title: Wavelet neural networks applied to pulping of oil palm fronds.
abstract: In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.
authors: School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia. zarita@cs.usm.my
authors: Zarita
authors: Z
authors: Zainuddin
authors: Wan Rosli
authors: WR
authors: Wan Daud
authors: Ong
authors: O
authors: Pauline
authors: Amran
authors: A
authors: Shafie
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D000431:Ethanol / Q000737:chemistry; D007220:Industrial Waste; D008422:Materials Testing; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D000073878:Palm Oil; D010209:Paper; D010938:Plant Oils / Q000737:chemistry*; D015203:Reproducibility of Results; D012972:Sodium Hydroxide / Q000737:chemistry; D012997:Solvents / Q000737:chemistry; D013499:Surface Properties; D013696:Temperature; D013718:Tensile Strength; D013997:Time Factors; D014865:Waste Disposal, Fluid / Q000379:methods*
chemical_list: D007220:Industrial Waste; D010938:Plant Oils; D012997:Solvents; D000431:Ethanol; D012972:Sodium Hydroxide; D000073878:Palm Oil