Reliable modelling of the sulphur properties to calculate the process parameters of the Claus sulphur recovery plant
Bibliographic record
Abstract
Abstract In order to handle the overwhelming effects of the removed hydrogen sulphide (H2S) from natural gas and industrial waste gases on the environment, H2S can be converted to elemental sulphur. Among the available processes for sulphur recovery, the most widely employed process is a modified Claus process. In this work, first, least square version of support vector machine (LS‐SVM) approach is utilized for determining the properties of sulphur including heat of vaporization, heat of condensation (S6, S8), heat of dissociation (S6, S8), and heat capacity of equilibrium sulphur vapours as a function of temperature. An illustrative example is given to show the usefulness of the presented computer‐based models with two parameters for designing and operation of the Claus sulphur recovery unit (SRU). According to the error analysis results, predicted values by the proposed intelligent models are in excellent agreement with the reported data in the literature for the aforementioned sulphur properties where the coefficient of determination (R2) is higher than 0.99 for all developed models. The average absolute relative deviation percent (%AARD) is less than 1.3 while predicting the heat capacity of equilibrium sulphur vapours. Other proposed models' predictions show less than 0.2% AARD from the target values. In addition, a mathematical algorithm on the basis of the Leverage approach is proposed to define the domain of applicability of the developed LS‐SVM models. It was found that the presented models are statistically valid and the employed data points for developing the models are within the range of their applicability.
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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 | 0.001 |
| 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".