Optimizing Hydrogen Liquefaction Efficiency Through Waste Heat Recovery: A Comparative Study of Three Process Configurations
Bibliographic record
Abstract
Hydrogen (H2) liquefaction is an energy-intensive process, and improving its efficiency is critical for large-scale deployment in H2 infrastructure. Industrial waste heat recovery contributes to energy savings and environmental improvements in liquid H2 processes. This study proposes a comparative framework for industrial waste heat recovery in H2 liquefaction systems by examining three recovery cycles, including an ammonia–water absorption refrigeration (ABR) unit, a diffusion absorption refrigeration (DAR) process, and a combined organic Rankine/Kalina plant. All scenarios incorporate 2 MW of industrial waste heat to improve precooling and reduce the external power demand. The simulations were conducted using Aspen HYSYS (V10) in combination with an m-file code in MATLAB (R2022b) programming to model each configuration under consistent operating conditions. Detailed energy and exergy analyses are performed to assess performance. Among the three scenarios, the ORC/Kalina-based system achieves the lowest specific power consumption (4.306 kWh/kg LH2) and the highest exergy efficiency in the precooling unit (70.84%), making it the most energy-efficient solution. Although the DAR-based system shows slightly lower performance, the ABR-based system achieves the highest exergy efficiency of 52.47%, despite its reduced energy efficiency. By comparing three innovative configurations using the same industrial waste heat input, this work provides a valuable tool for selecting the most suitable design based on either energy performance or thermodynamic efficiency. The proposed methodology can serve as a foundation for future system optimization and scale-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".