An expert model for estimation of the performance of direct dimethyl ether synthesis from synthesis gas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this work, an artificial neural network (ANN) has been trained and tested for estimation of the performance of direct synthesis of dimethyl ether (DME) from synthesis gas. Yield and selectivity of DME production and also conversion of CO could be predicted when temperature and pressure of reactor and H 2 /CO molar ratio in feed have been specified. The results of ANN estimation for yield of DME, selectivity of DME and CO conversion are in very good agreement with experimental values. For this development, database was collected from our previous experiment. The accuracy and trend stability of the trained networks were tested against unseen data. Different training schemes for the back‐propagation learning algorithm, such as: Scaled Conjugate Gradient (SCG), Levenberg–Marquardt (LM), Gradient Descent with Momentum (GDM), variable learning rate Back propagation (GDA) and Resilient back Propagation (RP) methods were used. The SCG algorithm with seven neurons in the hidden layer shows the best suitable algorithm with the minimum average absolute relative error 0.05231.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it