Prediction Model for Transient NOx Emission of Diesel Engine Based on CNN-LSTM Network

Abstract

In order to address the challenge of accurately predicting nitrogen oxide (NOx) emission from diesel engines in transient operation using traditional neural network models, this study proposes a NOx emission forecasting model based on a hybrid neural network architecture combining the convolutional neural network (CNN) and long short-term memory (LSTM) neural network. The objective is to enhance calibration efficiency and reduce diesel engine emissions. The proposed model utilizes data collected under the thermal cycle according to the world harmonized transient cycle (WHTC) emission test standard for training and verifying the prediction model. The CNN is employed to extract features from the training data, while LSTM networks are used to fit the data, resulting in the precise prediction of training NOx emissions from diesel engines. Experimental verification was conducted and the results demonstrate that the fitting coefficient (R2) of the CNN-LSTM network model in predicting transient NOx emissions from diesel engines is 0.977 with a root mean square error of 33.495. Compared to predictions made by a single LSTM neural network, CNN neural network predictions, and back-propagation (BP) neural network predictions, the root mean square error (RMSE) decreases by 35.6%, 50.8%, and 62.9%, respectively, while the fitting degree R2 increases by 2.5%, 4.4%, and 6.6%. These results demonstrate that the CNN-LSTM network prediction model has higher accuracy, good convergence, and robustness.

An Interpretable Time Series Forecasting Model for Predicting NOx Emission Concentration in Ferroalloy Electric Arc Furnace Plants

“…Yang, Wang, and Li [7] focused on using LSTM networks to model the relationship between the operational parameters and NOx emissions in a 660 MW boiler. To enhance NOx emissions prediction in diesel engine transient environments, Shen et al [25] proposed a prediction model based on a hybrid neural network architecture that combines the feature extraction capabilities of a convolutional neural network (CNN) with the time series prediction proficiency of LSTM networks. In addition to models considering the temporal dynamics of the operating variables in facilities, research has been conducted to modify the characteristics and purposes of prediction in facilities or enhance the performance of existing models.…”

“…Lightweight CNN could offer the advantage of efficient computation and reduced complexity, making them more suitable for real-time NOx emission prediction tasks in coal-fired boilers. Li et al's [25] study presents a CNN-based model for the accurate prediction of NOx emissions from a coal-fired power plant boiler. An attention mechanism was integrated into the CNN-based model, with the attention module focusing on the interdependencies between channels in the input feature maps to capture important information in latent space.…”

“…f t , i t , and o t denote the forget, input, and output gates and state vector at time t. ⊗ represents element-wise multiplication. When applied to EAFs, utilizing their strengths in learning the sequence of features [7,25,34,37], the LSTM network can offer advantages in improving the accuracy of predicting NOx emissions typically associated with the operations of these furnaces.…”

Advanced Flame front Detection in Combustion Processes Using Autoencoder Approach

“…CNNs, or Convolutional Neural Networks, represent a deep learning architecture incorporating convolutional structures. Comprising key elements like convolution, pooling, and fully connected layers [30], CNNs leverage the convolution layer for feature extraction, followed by pooling layers that reduce the number of parameters and enhance training efficiency by conveying data information to subsequent network layers. Ultimately, the fully connected layer employs linear transformation to produce output results.…”

“…Results showcase the superiority of the proposed method over the base-reference approach. The autoencoder architecture [27][28][29][30] (from now on AE) exhibits higher sensitivity levels, indicating its superior capability in accurately identifying pixels outside the flame edge, leading to reduced overestimations if compared to the method used as the base reference (from now on BR). Moreover, AE demonstrates improved accuracy, precisely delineating both edge and non-edge pixels, which significantly enhances the representation of combustion evolution.…”