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660MW燃煤炉的NOx排放预测与多层梯度增强决策树,考虑多种操作模式
Ziwei Wang1, Yongzan Zhou2, Yukun Zhu3
1School of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
ACS omega
|November 25, 2024
概括
本研究提出了一种新的数据驱动方法,使用多层渐变增强决策树 (mGBDT) 框架来准确预测燃煤发电厂的氧化 (NOx) 排放,改善环境控制.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 氧化 (NOx) 是燃煤发电厂的主要污染物.
- 准确的NOx预测对于优化炉控制和减少排放至关重要.
研究的目的:
- 开发一个数据驱动的方法来预测炉出口的NOx度.
- 提高燃煤发电厂中NOx排放预测的准确性.
主要方法:
- 使用内核独立组件分析 (KICA) 来消除非线性相关性.
- 集成的物理接地变量与KICA提取的特征.
- 采用强大的高斯混合模型 (RGMM) 来识别操作模式.
- 开发了模式特定的多层梯度增强决策树 (mGBDT) 模型.
- 使用粒子群优化 (PSO) 和10倍交叉验证优化了超参数.
主要成果:
- 获得了0.947.7的确定系数 (R2).
- 获得的平方根平均误差 (RMSE) 为6.09 mg/m3.
- 报告的平均绝对误差 (MAE) 为4.009 mg/m3.
- 与其他五款车型相比,表现出卓越的性能.
结论:
- 拟议的mGBDT框架有效预测NOx排放.
- 该方法提高了炉控制和减排战略.
- 这种数据驱动的方法在NOx预测准确性方面取得了显著的改进.
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