Abstract
Malaysia is the second-largest palm oil producer in the world. Nevertheless, limited research was found on using oil palm trunk (OPT) for glucose production. The objective of this study is to optimise the glucose production from OPT via simultaneous enzymatic process. Response Surface Methodology (RSM) was adopted to optimise the mass of OPT, stirring speed, and the hydrolysis time for glucose production. All the three parameters were significant with p < 0.001. Quadratic regression model well described the experiment data with predicted R2 = 0.9700 and adjusted R2 = 0.8828. Meanwhile, artificial neuron network (ANN) predicted the data with correlation R = 0.9612 and mean square error = 0.0021. The highest concentration of glucose, 30.1 mmol/L, was produced by using 30 g of OPT, 225 rpm for 16 h at 60 ℃. The prediction from both RSM and ANN are comparable and highly accurate.
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The authors are grateful for the support by SEGi University and Malaysian Palm Oil Board (MPOB) for providing the OPT sample.
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Kee, C.M., Chin, W.C., Chun, T.H., Bukhari, N.A. (2022). Optimisation and Prediction of Glucose Production from Oil Palm Trunk via Simultaneous Enzymatic Hydrolysis. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2021. Lecture Notes in Networks and Systems, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-93247-3_6
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