Advanced adaptive neuro‐fuzzy inference system controller for optimizing <scp>pH</scp> neutralization process control
Bibliographic record
Abstract
Abstract The heavy reliance of modern industries on chemical processes to facilitate the mass production of cosmetics, beverages, food products, and pharmaceuticals has in turn contributed to the heightened significance of pH value regulation that supports product quality assurance. However, the process of pH control is difficult due to its highly sensitive, dynamic, and nonlinear nature. The conventional control approaches like proportional integral derivative (PID) and proportional integral (PI) controller are inept at handling the complex process of pH control. Thereby, in this work adaptive neuro‐fuzzy inference system (ANFIS), which combines the accuracy of fuzzy inference system (FIS) and learning capability of adaptive neural network (ANN) is applied for pH process regulation. Moreover, the controller operation is improved further with the application of chicken swarm optimization (CSO) for tuning its input parameters. The primary goal is to accomplish effective load regulation and appropriate set‐point tracking using smoother control signal. According to the derived simulation outcomes, it is observed that both the industrial and standard structure of the proposed chicken swarm (CS)‐ANFIS controller outperforms other existing control techniques with better disturbance rejection, set‐point tracking and excellent sensitivity to change in model parameters.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".