Date: August 29, 2023
This article is published by WateReuse Association in collaboration with National Alliance for Water Innovation (NAWI). Lauren Nicole Core is water specialist consultant with the World Bank Group and communications lead with Lawrence Berkeley National Laboratory, Berkeley, CA; lcore@worldbank.org and lncore@lbl.gov.
Artificial Intelligence (AI) grabs headlines for its ability to mimic, in its own distinctive way, human expression, from writing to artwork. However, its most important applications venture beyond generative AI to address challenges that have confronted humankind since the dawn of civilization. Secure and sustainable access to freshwater, for instance. Our most important natural resource has never been more severely threatened, and the global need for new sources of usable freshwater has never been as great.
As such, nations, regions, and communities around the world are actively pursuing alternative water supplies such as brackish water or potable reuse of municipal reclaimed water. However, purifying these sources means increases in energy, chemical use, and the need for talented, highly trained advanced water treatment operators.
It is therefore important to find ways to drive down the costs of energy and chemicals associated with water treatment, and to support operators.
In an effort to address such challenges, research supported by the United States Department of Energy’s National Alliance for Water Innovation (NAWI) is using AI and machine learning (ML) to reduce energy and chemical use, improve operational support, increase treatment system uptime, and improve confidence in purified water quality. The research aims to lower the cost of Reverse Osmosis (RO)-based advanced treatment (RBAT) by developing new—and improving existing—technological solutions to make treatment of nontraditional waters competitive with conventional water sources for specific end-use applications.
“The researchers are part of the NAWI Alliance, which focuses on making desalination and water treatment technologies more efficient, effective, and reliable,” explains Peter Fiske, Executive Director of NAWI. “These technologies will enable 90% of our current non-traditional water sources to achieve pipe parity – when the levelized cost for treating and reusing nontraditional water sources are equal to the cost of today’s marginal water supplies.”
NAWI is the largest federal investment in water treatment, desalination, and water reuse since the 1960s. The innovative national research program and public-private partnership brings together industry, academic, national laboratory, and other stakeholders across the country to advance next-generation desalination and water recycling technologies.
Reverse Osmosis to Reverse Water Insecurity
RO is a long-proven water treatment process that lies at the heart of most potable reuse systems. RO-based facilities can render water from nontraditional sources safe for use in a wide range of applications, lessening our reliance on groundwater and other often overtaxed freshwater supplies.
But the effectiveness of RO comes at a cost. The RBAT approach consumes a great deal of energy. This limits its scalability, especially for less affluent communities, and leads to questions about more environmentally sustainable alternatives.
Nevertheless, “the adoption of water reuse is gaining momentum. Studies like this that help improve the energy efficiency of water reuse keep that momentum strong, improving the health and resilience of communities across the United States,” said WateReuse Association Executive Director Patricia Sinicropi.
Sampling Wastewater…Without Samples
Researchers use AI to deliver more efficient testing and assessment of wastewater and to eliminate contaminants more adaptively and cost-effectively than traditional control methods allow. Take N-nitrosodimethylamine (NDMA) for example. NDMA once played a key role in the production of rocket fuel, but these days is mainly present in water as a disinfection byproduct. It is considered extraordinarily carcinogenic based on a study linking it to liver tumors in mice.
Because it is a very small molecule, NDMA can partially pass through RO. So, RBAT uses ultraviolet (UV) radiation to destroy NDMA, among other water quality benefits. This method destroys certain chemicals found in water through photolysis, which uses intense UV light to shatter the chemical bonds characteristic of targeted contaminants. UV also inactivates many pathogens, and in combination with certain chemicals like hydrogen peroxide, can further break down unwanted contaminants. Like RO, this process consumes a great deal of energy.
Traditionally, water treatment engineers have sampled wastewater from the multiple treatment processes that make up an RBAT array and sent them to be tested for NDMA and other dangerous contaminants. This process can take up to one month. These samples contribute to a database reaching back months or years, and the updated series is analyzed to identify the highest concentration of NDMA across all samples. Since NDMA is an important contaminant and measuring it at a lab is slow and expensive, engineers and operators assume the worst. The intensity of UV light is set based on the highest or near-highest NDMA levels ever measured, and not changed.
