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Ethyleneglycol pulp from tagasaste.

L Jiménez1, A Pérez, M J De la Torre

  • 1Departamento de Ingeniería Química, Universidad de Córdoba, Córdoba, Spain. iq1jiall@uco.es <iq1jiall@uco.es>

Bioresource Technology
|July 24, 2007
PubMed
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This study optimized ethyleneglycol pulping of tagasaste (Chamaecytisus proliferus) for high pulp yield. The process yields non-white paper suitable for specific applications.

Area of Science:

  • Pulp and Paper Science
  • Biomass Utilization
  • Chemical Engineering

Background:

  • Tagasaste (Chamaecytisus proliferus) is a potential lignocellulosic resource for pulping.
  • Optimizing pulping conditions is crucial for maximizing pulp yield and desired paper properties.

Purpose of the Study:

  • To investigate the impact of ethyleneglycol pulping variables on tagasaste pulp yield and paper quality.
  • To develop a predictive model for pulping outcomes based on operational parameters.

Main Methods:

  • Experimental factorial design was employed to study ethyleneglycol pulping.
  • Operational variables included ethyleneglycol concentration, temperature, time, and PFI beating revolutions.
  • A fuzzy neural network model was used for predicting pulp yield and paper properties.

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Main Results:

  • Optimized conditions yielded high pulp (56.85%) with low brightness (22.51%).
  • Paper properties evaluated included breaking length, stretch, burst index, and tear index.
  • The fuzzy neural network model predicted outcomes with less than 15% error.

Conclusions:

  • Ethyleneglycol pulping of tagasaste can be optimized for high pulp yield.
  • The resulting pulp's low brightness makes it suitable for non-white paper production.
  • Predictive modeling aids in efficient process optimization for biomass pulping.