Calorific value prediction models of processed refuse derived fuel 3 using ultimate analysis
Bibliographic record
Abstract
Various models have been developed to predict the calorific value of Biomass but only a few models exist to predict this measure for the urban waste like Refuse Derived Fuel (RDF). In this paper, new models are introduced to predict the calorific value of RDF, as more advanced studies are required to be conducted with a focus on a distinct group of RDFs for validating the robustness of the models in the existing literature. The calorific value based on ultimate (elemental) analysis considers the contents of C, H, N, S, and O elements in RDF. Using empirical and machine learning methods, the newly established models accurately predicted the calorific value of the samples provided by a local municipality situated in Edmonton, Alberta, Canada. Furthermore, these new models demonstrated a lower bias and average absolute error than the other twelve previously published models pertinent to RDF material. Based on the established workflow the ultimate analysis-based models gave a higher coefficient of determination (R2) value in the range 0.78 − 0.80, indicating that the developed model improves the prediction of calorific value for RDF. The newly developed machine-learning models showed better results than the empirical models developed in this study implying that complex correlations can be dealt effectively while predicting calorific values for RDF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".