Grantee Research Project Results
Final Report: Decreasing the Energy Use in Wastewater Treatment
EPA Grant Number: SV839490Title: Decreasing the Energy Use in Wastewater Treatment
Investigators: Lampert, David , Stine, James , Thomison, David , Wiseman, Rabecca , Rui, Cai , Ahmadvand, Maryam , Atoufi, Hossein , Pamula, Abhiram , Erra, Rachana , Overacker, Nick , Barnes, Dominique , Shaw, Madelyn , Robison, Brooks
Institution: Oklahoma State University
EPA Project Officer: Page, Angela
Phase: II
Project Period: May 1, 2019 through April 11, 2020 (Extended to April 30, 2022)
Project Amount: $75,000
RFA: P3 Awards: A National Student Design Competition for Sustainability Focusing on People, Prosperity and the Planet - Phase 2 (2019) Recipients Lists
Research Category: P3 Awards , P3 Challenge Area - Safe and Sustainable Water Resources
Objective:
Municipal wastewater treatment plants (WWTPs) consume between 0.8% and 3% of all electricity in the United States. Energy savings measures in WWTPs have the potential for substantial pollution reduction. WWTPs reduce concentrations of nutrients to prevent dissolved oxygen (DO) concentrations from dropping in receiving water bodies. Aeration processes in WWTPs facilitate the growth of microorganisms that consume biochemical oxygen demand (BOD) to protect receiving waters. Blowers used in aeration processes consume substantial amounts of electricity to supply the air. Many facilities supply excess oxygen to ensure water quality compliance with little effort made to the energy costs, particularly in smaller communities with limited resources.
This project has attempted to address a critical need for new technologies to reduce aeration costs in WWTPs. Given the high energy demands of WWTPs, identifying mechanisms to reduce energy costs protects natural habitats and ecosystems across the planet that are stressed by climate change and other forms of air pollution. Increased wastewater energy efficiency preserves the integrity of the planet’s resources for future generations of people and improve air quality. New technologies that reduce aeration costs and increase prosperity. The results of this project benefit all people in society, since pollution mitigation improves the lives of all the planet’s citizens.
The project has resulted in many educational benefits to both the student team and the project stakeholders. Participants learned about sustainability principles including resource depletion and climate change. The project incorporated many disciplines, including lab analysis, electronics development, modeling, economic analysis, field implementation, entrepreneurship, and life cycle assessment, which provided a unique interdisciplinary experience for the student team.
The goal of this research was to develop algorithms to forecast BOD in the effluent of WWTPs under different aeration strategies for optimization of aeration systems and assess the potential of this technology to prevent pollution and save costs at WWTPs. The research application site for this project/technology was the Stillwater, OK WWTP. The objectives of this project were to generate a large dataset of reactor performance over a range of operating conditions in a lab-scale system, develop and parameterize a predictive model of the process, demonstrate the capability to minimize aeration and ensure BOD compliance using the model, create a process model for a full-scale facility using the data from the lab-scale research to assess potential performance and cost savings, and apply the design to operate the Stillwater WWTP.
Summary/Accomplishments (Outputs/Outcomes):
A laboratory-scale system was developed to control aeration based on observations of water quality parameters from a sensor network. The prototype control technology was tested using a bench-scale WWTP aeration system to generate algorithms for aeration control based on machine learning. A variable frequency drive (VFD) and an air compressor are used to modify the air delivery rate to the wastewater. The system automates air delivery based on a set parameter thresholds from the sensor network and collects data on reactor performance. The sensor network and corresponding data acquisition system collect output including pH, temperature, conductivity, turbidity, dissolved oxygen, ammonia, nitrates, and UV absorbance in the influent and effluent of the WWTP aeration system. The data are composited into files and uploaded to an online folder for retrieval and analysis by the flow-control computer. The flow controller utilizes the data to control the DO delivery, while monitoring the concentrations of the various sensor parameters to ensure the DO delivered is still effectively treating the wastewater. The Python Programming Language was used to develop algorithms to control aeration by adjusting the VFD and to train machine learning models using the data from the lab system. There are several promising algorithms used to predict BOD, including Extreme Learning Machine based on an Improved Cuckoo Search algorithm, Back Propagation, Modular Neural Networks, Support Vector Regression, and Regression Trees. A control method that minimizes the aeration input subject to the constraint of meeting the effluent BOD and nutrient requirements was developed. The control system adapts to conditions in the reactor using data from the sensor array with a hybrid non-linear predictive control algorithm. Additional work is being performed to compare different machine learning approaches that are most appropriate. Initial testing demonstrates that the system has promise for continued development.
