AI-driven digital twin and delay-aware surrogate MPC framework for biogas production

Keyphrases

• Digital Twin 100%
• Biogas Production 100%
• Delay-aware 100%
• Digital Delay 100%
• AI-driven Digital Twin 100%
• Anaerobic Digestion Model 66%
• Biochemical Processes 33%
• Missing Data 33%
• Closed-loop 33%
• Methane 33%
• Nonlinear Dynamics 33%
• XGBoost 33%
• Input-output 33%
• Noisy Data 33%
• Random Forest 33%
• Particle Swarm Optimization 33%
• Steady-state Performance 33%
• Benchmarking Framework 33%
• Operational Constraints 33%
• Anaerobic Digester 33%
• Energy Systems 33%
• Computationally Expensive 33%
• Chemical System 33%
• Sliding Window 33%
• Bayesian Optimization 33%
• Data-driven Control 33%
• Seasonal Variability 33%
• Stamping 33%
• Ramp Rate Limits 33%
• Closed-loop Simulation 33%
• Industrial Data 33%
• Multi-step Prediction 33%
• Systematic Integration 33%
• Multi-day 33%
• Biogas Prediction 33%
• Model Training 33%
• Irregular Measurements 33%
• Sampling Requirements 33%
• TabPFN 33%
• Common Benchmarking 33%
• Actuator Delay 33%
• Bayesian Hyperparameter Optimization 33%
• Irregular Sampling 33%
• Machine Learning Surrogate 33%
• Feedback Correction 33%
• Long Delay 33%
• Constrained MPC 33%
• Sample Efficiency 33%

Chemical Engineering

• Artificial Intelligence 100%
• Biogas 100%
• Digital Twin 100%
• Anaerobic Digestion 50%
• Learning System 25%
• Methane 25%
• Long Short-Term Memory Network 25%
• Xgboost 25%
• Bayesian Optimization 25%