Effects of Injection Timing and Antioxidant on NOx Reduction of CI Engine Fueled with Algae Biodiesel Blend Using Machine Learning Techniques

Abstract

Fossil fuels are depended upon often in the transport sector. The use of diesel engines in all areas produce pollutants, such as NOx and CO, which cause serious environmental pollution and hazards, such as global climate change and breathing difficulties. Conventional fuel usage should be reduced, and there should be a shift toward alternative fuels. For compression ignition (CI) engines, microalgae biodiesel has been promoted as a clean, sustainable fuel. This is because it possesses desired traits, such as a quick rate of development, high productivity, and the capacity to turn CO2 into fuel. When algal biodiesel is used, pollutants, such as CO, UBHC, and smoke, are typically reduced, whereas NOx emissions are typically increased. The adoption of an exhaust gas recirculation technology and the advancement or delay of injection timing can effectively reduce NOx formation. Incorporating antioxidant chemicals such as butylated hydroxyl anisole (BHA) into fuel also minimizes NOx formation. In this study, the use of microalgae biodiesel as a substitute fuel for CI engines was investigated by altering the injection timing and adding each antioxidant in two doses. According to ASTM standard test procedures for biodiesel, the fuel qualities of various blends of algal biodiesel with antioxidants were tested and compared with the diesel fuel. The experiments were conducted using CI engines, and parameters were examined, such UBHC, CO, NOx, and smoke opacity. In comparison to diesel fuel, B20 + 30% BHA (21 bTDC) blends produced 49% lower oxides of nitrogen. The smoke, HC, and CO emissions of fuel blend B20 + 30% BHA (25 bTDC) were reduced by 33.33%, 32.37%, and 11.21%, respectively, compared with those of diesel fuel. The fuel blend B20 + 30% BHA (25 bTDC) showed the highest brake thermal efficiency of 14.52% at peak load condition. A multi-output regression deep long short-term memory (MDLSTM) model was designed to predict the performance and emissions of CI engines operating with varied fuel mixtures. The average RMSE and R2 values for the proposed MDLSTM were 0.38 and 0.9579, respectively.