基于物理信息神经网络的燃煤锅炉NO_x排放浓度预测方法

Forecasting Method for NOx Emission in Coal Fired Boiler Based on Physics-informed Neural Network

准确的NO_(x)浓度预测对保障燃煤锅炉安全运行和降低污染物排放具有重要意义。基于机器学习的NO_x排放浓度预测方法计算速度快、拟合精度高,但缺少可解释性,且过度依赖训练样本,在样本不充分的情况下模型泛化能力差。为此,该文提出一种基于物理信息神经网络的燃煤锅炉NO_x排放浓度预测方法,将煤量、氧量、分离燃尽风(separated overfireair,SOFA)开度与NO_x排放浓度之间的单调关系嵌入到神经网络中,促使模型服从机理约束,避免机器学习过拟合或欠拟合,提升模型在锅炉宽工况条件下的准确性。以某660 MW燃煤锅炉为研究对象,算例分析表明,提出的预测方法明显优于随机森林、支持向量机和神经网络等常规机器学习方法,即使在未知工况下也能遵循参数间单调性关系,具有较好的可解释性和泛化能力。

Accurate NO_(x)emission prediction significantly improves operation safety and reduces pollutant emissions in coal-fired power plants.Machine learning-based NOx emission prediction models have the advantages of fast computational speed and high fitting accuracy.However,these methods lack interpretability and overly rely on training samples,leading to poor generalization ability under insufficient data scenarios.Therefore,this paper introduces a novel forecasting method for NOx emission based on a physics-informed neural network(PINN).In PINN,the monotonic relationships between coal feed rates,oxygen levels,openings of separated overfire air(SOFA),and NOx emission concentration are embedded into the neural network model,prompting the model to obey the mechanisms and effectively inhibiting the overfitting or underfitting issues.This enhancement boosts the model’s accuracy under broad boiler operating conditions.Taking a 660 MW utility boiler as the research object,the results indicate that the proposed method significantly outperforms three traditional machine learning methods(random forest,support vector machine,and artificial neural network),reflecting superior prediction performance and generalization capacity.Moreover,this PINN model maintains adherence to the monotonic relationship between parameters even in unknown operating modes.