Researchers in Switzerland have found a way to dramatically cut cement’s carbon footprint by redesigning its recipe. Their system simulates thousands of ingredient combinations, pinpointing those that keep cement strong while emitting far less carbon dioxide in seconds.
The cement industry produces around eight percent of global CO2 emissions – more than the entire aviation sector worldwide. Researchers at the Paul Scherrer Institute (PSI) have developed an AI-based model that helps to accelerate the discovery of new cement formulations that could yield the same material quality with a better carbon footprint.
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Instead of relying solely on time-consuming experiments or complex simulations, the researchers developed a modelling approach based on machine learning. ‘This allows us to simulate and optimise cement formulations so that they emit significantly less CO2 while maintaining the same high level of mechanical performance,’ explained mathematician Romana Boiger. ‘Instead of testing thousands of variations in the lab, we can use our model to generate practical recipe suggestions within seconds – it’s like having a digital cookbook for climate-friendly cement.’
With their novel approach, the researchers were able to selectively filter out those cement formulations that could meet the desired criteria. ‘The range of possibilities for the material composition – which ultimately determines the final properties – is extraordinarily vast,’ said Nikolaos Prasianakis, head of the Transport Mechanisms Research Group at PSI. ‘Our method allows us to significantly accelerate the development cycle by selecting promising candidates for further experimental investigation.’
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Industrial by-products such as slag from iron production and fly ash from coal-fired power plants are already being used to partially replace clinker in cement formulations and thus reduce CO2 emissions. However, the global demand for cement is so enormous that these materials alone can’t meet the need. ‘What we need is the right combination of materials that are available in large quantities and from which high-quality, reliable cement can be produced,’ said John Provis, head of the Cement Systems Research Group at PSI.
Finding such combinations, however, is challenging. ‘Cement is basically a mineral binding agent – in concrete, we use cement, water and gravel to artificially create minerals that hold the entire material together,’ Provis explained. ‘You could say we’re doing geology in fast motion.’
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This geology – or rather, the set of physical processes behind it – is enormously complex, and modelling it on a computer is correspondingly computationally intensive and expensive, which is why the research team is relying on artificial intelligence.
The researchers made use of a neural network and generated the data required for training it themselves: ‘With the help of the open-source thermodynamic modelling software GEMS, developed at PSI, we calculated – for various cement formulations – which minerals form during hardening and which geochemical processes take place,’ explained Prasianakis. By combining these results with experimental data and mechanical models, the researchers were able to derive a reliable indicator for mechanical properties – and thus for the material quality of the cement. For each component used, they also applied a corresponding CO2 factor, a specific emission value that made it possible to determine the total CO2 emissions. ‘That was a very complex and computationally intensive modelling exercise.’
Instead of seconds or minutes, the trained neural network can now calculate mechanical properties for an arbitrary cement recipe in milliseconds – around 1,000 times faster than with traditional modelling,’ Boiger said.
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To determine an optimal recipe, the researchers formulate the problem as a mathematical optimisation task, looking for a composition that simultaneously maximises mechanical properties and minimises CO2 emissions. ‘Basically, we are looking for a maximum and a minimum – from this we can directly deduce the desired formulation,’ Boiger said.
To find the solution, the team integrated an additional AI technology into the workflow – so-called genetic algorithms, which are computer-assisted methods inspired by natural selection. This enabled them to selectively identify formulations that ideally combine the two target variables without having to blindly test countless recipes and then evaluate their resulting properties.
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Among the cement formulations identified by the researchers, there are already some promising candidates. ‘Some of these formulations have real potential,’ said Provis, ‘not only in terms of CO2 reduction and quality, but also in terms of practical feasibility in production.’
The study primarily serves as a proof of concept. ‘We can extend our AI modelling tool as required and integrate additional aspects, such as the production or availability of raw materials, or where the building material is to be used – for example, in a marine environment, where cement and concrete behave differently, or even in the desert,’ said Boiger.
‘This is just the beginning,’ said Prasianakis. ‘The time savings offered by such a general workflow are enormous – making it a very promising approach for all sorts of material and system designs.’
The research has been published in Materials and Structures.