Widely used as power equipment, diesel engines emit NO
x
, which significantly threatens the well-being of both the ecosystem
and individuals. The SCR system, which is employed to reduce NO
x
emissions from diesel engines, relies on precise control of the NO
x
emission levels. Addressing the challenge that traditional NO
x
emission prediction methods struggle to accurately forecast the
emissions under transient operating conditions, this article introduces a deep
learning model that integrates CNN, ECA, and BIGRU.
The model’s necessary experimental data were collected during the hot phase of
the WHTC, and input parameters were screened through correlation analysis. The
model employs a CNN for feature extraction, integrates an ECA module to refine
key feature processing, and utilizes BIGRU to capture temporal dynamics and
dependencies, yielding predictive outcomes. Additionally, the model employs the
Adam optimizer and combines it with BWO to adjust hyperparameters, thereby
elevating the model’s accuracy for predicting transient NO
x
emission.
Comparative analysis with existing CNN, LSTM, and CNN–LSTM models revealed that
CNN–ECA–BIGRU model had a notable reduction in MAE, RMSE, and MAPE, along with
an enhanced R
2 value. These improvements highlight the model’s superior predictive
accuracy and its robust nonlinear fitting ability.
