Fly ash carbon content prediction and combustion optimization adjustment based on ISSA-RF-SSA

侯儒伟; 田放; 蔡浩; 马华; 刘伯阳

doi:10.19666/j.rlfd.202503038

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Fly ash carbon content prediction and combustion optimization adjustment based on ISSA-RF-SSA

更新时间：2026-01-21

- Fly ash carbon content prediction and combustion optimization adjustment based on ISSA-RF-SSA
- Thermal Power Generation Issue 12, Pages: 134-141(2025)
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- DOI：10.19666/j.rlfd.202503038
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- Published：2025
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侯儒伟, 田放, 蔡浩, et al. Fly ash carbon content prediction and combustion optimization adjustment based on ISSA-RF-SSA[J]. Thermal Power Generation, 2025, (12): 134-141.
DOI：

侯儒伟, 田放, 蔡浩, et al. Fly ash carbon content prediction and combustion optimization adjustment based on ISSA-RF-SSA[J]. Thermal Power Generation, 2025, (12): 134-141. DOI： 10.19666/j.rlfd.202503038.

摘要

针对传统飞灰含碳量预测模型存在局部最优解陷阱和泛化能力不足的问题，在锅炉热态多工况试验的基础上，经数据采集、处理以及变量Pearson相关性分析和重要度排序筛选出28个关键特征参数，采用麻雀搜索算法（sparrow search algorithm

SSA）确定随机森林（random forest

RF）模型最优超参数，构建SSA-RF预测模型。模型验证结果表明：SSARF模型在训练集和测试集的均方根误差分别降至0.010 8和0.019 1，决定系数R~2提升至0.999 7和0.998 1，显示模型优异的预测准确性和泛化能力。进一步提出ISSA-RF-SSA算法，融合多种策略改进SSA，实现燃烧参数的全局极值寻优。工程验证显示，ISSA-RF-SSA算法预测飞灰含碳量与实际值误差为0.03百分点，该算法优化后锅炉实际飞灰含碳量由2.500%降至1.345%。研究结果表明，通过多策略改进的ISSA-RF-SSA方法显著提升了算法的寻优性能，为燃煤机组燃烧优化提供了新思路。

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