Reductions in greenhouse gas (GHG) generation and energy consumption in wastewater treatment plants

2013 ◽  
Vol 67 (5) ◽  
pp. 1159-1164 ◽  
Author(s):  
L. Yerushalmi ◽  
O. Ashrafi ◽  
F. Haghighat
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Greenhouse Gas ◽  
Wastewater Treatment Plants ◽  
Oxygen Demand ◽  
Anaerobic Digester ◽  
Biological Processes ◽  
Ghg Emission ◽  
Physical Chemical ◽  
Co2 Equivalent

Greenhouse gas (GHG) emission and energy consumption by on-site and off-site sources were estimated in two different wastewater treatment plants that used physical–chemical or biological processes for the removal of contaminants, and an anaerobic digester for sludge treatment. Physical–chemical treatment processes were used in the treatment plant of a locomotive repair factory that processed wastewater at 842 kg chemical oxygen demand per day. Approximately 80% of the total GHG emission was related to fossil fuel consumption for energy production. The emission of GHG was reduced by 14.5% with the recovery of biogas that was generated in the anaerobic digester and its further use as an energy source, replacing fossil fuels. The examined biological treatment system used three alternative process designs for the treatment of effluents from pulp and paper mills that processed wastewater at 2,000 kg biochemical oxygen demand per day. The three designs used aerobic, anaerobic, or hybrid aerobic/anaerobic biological processes for the removal of carbonaceous contaminants, and nitrification/denitrification processes for nitrogen removal. Without the recovery and use of biogas, the aerobic, anaerobic, and hybrid treatment systems generated 3,346, 6,554 and 7,056 kg CO2-equivalent/day, respectively, while the generated GHG was reduced to 3,152, 6,051, and 6,541 kg CO2-equivalent/day with biogas recovery. The recovery and use of biogas was shown to satisfy and exceed the energy needs of the three examined treatment plants. The reduction of operating temperature of the anaerobic digester and anaerobic reactor by 10°C reduced energy demands of the treatment plants by 35.1, 70.6 and 62.9% in the three examined treatment systems, respectively.

scholarly journals Benchmarking of energy consumption in municipal wastewater treatment plants – a survey of over 200 plants in Italy

2018 ◽  
Vol 77 (9) ◽  
pp. 2242-2252 ◽  
Author(s):  
M. Vaccari ◽  
P. Foladori ◽  
S. Nembrini ◽  
F. Vitali
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Municipal Wastewater ◽  
Energy Savings ◽  
Wastewater Treatment Plants ◽  
Specific Energy Consumption ◽  
Oxygen Demand ◽  
Energy Performance ◽  
High Energy ◽  
Municipal Wastewater Treatment

Abstract One of the largest surveys in Europe about energy consumption in Italian wastewater treatment plants (WWTPs) is presented, based on 241 WWTPs and a total population equivalent (PE) of more than 9,000,000 PE. The study contributes towards standardised resilient data and benchmarking and to identify potentials for energy savings. In the energy benchmark, three indicators were used: specific energy consumption expressed per population equivalents (kWh PE−1 year−1), per cubic meter (kWh/m3), and per unit of chemical oxygen demand (COD) removed (kWh/kgCOD). The indicator kWh/m3, even though widely applied, resulted in a biased benchmark, because highly influenced by stormwater and infiltrations. Plants with combined networks (often used in Europe) showed an apparent better energy performance. Conversely, the indicator kWh PE−1 year−1 resulted in a more meaningful definition of a benchmark. High energy efficiency was associated with: (i) large capacity of the plant, (ii) higher COD concentration in wastewater, (iii) separate sewer systems, (iv) capacity utilisation over 80%, and (v) high organic loads, but without overloading. The 25th percentile was proposed as a benchmark for four size classes: 23 kWh PE−1 y−1 for large plants > 100,000 PE; 42 kWh PE−1 y−1 for capacity 10,000 < PE < 100,000, 48 kWh PE−1 y−1 for capacity 2,000 < PE < 10,000 and 76 kWh PE−1 y−1 for small plants < 2,000 PE.

Wastewater treatment process impact on energy savings and greenhouse gas emissions

2014 ◽  
Vol 71 (2) ◽  
pp. 303-308 ◽  
Author(s):  
D. Mamais ◽  
C. Noutsopoulos ◽  
A. Dimopoulou ◽  
A. Stasinakis ◽  
T. D. Lekkas
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Greenhouse Gas ◽  
Energy Savings ◽  
Wastewater Treatment Plants ◽  
Specific Energy Consumption ◽  
Ghg Emissions ◽  
Wastewater Treatment Process ◽  
Conventional Activated Sludge

The objective of this research was to assess the energy consumption of wastewater treatment plants (WWTPs), to apply a mathematical model to evaluate their carbon footprint, and to propose energy saving strategies that can be implemented to reduce both energy consumption and greenhouse gas (GHG) emissions in Greece. The survey was focused on 10 WWTPs in Greece with a treatment capacity ranging from 10,000 to 4,000,000 population equivalents (PE). Based on the results, annual specific energy consumption ranged from 15 to 86 kWh/PE. The highest energy consumer in all the WWTPs was aeration, accounting for 40–75% of total energy requirements. The annual GHG emissions varied significantly according to the treatment schemes employed and ranged between 61 and 161 kgCO2e/PE. The highest values of CO2 emissions were obtained in extended aeration systems and the lowest in conventional activated sludge systems. Key strategies that the wastewater industry could adopt to mitigate GHG emissions are identified and discussed. A case study is presented to demonstrate potential strategies for energy savings and GHG emission reduction. Given the results, it is postulated that the reduction of dissolved oxygen (DO) set points and sludge retention time can provide significant energy savings and decrease GHG emissions.

