Greenhouse gas emission prediction and impact analysis of dual-fuel engine

This study explored the profound influence of greenhouse gas (GHG) emissions on global climate change by introducing an innovative prediction model. Utilizing a backpropagation (BP) neural network with dual-hidden layer, optimized through simulated annealing particle swarm optimization (SA-PSO), the model predicted emissions from a diesel/natural gas dual-fuel engine at 1500 rpm across four torque levels: 400, 800, 1200, and 1600 N & sdot;m. & sdot; m. The inputs included engine torque, injection timing, pressure, excess air coefficient, and natural gas substitution ratio, with CO2 2 and CH4 4 as outputs. Evaluation metrics-the coefficients of determination (R2) 2 ) of 0.9975 for CO2 2 and 0.9951 for CH4, 4 , the root mean square error (RMSE) of 0.062 % and 278.04 ppm, and the mean relative error (MRE) of 0.82 % and 5.35 %, respectively-demonstrated the model's accuracy. A quantitative analysis using the Mean Influence Value (MIV) algorithm showed engine torque's pivotal role in emissions at a medium engine speed with contribution rates of 48.5 % and 40.3 % for CO2 2 and CH4 4 emissions, respectively. Notably, at a lower load condition (engine torque = 400 N & sdot;m), & sdot; m), the natural gas substitution ratio was identified as having the most substantial impact on emissions. This study presents a novel approach to predicting and reducing GHG emissions from dual-fuel engines.