Soft computing analysis for estimation of the combustion characteristics of a biogas-fuelled spark ignition engine

The need to transition towards renewable energy sources has led to the use of fuels like biogas in internal combustion (IC) engines. In the present investigation, the combustion of an SI engine is experimentally investigated for various ignition advances (IA) and further evaluated by genetic algorithm (GA). As experimentation takes much time and effort, a soft computing technique is used to predict the cylinder pressure variations for various ranges of IA. The in-cylinder pressure is predicted from the GA model for the IA range of 33° to 47° bTDC and further compared with the data obtained from the SI engine at a compression ratio (CR) of 10:1. Subsequently, a mathematical model is also obtained to establish a mathematical relation between the cylinder pressure variations at each crank angle for various values of IA. The predicted peak cylinder pressure obtained using the genetic algorithm and the mathematical model shows high accuracy (higher than 90% and 94% respectively). The average correlation coefficient for the predicted cylinder pressure using both techniques is found to be 0.99. This can reduce the need to conduct repeated experiments for the biogas-fuelled SI engine used in this study.