Design Optimization of a Solar Fuel Production Plant by Water Splitting With a Copper‐Chlorine Cycle
Notice bibliographique
Résumé
Due to growing concerns about climate change, the production of renewable fuels has attracted the attention of many researchers recently. Solar energy is an abundant energy resource and a good alternative for the transition toward a more sustainable energy future. Solar energy can be used in many ways to produce solar fuels, for instance, by cracking of hydrocarbons or splitting of water into hydrogen and oxygen. Among these methods, the utilization of thermochemical cycles to produce hydrogen from water is one of the most promising ways of producing solar fuel. The copper-chlorine (Cu-Cl) high-performance thermochemical cycle requires relatively lower temperatures compared to other thermochemical cycles and is a good choice for integration with solar power tower systems. In this study, the design and optimization of a standalone plant for hydrogen generation, powered by solar energy, is investigated. To supply the required thermal energy of the Cu-Cl cycle, and also, to store the thermal energy throughout the night, a high-temperature carbonate molten salt (LiNaK) is used. Also, a three-stage super-critical steam Rankine cycle with four feedwater heater subsystems is also integrated into the solar production plant to provide the required electrical needs of the system. Therefore, there is no need for any auxiliary fuel or electricity to have a stable and continuous operation of the solar fuel production plant. Also, a modified heat exchanger network is utilized for the Cu-Cl cycle to use the available waste heat of the cycle and improve the system overall thermal efficiency. The technical and economic performance of the integrated system is investigated, and energy and exergy efficiencies, along with the investment and hydrogen production costs are evaluated. Based on the thermodynamic results, an efficiency of 40.4% for the Cu-Cl cycle and 45.0% for the supercritical Rankine cycle is obtained. The overall energy efficiency of the system is 28.6%, while the exergy efficiency of the overall system is 29.5%. An economic analysis showed that the investment cost of the solar thermochemical plant for a plant capacity of 1524 kg of hydrogen per hour is 516 million dollars. Using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for multicriteria optimization, the optimum cost of producing hydrogen will be $2.98/kg H 2 while maintaining solar to hydrogen and exergetic efficiencies of 29.2% and 30.1%, respectively. Also, a comparison of the optimized proposed system with other renewable and nonrenewable hydrogen production systems indicates that this method is quite competitive among the other available methods.
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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 ».