A review of the application of response surface methodology in nanofiltration: Insights into process modeling, parametric analysis, and optimization
Bibliographic record
Abstract
Nanofiltration (NF) is a promising membrane technology for water treatment, desalination, and various industrial applications. To optimize NF performance, it is essential to thoroughly understand the interactions between operating parameters, membrane characteristics, and overall system efficiency. Response Surface Methodology (RSM) has become a valuable statistical tool for modeling and optimizing NF processes by systematically evaluating the effects of different parameters and predicting optimal conditions. This review provides an in-depth assessment of RSM applications in NF, focusing on key aspects such as permeate flux, contaminant rejection, energy efficiency, fouling mitigation, and membrane design. Special focus is placed on how RSM enhances energy efficiency in NF hybrid systems, improves membrane longevity, and advances process sustainability. Furthermore, RSM has played a crucial role in developing predictive models that assist in decision-making regarding NF system optimization. Future research should investigate the integration of RSM with emerging computational techniques, including machine learning, digital twins, and real-time monitoring, to create intelligent, self-adaptive NF systems. Additionally, incorporating sustainability metrics, such as life cycle assessment and techno-economic analysis, into RSM tools will aid in developing cost-effective and environmentally sustainable NF processes. By combining statistical modeling with modern computational approaches, RSM continues to drive advancements in NF technology, leading to more efficient and sustainable solutions for water treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".