Machine learning helps operators make real-time decisions to save time and money.
Water Planet Inc.

Over the past half-century, there have been tremendous advancements in the efficiency and scale of conventional desalination technologies. In 2015, more than 18,000 global desalination plants were in operation, providing more than 23 billion gallons per day of potable drinking water in 150 different countries, according to the International Desalination Association’s .

Yet, fresh drinking water remains one of Earth’s most precious commodities. Although conservation efforts and policy initiatives are critical, technological advancements will be needed to ensure that supply keeps up with demand.

The dominant desalination technology is reverse osmosis (RO) membrane technology, which accounts for 60 percent of the global capacity, and is expected to grow in the coming years, according to the International Renewable Energy Agency’s “Water Desalination Using Renewable Energy: Technology Brief” (2012). This increase is due to significant improvements in RO membranes, pre-treatment and energy recovery, which have decreased RO desalination cost.

However, cost reductions have begun to plateau, and the process is still dogged by high-energy consumption and performance instability caused by sensitivity to variations in feed-water quality. The industry has reached a point where advancements in the economics of complex computational processing are providing real solutions. Artificial intelligence (AI), or machine learning, is the future of the water industry.

Semi-autonomous computers are everywhere: Autopilot is making air travel safer and faster; mobile phones are like pocket-size personal assistants; and self-driving cars are promising to increase safety in daily commutes. Yet, the operation of vital public infrastructure—water and wastewater treatment plants—is still reliant on Victorian-era technologies. Plant operators may be seen standing in front of a water treatment system, waiting for something to go wrong.

Despite the technologically advanced age, water treatment operators and engineers remain the first line of defense to combat complex, natural and dynamic system disruptions. Even well-trained, experienced plant personnel require days to process data, analyze trends and prescribe changes needed at any given time.

Such delays in response can lead to overstress and reduced lifetime of process equipment (e.g., pumps, membranes, etc.), lowered throughput, and the overuse of costly consumables (e.g., cleaning chemicals, antiscalants, etc.). However, AI can play a pivotal role in making society’s current desalination infrastructure more cost-effective, energy efficient and, ultimately, better equipped to self-adapt and self-optimize to the inevitable variability of process conditions.

The advanced mathematics used to optimally maintain a complex water treatment system cannot be processed by humans in real-time. On the other hand, computers excel at rapid computing—around the clock and with perfect memory. Advanced computing and control philosophies will allow operators, engineers and their companies to make more informed decisions in a timely manner.

AI technology can be used to improve the desalination process by optimizing supplemental equipment surrounding the desalination membrane. One self-adaptive flux enhancement and recovery control technology appears to be the first application of AI as an active, real-time control platform in the water industry.

The technology monitors key operating parameters, performs real-time data analytics and makes predictions about when and what future maintenance actions will be required for upstream ultrafiltration pretreatment systems, which act as primary barriers protecting downstream RO membranes. In doing so, it provides the ability for the pretreatment filter to adapt to high fouling events buffering against the effects of these events on downstream membranes—maximizing plant uptime and potable water production.

Integrating AI into the water industry will maximize the potential of current technology while freeing valuable time for experts to focus on these higher-level advancements.