Modeling of a port fuel injection spark-ignition engine with different compression ratios using methanol blends with the response surface methodology

In this study, the response surface methodology was applied to verify the optimum compression ratio, methanol percentage, and engine load in order to obtain the best levels of engine response that will occur when using methanol (0, 10, and 20% by vol.) in a spark-ignition engine under different compression ratio (6.0:1, 8.0:1, and 10.0:1) and engine load (8, 10, and 12 kg) conditions. A response surface methodology aided by analysis of variance was created using the three-factor and three-level central composite full design with the results of the experiment. With the created model, optimum methanol percentage, compression ratio, and engine load levels corresponding to the finest brake thermal efficiency, brake-specific fuel consumption, carbon monoxide, carbon dioxide, hydrocarbon, and nitrogen oxide emission levels were determined. According to the optimization results, the optimum methanol percentage, compression ratio, and engine load were found to be 10.5%, 6.0:1, and 12 kg, respectively. Hydrocarbon, nitrogen oxide, carbon monoxide, carbon dioxide, brake thermal efficiency , and brake-specific fuel consumption corresponding to optimum operating conditions were determined as 63.568 ppm, 840.643 ppm, 0.365%, 14.059%, 28.199%, and 0.286 kg/kWh, respectively. To test the reliability of the response surface methodology results, experiments with optimal methanol, compression ratio, and engine load were carried out and compared with the response surface methodology findings. As a result, it can be said that the response surface methodology is a successful application for the optimization of a spark-ignition engine using methanol as an alternative fuel with different engine parameters.

Automated summary

In this study, the response surface methodology was used to optimize a spark-ignition engine using methanol as an alternative fuel. The optimum compression ratio, methanol percentage, and engine load were determined to achieve the best engine response. The results showed that the response surface methodology successfully improved the engine performance, increasing thermal efficiency and reducing emissions.