6 Search Results

The success of a future clean hydrogen infrastructure will depend on technology performance, operating conditions, and system configuration, which will be integrated to meet specific end-use requirements. We introduce a Systems-to-Atoms framework to cosimulate material, device, and system design for clean hydrogen storage and transport. Here we present a demonstration scenario in which hydrogen is distributed using a liquid organic hydrogen carrier to 700 bar refueling stations. Cross-scale analysis on hydrogen release at the refueling station reveals that while a high reaction pressure (up to 100 bar) may not enhance the catalyst performance, it can lead to system-level cost savingsmore » (up to 5% of total refueling station cost) due to savings in downstream gas compression. Further, a typically low-performing dehydrogenation catalyst material (e.g., copper) may be preferred over a high-performing catalyst (e.g., palladium) at different operating conditions when considering overall system affordability and supply chain resilience.« less

Multiscale optimization problems require the interconnection of several models of distinct phenomena which occur at different scales in length or time. However, the best model for any particular phenomenon may not be amenable to rigorous optimization techniques. For instance, molecular interactions are often modeled by computational chemistry software packages that cannot be easily converted into optimization constraints. Data-driven surrogate models can overcome this problem. By choosing surrogates with functional forms that are convertible to a mixed-integer linear model, one can connect and optimize these surrogates instead of the underlying models. We demonstrate the interconnection of linear model decision trees tomore » optimize across three scales of a formic acid dehydrogenation process. We show that optimizing across all three scales simultaneously leads to a 40% cost savings compared to optimizing each model independently. Furthermore, the surrogates retain some relevant physical behaviors and provide insights into the optimal design of this process.« less

Roughly 10% of the CO₂ emissions from iron and steel making are attributable to the direct release of CO₂ from the thermal decomposition of carbonates to produce flux, mainly CaO, used for impurity removal. Notably, these direct emissions remain even if carbon-based steelmaking is replaced by hydrogen-based steelmaking. After removing impurities from the molten metal, this flux becomes the solid waste product called ‘slag’, a primarily Ca-silicate material. The transformation of slag back into carbonates is thermodynamically spontaneous with negative ΔG in the ambient environment, meaning that ~10% of the CO₂ emissions from iron and steel making could be negatedmore » if equipment and methods were developed to support CO₂ mineralization. However, the rate of CO₂ mineralization using slag is slowed by several environmental, geometric, and processing factors. We leverage an experimentally verified model of CO₂ mineralization to determine how to efficiently accelerate the process. Increasing the crystallinity of slag, increasing the relative humidity, and reducing the grain size of slag particles provide the greatest increase in CO₂ mineralization rate at the lowest energy penalty. Increasing the concentration of CO₂ and the temperature provide only modest increases in the CO₂ mineralization rate while incurring a substantial energy penalty. For steelmaking slags, CO₂ mineralization represents low-hanging fruit as the current reuse pathways are low value. For ironmaking slag, replacing the production of amorphous slag for the cement industry with the production of crystalline slag for CO₂ mineralization becomes financially preferable when a carbon price/tax exceeds 67.40 USD/t-CO₂.« less