Modeling, polynomial regression, and artificial bee colony optimization of SI engine performance improvement powered by acetone–gasoline fuel blends

Abstract

The current study attempts to improve the performance of SI engines by employing two alternative acetone–gasoline mixtures. This investigation applies the Artificial Bees Colony Algorithm (ABC) to determine the optimum acetone–gasoline blends and engine speed to increase engine performance further and minimize fuel consumption. The SI engine performance of one-cylinder, four-stroke powered by neat gasoline fuel (AC0), 5% of acetone by volume (AC5), and 10 % of acetone by volume (AC10) has been investigated experimentally. Tests were carried out at speed rates from 1,000 to 3,600 rpm. The gasoline engine was integrated into an eddy-current dynamometer to evaluate the performance indexes. It was revealed that the overall performance of the gasoline engine is enhanced when acetone is blended with gasoline. The AC10 exhibited better engine brake torque (BT) and brake thermal efficiency (BTE) than pure gasoline, with 4.39 % and 6.9 %, respectively. According to the optimization findings, a 10% acetone concentration and engine speeds of 2889 rpm and 2769 rpm produced the best results in terms of BT and BTE, which were 7.776 N.m and 29.992%, respectively. However, at 2401 rpm of engine speed, a minimum BSFC of 0.2986 was reached without acetone. This result demonstrates that the ABC algorithm can precisely forecast the optimal position in terms of engine effectiveness and fuel consumption.

Keywords

• SI engine performance
• Optimization
• Acetone–gasoline
• Polynomial regression
• Alternative fuel
• Artificial bees colony algorithm