Energy Consumption Of Wastewater Treatment Plants Research Articles

Energy consumption prediction in water treatment plants using deep learning with data augmentation

Wastewater treatment plants (WWTPs) are energy-intensive facilities that play a critical role in meeting stringent effluent quality regulations. Accurate prediction of energy consumption in WWTPs is essential for cost savings, process optimization, regulatory compliance, and reducing carbon footprint. This paper introduces an efficient approach for predicting energy consumption in WWTPs, leveraging deep learning models, data augmentation, and feature selection. Specifically, Spline Cubic interpolation enriches the dataset, while the Random Forest model identifies important features. The study investigates the impact of lagged data to capture temporal dependencies. Comparative analysis of five deep learning models on original and augmented datasets from Melbourne WWTP demonstrates substantial performance improvement with augmented data. Incorporating lagged energy consumption data further enhances accuracy, providing valuable insights for effective energy management. Notably, the Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BiGRU) models achieve Mean Absolute Percentage Error (MAPE) values of 1.36% and 1.436%, outperforming state-of-the-art methods.

Minimizing greenhouse gases emissions and energy consumption from wastewater treatment plants via rational design and engineering strategies: A case study in Mashhad, Iran

Wastewater treatment plants (WWTPs) play direct and indirect roles in the emission of greenhouse gases (GHG) produced by biological processes and energy consumption, respectively. Nowadays, reducing GHG emission and the energy consumption of WWTPs are of interest. In this study, at first, the GHG emissions and energy consumption of the Mashhad WWTP (MLE and aerobic digestion process) were compared with energy self-sufficient WWTPs. In the next stage, four scenarios were developed to reduce GHG emissions and energy consumption. According to the results, energy consumption and GHG emissions in this WWTP is outside of the acceptable value. By changing aerobic digestion to anaerobic digestion, the energy consumption will decreases from 1 kWh/m3 to 0.58 kWh/m3, and the total amount of CO2 equivalent (CO2e-T) increases from 0.64 kg CO2e/m3 to 1.65 kg CO2e/m3. In order to reduce energy consumption and GHG emissions, anaerobic digester and CHP must be used together. This process leads to reduced energy consumption and GHG emissions by about 70% and 53% , respectively. The lowest (highest) GHG emissions will occur in MLE with anaerobic digester and CHP (without CHP). Based on this study, GHG emissions and energy consumption can be controlled by selecting a correct process for WWTPs.

Co-optimization of wastewater treatment plants interconnected with smart grids

Wastewater treatment plants (WWTPs) and smart grids of urban systems are in a strong nexus. Energy consumption of WWTPs will directly impact the economy of the smart grids, while the energy forecast, marketing, and resource allocation of smart grids will affect the economy of WWTPs. As such, these two critical infrastructures need to be operated and managed cooperatively to maximize the mutual economic benefits. In this paper, a mixed integer nonlinear programming (MINLP) co-optimization model is developed to address the day-ahead economic dispatch problem of smart grids embedded with interdependent links among wastewater treatment plants and smart grids. The energy demand of WWTPs is optimized by minimizing the energy consumption of the aeration module, mixed liquor pumping, primary clarifier’s influent pumping, secondary clarifier’s sludge pumping, and mixing devices that are integrated in the dynamic economic dispatch problem of smart grid. Additionally, biochemical oxygen demand (BOD) and total Kjedhal nitrogen (TKN) concentrations of the treated wastewater is also incorporated in the economic dispatch problem to ensure of the high quality of the WWTP’s effluent. Electricity consumption of the buildings is also added as a constraint to the economic dispatch, serving as a dynamic load for smart grid. Case studies are conducted to examine the impact of wastewater flow rate, influent BOD and TKN concentrations, battery efficiency, and end-of-day battery’s state of the charge constraints on economic dispatch of the smart grid. The results show that total operational cost and energy consumption of the integrated WWTP-smart grid is increased only by 3.4% and 1.75%, respectively, as the daily influent flow rate and BOD & TKN concentrations are increased from their minimum to maximum thresholds.

Towards an Energy Self-Sufficient Resource Recovery Facility by Improving Energy and Economic Balance of a Municipal WWTP with Chemically Enhanced Primary Treatment

The recent trend of turning wastewater treatment plants (WWTPs) into energy self-sufficient resource recovery facilities has led to a constant search for solutions that fit into that concept. One of them is chemically enhanced primary treatment (CEPT), which provides an opportunity to increase biogas production and to significantly reduce the amount of sludge for final disposal. Laboratory, pilot, and full-scale trials were conducted for the coagulation and sedimentation of primary sludge (PS) with iron sulphate (PIX). Energy and economic balance calculations were conducted based on the obtained results. Experimental trials indicated that CEPT contributed to an increase in biogas production by 21% and to a decrease in sludge volume for final disposal by 12% weight. Furthermore, the application of CEPT may lead to a decreased energy demand for aeration by 8%. The removal of nitrogen in an autotrophic manner in the side stream leads to a further reduction in energy consumption in WWTP (up to 20%). In consequence, the modeling results showed that it would be possible to increase the energy self-sufficiency for WWTP up to 93% if CEPT is applied or even higher (up to 96%) if, additionally, nitrogen removal in the side stream is implemented. It was concluded that CEPT would reduce the operating cost by over 650,000 EUR/year for WWTP at 1,000,000 people equivalent, with a municipal wastewater input of 105,000 m3/d.

