Conceptual Exploration of Waste Heat Recovery Solutions : A Case Study of Low-temperature Waste Heat in Gasket Manufacturing in Guangde, China
Notice bibliographique
Résumé
CO2 emissions have a significant impact on the climate, with a substantial portion originating from the industrial sector. To address this, companies like Alfa Laval aim to reduce their greenhouse gas (GHG) emissions across the entire value chain, particularly in Scope 3, which includes emissions from their suppliers. To contribute to Scope 3 emissions reduction, this thesis focuses on exploring the potential for waste heat recovery (WHR) within a gasket manufacturing process (GMP) operated by a supplier to Alfa Laval. A mixed-methods approach combining qualitative and quantitative studies was employed and conducted through a case study and a literature study. The case study contained an interview,site visits, and empirical data collection from a specific gasket manufacturing facility located in Guangde, China. The literature study provided a deeper understanding of WHR concepts, mechanisms, and relevant technologies. The methodological framework consisted of four phases: Exploration, Case study, Conceptualization and Verification. A mapping of the GMP was conducted to identify the processes where waste heat had the potential to be utilized. The identified waste heat streams were examined to understand their potential in terms of form, temperature, flow rate, cleanliness, and availability. To estimate the magnitude of generated waste heat within the GMP, a quantification of waste heat was conducted. Conceptual solutions for heat recovery were then developed, tailored to the specific characteristics of each waste heat stream. These conceptual solutions were verified by estimating the potential reductions in energy usage, CO2 emissions, and costs. Five subprocesses were identified with waste heat potential for WHR: thermal oil boilers, compressors, presses, ovens and local exhaust ventilation (LEV) system. All waste heat streams were gaseous and low-temperature, all streams below 100 °C except for boilers, which reached a maximum temperature of 118 °C, with varying levels of contamination and availability. Despite these challenges, waste heat streams from boilers and compressors showed the greatest potential for WHR. Conceptual solutions for WHR included a preheating chamber for sensible preheating, anabsorption chiller to provide cooling, and an Organic Rankine Cycle (ORC) system to generate electricity. Additionally, a heat encapsulation solution was proposed to mitigate waste heat dispersion, representing an initial step toward improving waste heat management and addressing the high temperature work environmnet within the facility. Among the solutions, the absorption chiller and ORC system showed the highest potential for savings, while simultaneously addressing the high cooling and electricity demands within the GMP. The case study revealed communication and knowledge gaps in energy efficiency measures (EEMs) between supply chain partners. It also highlighted the high natural gas usage in the facility, suggesting that focusing on EEMs to reduce natural gas could significantly lower Scope 3 emissions. Future efforts should include further exploration of EEMs and energy management practices, improved communication, and studies to verify the technical feasibility and cost-effectiveness of the proposed WHR solutions. Lastly, it is crucial to conduct further studies on the conceptual solutions to verify them comprehensively. These solutions remain theoretical at this stage, with no computational modeling, simulations, or experimental verification undertaken. Consequently, there is a lack of evidence regarding their technical feasibility and cost-effectiveness. It is essential to consider all aspects before implementing a conceptual solution to ensure it is viable in the long run, and sustainable from economic, environmental, and social perspectives.
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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 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 ».