The University of Chicago and partners are looking at artificial intelligence (AI) based solutions to build systems that would work to use wastewater as scientists across the world look for sustainable sources of energy and water as cities continue to boom. 

The researchers are working on two new projects to test methods to make "intelligent" water systems to recover nutrients and clean water. 

"Water is an indispensable resource of our society, as it is required for sustaining life and economic prosperity," said Junhong Chen, the Crown Family Professor in the Pritzker School of Molecular Engineering at the University of Chicago and lead water strategist at Argonne National Laboratory. "Our future economy and national security greatly depend on the availability of clean water. However, there is a limited supply of renewable freshwater, with no substitute."

This initiative was after the announcement of the US Department of Energy that the University of Chicago, along with Argonne National Laboratory, Northwestern University and other partners, will receive funding of $2 million, to develop an AI-based system to recover energy, nutrients and fresh water from municipal wastewater.

The approach will combine AI with machine learning (ML) to learn about the system dynamics, mathematical modelling for optimizing energy and nutrient recovery, and life-cycle analysis and modelling with respect to both science and economics to guide system design. The systems will also play a crucial role in developing designs and materials for efficient solar steam generation and wireless sensors for real-time water quality monitoring.

The intelligent system concept for municipal wastewater recovery, if successful will also be applicable to other wastewaters, including industrial and agricultural systems. 

The US government hopes that this funding will help create a smart water resource recovering system to significantly reduce the energy consumption of the current US treatment system of municipal wastewater, and become energy positive at a national scale. "This project is an important step forward in realizing Argonne's strategic plan to enhance our leadership in water-related science through pioneering research, discoveries and innovations using artificial intelligence," said Chen.

To treat and reuse wastewater, the contamination needs to be removed because water-contaminating chemicals such as poly-fluoroalkyl substances, or PFAS, may lead to severe environmental and health effects, such as low infant birth weight, cancer, and thyroid hormone disruption. However, to detect such harmful elements currently is extremely expensive, time-consuming and required highly skilled staff and equipment. The vast number of contaminants—over 4,000 in the PFAS family alone—also prohibits the conventional development of biological or chemical probes.

The team is working on a second project on is using AI in molecular engineering to detect and remove water contaminants. The project is developing a platform to use molecular simulation, organic synthesis, and AI to discover large molecular space of the possible PFAS so that the platform can locate, design and forge new chemical probes for identifying and removing these water contaminants. If successful, these systems can be deployed across the globe to remove water contaminants in municipalities and industrial areas. 

It is funded through the Discovery Challenge program from the Center for Data and Computing (CDAC), with support from UChicago's Office of Research and National Laboratories Joint Task Force Initiative.