AIM3: Additive and intelligent manufacturing of multi-functional membranes

Sustainable membrane-based technologies can cut energy, operational and capital costs for energy-intensive processes such as CO2 capture from (bio)methane and removal of methane from H2, while contributing to net-zero target. These purification and separation processes account for 10-15% of the world's energy consumption and membrane technologies could save $4 billion in energy costs annually.

Over 670 biogas plants are currently operational in the UK, generating nearly 12 TWh year-1 now. This means that significant amount of biogas will need to be purified because biogas contains ~15-50% CO2. These plants are already preventing 5.1 million tonnes of CO2 from being emitted each year. Moreover, the global biogas potential is expected to reach 80 Mtoe in 2040, which will contribute to a reduction in CO2 emissions, by displacing the use of more polluting fuels. In addition to this, gas and electricity companies such as National Grid, have been looking for future purification technologies to utilise on H2, which is mixed with CO2 and methane and needs to be purified.
Membranes are barrier films that selectively separate molecules based on their properties e.g. size or shape. The wide-spread implementation of this technology depends on the performance of the membrane materials and their manufacturing cost. Although membranes from polymers are dominating the global membrane market their performance suffers. Therefore, new materials are required.

Current efforts are directed at membranes from extremely porous materials such as zeolite imidazole framework (ZIF) and metal-organic framework (MOF) membranes. One gram of these materials contains surface areas equal to 2.5 football pitches. However, their production is expensive. Therefore, innovative techniques are required to manufacture these membranes cheaper at commercial scale.

In our previous project, we increased the surface area of ZIF membranes by coating them on folded substrates via electrochemistry. However, the limited availability of flexible substrates was one of the challenges. Moreover, the existing flexible surfaces are only available in small areas (e.g., 1 cm2) and needed to be manually folded, which is not practical. We also used artificial intelligence methods (machine learning models) to reduce the number of experiments during the project. However, basic models did not work to define the membrane synthesis.

Therefore, this proposal will deliver an additive manufacturing approach for manufacturing 3D folded flexible substrates. We will also define complex ZIF/MOF membrane synthesis system via complex machine learning models rather than classical approach. These will ensure scale up manufacturing of super-compacted membranes that is our original goal and enable wide-spread application of membrane-based separation technologies for a sustainable future.