Life cycle and economic viability assessment of clean hydrogen as a fuel in corn drying
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».