论文标题
神经网络在降解燃气轮机的NOx排放预测中的应用
Application of Neural Network in the Prediction of NOx Emissions from Degrading Gas Turbine
论文作者
论文摘要
本文旨在应用神经网络算法来预测天然燃气轮机降解的过程响应（NOX排放）。预测建模中考虑了九个不同的过程变量或预测因子。据发现，通过神经网络算法训练的模型应在培训和验证集中使用最新数据，以计算系统退化的影响。训练和验证集的R平方值证明了模型的有效性。残留物图没有任何明确的模式，表明该模型是合适的。证明了过程变量的重要性的排名，并且预测概况证实了过程变量的重要性。通过使用神经网络算法训练的模型表明了过程变量的最佳设置，以达到降解的燃气轮机系统中NOX排放的最小值。
This paper is aiming to apply neural network algorithm for predicting the process response (NOx emissions) from degrading natural gas turbines. Nine different process variables, or predictors, are considered in the predictive modelling. It is found out that the model trained by neural network algorithm should use part of recent data in the training and validation sets accounting for the impact of the system degradation. R-Square values of the training and validation sets demonstrate the validity of the model. The residue plot, without any clear pattern, shows the model is appropriate. The ranking of the importance of the process variables are demonstrated and the prediction profile confirms the significance of the process variables. The model trained by using neural network algorithm manifests the optimal settings of the process variables to reach the minimum value of NOx emissions from the degrading gas turbine system.
