Hybrid model predicts best tech for slashing industrial emissions in the chemical industry

Separating and purifying closely related mixtures of molecules can be some of the most energy-intensive processes in the chemical industry, and contributes to its globally significant carbon footprint. In many cases, traditional industrial separation protocols could be replaced using the latest energy-efficient nanofiltration membranes—but testing the best separation technology for each industrial use case is slow and expensive.

A computational tool that can reduce this work by comparing separation technologies for a given chemical mixture, and predict the most efficient and inexpensive technology for the task, has been developed by researchers at KAUST. The work is published in the journal Nature Energy.

“We are able to predict the separation of millions of molecules relevant across industries such as pharmaceuticals, pesticides, and pigments,” says Gyorgy Szekely, who led the research.

Commercial nanofiltration membranes can slash the energy cost of chemical separations, compared to traditional heat-driven methods such as evaporation and distillation, by selectively filtering out the desired product. Nanofiltration does not work in all cases, however.

“Predicting the separation performance of membranes for different chemical mixtures is a notoriously difficult challenge,” Szekely says.

To develop their overall chemical separation technology selection tool, Szekely and his team compiled a collection of nearly 10,000 nanofiltration measurements from the scientific literature, focusing on commercially available membranes.

The researchers used machine learning to analyze the data, generating an AI model able to predict the nanofiltration performance for untested chemical mixtures. This information was combined with mechanistic models to estimate the energy and cost requirements of a chemical separation if it was performed by nanofiltration, evaporation or extraction.

“Our novel hybrid modeling approach enables us to evaluate millions of potential separation options, to identify the most suitable and energy-efficient technology for any given chemical separation task,” says Gergo Ignacz, a member of Szekely’s team. “This will allow industry to make better-informed decisions that significantly reduce operating costs, energy consumption, and carbon emissions.”

The predictive power of the hybrid model was experimentally validated using three industrially relevant case studies, Szekely says. “We found an excellent match between the values that our model predicted, and measured values for these processes.”

The researchers showed that the carbon dioxide emissions of pharmaceutical purifications could be reduced by up to 90% by selecting the most efficient technology for the task. Overall, the energy consumption and carbon dioxide emissions of industrial separations could be cut by an average 40% using this method, they estimated.

One surprising finding was the stark difference between the best method and the other two methods for any given separation, Ignacz says. “For most cases, either nanofiltration, evaporation, or extraction emerged as a clear winner, with one method significantly outperforming the others based on economic and energy metrics, leaving little middle ground.”

Although the predictive power of the model proved to be high, there is still room for improvement and further validation, Szekely says. “Our tools are available as open access through the OSN Database at www.osndatabase.com, and we encourage the community to use them.”