Environmental Effects of Exhaust Emission from Spark Ignition Engine Fuelled with 4% HDPE Pyrolysis Oil-Gasoline Blend: Artificial Neural Network Modelling
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The present study investigates the environmental impacts of exhaust emissions from a spark-ignition (SI) engine fueled with a 4% High-Density Polyethene (HDPE) pyrolysis oil gasoline blend. Using Artificial Neural Network (ANN) modelling, the research focuses on predicting and analysing key emissions parameters, including carbon monoxide (CO), nitrogen oxides (NOx), oxygen (O2), hydrocarbons (HC), and carbon dioxide (CO2). A comprehensive dataset, encompassing various operational conditions, load, and speed, is collected from experiments. The analysis involves feature selection, data preprocessing, and the design of a feedforward backpropagation neural network architecture. The model is trained, tested, and validated on the dataset, with performance evaluated against environmental standards and regulations. Results from the trained ANN are then utilized to assess the environmental impact of the fuel blend under different scenarios. Sensitivity analysis identifies influential factors affecting emissions, providing insights into the complex relationship between input features and environmental effects. The study concludes with a detailed interpretation of findings, highlighting potential future considerations for mitigating environmental impacts associated with the use of HDPE pyrolysis oil-gasoline blends in SI engines. This research contributes to a deeper understanding of the interplay between fuel composition and environmental sustainability.
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