基于GRNN的船舶柴油机NO_x排放预测

Prediction of NO_x Emissions from Four-stroke Marine Diesel Engine Based on General Regression Neural Network

基于4190ZLC-2船用四冲程增压柴油机实验测得的数据,运用广义回归神经网络(GRNN)相关理论,以转速、功率、喷油提前角作为样本的输入量,以排放气体氮氧化物(NO_x)的体积分数作为样本的输出量,对输入数据进行归一化处理,对输出数据进行反归一化处理,建立船舶柴油机广义回归神经网络排放预测模型,在推进特性与负荷特性工况下利用该模型进行柴油机NO_x的排放预测。仿真结果表明,所提出的模型具有较高的预测精度,可为柴油机减少NO_x排放提供依据。

Based on the experimental data measured from the 4190ZLC -2 marine four-stroke turbo- charged diesel engine, the related theory general regression neural network （GRNN） was employed to establish the model for predicting the emission of nitrogen oxides （ NOx ） from marine diesel engine. The prediction was carried out on the model by setting the rotation speed, power and fuel injection advance angle as the input, and the volume fraction of nitrogen oxides （ NOx ） within the exhausted gases as the output. The inputs of were normalized and the outputs were anti-normalized. The model was finally used to predict nitrogen oxides emissions under the condition of propulsive characteristic and load characteristic. Results show that the proposed model has higher prediction accuracy and is suitable for providing reference for the reduction of NOx emissions.