Multi-Scale Digital Twin Framework with Physics-Informed Neural Networks for Real-Time Optimization and Predictive Control of Amine-Based Carbon Capture: Development, Experimental Validation, and Techno-Economic Assessment

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🔬研究者:Provides a novel methodology combining PINNs with multi-scale modeling for CCS, with experimental validation and sensitivity analysis.

🏢実務担当者:Offers a framework for real-time optimization and predictive control that can be integrated into existing carbon capture plants to reduce costs and energy consumption.

Carbon capture and storage (CCS) is essential for achieving net-zero emissions, yet amine-based capture systems face significant challenges including high energy penalties (20–30% of power plant output) and operational costs ($50–120/tonne CO2). This study develops and validates a novel multi-scale Digital Twin (DT) framework integrating Physics-Informed Neural Networks (PINNs) to address these challenges through real-time optimization. The framework combines molecular dynamics, process simulation, computational fluid dynamics, and deep learning to enable real-time predictive control. A key innovation is the sequential training algorithm with domain decomposition, specifically designed to handle the nonlinear transport equations governing CO2 absorption with enhanced convergence properties.The algorithm achieves prediction errors below 1% for key process variables (R2> 0.98) when validated against CFD simulations across 500 test cases. Experimental validation against pilot-scale absorber data (12 m packing, 30 wt% MEA) confirms good agreement with measured profiles, including temperature (RMSE = 1.2 K), CO2 loading (RMSE = 0.015 mol/mol), and capture efficiency (RMSE = 0.6%). The trained surrogate enables computational speedups of up to four orders of magnitude, supporting real-time inference with response times below 100 ms suitable for closed-loop control. Under the conditions studied, the framework demonstrates reboiler duty reductions of 18.5% and operational cost reductions of approximately 31%. Sensitivity analysis identifies liquid-to-gas ratio and MEA concentration as the most influential parameters, with mechanistic explanations linking these to mass transfer enhancement and reaction kinetics. Techno-economic assessment indicates favorable investment metrics, though results depend on site-specific factors. The framework architecture is designed for extensibility to alternative solvent systems, with future work planned for industrial-scale validation and uncertainty quantification through Bayesian approaches.

• semanticscholar https://doi.org/10.3390/pr14030462first seen 2026-05-05 23:37:58