Modeling of PVA Degradation in a Continuous Photochemical Reactor using Experimental Step Testing and Process Identification
Bibliographic record
Abstract
In AOP processes, the flow of oxidant must be controlled to minimize the oxidant residuals in a post biological treatment and to maximize the total organic carbon (TOC) removal and degradation. Designing a controller to regulate the hydrogen peroxide (H2O2) begins with a dynamic model determination of a chemical process. Therefore, a step testing technique is employed to construct a dynamic model of the UV/H2O2 degradation of polyvinyl alcohol (PVA) process based on pH and TOC responses to H2O2 step change. The experimental design consists of three different initial PVA concentrations, of 60.0, 280.0, and 500.0 mg PVA/L. Eight experimental tests were conducted for different hydrogen peroxide mass flowrates ranging from 0.336 to 125 mg H2O2/min. For every test, a transfer function was experimentally determined to describe the dynamics of the UV/H2O2 photochemical reactor for the degradation of PVA. System identification toolbox in Matlab software was used to determine first order plus time delay (FOPTD), second order plus time delay (SOPTD) and ARX polynomial models. The transfer functions and ARX models are a good model representation of the pH response data of a specific step change of H2O2 concentration. For example, the standard deviation of the process gain of test # 1 and its replicate was calculated to be 1.18 and standard deviation of the time constant was calculated to be 1.27. The pH response of the first test was fitted with a FOPTD model with a data fitting score of 88.8%. Test # 2 pH response data was fitted with a SOPTD transfer function with data fitting score of 83.6%. Tests # 6 and 7’s pH response was fitted with a FOPTD model with a data fitting score of 94.3 and 87.7 % respectively. The different transfer functions obtained for the low, average, and high PVA concentrations indicate the nonlinearity aspect of polymer systems. All quality models are quite reliable estimations of the pH and TOC response data, since they were developed from experimental tests and parameter estimation techniques based on nonlinear regression approach.
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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) | 0.001 | 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 | 0.001 |
| 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".