ARX-PID/NARX-PID Modelling and Control of UV/H2O2 TubularPhotoreactor for PVA Degradation in Water
Bibliographic record
Abstract
Various industries rely on water-soluble polymers as additives, therefore, it is imperative to treat them so that they do not enter our environment as persistent pollutants. Though used as a leading method for degradation of organic pollutants, biological treatment can only treat wastewater that is readily degradable by living organisms. If that is not possible, researchers mainly focused on the oxidation of polyvinyl alcohol (PVA) in an Advanced Oxidation Process (AOP) such as UV/H2O2 process as an easy process owing to its relatively developed kinetics, non-selective degradation, low cost, and ease in operation at large-scale [1-3]. In particular, the UV/H2O2 process requires modelling to establish a control system that prevents adverse effects of PVA and residual hydrogen peroxide (H2O2) to the aquatic system and subsequent biological processes by maintaining H2O2 residuals in the treated effluent within a safe level. During this study, the performance of black-box methods was examined in determining the dynamics of polyvinyl alcohol degradation in various UV/H2O2 systems, where process inputs and responses involved hydrogen peroxide concentration and acidity, respectively. The complete data analysis and model fitting is undertaken with MATLAB R2019b software. Models including the linear AutoRegressive with eXogenous Input (ARX), the nonlinear ARX (NARX), and the Hammerstein-Wiener model are compared for their success in providing an accurate representation of statistical dynamics. Comparatively, the sigmoidnetwork-based NARX was better at representing the process dynamics when compared to others. The study also explores the design of PID controllers via ARX and sigmoid-network-based NARX models and analyzes controller performance for set-point tracking and disturbance rejection. The ARX-P, ARX-PI, NARX-P, and NARX-PI controllers were not adequate for a good control design as they exhibit higher offset from set-point, higher overshoot, and longer settling time. The ARX-PID and NARX-PID provide adequate closed-loop responses, while NARX-PID seems better suited for the studied process. However, the ARX-PID has a higher IAE and produces a more robust output response against disturbances as it is adequate for processing disturbances but less useful in tracking process set-points. As a result, to compensate for the complexity in generating models and tuning the parameters with the NARX model, an approximation based on the ARX model is appropriate when its implementation will be followed by another set-up for H2O2 elimination. Although feasibility of linear control scheme was presented, it is suggested that similar scenarios should be considered in future designs, validations, and performance evaluations of the entire controlled system, especially for improving controller robustness by implementing multivariate adaptive controls and linear or non-linear predictive controls
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".