Taguchi and ANN-based optimization method for predicting maximum performance and minimum emission of a VCR diesel engine powered by diesel, biodiesel, and producer gas
Randomized trial investigates maximum performance and minimum emissions in a variable compression ratio diesel engine, suggesting biodiesel as a viable alternative.
Key Points
The study aims to optimize output parameters of a variable compression ratio diesel engine to enhance performance and reduce emissions.
Engine tests conducted with diesel, biodiesel, and producer gas under varying loads and compression ratios.
Performance parameters like brake thermal efficiency and emissions including CO, HC, NOx measured during trials.
Optimization employed the Taguchi method, followed by prediction using artificial neural networks.
Minimum emissions achieved were 0.58% CO, 42% HC, 191 ppm NOx with maximum BTE of 21.56% at 16.5 CR and 10 kg load.
The ANN model rated for precision, with R2 correlation coefficients of 1, 0.95552, 0.94367, and 0.97789 for different datasets.
Biodiesel from Calophyllum inophyllum oil identified as a suitable substitute for conventional diesel.