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17
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2
This article describes aspects of the development of an artificial intelligence (AI)-based prognostic modeling and performance optimization of a single-cylinder CI engine powered by biodiesel-diesel blends. It is a tool based on gene expression programming (GEP) followed by response surface methodology (RSM). RSM is employed to establish an explicit mathematical relationship between input and outputs. A database of experimental data on a computerized engine test bench was collected for model development and its testing. The prognostic ability of the GEP model was verified by error analysis, where the coefficient of determination (R2) and mean absolute percentage error (MAPE) varied marginally within the range of 0.979 ± 0.020 and 2.15 ± 0.25, respectively. The model captures adequate trends. Optimum input conditions of engine load, biodiesel-diesel blending ratio, fuel injection pressure, and fuel injection timing are observed to be 60.49 %, 14.32 %, 231.35 bar, and 23.7° bTDC, respectively, while optimized results of brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and peak in-cylinder pressure (Pmax) are found to be 24.28 %, 0.3135 kg/kWh, and 58.95 bar, respectively. GEP approach followed by RSM is observed to be a robust tool.<
Alan : Eğitim Bilimleri; Fen Bilimleri ve Matematik; Sağlık Bilimleri; Sosyal, Beşeri ve İdari Bilimler
Dergi Türü : Uluslararası
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