Effect of foam on temperature prediction and heat recovery potential from biological wastewater treatment

Corbala-Robles, L.; Volcke, Eveline; Samijn, A.; Ronsse, Frederik; Pieters, Jan

doi:10.1016/j.watres.2016.03.031

Cited by 15 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 4 This is the overall minimum, thus this amount of energy could be used to raise the water temperature in the aerated tank with the help of heat pumps and heat exchangers even if losses are accounted for. Of course, the exact amount of reclaimable heat depends on several factors, including the temperatures of the wastewater and the receiving surface water, heat loss, efficiency of heat pumps and foaming [25], among others. Assuming that the temperature in the aeration basin can be raised by 6 °C, it would theoretically result in an 80% increase in the growth rate according to eq.…”

Section: Resultsmentioning

confidence: 99%

Enhanced Pollution Removal with Heat Reclamation in a Small Hungarian Wastewater Treatment Plant

Somogyi¹,

Sebestyén²,

Kovács³

et al. 2018

J. sustain. dev. energy water environ. syst.

View full text Add to dashboard Cite

The aim of the research is to outline the possibilities of utilizing waste heat in small municipal wastewater treatment plants. The facility, which was chosen as case-study, accepts about 2,300 m 3 of raw sewage daily. In wintertime the wastewater temperature decreases to 10-14 °C which results in lower nitrification capacity based on measurement and validated model results. The excess heat of the wastewater would serve to increase the temperature of the aeration tank in order to enhance the microbiological activity and thus the efficiency of pollutant removal. The amount of reusable waste heat is calculated and with the help of dynamic simulation the effluent quality was determined to compare it with the original results. Increasing the temperature by 6 °C in the aerated tank, ammonium removal could be improved by 61%. This way not only the heat, but the nutrient pollution could be mitigated, too.

show abstract

Section: Resultsmentioning

confidence: 99%

Enhanced Pollution Removal with Heat Reclamation in a Small Hungarian Wastewater Treatment Plant

Somogyi¹,

Sebestyén²,

Kovács³

et al. 2018

J. sustain. dev. energy water environ. syst.

View full text Add to dashboard Cite

show abstract

“…This allows prediction of temperature in the context of wastewater treatment and has already been sufficiently demonstrated in several studies. 23,24 Corbala-Robles et al 25 extended previous temperature prediction models in biological reactors, in particular considering the effect of a foam layer, which significantly affects heat loss through the surface as a major factor influencing reactor temperature. A systematic description and calculation of heat fluxes in biochemical reactors within a plant-wide model, coupling the ASM reactions and multi-phase biochemical transformations with heat balances, has also already been illustrated, e.g.…”

Section: Heat Recoverymentioning

confidence: 99%

Resource recovery and wastewater treatment modelling

Solon

Volcke

Spérandio

et al. 2019

Environ. Sci.: Water Res. Technol.

View full text Add to dashboard Cite

show abstract

“…Inflow shows the overall treatment workload, together with aeration demands and costs [14]. Temperature as a hydrological variable is a key factor in biological reactions with a close connection to the further prediction of microbial water quality [30], [31], and is also an important input for potential heat recovery models [32]. Influent and effluent BODs are classical measures of organic compound contents in raw and treated wastewater.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

et al. 2020

View full text Add to dashboard Cite

The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend and predict the plant behavior to support process design and controls, improve system reliability, reduce operational costs, and endorse optimization of overall performances. Deep learning technologies as proven data-driven soft-sensors should be developed for WWTP applications to tackle the process of non-linearity and the dynamic nature of environmental data. This study adopts deep learning-based models as softsensors to forecast WWTP key features, such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption. We constructed six deep learning models derived from long short-term memory (LSTM) and gated recurrent unit (GRU), namely traditional LSTM and GRU, the exponentially smoothed LSTM, and the adaptive version of LSTM and smoothed LSTM. The employment of a smoothed LSTM technique is expected to reduce the outlier effect and to improve forecasting accuracy. Meanwhile, the usage of adaptive deep models will enhance the capabilities of the LSTM to quickly and accurately follow the trend of future data. We compared the performance of these models with Bi-directional LSTM (BiLSTM) and the seasonal decomposition using local regression. The historical records from a coastal municipal WWTP in Saudi Arabia are used to verify the investigated models' effectiveness. The proposed models provide promising forecasting results but require no assumptions on the data distributions. In terms of efficiency, GRU based models converge faster than LSTM based models. In terms of accuracy, the LSTM soft-sensor shows overall the optimal result for all key features followed by the exponentially-smoothed GRU and LSTM. By contrast, the adaptive models achieved the lowest forecasting performance compared to the other models. These findings will benefit practitioners to achieve data-driven WWTP management.

show abstract

Effect of foam on temperature prediction and heat recovery potential from biological wastewater treatment

Cited by 15 publications

References 18 publications

Enhanced Pollution Removal with Heat Reclamation in a Small Hungarian Wastewater Treatment Plant

Enhanced Pollution Removal with Heat Reclamation in a Small Hungarian Wastewater Treatment Plant

Resource recovery and wastewater treatment modelling

Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

Contact Info

Product

Resources

About