Prediction of emission characteristics of diesel/n-hexanol/graphene oxide blended fuels based on fast outlier detection-sparrow search algorithm-bidirectional recurrent neural network

This study aims to utilize deep learning (DL) technology to address the issue of carbon soot emission in traditional internal combustion engines. Spraying and combustion experiments were conducted in a constant volume combustion (CVC) chamber using n-hexanol and graphene oxide nanoparticles as partial substitutes for diesel, with the goal of predicting their emission characteristics based on experimental data. Initially, spray and combustion data under different operating conditions were collected using the CVC experimental setup, and outliers were removed using the fast outlier detection (FOD) algorithm. Subsequently, the learning rate and the number of hidden layer neurons in the Bidirectional Recurrent Neural Network (BiRNN) model were optimized using the Sparrow Search Algorithm (SSA) to enhance the model's accuracy and generalization ability. Unlike previous research methods, the novel FOD-coupled SSA optimization method used in this study ensures the optimal parameter configuration of the speed prediction model while accelerating the convergence speed, which ultimately improves the stability of the model. In addition, the optimal output variables were determined based on the Spearman and Pearson correlation coefficient rules. Finally, the effectiveness of the predictive model was validated using evaluation metrics such as root mean square error (RMSE) and R-squared (R2). The results indicate that the KL coefficient for D80H20GO20 fuel in the CVC device is minimal. The FOD-SSA-BiRNN model achieved R2 values of 0.99409, 0.99529, and 0.99692 for D100, D80H20, and D80H20GO20 fuels, respectively. Therefore, this study provides an effective method for the online accurate prediction of carbon soot emissions in mixed fuels.