Modeling the Performance of Fuzzy Expert System for Prediction of Combustion, Engine Performance, and Exhaust Emission Parameters of a Spark Ignition Engine Fueled With Waste Bread Bioethanol-Gasoline Blends

This article focuses on the use of a rule-based Mamdani-type fuzzy expert system for the prediction of Pmax, HRRmax, ID, and CD as combustion parameters, BTE and BSFC as engine performance parameters, and CO, CO2, HC, and NOx as exhaust emission parameters of fuel blends formed by blending waste bread bioethanol with gasoline in different proportions. For modeling of 55 test conditions created by being operated test engine with 11 different test fuels under five different engine loads. As a result of the study, while combustion parameters were predicted with correlation coefficients in the range of 0.948-0.973% for waste bread bioethanol-gasoline blends, correlation coefficients for engine performance and exhaust emission parameters were in the range of 0.968-0.977% and 0.955-0.991% respectively. Similarly, the ranges of correlation coefficients obtained for sugar beet bioethanol-gasoline blends with fuzzy expert system were as follows: 0.967-0.971% for engine performance parameters, 0.955-0.978% for exhaust emission parameters, and 0.951-0.964% for combustion parameters. These results prove that costly and labor-intensive engine tests can be predicted with minimum effort and high accuracy with the developed model.

Automated summary

This article discusses the use of a rule-based Mamdani-type fuzzy expert system to predict the combustion parameters, engine performance parameters, and exhaust emission parameters of waste bread bioethanol-gasoline blends and sugar beet bioethanol-gasoline blends. The study shows that the developed model can accurately predict the relevant parameters of different fuel blends with high precision.