On-line characterization of wood chip brightness and chemical composition by means of visible and near-infrared spectroscopy

Author:

Hans Guillaume1ORCID,Allison Bruce1

Affiliation:

1. Smart Manufacturing , FPInnovations , 2665 East Mall , Vancouver , BC V6T 1Z4 , Canada

Abstract

Abstract Historically, on-line and real-time measurement of wood chip properties in the pulp and paper industry has been a challenge and has hampered the development of advanced process control strategies. In this study, visible and near-infrared (VIS-NIR) spectroscopy is investigated as a means to characterize wood chip brightness and chemical composition (i.e. extractives, lignin and holocellulose content) on-line. The estimated standard error on the holocellulose reference measurement was significantly reduced using data reconciliation. VIS-NIR calibration models were developed using partial least square regression. Derivative and baseline correction were found to be the most appropriate pre-processing methods. Model desensitization to the influence of moisture content and temperature by means of external parameter orthogonalization resulted in more robust models critical for on-line applications under harsh industrial conditions. Wavelength selection improved model accuracy for all properties. A comparison of two different spectrometer and probe combinations demonstrated that, after wavelengths selection, a non-contact measurement of wood chips performs as well as a contact measurement of wood powder for monitoring chemical composition. On-line prediction of wood chip brightness and chemical composition using the developed VIS-NIR models was demonstrated over 7 months in a kraft pulp mill processing both hardwood and softwood chips.

Publisher

Walter de Gruyter GmbH

Subject

Biomaterials

Reference51 articles.

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2. Achiche, S., Baron, L., Balazinski, M., and Benaoudia, M. (2007). Online prediction of pulp brightness using fuzzy logic models. Eng. Appl. Artif. Intell. 20: 25–36. https://doi.org/10.1016/j.engappai.2006.04.002.

3. Acquah, G.E., Via, B.K., Fasina, O.O., and Eckhardt, L.G. (2015). Non-destructive prediction of the properties of forest biomass for chemical and bioenergy applications using near infrared spectroscopy. J. Near Infrared Spectrosc. 23: 93–102. https://doi.org/10.1255/jnirs.1153.

4. Allen, L.H. (1987). Pitch control during the production of aspen kraft pulp, Report MR116. FPInnovations.

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