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Modeling of acetosolv pulping of oil palm fronds using response surface methodology and wavelet neural networks


Mathematical models based on response surface methodology (RSM) and wavelet neural networks (WNNs) in conjunction with a central composite design were developed in order to study the influence of pulping variables viz. acetic acid, temperature, time, and hydrochloric acid (catalyst) on the resulting pulp and paper properties (screened yield, kappa number, tensile and tear indices) during the acetosolv pulping of oil palm fronds. The performance analysis demonstrated the superiority of WNNs over RSM, in that the former reproduced the experimental results with percentage errors and mean squared errors between 3 and 8% and 0.0054–0.4514 respectively, which were much lower than those obtained by the RSM models with corresponding values of 12–40% and 0.0809–9.3044, further corroborating the goodness of fit of the WNNs models for simulating the acetosolv pulping of oil palm fronds. Based on this assessment, it validates the exceptional predictive ability of the WNNs in comparison to the RSM polynomial model.

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Financial support from Universiti Sains Malaysia through Research University Grants No. 1001/PTEKIND/8140151 and 1001/PTEKIND/814240, and Directorate General of Higher Education of Indonesia for sponsoring postgraduate studies of Nasrullah is gratefully acknowledged.

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Correspondence to Zarita Zainuddin.

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Razali, N., Ong, P., Ibrahim, M. et al. Modeling of acetosolv pulping of oil palm fronds using response surface methodology and wavelet neural networks. Cellulose 26, 4615–4628 (2019).

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  • Oil palm fronds
  • Wavelet neural networks
  • Response surface methodology
  • Acetosolv pulping
  • Environmentally friendly process
  • Pulp and paper properties