Life cycle and economic viability assessment of clean hydrogen as a fuel in corn drying
Bibliographic record
Abstract
The framework illustrates the life cycle and economic assessment of hydrogen from four resources and conventional fuels for corn drying based on two delivery scenarios (pipeline and trucking). The process integrates production and delivery of the above-mentioned fuels via pipeline and trucking, their combustion in corn drying, and calculates the carbon intensity and energy costs associated with corn drying using these fuels. • Life cycle and economic assessment of hydrogen and fossil fuels in corn drying. • Transportation via trucking and pipeline scenarios is analyzed. • Green hydrogen reduces emissions above 90 % compared to current fuels. • Pipeline scenario makes hydrogen more cost-competitive. • Sensitivity analysis makes hydrogen viable for sustainable grain drying. This study presents a comparative life cycle and economic assessment of using clean hydrogen as a sustainable alternative to natural gas and propane for corn grain drying. The study compares the environmental performance limited to GWP 100 and cost-effectiveness of hydrogen from various renewable sources (hydro, wind, solar) and plasma pyrolysis of natural gas against conventional fossil fuels under two delivery scenarios: pipeline and trucking. A life cycle assessment is conducted using Open LCA to quantify the carbon intensity of each fuel from cradle to combustion at multiple energy requirements, based on four burner efficiencies across each scenario. In parallel, economic analysis is conducted by calculating the fuel cost required per ton of dried corn grains at each efficiency across both scenarios. The results indicate that green hydrogen consistently outperforms current fuels in terms of emissions, but it is generally more expensive at lower burner efficiencies and in trucking scenarios. However, the cost competitiveness of green hydrogen improves significantly at higher efficiency, and with pipeline infrastructure development, it can become more economical when compared to propane. Hydrogen produced via plasma pyrolysis offers high environmental and economic costs due to its electricity and natural gas requirements. Sensitivity analysis further explores the impact of a 50% reduction in hydrogen production and transportation costs, revealing that hydrogen could become a viable option for grain drying in both pipeline and trucking scenarios. This study highlights the long-term potential of hydrogen in reducing carbon emissions and offers insights into the economic feasibility of hydrogen adoption in agricultural drying processes. The findings suggest that strategic investments in hydrogen infrastructure could significantly enhance the sustainability of agricultural practices, paving the way for a greener future in food production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".