Water quality and energy consumption data from monthly operating reports were compiled into a relational database for the Stillwater WWTP. These data were used to analyze the potential for new aeration control technology to improve performance. The majority (73%) of the electricity at the Stillwater WWTP is used to power the blowers for aeration. The total electricity costs for the Stillwater WWTP in 2016 were approximately $800,000, and the total electricity consumed was 3,610 MWh. A process model was developed for the WWTP and used to analyze the potential for improved aeration delivery. Targeted DO levels for optimal aeration are often near 2 mg/L; the aeration basins in Stillwater operate far above these levels. Substantial decreases in energy efficiency are associated with operation at concentrations near saturation levels of 9 mg/L. The modeling and analysis of operations at lower DO indicate that the Stillwater WWTP could reduce electricity costs by nearly $300,000.
WWTPs in Oklahoma serving populations between 10,000 and 100,000 customers were surveyed to assess energy savings potential and current operational practices. These facilities are the expected target for the proposed technology. Larger cities are more aware of energy consumption, while smaller communities are often serviced by septic tanks, aeration ponds, and trickling filters. Facilities with the greatest energy efficiency were around 1 MWh per million gallons (MG) treated. Facilities on the lower end of energy efficiency including the Stillwater WWTP, which have energy expenditures near 2 MWh per MG. Most of these WWTPs change aeration operations seasonally. Savings of up to 50% could likely be achieved in these facilities, if performance could match that of other WWTPs. The data from the 12 facilities were extrapolated to other WWTPs across the state to determine aeration energy consumption and potential savings. Total energy consumption for aeration in mid-sized WWTPs in Oklahoma was over 54 GWh. Assuming these facilities could reduce their aeration energy to 1 MWh per MG, a savings of 17,575 MWh of electricity could be achieved, which corresponds to cost savings of over $2.7 million per year.
The project addressed the majority of the goals set out in the original proposal. The biggest remaining issue is to implement new technologies at WWTPs. The COVID-19 pandemic created many issues with access to facilities that increased the complexity of this challenge. The project team is still working on implementing the strategy at the time of this report.
Conclusions:
This project demonstrated that a control system could be developed using existing and emerging water quality sensors and a VFD-based aeration system for process control. Machine learning appears to have great promise to optimize the application of this system to meet WWTP effluent requirements while minimizing energy consumption. The technology has the potential to save over $2.7 million per year and over 17 GWh of electricity if it were successfully applied across Oklahoma.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
Other project views: | All 5 publications | 1 publications in selected types | All 1 journal articles |
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Type | Citation | ||
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Miranda MA, Ghosh A, Mahmodi G, Xie S, Shaw M, Kim S, Krzmarzick MJ, Lampert DJ, Aichele CP. Treatment and Recovery of High-Value Elements from Produced Water. Water 2022;14(6):880. |
SV839490 (Final) |
Exit Exit |
Supplemental Keywords:
Engineering, clean technologies, waste reduction, life-cycle analysis, modeling, monitoring, sustainable development, south central, states: Oklahoma, OKProgress and Final Reports:
Original AbstractP3 Phase I:
Decreasing the Energy Use in Wastewater Treatment | Final ReportThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.