DEVELOPMENT OF HEMP FIBER-REINFORCED FIRE-RESISTANT COMPOSITES FOR SUSTAINABLE MINING VENTILATION SYSTEMS
Notice bibliographique
Résumé
Saskatchewan's economy thrives on two critical sectors: agriculture and mining. With an increasing focus on sustainability, this research aligns with Saskatchewan's growing emphasis on agricultural waste management and sustainable mining operations. The hemp industry, in particular, has seen a significant rise in production within Saskatchewan and across Canada, creating opportunities to utilize hemp fibers in innovative ways. The sustainable development of materials for the ventilation duct for underground mining operation could have far-reaching impacts on both the agricultural and mining sectors in Saskatchewan, which is an important goal of this research. To achieve the goal, the hemp fibers were treated with different concentrations of sodium hydroxide (NaOH) to improve their surface properties and compatibility with polymers. In addition, ammonium polyphosphate (APP) was applied in various combinations to further enhance fire resistance. The effectiveness of these treatments was verified through Scanning Electron Microscopy (SEM) analysis, which provided insights into the fiber surface modifications. The treated hemp fibers were used to fabricate nonwoven hemp fiber fabrics. These fabrics underwent a series of mechanical and physical tests, including tensile tests, water absorption tests, and flammability assessments, to determine their suitability for further composite fabrication. A hand layup method was employed to develop hemp fiber-reinforced polymer composites, with bio-based epoxy resin serving as the matrix. The fiber-to-matrix ratio was maintained at 30:70. A comprehensive range of tests was then conducted on the resulting composites, including tensile, flexural, and Izod impact tests, as well as water absorption tests, density measurements, and flame resistance evaluations using the UL94 vertical and horizontal flame tests. Additional performance assessments, such as cone calorimeter tests, thermal conductivity evaluations, and Thermogravimetric Analysis (TGA), were performed to further investigate the material's thermal stability and fire resistance properties. These tests provided a thorough understanding of the mechanical, thermal, and fire-resistant capabilities of the developed composites. To optimize the manufacturing parameters for the hemp fiber-reinforced polymer composites, a Taguchi L9 orthogonal array was used for the experimental design. Linear regression analysis was carried out using MINITAB software to develop predictive equations for key performance indicators. Among these equations, the most influential ones were identified and subsequently applied to a multi-objective optimization process using genetic algorithm (GA) in MATLAB Global Optimization Tool. The optimization process focused on improving the composite's mechanical and thermal performance by fine-tuning the manufacturing parameters, including fiber treatment concentrations and resin formulations. This research presents a sustainable pathway for utilizing hemp fiber in the development of fire-resistant composites, particularly for underground mining applications. By combining agricultural waste management with innovations in material science, the study not only contributes to reducing environmental impact but also offers a practical solution for creating safer and more sustainable mining operations.
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 | 0,000 | 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) | 0,001 | 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 ».