Filter

PrISMa to optimize CO2 capture

Zurich/Lausanne/Edinburgh – Researchers from the Swiss Federal Institutes of Technology in Zurich and Lausanne and Heriot-Watt University have developed the PrISMa model platform. Computer simulations coupled with artificial intelligence will be used to optimize CO2 capture measures.

A research team from the Swiss Federal Institutes of Technology in Zurich (ETH) and Lausanne (EPFL) and Heriot-Watt University in Edinburgh have developed the PrISMa platform (Process-Informed design of tailormade Sorbent Materials). PrISMa serves to simplify measures for CO2 capture. According to a press release, the use of advanced simulations combined with machine learning is an innovative tool that seamlessly combines material science, process design, techno-economics and life cycle analysis. This concept allows the perspectives of different interest groups to be brought together.

PrISMa considers four levels for this purpose: First, potential materials are examined and offered for their adsorption properties by means of computer simulation. At the process level, data such as recovery, energy consumption and other parameters are evaluated. The technical-scientific analysis evaluates the economic and technical feasibility of a CO2 capture plant. At the life cycle level, PrISMa considers the environmental impact over the entire life cycle of the system and its sustainability.

PrISMa is intended to provide valuable insights for various interest groups. Engineers receive tools for the development of efficient and cost-effective processes, while chemists also receive information on the molecular properties of materials for carbon dioxide capture.

"One of the unique features of the PrISMa platform is its ability to predict the performance of new materials using advanced simulations and machine learning," Berend Smit, Professor of Chemical Engineering at EPFL, is quoted as saying in the press release. "This innovative approach accelerates the discovery of high-performance materials for CO2 capture and outperforms traditional trial-and-error methods." ce/eb

View full article