Modelling and Multi-Objective Optimization of the Sulphur Dioxide Oxidation to the Sulphur Trioxide Process
Bibliographic record
Abstract
In this thesis, the catalytic oxidation of sulphur dioxide (SO₂) to sulphur trioxide (SO₃), which is a critical step in the production of sulphuric acid (H₂SO₄), was studied under adiabatic operating conditions. The oxidation process is taking place in a heterogeneous plug flow reactor. Because the SO₂ oxidation is a highly exothermic equilibrium reaction, a series of packed bed catalytic reactors with intercooling heat exchangers is required to achieve high SO₂ conversion. To predict the effect of the operating conditions such as the temperature and the pressure on the oxidation as well as to model mathematically the reactor, it is essential to find an appropriate kinetic rate equation. In this study, various kinetic models were evaluated to select the kinetic model that appeared to be the most representative of available experimental data. In this regard, the residual sum of squares of the differences between the predicted and experimental conversion values was used to compare the various kinetic models. The model which showed the better fitting of the experimental data was the one proposed by Collina et al. The SO₂ oxidation reactor model was developed in order to propose a methodology to perform the multi-objective optimization of many process strategies involving a number of catalytic beds and different reactor configurations. The temperature and the length of each catalytic bed are considered as decision variables to determine the optimal values of the three objectives: the SO₂ conversion, the SO₃ productivity and the catalyst weight, where the first two need to be maximized whereas the last one need to be minimized. The optimization process is comprised of two main steps. First, the Pareto domain, which contains a representative number of non-dominated solutions, was circumscribed using a non-sorting genetic algorithm. Secondly, the Pareto domain was ranked with the Net Flow method (NFM) to determine the highest-ranked Pareto-optimal solution. For ranking the Pareto domains of all strategies, a greater emphasis was placed on the SO₂ conversion because unreacted SO₂ needs to handle at the exit of the process in addition to decrease the amount of sulphuric acid produced. Results show that the process comprised of four catalytic beds with an intermediate SO₃ absorption column provides higher SO₂ conversion in comparison with the process with four catalytic beds without an intermediate absorption column. However, the enhanced conversion is achieved at the expense of higher operating costs. The optimum value of the total bed length for the four catalytic beds without an intermediate SO₃ absorption column commonly used industrially, is very closed to its minimum or ideal (5% difference), which clearly shows that the minimum catalyst weight almost prevails in this strategy to reach a relatively high SO₂ conversion in the vicinity of 97%.
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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 | 0.001 |
| 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".