metadata
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)