Comparison between response surface methodology and artificial neural network: Application in three‐product hydrocyclones
Bibliographic record
Abstract
Abstract Modelling a process or equipment is a profitable strategy to build better control strategies, predict fault conditions, and optimize the processes. Different approaches could be explored to achieve the development of better models. This paper investigates the use of experimental data generated by a central composite rotatable design (CCRD) to develop models capable of predicting the performance of a three‐product hydrocyclone for several setups with different dimensional parameters values. Two different modelling strategies are explored: response surface methodology (RSM) and artificial neural networks (ANN). With the RSM models, it was possible to evaluate the statistical importance of the input variables to each output variable. The ANN models showed improved coefficients of determinations ( R 2 ) compared to the RSM models, presenting values higher than 97% for all cases, while the RSM models ranged from 79.07%–88.83%. The ANN was demonstrated to be the most effective method to model the physical problem of three‐product hydrocyclones, and it captured its non‐linearities. It was shown that the combination of the design of experiments and ANN to analyze this physical problem is successful and may also be applied to other problems. As far as we have knowledge, a work regarding the comparison of both RSM and ANN methods applied to three‐product hydrocyclones was not found in the literature; this absence was the motivation for this work.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".