Assessing thermoelectric membrane distillation performance: An experimental design approach
Bibliographic record
Abstract
• Experimental design provides a robust evaluation of the performance of thermoelectric membrane distillation. • Minimizing energy consumption in thermoelectric membrane distillation requires recirculation of the hot feed. • Adjusting the feed flowrate can reduce energy consumption in thermoelectric membrane distillation by up to 34%. • Derivation of a nonlinear mathematical model that predicts the energy consumption in thermoelectric membrane distillation systems. Thermoelectric membrane distillation has shown promise as a new membrane distillation technique capable of improving energy consumption metrics. This study features an experimental design approach to investigating the performance of a thermoelectric membrane distillation system. Screening and full factorial designs were implemented in Minitab 16 to determine the optimal process conditions for minimizing the specific energy consumption of the system. The process parameter with the most significant impact on the specific energy consumption of thermoelectric membrane distillation systems was determined and a mathematical model for predicting the specific energy consumption was derived. The study showed that adjusting the feed flowrate, the most influential continuous parameter, from a sub-optimal level to an optimal level, while keeping other process variables at their optimal levels, could lead to a 34% reduction in the system's specific energy consumption. At the optimized process parameters of the thermoelectric membrane distillation system, the minimized specific energy consumption fell about 35% below the threshold value of 1,000 kWh/m 3 found among the efficient membrane distillation systems in the literature. • Thermoelectric heat exchanger provides the driving force for the membrane distillation process • Seven process variables are assumed to influence the energy consumption of the distillation process • The variables are screened before being analyzed in a full factorial experimental design
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".