Advertisement

Cellulose

pp 1–14 | Cite as

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

  • Nasrullah Razali
  • Pauline Ong
  • Mazlan Ibrahim
  • Wan Rosli Wan Daud
  • Zarita ZainuddinEmail author
Original Research
  • 17 Downloads

Abstract

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.

Graphical abstract

Keywords

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

Notes

Acknowledgments

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.

References

  1. Dapia S, Sixta H, Borgards A, Harms H, Parajo J (2003) TCF bleaching of hardwood pulps obtained in organic acid media: production of viscose-grade pulps. Holz als Roh-und Werkstoff 61:363–368CrossRefGoogle Scholar
  2. Ferrer A, Vega A, Rodríguez A, Jiménez L (2013) Acetosolv pulping for the fractionation of empty fruit bunches from palm oil industry. Bioresour Technol 132:115–120CrossRefGoogle Scholar
  3. Jiménez L, Angulo V, Caparrós S, Ariza J (2007) Comparison of polynomial and neural fuzzy models as applied to the ethanolamine pulping of vine shoots. Bioresour Technol 98:3440–3448CrossRefGoogle Scholar
  4. Keynia F, Heydari A (2019) A new short-term energy price forecasting method based on wavelet neural network. Int J Math Oper Res 14:1–14CrossRefGoogle Scholar
  5. Kleinert TN (1971) Organosolv pulping and recovery process. Google PatentsGoogle Scholar
  6. Kleinert T, Tayenthal K (1931) Über neuere versuche zur trennung von cellulose und inkrusten verschiedener hölzer. Angew Chem 44:788–791CrossRefGoogle Scholar
  7. Leh CP, Rosli WW, Zainuddin Z, Tanaka R (2008) Optimisation of oxygen delignification in production of totally chlorine-free cellulose pulps from oil palm empty fruit bunch fibre. Ind Crops Prod 28:260–267CrossRefGoogle Scholar
  8. Ligero P, Villaverde J, Vega A, Bao M (2007) Acetosolv delignification of depithed cardoon (Cynara cardunculus) stalks. Ind Crops Prod 25:294–300CrossRefGoogle Scholar
  9. Mathworks (2016) Matlab Inc, Natick, MA, p 488Google Scholar
  10. McDonough TJ (1993) The chemistry of organosolv delignification. Tappi J 76:186–193Google Scholar
  11. Mussatto SI, Dragone G, Rocha GJ, Roberto IC (2006) Optimum operating conditions for brewer’s spent grain soda pulping. Carbohydr Polym 64:22–28CrossRefGoogle Scholar
  12. Nasrullah R (2013) Pengoptimuman pemulpaan asetosolv pelepah kelapa sawit dan kesan pencampurannya dengan pulpa sekunder. Ph.D. Thesis, Universiti Sains MalaysiaGoogle Scholar
  13. Nimz H, Granzow C, Berg A (1986) Acetosolv pulping. Eur J Wood Wood Prod 44:362CrossRefGoogle Scholar
  14. Page D (1969) A theory for the tensile strength of paper. Tappi 52:674–681Google Scholar
  15. Rodríguez A, Serrano L, Moral A, Pérez A, Jiménez L (2008) Use of high-boiling point organic solvents for pulping oil palm empty fruit bunches. Bioresour Technol 99:1743–1749CrossRefGoogle Scholar
  16. Rodríguez A, Sánchez R, Ferrer A, Requejo A (2011) Simulation of Hesperaloe funifera diethanolamine pulping by polynomial and neural fuzzy models. Chem Eng Res Des 89:648–656CrossRefGoogle Scholar
  17. Sahin HT, Young RA (2008) Auto-catalyzed acetic acid pulping of jute. Ind Crops Prod 28:24–28CrossRefGoogle Scholar
  18. Sarkanen KV (1980) Acid-catalyzed delignification of lignocellulosics in organic solvents. In: Progress in biomass conversion, vol 2. Elsevier, pp 127–144Google Scholar
  19. Shimada K, Hosoya S, Tomimura Y (1991) International symposium on wood and pulping. Chemistry notes. TAPPI Press, AtlantaGoogle Scholar
  20. Sixta H et al (2004) Evaluation of new organosolv dissolving pulps. Part I: preparation, analytical characterization and viscose processability. Cellulose 11:73–83CrossRefGoogle Scholar
  21. Tu Q, Fu S, Zhan H, Chai X, Lucia LA (2008) Kinetic modeling of formic acid pulping of bagasse. J Agric Food Chem 56:3097–3101CrossRefGoogle Scholar
  22. Turkan Y, Hong J, Laflamme S, Puri N (2018) Adaptive wavelet neural network for terrestrial laser scanner-based crack detection. Autom Constr 94:191–202CrossRefGoogle Scholar
  23. Vázquez G, Antorrena G, González J (1995) Kinetics of acid-catalysed delignification of Eucalyptus globulus wood by acetic acid. Wood Sci Technol 29:267–275CrossRefGoogle Scholar
  24. Vázquez G, Antorrena G, González J, Freire S, Lopez S (1997) Acetosolv pulping of pine wood. Kinetic modelling of lignin solubilization and condensation. Bioresour Technol 59:121–127CrossRefGoogle Scholar
  25. Wan Rosli W, Law K, Zainuddin Z, Asro R (2004) Effect of pulping variables on the characteristics of oil-palm frond-fiber. Bioresour Technol 93:233–240CrossRefGoogle Scholar
  26. Wanrosli W, Mazlan I, Law K, Nasrullah R (2011) Influences of the operating variables of acetosolv pulping on pulp properties of oil palm frond fibres. Maderas Ciencia y tecnología 13:193–202CrossRefGoogle Scholar
  27. Xu L, Du X, Wang B (2018) Short-term traffic flow prediction model of wavelet neural network based on mind evolutionary algorithm. Int J Pattern Recognit Artif Intell.  https://doi.org/10.1142/S0218001418500416 Google Scholar
  28. Zainuddin Z, Ong P (2012) An effective and novel wavelet neural approach in classifying type 2 diabetics. Neural Netw World 22:407–428CrossRefGoogle Scholar
  29. Zainuddin Z, Wan Daud WR, Pauline O, Shafie A (2011) Wavelet neural networks applied to pulping of oil palm fronds. Bioresour Technol 102:10978–10986CrossRefGoogle Scholar
  30. Zhang Q, Benveniste A (1992) Wavelet networks. IEEE Trans Neural Netw 3:889–898CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Chemical EngineeringUniversitas Syiah KualaBanda AcehIndonesia
  2. 2.Faculty of Mechanical and Manufacturing EngineeringUniversiti Tun Hussein Onn MalaysiaParit Raja, Batu PahatMalaysia
  3. 3.Bioresource, Paper and Coating Division, School of Industrial TechnologyUniversiti Sains MalaysiaUSMMalaysia
  4. 4.School of Mathematical SciencesUniversiti Sains MalaysiaUSMMalaysia

Personalised recommendations