Hybrid smart model to determine concentration of acidic gases in absorption tower of sweetening process
Bibliographic record
Abstract
Abstract The natural gases produced from underground reserves may contain sour gases such as H 2 S and CO 2 . If the amount of these gases is greater than the standard level, it will be harmful to humans and the environment. Hence, it appears important to accurately determine the concentration of H 2 S and CO 2 in various equipment/units of a natural gas sweetening plant. In this study, a new connectionist approach is introduced to obtain the concentration of outlet acid gases of the sweetening tower of the South Pars gas refinery (in Iran), by utilizing a conventional connectionist tool optimized by an imperialist competitive algorithm (ICA). This ICA strategy is applied to optimize the weights, biases, and number of neurons of the artificial neural network (ANN) model. The input parameters in this modelling strategy include time, the amount of H 2 S in the output amine, the flow rate of input sour gas, the temperature of sea water for cooling, the mass flow rate of low‐pressure steam to the amine–amine heat exchanger, and the flow rate of input amine. The performance of the hybrid deterministic tool, ANN‐ICA, is compared with the ANN‐back propagation (BP) method. The coefficient of determination and mean squared error to forecast the concentration of output H 2 S are 0.8931 and 0.0125 for the ANN‐BP algorithm and 0.9057 and 0.0104 for the ANN‐ICA algorithm, respectively. These statistical values are 0.8428 and 0.0001 for the ANN‐BP model and 0.9307 and 0.000 05 for the ANN‐ICA model, respectively, while predicting output CO 2 concentration. The results confirm that the ANN‐ICA is a more reliable approach, compared to ANN‐BP, to forecast the concentration of acidic gases leaving the absorption tower.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".