| --- |
| title: PFAS-SBEAD AI Optimization Pipeline |
| emoji: ⚗️ |
| colorFrom: blue |
| colorTo: purple |
| sdk: docker |
| app_port: 8501 |
| pinned: false |
| --- |
| |
| # Closed-Loop PFAS-SBEAD Optimization Pipeline |
|
|
| AI-driven optimization platform for PFAS degradation using Sidestream Bioelectrochemical Anaerobic Digestion (SBEAD) reactor system. |
|
|
| ## Features |
|
|
| - **PFAS Degradation Prediction**: XGBoost regressor predicting degradation percentage |
| - **Fluoride Release Modeling**: Random Forest for fluoride release estimation |
| - **Short-Chain Risk Classification**: Gradient Boosting classifier for short-chain PFAS accumulation risk |
| - **Reactor Instability Detection**: Isolation Forest anomaly detection for stability monitoring |
| - **Closed-Loop Optimization**: Bayesian-inspired recommendations for next experiments |
| - **Mass Balance Analysis**: Complete PFAS mass balance accounting (adsorption vs degradation) |
| - **Sensitivity Analysis**: Feature correlation and SHAP-proxy importance rankings |
|
|
| ## AI Objective Function |
|
|
| ``` |
| AI Score = 0.40 × PFAS degradation + 0.30 × fluoride release |
| - 0.15 × short-chain PFAS risk - 0.10 × energy input - 0.05 × instability |
| ``` |
|
|
| ## Dataset |
|
|
| 120 experimental records covering: |
| - Reactor parameters (OLR, HRT, pH, temperature, COD, VFA, alkalinity) |
| - Electrochemical parameters (voltage, current, current density, conductivity) |
| - PFAS parameters (initial/final concentration, degradation, adsorption) |
| - Degradation indicators (fluoride release, defluorination, short-chain formation) |
| - Stability indicators (pH drop, VFA accumulation, ORP drift, current instability) |
|
|