A Data-Driven Soft Sensor to Forecast Energy Consumption in Wastewater Treatment Plants: A Case Study

Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With the increase of installed WWTPs worldwide, the modeling and forecast of their energy consumption have become a critical factor in WWTP design to meet environmental and economic requirements. The accurate and swift energy consumption forecasting soft-sensors are not only supportive to the daily electric and financial budgeting by WWTP practitioners on the micro-scale, but also beneficial to local municipal operation and fundamental to regional environmental impact estimation on the macro-scale. Energy consumption in WWTPs is influenced by different biological and environmental factors, making it complicated and challenging to build soft-sensors. This article intends to provide short-term forecasting of WWTP energy consumption based on data-driven soft sensors using traditional time-series and deep learning methods. Ten data-driven soft sensors, including the ordinary least square, exponential smoothing state space, local regression, auto-regressive integrated moving average (ARIMA), structural time series model, Bayesian structural time series, non-linear auto-regressive, long short-term memory with and without updates, and gated recurrent units have been investigated and compared for WWTP energy consumption forecasting. Energy consumption time-series data from a membrane bioreactor-based WWTP in the middle east is used to evaluate the performances of the proposed soft-sensors. Results showed that ARIMA achieved slightly improved performances, among others. The employment of adaptive deep learning-based soft sensors is expected to enhance the capabilities of the deep models to quickly and accurately follow the trend of future data.

The correlations among wastewater internal energy, energy consumption and energy recovery/production potentials in wastewater treatment plant: An assessment of the energy balance

Given the economic and environmental importance of energy use in wastewater treatment plants (WWTPs), the need to assess the energy balance of WWTPs has become a growing concern. Previous studies have suggested that energy balance or even net energy production may be achieved in WWTPs under specific conditions. However, information regarding the energy consumption and the energy recovery/production potential in WWTPs as a function of the influent characteristics is still very limited. In this paper, by exploring the correlations among wastewater internal energy, energy consumption and energy recovery in WWTPs, a novel net energy consumption (NEC) model was developed for predicting the energy self-sufficiency level of WWTPs. From our results, exponential regression showed a high accuracy in predicting the annual energy consumption, the annual excess sludge production and the bioreactor footprints in WWTPs. Wastewater with more internal energy which is determined by influent chemical oxygen demand (COD) concentration and flow rate, not only leads to higher energy consumption in WWTPs, but also results in an increase in the excess sludge production, bioreactor footprints and wastewater volume. This means that the WWTPs could achieve energy saving or even net energy production by incorporating sludge incineration, photovoltaic (PV) generation and thermal energy recovery. By combing regression analysis with theoretical formula, the annual net energy demand of WWTPs reached −0.187–0.466 kWh·m−3 in the range of wastewater condition studied (the influent COD concentration range of 60–800 mg·L−1 and the flow rate range of 1296–100,000 m3·d−1). The NEC model reveals that the net zero energy consumption may be achieved by integrating the better understandings of wastewater internal energy, energy conversion methods and environmental media energy, which is of value to policy makers for the planning of new WWTPs and provides theoretical support for the selection of available energy recovery methods.

Investigating the impacts of extraneous water on wastewater treatment plants.

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.

Operating costs and energy demand of wastewater treatment plants in Austria: benchmarking results of the last 10 years.

This work presents operating costs and energy consumption of Austrian municipal wastewater treatment plants (WWTPs) (≥10,000 PE-design capacity) that have been classified into different size groups. Different processes as well as cost elements are investigated and processes with high relevance regarding operating costs and energy consumption are identified. Furthermore, the work shows the cost-relevance of six investigated cost elements. The analysis demonstrates the size-dependency of operating costs and energy consumption. For the examination of the energy consumption the investigated WWTPs were further classified into WWTPs with aerobic sludge stabilisation and WWTPs with mesophilic sludge digestion. The work proves that energy consumption depends mainly on the type of sludge stabilisation. The results of the investigation can help to determine reduction potential in operating costs and energy consumption of WWTPs and form a basis for more detailed analysis which helps to identify cost and energy saving potential.

STUDY ON GREENHOUSE GAS EMISSIONS FROM WASTEWATER TREATMENT PLANTS

One of the causes of climate change is the accumulation of greenhouse gases (GHGs). Methane (CH4) is one of the greenhouse gases, with an ability to trap heat in Earth's atmosphere 21 times greater than carbon dioxide (CO2) per unit mass and in the same period of time. Because the lifetime of methane in the atmosphere is relatively short, it could be kept under control. Wastewater treatment plants (WWTPs) are anthropogenic sources of CH4 emissions. Anaerobic digestion of the sludge resulting from wastewater treatment produces biogas, which contains large amounts of CH4. The paper aims to study the opportunities to control and even eliminate methane emissions resulted from WWTPs. The main solution to achieve that is by an intensive usage of biogas. The methane contained in the biogas (resulted from sludge anaerobic digestion) can be used for local production of energy required in the WWTP. Power consumption of a WWTP was monitored, together with its biogas production. The necessary consumptions of power and heat were calculated and compared with the potential energy of methane in the biogas obtained. It resulted that an important part of the WWTP energy demand may be covered by implementing a local solution to generate energy from biogas burning. The implementation of systems that produce biogas and use it further to produce energy for own consumption is an important step towards controlling the level of methane in the atmosphere, especially in large WWTPs. The carbon footprint of energy consumption in WWTP is also reduced.