Artificial neural network based forecasting of diesel engine performance and emissions utilizing waste cooking biodiesel

Ecological and environmental problems resulting from fossil fuels are due to the harmful emissions released into the atmosphere. The rising interest in searching for alternative fuels like biodiesel is growing to solve these problems. Waste cooking oil (WCO) is transformed into methyl ester and combined with biodiesel in percentages of 25, 50, 75, and 100%. Research is done on the impacts of methyl ester blends on engine performance and emissions. Compared to diesel, the methyl ester combination showed 25% lower brake power and 24% loss in thermal efficiency at maximum load and 1500 rpm. However, diesel fuel showed 23% lower specific fuel consumption increase than biodiesel. Compared to diesel, methyl ester exhibits 15% lower air-fuel ratio and 4% volumetric efficiency. Biodiesel lowers CO, HC, and smoke concentrations by 12, 44, and 48%, respectively, compared to diesel. Biodiesel emits 23% higher NOx at 1500 rpm and 100% engine load. To predict the emissions and performance of different percentages of biodiesel at engine speed variation, an artificial neural network (ANN) model is presented. ANN modeling minimizes labor, time, and finances and uses nonlinear data. Predictions were produced about the brake output power, specific fuel consumption, thermal efficiency, air-fuel ratio, volumetric efficiency, and emissions of smoke, CO, HC, and NOx as a function of engine speed and blend ratio. All correlation coefficients (r) over 0.99 and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>R</mml:mi> <mml:mn>2</mml:mn></mml:msup> </mml:math> values were beyond 0.98 for all variables. There were low values of MSE, MAPE, and MSLE with significant predictive ability. WCO's biodiesel is a viable diesel engine replacement fuel.