Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

Abstract

The study utilized neural network modeling to forecast the fuel consumption in compression ignition engines fueled by diesel, HDPE PO, PP PO, and LDPE PO. Using empirical data, a neural network model was constructed and used to estimate specific fuel consumption (SFC). Employing orthogonal arrays and parameter adjustments ensured accurate prediction of SFC, which was validated through experimentation. The multilayer perception network, coupled with traditional backpropagation, facilitates the nonlinear mapping of inputs to outcomes. In the LM10TP architecture, the key metrics from the training set included an impressive R-squared value of 1, indicating a perfect fit with a root mean square error (RMSE) of 0.0012 and a mean square error (MSE) of 1.5143E-06. Similarly, the validation set exhibited robust performance metrics with an R-squared value of 0.9999, an RMSE of 0.0011, and an MSE of 1.2185E-06. These metrics underscore the efficacy of neural networks in both the training and validation phases, affirming their utility as reliable predictive tools for SFC. Overall, this study highlights the effectiveness of neural network modeling for accurately predicting fuel consumption in compression ignition engines across diesel and polymer-based fuels. By leveraging empirical data and sophisticated modeling techniques, this study contributes to advancing predictive capabilities in the field, offering valuable insights for optimizing engine performance and fuel efficiency.