# A Deep Learning Method for the Prediction of Pollutant Emissions from Internal Combustion Engines
## 1 Introduction
### Present Contribution  
This research investigates the possibility of integrating predictive models into the engine control unit (ECU), with the aim of eliminating the need for cumbersome portable devices during certification cycles, monitoring combustion quality in real time, and enabling remote emission monitoring from any location. Tests were carried out on an SI single-cylinder research engine fueled with conventional gasoline E5, considering a relative air excess index (\(\lambda\)) equal to 1.0 and a wide range of different operating conditions. The experimental setup involved collecting data coming from ECU and a raw gas analyzer. These data served as training data for the ML architecture, which was developed to forecast the composition of exhaust gases based on the observed behavior of the engine. To accomplish this mission, different Feed-Forward neural networks with a back propagation (BP) optimizer [30] were tested.  
In the current study, research was conducted on fine-tuning parameters such as the number of neurons, hidden layers, and input variables in ANN structures to enhance the prediction performance. By conducting a series of experiments and validations iteratively, the ML architecture ideal design was established.  
The current study extensively tested different ANN architectures, focusing on configurations optimized for pollutant emission prediction in a spark-ignition engine. The models were trained on dynamic engine operating cycles, allowing them to predict emissions under varying real-world conditions. This dynamic approach contrasts with earlier studies that focused more on static engine conditions. This research created and tested five unique dynamic cycles designed to stress the predictive capabilities of the network. Each cycle represented different engine speeds, torques, and throttle conditions, providing a more thorough evaluation of the model's robustness across various operating conditions. The capacity of the optimized model in generalizing over various cycles (engine speeds, throttle openings, and torque settings) demonstrates its superior robustness. Previous models, by comparison, may have shown limited performance outside of specific test conditions.  
The findings indicate that the proposed model achieves convergence throughout the training process while avoiding overfitting. This demonstrates its proficiency in efficiently extracting knowledge from the dataset and generating accurate predictions. The results indicate that the model holds considerable promise for predicting pollutant emission concentrations in SI engines. Indeed, for each of the aforementioned dynamic cycles, the prediction of the analyzed pollutant emissions yields an average root mean square error (RMSE) of less than 5%, with a maximum of 5.87% and a minimum of 1.57%. Throughout, it consistently remains below the 10% acceptability threshold, thereby ensuring compliance with high quality standards [31]. Therefore, the BP neural network model has potential applications as an onboard virtual tool for estimating emissions in real driving conditions through the presented ANN predictive model.