Deep learning models for forecasting sour gas generation in a petroleum refinery
Bibliographic record
Abstract
Abstract Sour water stripping is a critical process in petroleum refineries, essential for the safe handling and disposal of wastewater that contains hazardous components such as hydrogen sulphide (H₂S) and ammonia (NH₃). Effective management of sour gas, the product of sour water stripping, is crucial to minimize environmental impacts of release of pollutants like sulphur dioxide (SO₂) and nitrogen oxides (NOₓ). This study explores the application of advanced deep learning models for forecasting sour gas generation in a refinery setting. Utilizing a comprehensive dataset from a sour water stripper unit, various deep learning architectures, such as recurrent neural networks (RNNs), long short‐term memory networks (LSTMs), bidirectional LSTMs (BiLSTMs), one dimensional convolutional neural network (1D‐CNN), and few hybrid models were employed to predict sour gas output. The evaluation metrics indicate that the 1D‐CNN and two‐layer LSTM models outperformed the other models, whereas the CNN‐LSTM encoder–decoder model did not result in good prediction among all the models studied. These findings underscore the capability of deep learning techniques to improve predictive accuracy and enhance operational efficiency in refinery sour gas management.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".