Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 28, 2026Energy Conversion and ManagementOpen Access

System-level techno-economic analysis of hydrogen-fuelled genset internal combustion engines with exhaust-driven power recovery

View Full Paper
Ask AI
Bookmark
Share

Authors

ARAgill Aynngran Raj Kumara RajCCChee Choy ChowMCMeng Choung Chiong

Discussion

Loading...

Member takes

Overview

Simulation study demonstrates exhaust heat recovery lowers electricity costs by 18.6% in hydrogen generator systems, highlighting waste energy integration as key to clean power.

Key Points

  • To evaluate and compare the thermodynamic and techno-economic performance of gasoline, open-cycle hydrogen, and waste-heat-recovery-integrated hydrogen generator systems.
  • Conducted unified thermodynamic and techno-economic modeling using an ASPEN-based process simulation framework comparing gasoline, open-cycle hydrogen, and waste-heat-recovery setups.
  • Integrated an exhaust-mounted electric turbogenerator coupled with a proton exchange membrane electrolyser to harvest exhaust energy for in-situ hydrogen regeneration.
  • Assessed system performance and levelised cost of electricity under operating parameters of 5 hours per day and 365 days per year across grey and green hydrogen scenarios.
  • At maximum stable equivalence ratios examined (φ = 1.045 for gasoline, φ = 0.857 for hydrogen), open-cycle hydrogen engines showed a 17.5% lower brake thermal efficiency than gasoline (33.4% vs. 40.5%).
  • Coupling an electric turbogenerator waste heat recovery system raised apparent extended system-level energy utilisation factors to 54.4% in ASPEN simulations and 52.3% theoretically.
  • Waste heat recovery reduced the levelised cost of electricity for grey hydrogen to 0.727 MYR kWh⁻¹, representing reductions of 39.4% relative to open-cycle hydrogen and 18.6% relative to gasoline.

Cite This Study

Raj et al. (2026) studied this question.

synapsesocial.com/papers/6a91469ad15324a1df3aa65fhttps://doi.org/10.1016/j.enconman.2026.122082
View Full Paper
Ask AI
Bookmark
Share