Developing a GIS-based MINLP framework for optimizing the waste-to-energy supply chain with plant scale considerations
Bibliographic record
Abstract
Municipal solid waste (MSW) can be used to reduce reliance on fossil fuels and help transition towards a circular economy. The MSW supply chain is complex and needs to be optimized to minimize total supply chain costs. There are few studies on the integrated optimization of the MSW supply chain with a waste-to-energy (WtE) conversion facility. This study develops a framework to optimize various cost parameters, including feedstock collection, transportation, capital, maintenance, and operations costs simultaneously through an integrated GIS-based mixed-integer nonlinear programming (MINLP) model considering the WtE conversion facility scale. A GIS location-allocation analysis using the fuzzy analytic hierarchy process (FAHP) approach was used to identify candidate sites for WtE facilities based on environmental, economic, and social criteria. GIS origin-destination cost functions were used to determine the precise distances from transfer stations by road and rail to candidate facilities and subsequently landfills. These outputs were then used for the MINLP framework to optimize the number, location, and size of WtE facilities. A case study for Western Canada was conducted considering 447 landfills in 4 provinces. More than 5800 candidate locations and 598 transfer stations were screened. The results show that at a discount rate of 10%, the electricity production cost is competitive at only 10.99 $/MWh when the gate fee (charged by a WtE facility for processing waste) of 35 $/tonne and a carbon credit of 15 $/tonne are considered as a source of revenue, accounting for all cost factors. The optimal site is in Foothills County, Alberta, and it has a size of 183 MW. The sensitivity analysis shows the most sensitive parameters on the per-unit electricity production cost are plant efficiency, capital cost, and gate fees. The associated uncertainties in the cost of electricity production are 10.99±6.88 $/MWh. The results of the study can help in making investment decisions and policy formulation. • An integrated GIS-based MINLP model is developed for the biomass supply chain • Framework includes plant scales, actual biomass collection points and distances • The optimal plant site is in Foothills County, Alberta, Canada, at a size of 183 MW • The electricity production cost is competitive at only 10.99±6.88 $/MWh • Key sensitivity parameters are plant efficiency, capital cost, and gate fee on EPCs
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".