Data-driven Modeling for Over-temperature Prediction of Platen Superheater in Coal-fired Boiler

FAN Yuchen; ZHOU Yongqing; WEI Chang; LIU Xin; WANG Heyang

doi:10.13334/j.0258-8013.pcsee.241152

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Data-driven Modeling for Over-temperature Prediction of Platen Superheater in Coal-fired Boiler

更新时间：2026-01-17

- Data-driven Modeling for Over-temperature Prediction of Platen Superheater in Coal-fired Boiler
- Vol. 45, Issue 19, Pages: 7634-7643(2025)
- 作者机构：
  
  1. 天津大学机械工程学院, 天津市津南区,300354
  2. 烟台龙源电力技术股份有限公司,山东省,烟台市,264006
- 作者简介：
- 基金信息：
- DOI：10.13334/j.0258-8013.pcsee.241152
  CLC：
- Published：2025
- 稿件说明：
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FAN Yuchen, ZHOU Yongqing, WEI Chang, et al. Data-driven Modeling for Over-temperature Prediction of Platen Superheater in Coal-fired Boiler[J]. 2025, 45(19): 7634-7643.
DOI：

FAN Yuchen, ZHOU Yongqing, WEI Chang, et al. Data-driven Modeling for Over-temperature Prediction of Platen Superheater in Coal-fired Boiler[J]. 2025, 45(19): 7634-7643. DOI： 10.13334/j.0258-8013.pcsee.241152.

摘要

燃煤锅炉普遍采用空气分级燃烧技术，此举虽可大幅降低NOx生成，但造成炉内火焰中心上移，导致屏式过热器(屏过)管壁超温严重。此外，调峰运行使锅炉负荷经常性不规则变化，进一步恶化了屏过传热，使爆管泄漏事故频发。为指导锅炉安全可靠运行，提出一种基于遗传算法优化超参数的深度神经网络模型(deep neural network model with its hyperparameters optimized by genetic algorithm，GA-DNN)，通过构建炉内风煤侧及汽水侧运行参数与屏过30片管屏出口温度之间的映射关联，对屏过超温进行分析和预测。该模型可实现对不同负荷工况下屏过温度分布的准确预测，在此基础上能够以97.5%以上的准确率识别出当前及未来5 min屏过超温(> 550℃)的运行工况，同时可在89.2%的准确率下预测出未来5 min屏过超温最严重的管屏所在区域。

Abstract

Air-staging combustion technology is widely used in coal-fired boilers to reduce NOx emissions. However

it causes high-temperature flame to move upwards in the furnace and aggravates the tube over-temperature of the platen superheater (PLSH) located in the upper furnace. Moreover

the rapid changes of unit load under load-cycling mode further aggravate the tube over-temperature of boiler PLSH. In order to guide the safe and reliable operation of boilers

a deep neural network model with hyperparameters optimized by genetic algorithm (GA-DNN) is proposed to predict the over-temperature of boiler PLSH. By establishing the mapping correlation between the air

coal

and steam parameters and 30 tube panel temperatures at PLSH-outlet

the model can accurately predict the temperature distribution of the PLSH under different load conditions. On this basis

it can identify the over-temperature operational conditions (> 550℃) of PLSH for the present and for the next 5 min with over 97.5% precision. Additionally

it can predict the region with the worst PLSH in the next 5 min

with an accuracy rate of 89.2%.

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