Optimizing Wastewater Use for Green Hydrogen Production: AI Unlocks Clean Energy Potential

Scientists from the Departments of Inorganic Chemistry and Chemical Engineering of the University of Malaga participated in an international collaboration that has optimized, through artificial intelligence, the process of producing bio-hydrogen from wastewater. It is a consortium involving researchers from countries such as Vietnam, South Korea, India and Taiwan. Their work is published in the journal Energy.

"Developing processes for the use and valorization of wastewater is necessary to improve the sustainability of water resources and protect the environment," says Enrique Rodríguez Castellón, Professor at the Faculty of Sciences, one of the authors of this study, who adds that hydrogen is an "essential raw material in the chemical and metallurgical industries and a key energy vector in decarbonization."

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Therefore, as noted in this study, the use of wastewater to produce green hydrogen—considered to be the fuel of the future—is a sustainable process with great potential, since it contributes to saving drinking water, optimizing waste and reducing the use of fossil resources.

A New Path

This research has precisely succeeded in optimizing the efficiency of this process, which is carried out through dark fermentation—a method that consists in using anaerobic microorganisms to break down the organic matter present in wastewater to produce bio-hydrogen—although, so far, it has been possible with variables that affect its efficiency and limits in its commercial use.

Therefore, the use of artificial intelligence and machine learning opens a new path to creating predictive models to enhance chemical processes such as dark fermentation. "These models facilitate the identification and learning of patterns, leading to greater accuracy in predictions and system control," says Rodríguez Castellón.

A Novel Method

The study by this international consortium has proven that developing predictive models for this process to improve its efficiency is possible, fine-tuning the procedure and saving time and costs. Moreover, the study describes a novel AI-assisted method that would displace more conventional ones, using real-world test data to build predictive models. It has been used to optimize energy recovery and minimize organic waste from the process, improving its sustainability.