A sustainability assessment model for selecting pre-processing equipment in hemp-based biocomposite supply chains under techno-economic, environmental, and social measures
Bibliographic record
Abstract
Biocomposites have become a significant sustainable alternative in green engineering. The production of biocomposites relies on strategic decisions regarding biomass collection and size reduction during pre-processing within the supply chain. This dissertation aims to evaluate different biomass collection and pre-processing equipment configurations in a case study of hemp-based biocomposites supply chain in Saskatchewan, Canada, to identify the most sustainable operational options. A novel sustainable decision-making framework is proposed in three phases, integrating economic, environmental, social (job opportunity), and technical considerations. The first phase employs a Techno-Economic Analysis tool to quantify the economic criteria of pre-processing scenarios, including Mean Net Present Value derived from Monte Carlo simulations, Net Present Value under Risk, and selling price ranges. The selling price is identified as the most influential factor, contributing 72%-78% to Net Present Value fluctuations. In the second phase, environmental impacts of the scenarios are assessed using an attributional Life Cycle Assessment tool. The cultivating and harvesting stage, linked to the use of biomass, fertilizers, and diesel fuels, is identified as a critical contributor to the environmental impact in all the important impact categories. Additionally, the social dimension is evaluated by estimating potential job creation associated with this biocomposite supply chain. Technical factors are captured by gathering industrial expert insights on product quality, system reliability, and Technology Readiness Levels (this dataset in particular included an Unreliability Factor). The outputs from the first two phases are used as the inputs for the third phase, for which an Analytic Network Process model is employed with interdependencies among criteria and alternatives. To assess the robustness of the alternatives’ rankings from the Analytic Network Process model, a sensitivity analysis using Non-Linear Programming is developed. Ultimately, the proposed framework effectively selects best hemp pre-processing equipment (namely half-screen hammer mill and round baler) by introducing sustainable metrics. Incorporating “interdependencies” among criteria and alternatives enhances the solution's robustness for decision-makers, as validated by the sensitivity model compared to the conventional Analytical Hierarchy Process. This Analytic Network Process-based supply chain framework could be adapted to various biocomposite contexts and production regions by modifying input data, broadening its impact.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".