Prediction of spark ignition engine performance and emissions using RERNN approach

This paper suggests a Recalling Enhanced Recurrent Neural Network (RERNN) method for accurately predicting the performance and emissions of spark ignition (SI) engines. The RERNN method is an efficient neural network technique. The RERNN model is also trained using a better conjugate method that speeds up convergence by using a generalized Armijo search technique. The proposed method analyzes combustion phasing, exhaust temperature, engine-out emissions, burn duration, mean effective pressure, ignition lag, and maximum pressure rise rate. By then, the proposed technique is implemented on the MATLAB platform and is evaluated for its performance against existing techniques. The proposed RERNN method achieves a better outcome than other existing Seagull Optimization Algorithm (SOA), Grasshopper Optimization Algorithm (GOA), and Wild Horse Optimizer (WHO) techniques. The proposed method shows the error in carbon monoxide (CO) is 0.18% and carbon dioxide (CO2) is 0.29% at a high speed of 900 rpm compared with other existing approaches.