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- W3134393670 abstract "In this work, comparative assessment of predicted biodiesel yield using response surface methodology (RSM) and artificial neural network (ANN) is implemented. The artificial neural network was trained using RSM based experimental data. The importance of each independent variable on the response was evaluated using sensitivity analysis. This study shows that ANN and RSM models closely predicted the yield with an R2 value of 99.7 and 99.2 respectively. However, the ANN model precisely predicted the biodiesel yield with the least mean square error value of 0.00033, which is significantly lower than the yield predicted using RSM. The ANN simulation results show good agreement with experimental data. It can be inferred that ANN is a better tool compared to RSM and can be used for accurately predicting biodiesel yield that thereby effectively reducing the time-consuming and expensive experimental test." @default.
- W3134393670 created "2021-03-15" @default.
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- W3134393670 date "2021-01-01" @default.
- W3134393670 modified "2023-10-18" @default.
- W3134393670 title "Comparative assessment of response surface methodology and artificial neural networks in forecasting biodiesel yield from waste cooking sunflower oil" @default.
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- W3134393670 doi "https://doi.org/10.1016/j.matpr.2021.02.291" @default.
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