ARX/NARX modeling and PID controller in a UV/H2O2 tubular photoreactor for aqueous PVA degradation
Bibliographic record
Abstract
Water-soluble polymers are widely employed as additives in many different industries. The need to treat sewage contaminated with water-soluble polymers is essential to prevent persistent pollutants from entering our environment. The advanced oxidation process (AOP), UV/H 2 O 2 process, is used in this study to degrade polyvinyl alcohol (PVA) in aqueous solutions. However, a suitable modeling approach and a control scheme are required to remove the PVA polymer and reduce hydrogen peroxide (H 2 O 2 ) residual in the treated effluent within a safe level to prevent adverse effects on the aquatic system as well as subsequent biological processes. This study presents black-box modeling for identifying the dynamics of polyvinyl alcohol (PVA) degradation in a series of UV/H 2 O 2 photo-reactors, where the process input and response variables are inlet hydrogen peroxide concentrations and effluent pH , respectively. Data processing, model development, and process simulation are performed using MATLAB R2019b software. In this study, the linear AutoRegressive with eXogenous input (ARX), non-linear ARX (NARX), and Hammerstein-Wiener models are considered as base system models, where the sigmoid-network-based NARX produced the best representation with 82.24% of the trainset and 76.28% of the validation-set of the process dynamics. The design of PID controllers tuned using ARX and sigmoid-network-based NARX models are discussed, and the controller performance is analyzed for set-point tracking and disturbance rejection through simulation studies. The closed-loop response of ARX-PID and NARX-PID are deemed adequate. In fact, the NARX-PID performs much better for the studied process achieving an integrated absolute error (IAE) of 0.6211 with a settling time of 4.63 h, whereas the ARX-PID has lower IAE (0.4238) and produces a more aggressive controlled output response robust in disturbance rejection. Thus, ARX-PID is adequate for frequent process disturbances but less suitable for process set-point tracking.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".