scholarly journals Minimizing greenhouse gas emissions of an industrial wastewater treatment plant in terms of water–energy nexus

2021 ◽  
Vol 11 (12) ◽  
Author(s):  
Pelin Yapıcıoğlu ◽  
Özlem Demir
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Greenhouse Gas ◽  
Flow Rate ◽  
Wastewater Treatment Plant ◽  
Industrial Wastewater ◽  
Ghg Emissions ◽  
Treatment Plant ◽  
Ghg Emission ◽  
Water Energy Nexus

AbstractIn this paper, (CO2) and methane (CH4) emissions of an industrial wastewater treatment plant were monitored. GHG emissions originated from treatment processes were considered as the direct emissions and determined using closed chamber method. GHG emission due to energy consumption was regarded as the indirect emissions. In the second stage of the study, it was aimed to reduce GHG emissions in terms of water–energy nexus. If the plant is operated under design conditions, energy consumption would be lower according to water–energy nexus. Also, the effect of design conditions on GHG emissions was investigated. Firstly, the correlation was defined between GHG emissions and operational parameters in terms of chemical oxygen demand (COD) and wastewater flow rate using Monte Carlo simulation. Then, design COD and wastewater flow rate were simulated to determine the possible GHG emission for each month. The simulation results show that minimization of GHG emissions might be possible if wastewater plant is operated under design conditions. The minimum greenhouse gas emission in the result of the simulation study is 8.25 kg CO2-eq/d if the plant is operated under design COD and flow rate. Total reduction in GHG emissions is approximately 30% if the plant is operated under design conditions.

scholarly journals Energy audit in small wastewater treatment plants: methodology, energy consumption indicators, and lessons learned

2015 ◽  
Vol 72 (6) ◽  
pp. 1007-1015 ◽  
Author(s):  
P. Foladori ◽  
M. Vaccari ◽  
F. Vitali
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Wastewater Treatment Plants ◽  
Capacity Utilization ◽  
Oxygen Demand ◽  
Lessons Learned ◽  
Electrical Heating ◽  
Energy Audit ◽  
Energy Audits ◽  
Common Situation

Energy audits in wastewater treatment plants (WWTPs) reveal large differences in the energy consumption in the various stages, depending also on the indicators used in the audits. This work is aimed at formulating a suitable methodology to perform audits in WWTPs and identifying the most suitable key energy consumption indicators for comparison among different plants and benchmarking. Hydraulic-based stages, stages based on chemical oxygen demand, sludge-based stages and building stages were distinguished in WWTPs and analysed with different energy indicators. Detailed energy audits were carried out on five small WWTPs treating less than 10,000 population equivalent and using continuous data for 2 years. The plants have in common a low designed capacity utilization (52% on average) and equipment oversizing which leads to waste of energy in the absence of controls and inverters (a common situation in small plants). The study confirms that there are several opportunities for reducing energy consumption in small WWTPs: in addition to the pumping of influent wastewater and aeration, small plants demonstrate low energy efficiency in recirculation of settled sludge and in aerobic stabilization. Denitrification above 75% is ensured through intermittent aeration and without recirculation of mixed liquor. Automation in place of manual controls is mandatory in illumination and electrical heating.

scholarly journals Quantifying the energy consumption and greenhouse gas emissions of changing wastewater quality standards

2020 ◽  
Vol 81 (6) ◽  
pp. 1283-1295
Author(s):  
Iliana Cardenes ◽  
Jim W. Hall ◽  
Nick Eyre ◽  
Aman Majid ◽  
Simon Jarvis
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Energy Use ◽  
Electricity Consumption ◽  
Oxygen Demand ◽  
Quality Standards ◽  
Gas Emissions ◽  
Effluent Standards

Abstract Regulations to ensure adequate wastewater treatment are becoming more stringent as the negative effects of different pollutants on human health and the environment are understood. However, treatment of wastewater to remove pollutants is energy intensive, so has added significantly to the operation costs of wastewater treatment plants. Analysis from six of the largest wastewater treatment works in South East England reveals that the energy consumption of these treatment works has doubled in the last five years due to expansions to meet increasingly stringent effluent standards and population growth. This study quantifies the relationship between energy use for wastewater treatment and four measures of pollution in effluents from UK wastewater treatment works (biochemical oxygen demand, ammoniacal nitrogen, chemical oxygen demand and suspended solids). The linear regression results show that indicators of these pollutants in effluents, together with the extension of plants to improve wastewater treatment, can predict over 95% of energy consumption. Secondly, using scenarios, the energy consumption and greenhouse gas emissions of effluent quality standards are estimated. The study finds that tightening effluent standards to increase water quality could result in a doubling of electricity consumption and an increase of between 1.29 and 2.30 additional MTCO2 per year from treating wastewater in large works in the UK.

scholarly journals Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1149
Author(s):  
Pedro Oliveira ◽  
Bruno Fernandes ◽  
Cesar Analide ◽  
Paulo Novais
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Learning Approach ◽  
Sustainable Cities ◽  
Short Term ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.

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