PULP AND PAPER FROM OIL PALM FRONDS: WAVELET NEURAL NETWORKS MODELING OF SODA-ETHANOL PULPING

Zarita Zainuddin, Wan Rosli Wan Daud, Pauline Ong, Amran Shafie

Abstract


Wavelet neural networks (WNNs) were used to investigate the influence of operational variables in the soda-ethanol pulping of oil palm fronds (viz. NaOH concentration (10-30%), ethanol concentration (15-75%), cooking temperature (150-190 ºC), and time (60-180 min)) on the resulting pulp and paper properties (viz. screened yield, kappa number, tensile index, and tear index). Performance assessments demonstrated the predictive capability of WNNs, in that the experimental results of the dependent variables with error less than 6% were reproduced, while satisfactory R-squared values were obtained. It thus corroborated the good fit of the WNNs model for simulating the soda-ethanol pulping process for oil palm fronds.

Keywords


Oil palm fronds; Optimization; Pulp and paper; Soda-ethanol; Wavelet neural networks

Full Text: PDF

Welcome to BioResources! This online, peer-reviewed journal is devoted to the science and engineering of biomaterials and chemicals from lignocellulosic sources for new end uses and new capabilities. The editors of BioResources would be very happy to assist you during the process of submitting or reviewing articles. Please note that logging in is required in order to submit or review articles. Martin A. Hubbe, (919) 513-3022, hubbe@ncsu.edu; Lucian A. Lucia, (919) 515-7707, lucian.lucia@gmail.com URLs: bioresourcesjournal.com; http://ncsu.edu/bioresources ISSN: 1930-2126