Xianyong Peng

Conformal prediction guarantees interval coverage only when calibration and deployment data are exchangeable. We test joint state-of-charge (SOC) and state-of-health (SOH) intervals when this assumption fails across cells, discharge protocols, chemistries, and an electric-vehicle (EV) driving profile, with calibration data kept separate from training and model selection. At 90% nominal, SOC coverage is 90.9% on a random cycle split but 33.0–81.4% on held-out cells, at most 0.9% on cross-protocol windows with nonzero SOC, at most 5.16% across chemistries, and 0.02% on EV-profile cells (five-seed means). SOH coverage is below nominal under every shift. Four learned baselines (recurrent, convolutional, Transformer, and physics-informed) and an equivalent-circuit Kalman filter fall short on the hardest cells. Density-ratio and optimal-transport weighting, conformalised quantile regression, and group-conditional calibration reach nominal mean coverage on no held-out cell. Recalibrating on the deployed cell’s five latest labelled cycles restores coverage to 85.5–90.3% on held-out cells without retraining, but under protocol and EV-profile shift only with intervals over half the SOC range. Under chemistry shift, optimal-transport alignment configured on labelled target data lowers SOC error by 10–59%, yet only fine-tuning with target-domain calibration restores coverage (88.0–89.3%). Source-calibrated conformal intervals are not valid under deployment shift by default.

The flexible operation of coal-fired boilers poses significant challenges to thermal safety, particularly due to delayed responses in wall temperature under variable load conditions, which may lead to overheating risks and reduced equipment lifespan. To address this issue, we propose a PLO-KAN framework for high-precision prediction of high-temperature superheater wall temperatures. The framework integrates a Kolmogorov–Arnold Network (KAN) with learnable B-spline activation functions to enhance interpretability, a sliding-window strategy to capture temporal dependencies, and Polar Lights Optimization (PLO) for automated hyperparameter tuning, balancing local exploitation and global exploration. The method is validated using 10,000 operational samples from a 1000 MW ultra-supercritical once-through boiler, with 68 key features selected from 106 candidates. Results show that the proposed model achieves high accuracy and robustness in both single-step and multi-step forecasting, maintaining reliable performance within a five-minute prediction horizon. The proposed method provides an efficient and interpretable solution for real-time wall temperature prediction, supporting proactive thermal management and enhancing operational safety in coal-fired power plants.

To accommodate the high penetration of intermittent renewable energy sources like wind and solar power into the grid, coal-fired units are required to operate with enhanced deep peak-shaving and variable load capabilities. This study develops a dynamic model of the boiler–turbine coordinated control system (BTCCS) for ultra-supercritical once-through boiler (OTB) coal-fired units operating under wet conditions. A mechanistic model framework is established based on mass and energy conservation. In case of missing steady-state data, this work proposes a mechanism-integrated parameter identification method that determines model parameters using only dynamic running data while incorporating physical constraints. Model validation demonstrates that the proposed approach accurately reproduces the variable-load operation of the BTCCS within the range of 50–350 MW. Mean relative errors of output variables are all less than 7.5%, and root mean square errors of output variables are less than 0.3 MPa, 1.4 kg/s, 0.25 m, and 20.7 MW, respectively. Open-loop simulations further confirm that the model captures the essential dynamic characteristics of the system, making it suitable for simulation studies and control system design aimed at improving operational flexibility and safety of OTB coal-fired units under wet conditions.

This study presents an advanced NO_x reduction strategy for a 330 MW lignite-fired boiler using an immunological AI system: the SILO (Stochastic Immune Layer Optimizer) combustion optimizer inspired by artificial immune systems. The immunological AI optimizer adaptively models multi-variable interactions and fireball shape in real time, optimizing fuel–air mixing to reduce NO_x formation at the source. Unlike reactive secondary methods, the combustion optimizer reshapes the combustion process to reduce emissions while improving efficiency. Real-time temperature data from the AGAM acoustic system inform the combustion optimizer’s fireball modeling, ensuring combustion uniformity. A urea-based SNCR system serves as a secondary layer, controlled based on local furnace conditions to target thermal zones. Field results confirmed that SILO reduced NO_x emissions below 200 mg/Nm³, decreased urea consumption by up to 34%, and improved boiler efficiency by 0.29%. The architecture offers a scalable, DCS-integrated solution for aligning fossil-fueled operations with tightening emission standards.