Taguchi Approach and Multiple Regression Analysis for IDI Engine with RBME Optimization

Abstract
Diesel engines are suited to human usage. With the trend of Petro diesel running out, experts have been concentrating more on alternative fuels. One such alternative fuel whose fuel characteristics are more similar to diesel is bio-diesel. The majority of researchers came to the conclusion that by using 100% bio-diesel, emissions might be lowered while still retaining efficiency, with a minor increase in NOX emissions relative to diesel fuel. In this study, we looked at adding isopropanol to reduce these NOX emissions. An IDI (Indirect Diesel Injection) single-cylinder, four-stroke diesel engine is selected for testing. It is feasible to ascertain the engine characteristics and emissions of fuels like diesel and rice bran methyl ester (RBME) mixed with iso-propanol additive by conducting tests on IDI engines. The goal of the task is to determine the best settings for each answer, analyse the impact of each input component on the response, compare projected values using Taguchi's Additive Law and multiple regression techniques, and determine Confidence Intervals (CI). The regression formulas are derived from multiple regression analysis with a coefficient of regression up to 83% for the responses Exhaust Gas Temperature (EGT), Brake Thermal Efficiency (BThe), Brake Specific Fuel Consumption (BSFC), Hydrocarbons (HC), Carbon Monoxide (CO), Carbon Dioxide (CO2), Oxygen (O2), Nitrogen Oxides (NOX), and Smoke. RBME+2% Isopropanol is shown to be the optimum fuel overall based on engine performance and emission characteristics, particularly for EGT, BThe, BSFC, O2, NOX, and Smoke.