Simulation and control of membrane reactors for catalytic reduction of dissolved oxygen from water
Bibliographic record
Abstract
Removal of dissolved oxygen (DO) from water has gained much attention in recent decades to prevent different problems such as corrosion, bio‐fouling, and performance degradation in many industries. The traditional physical and chemical methods for DO removal have found wide application in industries. However, physical methods have low efficiency and chemical methods often produce undesirable products. Therefore, catalytic reduction by hydrogen has been regarded by a variety of industries recently. In this study, catalytic reduction of DO from water is examined using membrane reactors. The mathematical model of this system is developed while considering the axial dispersion, membrane permeation, and chemical reaction. The model is solved in steady state mode and the effect of various parameters on the DO removal is investigated. The results of steady state mode are used as initial conditions for solving the model in dynamic mode. The impact of operating conditions such as water flow rate, DO concentration of influent water, hydrogen flow rate, and hydrogen pressure on the performance of the DO process is studied. Results of the dynamic simulation suggest that hydrogen pressure is the best option to be used as a manipulated variable for control of effluent DO concentration. Finally, a PI controller is implemented to control the system. The closed loop responses indicate that a PI controller would perform well both in load rejection and setpoint tracking if it is tuned accurately.
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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) | 0.001 | 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) | 0.002 | 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".