Analysis of waste groundnut oil biodiesel production using response surface methodology and artificial neural network
作者： A.A. Ayoola , F.K. Hymore , C.A. Omonhinmin , O.C. Olawole , O.S.I. Fayomi
DOI: 10.1016/J.CDC.2019.100238
关键词: Pulp and paper industry 、 Yield (chemistry) 、 Transesterification 、 Biodiesel production 、 Response surface methodology 、 Catalysis 、 Mathematics 、 Correlation coefficient 、 Biodiesel 、 Linear regression
摘要: Abstract Investigation on the use of KOH and NaOH catalysts for waste groundnut oil (WGO) biodiesel production, as well comparative adoption response surface methodology (RSM) artificial neural network (ANN) modelling yield process parameters was carried out in this research work. Box–Benkhen experimental design adopted four considered were methanol-oil mole ratio (6–12), catalyst concentration (0.7–1.7 wt%), reaction temperature (48–62 °C) time (50–90 min). The results work reveal that produced higher biodiesel, compared to obtained from catalysed process. ANN model had 0.9241 regression coefficients (R) 0.8539 correlation (R2) while R R2 calculated RSM 0.9290 0.8516 transesterification Also, overall coefficient 0.9629 0.9272, 0.9210 0.8791, WGO production. Hence, typify robustness superiority over predicting solving complex problems specifically due larger values recorded.
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