伴随可再生能源发电装机容量快速增加,深度调峰过程中负荷多变、燃烧失稳等不稳定工况对火电机组的燃烧优化控制提出了更高要求,快速发展的人工智能技术与深度学习算法为锅炉参数预测建模及优化提供了重要手段。在机器学习算法方面,总结了特征筛选与建模算法的研究现状,提出了传统统计学方法与线性降维方法的科学解释性较差且不能很好地辨识高维数据,结合深度学习算法的特征筛选方法在处理复杂的火电机组数据时优势更明显;对比了多种神经网络在NOx排放浓度建模中的优缺点,其中长短期记忆神经网络与卷积神经网络在处理时序数据时效果更好、集成模型通过组合不同学习器的优势可提高整个模型的泛化能力和鲁棒性。在预测模型的应用方面,通过对SCR脱硝系统建立预测模型可以方便运行人员模拟并修正可调参数,同时作为软测量手段监测燃烧系统运行状态;引入NOx排放浓度预测模型的前馈控制和模型预测控制等先进控制手段可有效改善火电机组传统PID控制效果较差的问题;在多目标优化中NOx脱除效率通常与锅炉效率或脱硝成本共同作为优化目标,以期实现经济效益与社会效益的和谐统一。

With the rapid increase of installed capacity of renewable energy power generation,unstable conditions such as variable load andunstable combustion during deep peak regulation put forward higher requirements for combustion optimization control of thermal powerunits. The rapidly developing artificial intelligence technology and deep learning algorithm provides an important means for boiler parameterprediction modeling and optimization. In terms of machine learning algorithms, this paper summarized the research status of feature screening and modeling algorithms, and pointed out that traditional statistical methods and linear dimensionality reduction methods had poor scientific interpretation and can not identify high-dimensional data well, and feature screening methods combined with deep learning algorithms had more obvious advantages in processing complex thermal power unit data. The advantages and disadvantages of various neuralnetworks in NOx emission concentration modeling were compared. Among them, long short-term memory neural network and convolutionalneural network have better effects in processing time series data, and the integrated model can improve the generalization ability and robustness of the whole model by combining the advantages of different learners. In the application of prediction model, the establishment ofprediction model for SCR denitration system can facilitate operators to simulate and modify adjustable parameters, and at the same time,it can be used as a soft measurement method to monitor the operating state of the combustion system. Advanced control methods, such asfeedforward control and model predictive control, which introduce NOx emission concentration prediction model, can effectively improvethe poor effect of traditional PID control for thermal power units. In multi-objective optimization, NOx removal efficiency and boiler efficiency or denitrification cost are usually used as optimization objectives, in order to achieve the harmonious unity of economic and social benefits.

0 引言

1 NOx排放浓度背景

   1.1 火电脱硝系统介绍

   1.2 NOx排放浓度数据特征

2 特征筛选

   2.1 数据预处理

   2.2 特征提取

   2.3 特征选择

3 关于锅炉参数模型的机器学习算法

   3.1 支持向量机算法

   3.2 神经网络算法

   3.3 集成学习建模方法

4 应用场景

   4.1 锅炉运行参数监测与修正

   4.2 燃烧优化控制

   4.3 多目标优化问题

5 结语及展望