The Development of a GIS-Based Framework to Locate Biomass and Municipal Solid Waste Collection Points for an Optimal Waste Conversion Facility
Bibliographic record
Abstract
Highlights An integrated GIS-based tool was developed for optimally locating bioenergy facilities. Waste and lignocellulosic biomass potential and distribution were assessed for Alberta. A case study for Alberta’s Industrial Heartland identified facility locations for two scenarios. Ten optimal locations were identified across Alberta for bioconversion of waste and biomass feeds. Abstract . Quantifying the availability of feedstock and determining an optimal location are key to ensuring the sustainability of a waste to value-added (W2VA) facility. This study aims to identify lignocellulosic biomass (agricultural and forest residues) and municipal solid waste (MSW) potential, find geographical point-source locations for the distributed biomass, and identify optimal locations for W2VA facilities across the province of Alberta, Canada, using an integrated geographic information system (GIS) based approach. MSW potential is estimated using population and average annual waste generation per capita, while agriculture and forest residue are estimated using production data and harvesting residue factor. A GIS-based framework is developed to locate biomass collection points by latitude and longitude for distributed biomass and to estimate their associated biomass potential. An integrated framework is subsequently developed to optimally locate W2VA facilities that have minimal environmental, economic, and social impacts. An array of geographical constraints is then considered in a suitability analysis and network analysis framework. An estimate of the annual availability of feedstock using the most recent data shows MSW, agricultural residue, and forest residue potentials of 4,330,000 wet megagrams (Mg), 4,060,000 dry Mg, and 2,070,000 dry Mg, respectively, in Alberta. Optimal W2VA facility locations are identified for Alberta’s Industrial Heartland (AIH) considering waste heat from the areas as an additional energy source. Ten other locations where facilities can be operated sustainably are identified across the province. This study can be used as a framework by municipalities and communities in any jurisdiction in the world to geographically locate biomass source and collection points, along with their annual capacity, and the corresponding optimal site for a W2VA facility. Keywords: Biomass, Biorefinery, GIS, suitability analysis, Integrated methodology, Municipal solid waste, Sustainability, Waste management, Waste-to-energy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".