Tanumoy Banerjee

Despite major interest in the use of liquid hydrogen as a fuel for aviation and aerospace sectors, loss of hydrogen through boil-off continues to be a hurdle as it causes financial loss, indirect greenhouse gas emissions, and safety concerns. Herein, we perform techno-economic analysis, calibrated with experimental measurements, to assess the use of sorbents to capture and condition hydrogen boil-off events in the range of 1.5 to 70 tonnes per day at launch sites and airports. We evaluate the direct delivery of captured boil-off to combustion engines and fuel cells, comparing against boil-off re-liquification and compression. Results indicate probable cost-effectiveness at delivered liquid hydrogen prices above 0.9 U.S. dollar ($) per kg H₂ at airports, and above 10 $ per kg H₂ at launch sites where 10% or more of hydrogen is lost to boil-off.

Boil-off hydrogen (BOH) loss from liquid hydrogen (LH2) storage and transfer infrastructure poses a critical challenge to the use of hydrogen in emerging aviation, space, and heavy-duty mobility applications. As LH2 infrastructure expands, it becomes paramount to identify strategies to minimize BOH loss, enhance exergy efficiency, and reduce hydrogen leakage-related safety issues. This work presents a detailed comparison of cryogenic and non-cryogenic BOH management, including three new process designs with heat integration for BOH recovery and compression up to 700 bars: multi-stage mechanical compression, metal-hydride (MH)-based compression, and an integrated re-liquefaction-vaporization system. Specifically, this work describes a thermally integrated three-stage mechanical compressorintercooler system; a two-stage MH-based BOH compression architecture using different AB2 hydrides (magnesium-based and Ti-Zr-Mn-based); and a cryogenic re-liquefaction cycle coupled with stages of cryopump and a vaporization setup, all operating at the initial BOH condition of 2 bar, 60 K (−213 °C). A thorough techno-economic analysis (TEA) framework is developed, capturing the effects of process, economic, and operational parameters. While mechanical systems are dominated by initial capital cost and energy price, MH systems are sensitive to hydride cost and relevant thermodynamic parameters like reversible hydrogen uptake and cycle life. The re-liquefaction-vaporization pathway offers the lowest cost of capture and compression, especially at higher discharge pressures, and is sensitive to annual throughput and plant operational lifetime. MH compression shows cost competitiveness under favorable material performance, and mechanical compression remains economically competitive only at very high BOH capture capacity.

Quantitatively accurate computational predictions of metal hydride thermodynamics are challenging but critical for alloy performance optimization across a multitude of technological domains, including hydrogen storage, compression, purification, and getters. Recent machine learning approaches have demonstrated great success in this area, but can potentially suffer from several shortcomings since they rely on imbalanced experimental training data and can have poor out-of-distribution (ood) test performance. Here we circumvent such pitfalls by developing a computationally efficient, first principles-based workflow for direct prediction of metal hydride phase equilibrium, i.e., the pressure-composition-temperature (PCT) diagram. We then demonstrate its utility on predicting low stability hydrides derived from compositionally complex C14 Laves phase AB₂ alloys. Specifically, we computationally predict and then experimentally validate an AB₂ alloy series (z < 0.6 for Ti_2−zZr_zCrMnFeNi) with ideal hydriding thermodynamics for a two-stage metal hydride-based compressor for pressurizing boil off from liquefied hydrogen. Importantly, this study lays the groundwork for accurate and efficient discovery/optimization of ood, low-stability hydrides for which purely data-driven approaches lack sufficient accuracy.

Body-centered cubic (BCC) based multi-principal element alloy (MPEA) hydrides have demonstrated significant potential for compact and efficient hydrogen storage.

Quantitatively accurate computational predictions of metal hydride thermodynamics are challenging but critical for alloy performance optimization across a multitude of technological domains, including hydrogen storage, compression, purification, and getters. Recent machine learning approaches have demonstrated great success in this area, but can potentially suffer from several shortcomings since they rely on imbalanced experimental training data and can have poor out-of-distribution (ood) test performance. Here we circumvent such pitfalls by developing a computationally efficient, first principles-based workflow for direct prediction of metal hydride phase equilibria, i.e., the pressure-composition-temperature (PCT) diagram. We then demonstrate its utility on predicting low stability hydrides derived from compositionally complex C14 Laves phase AB2 alloys. Specifically, we computationally predict and then experimentally validate an AB2 alloy series (z < 0.6 for Ti_{2−z}Zr_{z}CrMnFeNi) with ideal hydriding thermodynamics for a two-stage metal hydride-based compressor for pressurizing boil off from liquefied hydrogen. Importantly, this study lays the groundwork for accurate and efficient discovery/optimization of ood, low-stability hydrides for which purely data-driven approaches lack sufficient accuracy.