Abstract：
The reduction of various nitrogen oxide (NOx) emissions from diesel engines is an important environmental issue due to their negative impact on air quality and public health. Selective catalytic reduction (SCR) has emerged as an effective technology to mitigate NOx emissions, but predicting the performance of SCR systems remains a challenge due to the complex chemistry involved. In this study, we propose using DNN models to predict NOx emission reductions in SCR systems. Four types of datasets were created; each consisted of five variables as inputs. We evaluated the models using experimental data collected from a diesel engine equipped with an SCR system. Our results indicated that the deep neural network (DNN) model produces precise estimates for exhaust gas temperature, NOx concentration, and De-NOx efficiency. Moreover, inclusion of additional input features, such as engine speed and temperature, improved the prediction accuracy of the DNN model. The mean absolute error (MAE) values for these parameters were 3.1 °C, 3.04 ppm, and 3.65%, respectively. Furthermore, the R-squared coefficient of determination values for the estimates were 0.912, 0.983, and 0.905, respectively. Overall, this study demonstrates the potential of using DNNs to accurately predict NOx emissions from diesel engines and provides insights into the impact of input features on the performance of the model.
摘要：
由于柴油机对空气质量和公众健康的负面影响，减少柴油机的各种氮氧化物（NOx）排放是一个重要的环境问题。选择性催化还原（SCR）已成为减少NOx排放的有效技术，但是，由于涉及复杂的化学过程，预测SCR系统的性能仍然是一个挑战。在这项研究中,我们建议使用DNN模型来预测SCR系统中的NOx减排量。创建了四种类型的数据集；每个数据集包含五个变量作为输入。我们使用从配备SCR系统的柴油发动机收集的实验数据评估了模型。我们的结果表明，深度神经网络（DNN）模型可以精确估计废气温度，NOx浓度,和去NOx效率。此外，包含其他输入功能，如发动机转速和温度，提高了DNN模型的预测精度。这些参数的平均绝对误差（MAE）值为3.1°C，3.04ppm,和3.65%，分别。此外，估计值的R平方确定系数分别为0.912,0.983和0.905.总的来说,这项研究证明了使用DNN准确预测柴油机NOx排放的潜力，并提供了对输入特征对模型性能影响的见解。
