Development of an integrated Municipal Solid Waste conversion and CO2 utilization process for the environmentally friendly production of transportation fuels
Bibliographic record
Abstract
The global rise in municipal solid waste (MSW) generation and the urgent need to decarbonize the transportation sector, particularly the aviation fuels and heavy trucks, have created a compelling opportunity for circular carbon technologies. This thesis presents the development and analysis of a waste-to-jet fuel production pathway that integrates MSW gasification, syngas conditioning, reverse water gas shift (RWGS) conversion, Fischer-Tropsch (FT) synthesis, and hydrocracking. The process design and simulation are performed using Aspen Plus, with a particular emphasis product selectivity and CO₂ utilization. Using the simulation results, a comprehensive techno-economic analysis (TEA) is carried out to estimate capital and operating expenditures, minimum selling prices, and project viability under Quebec’s low-carbon electricity conditions. Additionally, a detailed life cycle assessment (LCA) is conducted in OpenLCA to evaluate the environmental footprint of the proposed system compared to conventional pathway of jet fuel production and waste incineration. The LCA results, based on ReCiPe methodology, indicate that the MSW-to-jet fuel pathway can achieve net negative greenhouse gas emissions under low-carbon electricity scenarios. The findings highlight the dual benefit of MSW valorization: reducing landfill dependency and enabling the production of sustainable aviation fuel (SAF) with a significantly lower carbon intensity. This research demonstrates that MSW can serve as a viable feedstock for decarbonized fuel production, offering a scalable and economically competitive solution aligned with global climate and waste management goals.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".