Abstract

In the pulp and paper and biofuel industries, real-time online characterization of biomass gross calorific value is of critical importance to determine its quality and price and for process optimization. Near infrared spectroscopy is a relatively low-cost technology that could potentially be used for such an application. However, the near infrared spectra are also influenced by biomass temperature and moisture content. In this study, external parameter orthogonalization is employed to remove simultaneously the influence of temperature and moisture content on the spectra before predicting gross calorific value. External parameter orthogonalization is of particular interest when one desires to transfer information from one modeling experiment to another, such as when developing a calibration model for a new property from the same material, or when it would be more efficient to divide the experimental effort. External parameter orthogonalization (EPO) was found to be an effective method for desensitizing a partial least squares calibration model to the influence of temperature and moisture content, enabling robust and accurate prediction of biomass gross calorific value. Partial least square models developed with external parameter orthogonalization always provided equal or better performance than models developed without external parameter orthogonalization. The paper shows that experimental efforts and costs can be reduced by approximately one half while maintaining prediction accuracy and model robustness.

© 2019 The Author(s)

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