A Data-Science Digital Twin-Enabled Thermal Performance Optimization for Reducing Energy Losses in Industrial Steam Generation Systems

Abstract

Steam generation is one of the largest energy-consuming processes in process industries, and even well-maintained boilers lose a significant portion of fuel energy through flue gas, incomplete combustion, surface radiation, and blowdown. Conventional boiler operation typically relies on fixed set-points and calendar-based maintenance, which may not adequately capture the time-varying effects of load, fuel quality, and heat-transfer fouling. This paper proposes a digital twin framework that integrates a physics-based thermal model of the steam generator with a data-driven residual learner, an extended Kalman filter for fouling estimation, a CUSUM-based anomaly detector, and a constrained optimizer. The proposed twin continuously estimates fouling, energy-loss components, and exergy performance, while providing recommendations for excess-air adjustment and cleaning decisions that can be communicated to operators or supervisory control systems. The framework is evaluated through a simulation-based case study of a 40 t/h natural-gas-fired water-tube boiler operating under varying loads over a 60-day period. The calibrated digital twin reproduces stack temperature with an R² of 0.96 and an RMSE of 1.90 °C, while the extended Kalman filter estimates the fouling index with an RMSE of 0.008, compared with 0.053 for direct inversion. Compared with the fixed-set-point baseline, twin-guided operation increases mean thermal efficiency from 89.7% to 91.2%, representing an improvement of 1.49 percentage points, reduces fuel consumption by 1.6%, and avoids approximately 537 t of CO₂ emissions annually. Sensitivity, sensor-bias, fault-detection, exergy, and economic analyses further indicate that the performance improvement is robust under different operating conditions. At an assumed natural gas price of 8 USD/GJ, the estimated investment payback period is approximately 0.9 years. These findings suggest that model-based and condition-aware operation can provide a practical, low-capital pathway for improving energy efficiency and reducing emissions in existing industrial steam generation systems.