Optimization of a SNCR/LN NOx Reduction System using Model Predictive Control
Notice bibliographique
Résumé
An increase awareness in climate change has pushed governments to tighten regulations surrounding environmental emissions limits for industry. Existing industrial plants are required to meet these new regulations, which requires the implementation of innovative technologies. These retrofits are very costly for older facilities to both implement and maintain. Application of one such system at a Metro Vancouver Waste to Energy facility utilized a Low NOxtm and Selected Non-Catalytic Reduction to reduce the plant’s NOx output. This project, completed in 2013, did not perform well due to the requirement of an operator to manually balance the Low NOxTM and Selected Non-Catalytic Reduction system. The manual balancing resulted in an estimated 40% more Ammonia being used at an estimated cost of $ 48 000.00 per year. This project provided the feasibility, design, and configuration of an advanced control algorithm, Model Predictive Control, to maximize the performance of these two systems and to reduce the overall operational cost of the system. \n \nAdvanced process control has a slow adoption rate in Industry especially in smaller facilities, where portray the benefits of newer technologies is an uphill battle. As a result, this project was structured as a Front-End Engineering and Design (FEED) project. The project involved a performance analysis, cost-benefit analysis, design work, and proof of concept configuration in a Digital Twin of the plant’s Distributed Control System. A detailed evaluation of the original control strategy was performed to determine its limitations and constraints. A cost-benefit study showed the benefits of an optimized system. Design documents were created to provide a base for the modifications that would be required to implement the new control strategy. A Digital Twin of the site’s control system was created and used as a development system. The new MPC controller was configured using standard function block programming and was added to the site’s Human Machine Interface. To create the prediction model for the MPC controller, a training set of data was created by performing tests on the live system, and the created model was verified against a separate set of data. The model was then evaluated and refined before creating a simulation and testing the final configuration. \n \nIt was found that an optimized control strategy would result in higher utilization of Low NOxtm and a overall reduction of Ammonia usage. Additionally, it was found that the Ammonia became more effective at a higher temperature, and further savings are attainable by operationally running the furnace at a temperature above 1050 ºC. The final optimization of the system showed significant saving opportunities. The implementation of MPC in this manner showed that implementing new technology can help aging facilities remain viable as emissions regulations continue to be lowered.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».