DEVELOPMENT OF HEMP FIBER-REINFORCED FIRE-RESISTANT COMPOSITES FOR SUSTAINABLE MINING VENTILATION SYSTEMS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".