Application of an integrated approach based on multi‐objective preference in the design and performance evaluation of efficient defoamers
Bibliographic record
Abstract
Abstract A probability‐based approach for multi‐objective optimization is employed to develop efficient defoamers, addressing the challenges of drilling fluid foaming and the complex task of synergistically optimizing multiple performance indices of existing defoamers. An L 16 (4 3 ) orthogonal array (comprising 16 experimental runs with 3 factors at 4 levels each) is selected for the optimization process. The evaluation factors include the ratio of polyoxyethylene polyoxypropylene glycerol polyether (GPE) to polypropylene glycol (PPG), reaction temperature, and reaction time. The evaluation indicators encompass the stationary defoaming time and density recovery of freshwater and saline slurries at both room and elevated temperatures. Each performance indicator is classified as either favourable or unfavourable. Partial and total preference probabilities are calculated for each attribute. The optimal preparation plan is identified through screening, yielding a GPE:PPG ratio of 1:1, a reaction temperature of 80°C, and a reaction time of 2 h. Experimental results demonstrate that the defoamer effectively eliminates foam in drilling fluids at dosages ranging from 0.2% to 0.3%, outperforming typical commercially available defoamers. The defoamer achieves a density recovery rate exceeding 81% for both freshwater and saline slurries at room and high temperatures. Characterization via infrared spectroscopy, thermal stability assessments, and fluorescence testing confirms that the defoamer retains excellent performance under the complex working conditions encountered in drilling fluids, showcasing strong adaptability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".