Abstract
Response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) were tested in the modeling of acid pretreatment of palm kernel oil with a very high acid value (22±0.1mg KOH/g oil). This was investigated considering methanol/oil molar ratio (1.3:1–3.8:1), catalyst loading (0.3–0.5vol.%) and time (20–40min) using Box Behnken design. The developed RSM, ANFIS and ANN models described the process with high accuracy (coefficient of determination, R2>0.99 and average absolute deviation, AAD=2.72–23.96%). RSM, RSM coupled with generic algorithm (GA), ANFIS-GA and ANN-GA were applied to optimize the process for best operating condition and ANN-GA gave the minimum acid value (0.64mg KOH/g) under the best optimal condition of methanol/oil molar ratio 3.4:1, catalyst loading 0.39vol.% and time 24.06min. Based on the statistical indices obtained, RSM performed the least, while ANN marginally outperformed ANFIS. GA proved to be superior to RSM in the optimization of the esterification process.
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