance regardless of biological processes. Simulating a 3 °C lower influent temperature, the temperature in the activated sludge unit dropped by 2.8 °C, which had a negative impact on nitrogen removal.

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“…In this paper, a heat transfer model for sewer systems is presented that can be easily integrated with other models available in the literature (upstream wastewater generation models from households (Wärff et al 2020), standard WWTP models (Henze et al 2000) and heat recovery equipment models describing the energy recovery, temperature variation etc. in heat exchangers and heat pumps (Geankoplis 1993;Arnell & Saagi 2020)) that can eventually promote system-wide studies (Arnell et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…In such cases, the availability of the entire model toolbox in the same simulation software is advantageous. In this study, a wastewater generation model (Wärff et al 2020), WWTP model (Arnell et al 2021) and heat transfer equipment models (Arnell & Saagi 2020) are developed using the same simulation platform (Matlab/Simulink) and can be easily integrated with both the mechanistic and conceptual sewer heat transfer models. This can significantly improve the possibilities for city-wide heat recovery studies with a wider scope.…”

Section: Introductionmentioning

confidence: 99%

Modelling temperature dynamics in sewer systems – comparing mechanistic and conceptual modelling approaches

Saagi

Arnell

Reyes

et al. 2021

Water Science and Technology

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The vast majority of the energy consumed for urban water services is used to heat tap water. Heat recovery from wastewater is consequently an area of rapidly growing concern, both in research and by commercial interest, promoting the path towards a circular economy. To facilitate a system-wide evaluation of heat recovery from wastewater, this paper compares two one-dimensional models (mechanistic and conceptual) that can describe wastewater temperature dynamics in sewer pipe systems. The models are applied to successfully predict downstream wastewater temperature for sewer stretches in two Swedish cities (Linköping & Malmö). The root mean squared errors for the mechanistic model (Linköping Dataset1 – 0.33 °C; Linköping Dataset2 – 0.28 °C; Malmö – 0.40 °C) and the conceptual model (Linköping Dataset1 – 0.32 °C; Linköping Dataset2 – 0.20 °C; Malmö – 0.44 °C) indicate that both models have similar predictive capabilities, encouraging the use of conceptual models to reduce data requirements and model calibration efforts. Both models are freely distributed and can be easily integrated with wastewater generation and treatment models to facilitate system-wide wastewater temperature dynamics analysis.

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Modelling heat recovery potential from household wastewater

Cited by 16 publications

References 3 publications

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

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

Plant-wide modelling and analysis of WWTP temperature dynamics for sustainable heat recovery from wastewater

Modelling temperature dynamics in sewer systems – comparing mechanistic and conceptual modelling approaches

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