Soft-sensing modeling of chemical oxygen demand in photo-electro-catalytic oxidation treatment of papermaking wastewater
Bibliographic record
Abstract
Photo-electro-catalytic (PEC) oxidation has been widely recognized as an effective technology for advanced treatment of papermaking wastewater. To optimize the oxidation process, it is important of monitor continuously the chemical oxygen demand (COD) of inflow and outflow wastewater. However, online COD sensors are expensive difficult to maintain, and therefore COD is usually analyzed off-line in laboratories in most cases. The objective of this study is to develop an inexpensive method for on-line COD measurement. The oxidation-reduction potential (ORP), pH, and dissolved oxygen (DO) of wastewater were selected as the key parameters, which consists of four different types of artificial neural network (ANNs) methods:multi-layer perceptron neural network (MLP), back propagation neural network (BPNN), radial basis neural network (RBNN) and generalized regression neural network (GRNN). These parameters were applied in the development of COD soft-sensing models. Six batches of papermaking wastewater with different pollution loads were treated with PEC technology over a period of 90 minutes, and a total of 546 data points was collected, including the on-line measurements of ORP, pH and DO, as well as off-line COD data. The 546 data points were divided into training set (410 data, 75% of total) and validation set (136 data, 25% of total). Four statistical criteria, namely, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), and determination coefficient (R2) were used to assess the performance of the models developed with the training set of data. The comparison of results for the four ANN models for COD soft-sensing indicated that the RBNN model behaved most favorably, which possessed precise and predictable results with R2=0.913 for the validation set. Lastly, the proposed RBNN model was applied to a new batch of PEC oxidation of papermaking wastewater, and the results indicated that the model could be applied successfully for COD soft-sensing for the wastewater.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".