Uncertainty evaluation of coal fineness measured by imaging technique: model, validation, and online application.
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| Title: | Uncertainty evaluation of coal fineness measured by imaging technique: model, validation, and online application. |
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| Authors: | Jin, Qiwen1 (AUTHOR), Chen, Lechong1 (AUTHOR), Zhu, Dian1 (AUTHOR), Shi, Zifu1 (AUTHOR), Xue, Zhiliang1 (AUTHOR) xuezhiliang@zju.edu.cn, Lin, Zhiming1 (AUTHOR) lzmfor@zju.edu.cn, Wu, Yingchun1 (AUTHOR), Wu, Xuecheng1,2 (AUTHOR) |
| Source: | International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 7, p2037-2062. 26p. |
| Subject Terms: | *Particle size distribution, *Pulverized coal, *Digital image processing, *Error analysis in mathematics, *Monte Carlo method, *Real-time computing |
| Abstract: | Pulverized coal fineness is a crucial parameter for both milling and boiler combustion systems in coal-fired power plants. Despite advancements in online measurement technologies, the uncertainty due to sample size and distribution properties has often been underestimated or overlooked. To address this gap, this work proposes exact particle size distribution (PSD) fitting for coal powder samples using doubly-truncated Rosin-Rammler (DTRR) function, a numerical method based on Monte-Carlo principle for quantitatively analyzing the uncertainty of coal fineness by simulating physical sampling and measurement process. A custom-made digital holographic particle analyzer (DHPA) is employed to acquire PSD data of coal powder from a 300 MWe coal-fired power plant. Systematic simulations are conducted to investigate the effects of sample size and distribution parameters on measurement uncertainty. Subsequently, a high-precision uncertainty model for pulverized coal fineness is developed and validated using both simulation and experimental data. The feasibility of applying this model to the online measurement of coal fineness is assessed, confirming that it can be directly integrated into online monitoring systems. This work provides a reference for error estimation in the fineness monitoring of fuel particles or other powders following DTRR distribution. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Pulverized coal fineness is a crucial parameter for both milling and boiler combustion systems in coal-fired power plants. Despite advancements in online measurement technologies, the uncertainty due to sample size and distribution properties has often been underestimated or overlooked. To address this gap, this work proposes exact particle size distribution (PSD) fitting for coal powder samples using doubly-truncated Rosin-Rammler (DTRR) function, a numerical method based on Monte-Carlo principle for quantitatively analyzing the uncertainty of coal fineness by simulating physical sampling and measurement process. A custom-made digital holographic particle analyzer (DHPA) is employed to acquire PSD data of coal powder from a 300 MWe coal-fired power plant. Systematic simulations are conducted to investigate the effects of sample size and distribution parameters on measurement uncertainty. Subsequently, a high-precision uncertainty model for pulverized coal fineness is developed and validated using both simulation and experimental data. The feasibility of applying this model to the online measurement of coal fineness is assessed, confirming that it can be directly integrated into online monitoring systems. This work provides a reference for error estimation in the fineness monitoring of fuel particles or other powders following DTRR distribution. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19392699 |
| DOI: | 10.1080/19392699.2025.2523375 |
