Optimal Planning of Integrated Nuclear-Renewable Energy System for Marine Ships Using Artificial Intelligence Algorithm
Notice bibliographique
Résumé
Ocean-going ships are one of the primary sources of Greenhouse Gas (GHG) emissions. Several actions are being taken to reduce the GHG emissions from maritime vessels, and integration of Renewable Energy Sources (RESs) is one of them. Ocean-going marine ships need a large amount of reliable energy to support the propulsive load. Intermittency is one of the drawbacks of RESs, and penetration of RESs in maritime vessels is limited by the cargo carrying capacity and usable area of that ship. Other types of reliable energy sources need to be incorporated in ships to overcome these shortcomings of RESs. Some researchers proposed to integrate fossil fuel-based generators like diesel generators and renewable energy in marine vessels to reduce GHG emissions. As the penetration of RESs in marine ships is limited, fossil fuel-based generators provide most of the energy. Therefore, renewable and fossil fuel-based hybrid energy systems in maritime vessels can not reduce GHG emissions to the desired level. Fossil fuel-based generators need to be replaced by emissions-free energy sources to make marine ships free from emissions. Nuclear energy is emissions-free energy, and small-scale nuclear reactors like Microreactors (MRs) are competent to replace fossil fuel-based generators. In this paper, the technical, environmental, and economic competitiveness of Nuclear-Renewable Hybrid Energy Systems (N-R HES) in marine ships are assessed. The lifecycle cost of MR, reliability of the proposed system, and limitations of integrating renewable energy in maritime vessels are considered in this study. The proposed N-R HES is compared with three different energy systems, namely ‘Standalone Fossil Fuel-based Energy Systems’, ‘Renewable and Fossil Fuel-based Hybrid Energy Systems’, and ‘Standalone Nuclear Energy System’. The cost modeling of each energy system is carried out in MATLAB simulator. Each energy system is optimized by using the Differential Evolution Algorithm (DEA), an artificial intelligence algorithm, to find out the optimal configuration of the system components in terms of Net Present Cost (NPC). The results determine that N-R HES has the lowest NPC compared to the other three energy systems. The performance of the DE algorithm is compared with another widely accepted artificial intelligence optimization technique called ‘Particle Swarm Optimization (PSO)’ to validate the findings of the DE algorithm. The impact of control parameters in the DE algorithm is assessed by employing the Adaptive Differential Evolution (ADE) algorithm. A sensitivity analysis is carried out to assess the impact of different system parameters on this study’s findings.
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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) | 0,002 | 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 ».