Chemical-Looping Technology: Application of Nonlinear Model Predictive Control and Reactor Network Modelling Using Biomass as a Fuel
Notice bibliographique
Résumé
As climate change becomes a more pressing issue, there is increasing research being performed to investigate greenhouse gas reduction strategies such as carbon capture technology and sustainable fuels. To this end, chemical-looping (CL) technologies such as chemical-looping combustion (CLC) and chemical-looping gasification (CLG) are being explored to improve the sustainability of energy generation. In addition, biomass has recently been studied as a renewable fuel for this process. Various works into CL technologies have been performed, but further investigation is required in the areas of process design and control in order to verify whether this technology can feasibly be implemented for energy generation and to determine the most effective implementation strategies for CL processes. \n \nThe aim of this thesis is to determine reactor design and control strategies which can be implemented to improve the energy generation, gasification efficiency, and carbon capture effectiveness of packed bed CLC and CLG. In this work, optimal control strategies for large-scale packed bed CLC are obtained by implementing nonlinear model predictive control (NMPC). For NMPC, a multiscale model is developed to simulate the plant behaviour and validated against multiple sources of experimental data, while a pseudo-homogeneous model is used as the internal NMPC model to reduce computational costs for implementation of feedback control. By manipulating the inlet air and inert gas fluxes in the oxidation stage, and the inlet fuel flux in the reduction stage, the outlet temperature and CO2 selectivity could be controlled in order to improve the energy generation and carbon capture effectiveness of the process. Then, a reactor network model was proposed to simulate packed bed biomass-fueled CLG and CLC, and validated using experimental data under both CLG and CLC conditions. Using this model, a variety of oxygen carrier (OC) bed lengths and locations were assessed to evaluate the resulting impact on the performance of CLG and CLC. For CLG, the highest gasification efficiency occurred with an OC/biomass ratio of 0.25 combined with a steam/biomass ratio of 1, and the OC placed near the reactor inlet. For CLC, a fully packed bed with steam as the inlet gas resulted in the highest outlet CO2 fraction. \n \nThese design and control strategies obtained through the NMPC scheme and reactor network model can be employed to improve the feasibility of chemical-looping technology. This would make it more CL more practical to implement in existing gasification and combustion processes, improving the sustainability of energy generation.
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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 | 0,001 | 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 ».