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Verified CAS / Academic Author2 Decoded Studies

Prof. PENG Baozi

National Institute of Clean-and-Low-Carbon Energy, Beijing; State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences

Co-Affiliations:National Institute of Clean and Low-Carbon Energy, Beijing 102209, China

Research Publications & English Decoded Briefs

Showing 2 publications
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(25)60600-1

Damage Mechanism of High Chromia Refractory in the Slag Tapping Hole of Commercial Entrained-Flow Gasifiers

The service life of refractory bricks in the slag tapping hole of entrained-flow gasifiers is a critical bottleneck for long-term stable operation. This study investigated the damage mechanism of high chromia refractories in four commercial coal-water slurry gasifiers by analyzing gasification coal samples and corroded refractory bricks. Slag characteristics, including crystallization and viscosity-temperature behavior, were evaluated. Results revealed that low-viscosity slag induces more severe refractory damage. To mitigate slag crystallization risk, a safe slag tapping temperature range is recommended as tICT−t2.5 when tICT exceeds t25. Interior morphology of corroded bricks exhibited cracks, primarily attributed to molten slag penetration and subsequent reactions with refractory material. SEM-EDS analysis of slag-aggregate and slag-matrix interfaces identified reduction in Cr2O3 content as the earliest damage characteristic. XRD detected no zirconium-containing spinel in cracks, indicating that thermal expansion mismatch between newly formed phases and the refractory matrix drives crack propagation. A damage mechanism is proposed: initial Cr2O3 depletion compromises both matrix and aggregate, facilitating slag ingress and new phase formation, ultimately leading to structural failure. Early detection or prevention of Cr2O3 reduction is essential to prolong refractory service life.

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Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(25)60611-6

Identification of Coal Characteristics by Near-Infrared Spectroscopy: Machine Learning Predictions and Experimental Validations

Rapid and accurate determination of coal properties is critical for process optimization, quality control, and supply chain management in the coal industry. This study integrates near-infrared (NIR) spectroscopy with machine learning algorithms to develop a fast and reliable framework for predicting coal components. Five algorithms—support vector machine (SVM), random forest (RF), temporal convolutional network (TCN), one-dimensional convolutional neural network (1D CNN), and gated recurrent unit (GRU)—were employed for regression modeling. Among these, the TCN model achieved the lowest mean absolute error (MAE) of 0.505, while the RF model exhibited the lowest root mean square error (RMSE) of 0.618, indicating robust predictive accuracy on the test set. The TCN model also demonstrated superior generalization with the lowest coefficient of variation (CV) of 0.042. Single-output models revealed differential performance: TCN was optimal for ash content prediction, while RF excelled for fixed carbon and low calorific value. Considering model parameters, computation time, and accuracy, RF, 1D CNN, and TCN were identified as the most efficient. SHAP value analysis identified key spectral peaks influencing predictions, with peak 52 (1141.35–1157.65 nm) showing the highest impact across all models. Distinct peaks were associated with specific components: peak 46 (1173.89–1179.23 nm) for ash, peak 18 (1382.19–1388.10 nm) for moisture, and peaks 5 and 28 (1914.79–1920.46 nm and 1267.38–1270.48 nm) for fixed carbon. This research provides a novel method for rapid coal testing and offers insights applicable to component analysis in other fields.

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