A Neuro-Fuzzy Model of Evaporator in Organic Rankine Cycle
Bibliographic record
Abstract
The Organic Rankine Cycle (ORC) is a propitious waste heat recovery (WHR) technology that allows recovery of wasted energy from low to medium temperature sources.This WHR method needs to be adopted as an Internal Combustion Engine (ICE) bottoming technology to mitigate its environmental effects and fulfil exhaust gas emission regulations .The evaporator is the most decisive element of the ORC cycle due to its high nonlinear behaviour and high thermal inertia.In this study, a neuro -fuzzy model of the evaporator is presented based on the data obtained from Finite Volume (FV) model of the evaporator.The simulation results are compared in terms of RMSE, error mean and standard deviation.The data obtained from ANFIS model reached a promising agreement with FV model.F or prediction of the evaporator outlet temperature, RMSEs of 0.152 and 1.33 obtained for the training and test data, res pectively.Furthermore, the ANFIS model was successfully able to predict the evaporator power with RMSE of 0.035 for the training and 0.2 for the test data.In addition, the ANFIS model compared to the FV model with twenty control volumes enhanced the simu lation time significantly.This clearly indicates the great potential of employing ANFIS model for real-time applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".