October 13, 2022
Chemical engineers at Heriot-Watt University have contributed to the development of a machine-learning model that accurately predicts the heat capacity of MOFs and other adsorbents.
The study, published today in nature materialsopens exciting opportunities for future carbon capture technologies.
Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores.
These pores, combined with the rich and diverse chemistry of MOFs, make them extremely versatile and promising for many applications, including carbon dioxide (CO2) capture from power plants and factories.
Susana Garcia, Professor of Chemical and Process Engineering and Associate Director of Carbon Capture and Storage at the Research Center for Carbon Solutions (RCCS), is advancing possibilities around new materials through her research that includes MOFs, energy separations, industrial and environmental applications.
She is currently leading the ACT funded program Project PRISMwhere a consortium of international researchers has developed a technology platform to accelerate the deployment of carbon capture technologies by integrating process engineering and basic sciences.
“The results of the PrISMa platform demonstrated the importance of material properties such as heat capacity to estimate the energy required to drive carbon capture and raised the need for this study,” said Professor Garcia.
“It’s about predicting the amount of energy needed to heat a substance by one degree.”
Professor Garcia’s team is also using the platform to decarbonize UK industry as part of efforts within the UK Industrial Decarbonization Innovation and Research Center – edric.
To demonstrate the impact of their work in the real world, the team simulated the performance of the materials in a carbon capture plant. Dr. Charithea Charalambous, Research Associate on Professor Garcia’s team and technical lead for the PrISMa modeling work, developed the process model.
Dr. Charalambos explains: “Our results show that with the correct heat capacity values for MOFs, the total energy cost of the carbon capture process is much lower than we originally calculated.”
“For some metal frameworks, this was a 50% reduction in energy costs, which significantly affected the technical and economic viability of the process.”
So far, all engineering calculations assume that all MOFs have the same heat capacity for the simple reason that almost no data are available. The surprising aspect of this work is that Professor Bernd Smit’s team at the École polytechnique fédérale de Lausanne, a public research university in Switzerland, used a big data approach for a problem where there are no data.
They realized that one could mechanistically learn how the local chemical environment affects the vibrations of every atom in MOFs and that these vibrations could be related to heat capacity. To test the predictions, the researchers assembled several MOFs and measured their heat capacity, which all agreed well with the model’s predictions.
Professor Garcia concluded: “This work highlights the importance of multidisciplinary and interdisciplinary research efforts in the fight against climate change, and demonstrates how AI can accelerate the resolution of urgent problems.”