Integrated model for power generation from biomass gasification: A market readiness analysis for northwestern Ontario
Bibliographic record
Abstract
Biomass gasification, utilized for various energy uses including power generation, provides an attractive option of energysecurity for remote rural communities and simultaneously helps in reducing greenhouse gas emissions. However, widerapplication of biomass gasification technology is limited due to a number of technical and economic challenges. Althougha few studies have developed process-based models for reducing the costs of biomass supply chains through efficient logisticsoperations, there is a dearth of integrated modeling to capture the behaviour of the entire production system. In thispaper an integrated non-linear dynamic mixed-integer programming model, using optimization and simulation techniques,is developed for biomass gasification power plants using General Algebraic Modeling System (GAMS) computersoftware. The major variables considered in the model are harvesting and processing costs, logistic costs for biomass feedstockdelivery and storage, capital costs of power plant, operation and maintenance costs including labour, insurance andcapital financing, and other regulatory costs. The model provides different cost-benefit trajectories depending upon thescale of power generation from biomass gasification, thereby providing the prospective investors with information regardingmarket potential of the technology. The application of the model for setting up of a biomass gasification plant at a typicallocation in northwestern Ontario shows decreasing costs with increasing plant capacity up to 50 MW. The total costper MWh production ranged from CAD 47.65 for a 50-MW plant to CAD 79.55 for a 10M-W plant. Key words: bioenergy, biomass, market potential, modeling, systems analysis
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".