The research team proposes to address the energy inefficiency typical of traditional RBAT-based treatment solutions by using an AI- and ML-based approach. It goes beyond direct sampling in favor of analyzing enormous datasets to train “soft sensors.” Soft sensors are AI models that use faster, cheaper data sources to predict the concentrations of slower, more challenging contaminants like NDMA. The result is a real-time estimate of key contaminants in a water source, including its most elusive. Researchers have demonstrated that AI can predict NDMA concentrations within ±3 parts per trillion of their actual value. This analysis informs the treatment process, reducing unnecessary UV treatments by roughly 50% while still reducing NDMA levels far below regulatory limits. Further AI research in the same NAWI project is modeling microfiltration and RO with AI to detect faults or optimize energy and chemical use within those steps of RBAT systems.
“The development and implementation of advanced controls for optimization, such as ML and AI, is an area that is ripe for innovation,” said Andrew Salveson, Project Lead and Water Reuse Chief Technologist, Disinfection Chief Technologist, and Project Manager at Carollo Engineering. “In some cases, advanced controls are built into individual processes, but the controls are not integrated across the entire system and don’t account for impacts on upstream or downstream processes. To the best of our knowledge, no studies have applied ML or AI to the operation of integrated RBAT trains in potable water reuse.”
Advanced Fault Detection for Better Reliability
This research organizes AI to support RBAT systems under two umbrellas: process control and fault detection. A pilot scheduled for 2024 will test the team’s efforts to provide better fault detection through real-time AI analysis. Adaptive process control and intelligent monitoring of treated-water supplies will allow water-treatment facilities to address fluctuations in water quality before they threaten to impact the delivery of usable freshwater.
This approach will also save energy and lower costs to water consumers, and researchers have hope that it will increase public confidence in its water supply. Those pilots will be conducted at Las Virgenes Municipal Water District (LVMWD) in Calabasas, California – an early adopter and innovator of such technological interventions – and Orange County Water District (OCWD) in Fountain Valley, California.
In advance of the pilot, researchers are currently conducting a series of simulations against historical data collected from high-frequency online sensors used by water utilities partnering with the project. These simulations encompass five different fault-detection and process-control methods, the results of which will be assessed at the end of this phase of the project. Researchers expect that more than one method will achieve their target of 10% energy savings, 20% cost savings, or 50% improved reliability. The most promising set of controls will be tested more rigorously at two demonstration-scale RO-based water-reuse facilities designed to produce potable water.
These are still early days for AI in general, and especially for its use as a facilitator of water reuse and as treatment of nontraditional water sources. But this research has already demonstrated significant advantages in water-testing methods, and researchers expect similar results in their efforts to inform fault detection and process controls with AI. Full implementation of the research team’s findings for commercial and municipal purposes will only be possible after a thorough real-life trial mirroring large-scale water treatment and reuse. For now, AI holds immense promise for a new generation of more efficient, steadier water treatment facilities capable of safely delivering freshwater from a wider variety of nontraditional sources than current technology allows.
Author’s note: The views expressed in this column do not necessarily represent the views of the US Department of Energy or the US government.
NAWI is a public-private partnership that brings together a world-class team of industry and academic partners to examine the critical technical barriers and research needed to radically lower the cost and energy of desalination. NAWI is led by DOE’s Lawrence Berkeley National Laboratory in collaboration with National Energy Technology Laboratory, National Renewable Energy Laboratory, and Oak Ridge National Laboratory, and is funded by the Office of Energy Efficiency and Renewable Energy’s Industrial Efficiency and Decarbonization Office.
There are many more NAWI-supported research projects and innovators leading the charge for a circular economy through desalination and water supply research. NAWI provides several opportunities for participation, from applying for Alliance membership to volunteering to advise a project team as a Project Support Group member. Through relentless dedication to enhancing water accessibility, purity, and affordability, NAWI’s visionary research instills optimism for a future where access to clean water becomes a reality for all.
Research Partners: Baylor University: Amanda Hering; Carollo: Amos Branch, Andrew Salveson, Charlie He, Wen Zhao, Daniel Hutton, Kyle Thompson; Las Virgenes Municipal Water District: Burt Bril, Darrell Johnson, John Zhao, Steve Jackson; National Water Research Institute: Kevin Hardy; Orange County Water District: Han Gu, Jana Safarik, Megan Plumlee; United States Military Academy: Katheryn Newhart; West Basin Municipal Water District: Alejandra Cano-Alvarado, Margaret Moggia, Uzi Daniel, Veronica Govea; Yokogawa Corporation: Steve Hayden, Yasuhiro Matsui.
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