The U.S. Department of Energy (DOE) recently awarded $27.5 million in grants for 16 research projects to advance resource recovery from wastewater (FY21 Research and Development for Advanced Water Resource Recovery Systems Selections Table | Department of Energy).  DOE has focused resources on water resource recovery facilities (WRRFs), mainly energy efficiency grants in the past, and has found that the amount of energy coming into WRRFs in the wastewater far exceeds the energy required to operate the WRRFs. These grants seek to harness that energy, generate enough to power the facility and even export energy in the form of renewable fuels. 

The North East Biosolids & Residuals Association (NEBRA) is assisting in one such research project being led by Jeffrey McCutcheon at the University of Connecticut’s Department of Chemical & Biomolecular Engineering.  The project titled “A Digitalization, Automation, and Optimization Platform for Improved Resiliency and Consistency of Distributed Anaerobic Digestion for Wastewater Resource Recovery” received $2 million in DOE funding.  Dr. McCutcheon has teamed with Dr. Baikun Li, from the Department of Civil and Environmental Engineering, who has led the development of millimeter-sized electrode array sensors for real-time in situ high-resolution profiling of multiple parameters in anaerobic digestion (AD) systems.  The researchers are particularly interested in AD systems that co-digest with food wastes.

DOE issued the Funding Opportunity Announcement (FOA) last summer with final submissions due in October 2020.  The grant program will further research, development and deployment of innovations in resource recovery technologies.  There were two Topic Area for this grant:  Topic Area 1 sought projects that are almost ready to pilot and Topic Area 2 was for projects that are almost ready to be deployed so as to give them a good nudge.  The UConn research project falls into Topic Area 1 as it seeks to test out Artificial Intelligence/Machine Learning (AI/ML) for more efficiently operating ADs.

According to Dr. Li, “These MEA sensor data will be fed into machine learning algorithms for data-driven modeling development, which has never been conducted in AD systems yet.  We expect such high-resolution profiling can provide unprecedented elucidation of biochemical variations along the depth of AD systems, and further enhance economic outcomes from Co-ADs.”  The UConn team will be developing a “machine learning algorithm-aided process model and control capabilities for ADs to predict and thus prevent system meltdown while simultaneously optimizing system performance for variable feedstocks for a number of DOE-specified metrics.”

UConn sought a co-digestion facility in Connecticut and originally teamed up with Quantum Biopower.  However, Quantum was unable to participate in the project so NEBRA went to work finding another co-digestion facility for the research.  NEBRA didn’t have to look any further than its member, the award-winning Greater Lawrence Sanitary District (GLDS) which has been co-digesting its sludges with waste food slurry for a while now (see recent presentation to the North East Digestion Roundtable if you want to learn more: https://youtu.be/qv2nxrWG8jI). 

Worcester Polytechnic Institute (WPI) in Massachusetts was the other local university institute to receive funding, also $2 million under Topic Area 1, for its project to turn wastewater solids into renewable natural gas (RNG).  The WPI research is being led by Chemical Engineering professor Michael Timko and will utilize hydrothermal gasification technology to generate RNG and extract nutrients for recycling as well.  NEBRA will be keeping a close eye on that research as well.  The potential for RNG is great with plans already in the works in other areas of the world to construct fueling facilities. 

Artificial Intelligence and Machine Learning (ML) are all the rage in wastewater right now because it can help operators do more with less.  In the June Water, Environment & Technology (WE&T) magazine, CDM Smith’s David Ubert suggested that automation is becoming a necessity and that AI/ML are “essential, not only to control the equipment in a facility but also to maintain a safe working environment, store valuable information/data for later use, identify potential issues, and achieve a host of other advantages.”  In AD, it could be used to maximize biogas production for example.

The Water Research Foundation (WRF) received $1.2 million under Topic Area 2 for “Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities” where they will deploy some of these emerging technologies at WRRFs.  WRF has teamed with Black & Veatch, the Hampton Roads Sanitation District, DC Water, Metro Wastewater Reclamation District, University of Michigan, Northwestern University, and Oak Ridge National Laboratory for that project.