Optimizing removal of elemental mercury from flue gas using halide‐impregnated red mud
Bibliographic record
Abstract
Abstract Mercury (Hg 0 ) emission from coal‐fired industrial plants poses severe threats to ecosystem sustainability and human health, urging the development of novel and cost‐effective adsorbents to treat industrial flue gas. Herein, the modification of industrial residual red mud (RM) through the impregnation of hydrogen halides (HH) and its adsorption characteristics for removing elemental mercury from combustion flue gas was reported. Experimental investigation of HH‐modified RM reveals that the hydrogen iodide (HI)‐modified RM with a concentration of 1.5 M had a maximum Hg 0 removal efficiency of 98%, whereas hydrogen bromide (HBr) 1.5 M modified RM had a maximum Hg 0 removal efficiency of 90%. The effect of various parameters, such as reaction temperature and halide concentrations, were also found to be influential for the adsorption efficiency of the modified RM. Moreover, it is important to highlight the chemisorption characteristics of HI‐modified RM, which significantly enhances the efficiency of the removal process. The Hg 0 removal efficiency increases with the increase in HI concentration (1.5 M @ 93%) with an optimal reaction temperature of 140°C. Furthermore, the maximum Hg 0 removal attained with a change in NO concentration was 98% at 200 ppm. However, increasing the SO 2 concentration reduces the efficiency of RM for removing Hg 0 in simulated coal combustion flue gas. The pseudo‐second‐order model ( R 2 = 0.98) accurately describes the adsorption of in kinetic investigations, indicating a chemisorption mechanism. This analysis of the chemisorption mechanism highlights the efficiency of halide‐modified industrial solid waste, which has the potential to be used in the design of economical and innovative adsorbents for reducing environmental pollution. The present study employed specific reaction parameters such as reaction temperature, halide loading contents, and different flue gas compositions, which had not been extensively explored.
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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 | 0.000 |
| 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 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".