Optimization of silver cementation yield in fixed bed reactor using factorial design and central composite design
Bibliographic record
Abstract
Abstract This work deals with cementation of silver onto iron grid in fixed bed reactor. The influence of several parameters is studied namely: initial concentration of silver [Ag+]0, flow rate, solution pH, and mass of iron. Moreover, their influence on the yield of the reaction of cementation is investigated statistically by the experimental design in view of industrial application. The estimation and the comparison of the parameter's effects are realised by using two‐level factorial design. The analysis of these effects permits to state that the most influential factor is the mass of iron with an effect of (+5.642), the second in the order is the initial concentration of silver ions (Ag+) with an effect of (+4.005), the third is the flow rate of the electrolytic solution with an effect of (+3.824). A central composite design methodology is employed to determine the optimum conditions for a silver cementation yield onto iron grid. For this end, the experimental results were approximated by a second‐order model as well as the surface contour plots and surface responses are drowned. The optimal conditions found for initial silver concentration, such as a flow rate, pH of the solution and mass of iron, are respectively: 21.25 mg/L, 4.43 L/min, 3.6 and 50 g. Under these conditions, the obtained silver cementation yield is 96.851%.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".