Power- and biomass-to-liquid processes with fuel-assisted solid oxide electrolysis cells and water gas shift-adjusted systems: A techno-economic analysis
Bibliographic record
Abstract
In the pursuit of mitigating climate change , sustainable aviation fuels (SAFs) present a promising solution for defossilizing long-haul air travel. Power- and biomass-to-liquid (PBtL) processes, which combine renewable hydrogen and non-crop-based biomass via Fischer–Tropsch (FT) synthesis, offer a pathway to SAF production. However, the high electricity demand for hydrogen production via electrolysis poses a significant economic challenge. Therefore, this study investigates the integration of fuel-assisted solid oxide electrolysis cells (FASOECs) and adjustments to the water gas shift (WGS) equilibrium in PBtL processes, to reduce the electricity demand for hydrogen production and adapt to potentially fluctuating electricity prices. The results indicate that WGS adjustments reduce specific electric energy demands but compromise carbon efficiency and fuel production rates. Conversely, FASOEC-based process configurations exhibit higher energy efficiencies when the FT tail gas purge stream is utilized in the FASOEC anode. Furthermore, all considered configurations are thermally self-sufficient when heat integration is performed. A techno-economic analysis using TEPET for Norway in 2023 reveals that the WGS-adjusted configurations consistently outperform FASOEC process variants in terms of net production costs (NPC). Among the evaluated configurations, the WGS-adjusted processes demonstrate the greatest economic competitiveness, with NPC values as low as 2.66 € 2023 /kg fuel (1.94 € 2023 /l fuel ), while the fuel-assisted PBtL recycle case generates the least economically competitive process variant with an NPC value of 3.22 € 2023 /kg fuel (2.35 € 2023 /l fuel ). Additionally, the FT tail gas purge stream emerges as a valuable resource for reducing specific electrolysis energy demands in the Purge-to-Fuel configuration, yielding a reduced NPC value of 3.00 € 2023 /kg fuel (2.19 € 2023 /l fuel ) while retaining the same carbon efficiency as the conventional PBtL process (3.12 € 2023 /kg fuel (2.27 € 2023 /l fuel )). Key cost drivers include electricity, SOEC stack replacement, and biomass, with the Acid gas cleaning unit identified as a major source of carbon losses ( ∼ 75 %). This study highlights critical trade-offs between energy and carbon efficiency, emphasizing the need for optimized purge stream utilization and WGS equilibrium adjustments to enhance the commercial viability of SAF production.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".