Neural Based pH System in Effluent Treatment Process
Bibliographic record
Abstract
This investigation considers the application of Artificial Neural Network (ANN) techniques to estimate the pH value for effluent treatment process. ANN has the ability to identify the non-linear dynamical systems from the input-output data. An important requirement of the application is robustness of the system against erroneous sensor measurements. The simulation model of the pH system for common effluent treatment plant (CETP) is developed using MATLAB 7.5, GUI tool box. A novel off-line and on-line training scheme for the neural network is developed by error back propagation training algorithm to model the pH system for CETP, accurately. For this purpose, a simple feed forward, back propagation neural network, with only one hidden layer, and sigmoidal activation functions is used. The training of such network is based on Input-Output data which is collected from Perundurai common effluent treatment plant (PCETP). Experimentation and simulation results on this neuro identifier of pH system for common effluent treatment plant are shown.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".