Energy Consumption, Energy-saving and Emissions Reduction of Wastewater Treatment Plants (WWTPs) in Wisconsin

2019 ◽  
Author(s):  
Ryoichi S. Amano ◽  
Ahmad I. Abbas ◽  
Muhannad Al-Haddad ◽  
Mohammad D. Qandil
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Energy Saving ◽  
Wastewater Treatment Plants ◽  
Emissions Reduction ◽  
Consumption Energy

scholarly journals Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants

2019 ◽  
Vol 9 (21) ◽  
pp. 4501 ◽  
Author(s):  
Yongteng Sun ◽  
Ming Lu ◽  
Yongjun Sun ◽  
Zuguo Chen ◽  
Hao Duan ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Energy Conservation ◽  
Energy Saving ◽  
Wastewater Treatment Plants ◽  
System Optimization ◽  
High Energy ◽  
Green Energy ◽  
Application Process ◽  
Conservation Technologies

High energy consumption is an important issue affecting the operation and development of wastewater treatment plants (WWTPs). This paper seeks energy-saving opportunities from three aspects: energy application, process optimization, and performance evaluation. Moreover, effective energy-saving can be achieved from the perspective of energy supply and recovery by using green energy technologies, including wastewater and sludge energy recovery technologies. System optimization and control is used to reduce unnecessary energy consumption in operation. Reasonable indexes and methods can help researchers evaluate the application value of energy-saving technology. Some demonstration WWTPs even can achieve energy self-sufficiency by using these energy conservation technologies. Besides, this paper introduces the challenges faced by the wastewater treatment industry and some emerging energy-saving technologies. The work can give engineers some suggestions about reducing energy consumption from comprehensive perspectives.

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.

scholarly journals Methodology for Energy Optimization in Wastewater Treatment Plants. Phase II: Reduction of Air Requirements and Redesign of the Biological Aeration Installation

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1143
Author(s):  
Ana Belén Lozano Avilés ◽  
Francisco Del Cerro Velázquez ◽  
Mercedes Llorens Pascual Del Riquelme
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Phase Ii ◽  
Wastewater Treatment Plants ◽  
Energy Optimization ◽  
Treatment Plant ◽  
Pressure Losses ◽  
San Pedro ◽  
Air Supply ◽  
Optimization Methodology

Phase I of the proposed energy optimization methodology showed how the selection of best management criteria for the biological aeration process, and the guarantee of its control at the wastewater treatment plant (WWTP) in San Pedro del Pinatar (Murcia, Spain) produced reductions of around 20% in energy consumption by considerably reducing the oxygen needs of the microorganisms in the biological system. This manuscript focused on phase II of this methodology, which describes the tools that can be used to detect and correct deviations in the optimal operating points of the aeration equipment and the intrinsic deficiencies in the installation, in order to achieve optimization of the oxygen needs by the microorganisms and improve the efficiency of their transfer from the gas phase to the liquid phase. The objectives pursued were: (i) to minimize the need for aeration, (ii) to reduce the pressure losses in the installation, (iii) to optimize the air supply pressures to avoid excessive energy consumption for the same airflow, and (iv) to optimize the control strategy for the actual working conditions. The use of flow modeling and simulation techniques, the measurement and calculation of air transfer efficiency through the use of off-gas hoods, and the redesign of the aeration facility at the San Pedro del Pinatar WWTP were crucial, and allowed for reductions in energy consumption in Phase II of more than 20%.

scholarly journals Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
ZhenHua Li ◽  
ZhiHong Zou ◽  
LiPing Wang
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Energy Saving ◽  
Wastewater Treatment Plant ◽  
Rbf Neural Network ◽  
Influence Factors ◽  
Treatment Plant ◽  
Fuzzy Clustering Method ◽  
Complete Set ◽  
The Relationship

Wastewater treatment plant (WWTP) is the energy-intensive industries. Energy is consumed at every stage of wastewater treatment. It is the main contributor to the costs of WWTP. Analysis and forecasting of energy consumption are critical to energy-saving. Many factors influence energy consumption. The relationship between energy consumption and wastewater is complex and challenging to identify. This article employed the fuzzy clustering method to categorize the sample data of WWTP and analyzed the relationship between energy consumption and the influence factors in different categories. The study found that energy efficiency in various categories was changed and the same influence factors in different types had different influence intensity. The Radial Basis Function (RBF) neural network was used to forecast energy consumption. The data from the complete set and categories was adopted to train and test the model. The results show that the RBF model using the date from the subset has better performance than the multivariable linear regression (MLR) model. The results of this study provided an essential theoretical basis for energy-saving in WWTP.

scholarly journals Investigating the impacts of extraneous water on wastewater treatment plants

2016 ◽  
Vol 75 (4) ◽  
pp. 847-855 ◽  
Author(s):  
S. Rödel ◽  
F. W. Günthert ◽  
T. Brüggemann
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Wastewater Treatment Plants ◽  
Measurement Data ◽  
Practical Investigations ◽  
Organic Load ◽  
Sewer Pipes ◽  
Purification Efficiency ◽  
Theoretical Approaches ◽  
Local Water

To demonstrate the effects of increased extraneous water on operation, purification, and energy efficiency, two wastewater treatment plants (WWTPs) have been investigated in detail under the research project ‘Sealing of sewer pipes – Effects on the purification performance of WWTPs and their impact on the local water balance’. Both treatment plants, after evaluating and analyzing the measurement data and information about them, were compared in the light of existing literature and other practical investigations. Furthermore, the results were assessed with respect to transferability to other treatment plants. In WWTP 1, extraneous water reduction led to lower energy consumption of certain plant components such as the pumping station and aeration. An increased percentage of extraneous water had an impact on the wastewater characteristics (e.g. organic load) in WWTP 2. A decrease in extraneous water increases the concentration of biodegradable matters; however, an increase in extraneous water increases the loads in the effluent. The results are in accordance with the theoretical approaches described in the literature and confirm the correlations between extraneous water and purification efficiency and energy consumption of WWTPs.

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.

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