Multivariable statistical analysis for enhancing performance indicators in direct contact membrane distillation
Bibliographic record
Abstract
ABSTRACT Orthogonal experimental design, correlation analysis and response surface charts were used to identify the parameters influencing the operational efficiency of direct contact membrane distillation (DCMD). The orthogonal array design method was used to optimize the number of experimental trials required for dependence analysis. The operating factors studied were hot feed properties (temperature, salinity, flowrate) and cold distillate characteristics (temperature, and flowrate). The impact of those factors on three DCMD performance indicators - cold distillate production rate, performance ratio and recovery ratio – was investigated. The most significant factors influencing each performance indicator were obtained from the quantitative values of main and interaction effects, and confirmed by using the Pearson product-moment correlation coefficients. The main effects of feed and distillate temperatures on the performance indicators were the greatest, indicating that the most significant factors were the feed and distillate temperatures. The maximum distillate production rate was obtained at feed and distillate temperatures of 90 and 15°C, respectively. The optimum recovery and performance ratios were obtained at a feed flowrate of 1.6 L/min but when feed temperature was kept at 70°C.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".