Biomass-Fired Organic Rankine Cycle-Based CHP for Community-Scale Applications
Bibliographic record
Abstract
The growing energy demand, depleting fossil fuel reserves, and global warming concerns call for a further increase in biomass energy utilization.At present, biomass is mostly used in small-scale applications where the production of electricity is technically and economically disadvantageous.On the other hand, district or communityscale CHP applications with higher efficiencies and lower specific investment costs are a better alternative.Biomass combined heat and power (BCHP) systems can reduce GHG emissions and also have the potential for higher overall energy efficiencies than conventional home heating methods.In this research, an organic Rankine cycle (ORC)-based BCHP for community-scale applications is investigated concerning technical and economic aspects.MDM (Octamethyltrisiloxane) is selected as the ORC working fluid, taking into account the cycle efficiency and system design.The heat of biomass combustion in the boiler is used to vaporize the organic working fluid in the evaporator.The working fluid vapor drives the turbine that spins an alternator.A mathematical model for the community-scale ORC BCHP system is developed to predict its operational performance.Various costs for the BCHP plant are analyzed and the cost of electricity (COE) is calculated.The community-scale or district BCHP plant generates 520.9 kWe electricity with the electrical efficiency reaching 17.24 % at a turbine inlet temperature of 250 °C, and provides hot water with a heating load of 2365.7kWth at a temperature of 79.2 °C.The COE of the BCHP plant is 98.2 $/MWh when not including CO2 credit.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".