In the heart of Saudi Arabia, researchers are tackling a pressing environmental challenge: the safe reuse of treated wastewater (TWW) for irrigation. Led by Syed Muzzamil Hussain Shah from the Interdisciplinary Research Centre for Membranes and Water Security at King Fahd University of Petroleum and Minerals, a recent study published in the *Water-Energy Nexus* (translated to English as *Water-Energy Connection*) is making waves in the agritech and energy sectors. The research focuses on reducing organic pollutants in TWW using ozone treatment and leveraging artificial intelligence (AI) to optimize the process.
Organic pollutants in TWW, particularly those measured by Biochemical Oxygen Demand (BOD), pose significant risks to both environmental and agricultural health. Shah and his team collected TWW samples from irrigation zones and subjected them to ozone treatment at a concentration of 0.83 mg/L for varying durations—0, 15, and 25 minutes. The results were promising. “We observed a significant increase in oxidation-reduction potential (ORP) levels post-ozonation, which is a strong indicator of the treatment’s efficacy in reducing organic pollutants,” Shah explained. ORP levels, initially ranging from 155 to 165 mV in untreated samples, rose to 208 mV and 218.30 mV after 15 and 25 minutes of ozone treatment, respectively. This increase correlated with a reduction in BOD levels, suggesting that ozone treatment could be a viable solution for improving water quality for agricultural use.
But the innovation doesn’t stop at ozone treatment. The study also integrated AI-assisted machine learning (ML) to predict ORP levels accurately. Several models, including ANFIS-M1, SVR-M1, and RLR-M1, were developed and evaluated. ANFIS-M1 emerged as the top performer, while SVR-M2 and RLR-M2 showed greater variability and lower accuracy. “The integration of AI allows us to optimize the ozone treatment process, making it more efficient and cost-effective,” Shah noted. This could have significant implications for the energy sector, where water management is a critical component of sustainable operations.
The commercial impact of this research is substantial. As water scarcity becomes an increasingly pressing issue, the ability to safely reuse TWW for irrigation could revolutionize agricultural practices. For the energy sector, this means more sustainable water management, reduced environmental impact, and potentially lower operational costs. “This research provides a roadmap for managing TWW in a way that benefits both agriculture and the environment,” Shah said. “It’s a step toward a more sustainable future.”
The study, published in the *Water-Energy Nexus*, highlights the potential of AI and ozone treatment to advance sustainable water management practices. As the world grapples with water scarcity and environmental challenges, innovations like these offer hope for a more sustainable future. The research not only addresses immediate environmental concerns but also paves the way for future developments in water treatment and reuse technologies. With further refinement, these methods could become standard practice in agricultural and industrial settings, shaping the future of water management.