Modeling of SCR denitrification reactor for thermal power units based on improved grey wolf optimization LSTM network

吴磊; 顾华; 姚一鸣; 张军; 苏军; 陈依

doi:10.19666/j.rlfd.202502032

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Modeling of SCR denitrification reactor for thermal power units based on improved grey wolf optimization LSTM network

更新时间：2025-11-21

- Modeling of SCR denitrification reactor for thermal power units based on improved grey wolf optimization LSTM network
- Thermal Power Generation Issue 11, Pages: 136-141(2025)
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- DOI：10.19666/j.rlfd.202502032
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- Published：2025
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吴磊, 顾华, 姚一鸣, et al. Modeling of SCR denitrification reactor for thermal power units based on improved grey wolf optimization LSTM network[J]. Thermal Power Generation, 2025, (11): 136-141.
DOI：

吴磊, 顾华, 姚一鸣, et al. Modeling of SCR denitrification reactor for thermal power units based on improved grey wolf optimization LSTM network[J]. Thermal Power Generation, 2025, (11): 136-141. DOI： 10.19666/j.rlfd.202502032.

摘要

针对传统机理建模方法预测选择性催化还原技术（SCR）脱硝反应器出口NO_x质量浓度精度不高的问题，提出了一种结合增强型灰狼优化算法（EGWO）与长短时记忆（LSTM）神经网络的混合预测模型。首先，基于主成分分析（PCA）对原始数据进行处理与筛选，实现输入变量的降维。然后，利用EGWO优化LSTM神经网络的超参数。最终，将输入变量作为EGWO-LSTM模型的输入，预测出口NO_x质量浓度。以国内某超超临界1 000 MW火电机组为例，仿真结果表明，该模型在误差控制方面表现最优，均方根误差较传统LSTM模型下降50.36%，较BP模型下降76.14%，模型平均绝对百分比误差仅为1.01%。EGWO相对于GWO收敛至最优解时的迭代次数更少，且具有更高的收敛精度。

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