基于深度极限学习机的柴油机尾气排放预测

准确预测拖拉机等柴油机械实际工况污染物排放在排放清单建立和区域污染物排放控制方面具有重要意义。基于拖拉机不同运行状态下发动机转速、油耗、燃烧比、CO、HC、NOX和PM等实测数据作为数据源，建立深度极限学习机(Deep Extreme Learning Machine，DELM）的预测模型，并对拖拉机怠速、行走和旋耕等基本工况下的污染物排放进行预测。为进一步评估DELM预测模型的适应性，将其与支持向量机(support vector machine, SVM)和前馈神经网络(Back propagation neural network, BPNN)模型进行对比分析。结果表明，1）DELM模型在预测排放时间序列方面具有一定优势，其预测拖拉机在怠速、行走和旋耕3种状态下的NOX、HC、CO和PM排放均方根误差均值分别为5.269×10-5、5.195×10-5、5.135×10-5和2.795×10-5。2）DELM模型与SVM和BP对比发现，DELM模型在鲁棒性以及适应性方面的优势显著。3）DELM方法的较高的准确度和泛化性，为基于发动机状态数据预测移动源尾气排放提供思路和方法。

Prediction of Diesel Engine Exhaust Emissions Based on Deep Extreme Learning Machine

Accurately predicting the actual working condition pollutant emissions of tractors and other diesel machinery is of great significance in the establishment of emission inventories and regional pollutant emission control. Based on the measured data such as engine speed, fuel consumption, combustion ratio, CO, HC, NOX and PM under different operating conditions of the tractor as the data source, a Deep Extreme Learning Machine (DELM) prediction model was established, and the tractor idling speed, and the pollutant emissions under basic working conditions such as tractor idling, walking and rotary tillage are predicted. In order to further evaluate the adaptability of the DELM prediction model, it is compared and analyzed with support vector machine (SVM) and back propagation neural network (BPNN) models. The results show that 1) The DELM model has certain advantages in predicting the emission time series. It predicts that the average root-mean-square error of the NOX, HC, CO, and PM emissions of the tractor in the three states of idling, walking and rotary tillage are 5.269×10-5, 5.195×10-5, 5.135×10-5 and 2.795×10-5. 2) The DELM model is compared with SVM and BP and it is found that the DELM model has significant advantages in robustness and adaptability. 3) The high accuracy and generalization of the DELM method provide ideas and methods for predicting mobile source exhaust emissions based on engine state data.