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Journal articles
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Open Access
Tetraethyl orthosilicate-containing dispersion coating — water vapor and liquid water barrier properties, TAPPI Journal September 2021

ABSTRACT: An aqueous styrene-butadiene latex dispersion coating containing in-situ processed tetraethyl orthosilicate (TEOS) applied on paperboard demonstrated improved water barrier performance. Coatings containing TEOS equivalent to 0.8% silicon dioxide (SiO2; dry basis) exhibited water vapor performance of < 25 g/m2/day (23°C, 50% relative humidity [RH]) and liquid water barrier performance Cobb 1800 s of < 6 g/m2, when applied as a single-layer 18 g/m2 coating. Cobb 1800 s barrier performance was still good (< 11 g/m2) at coat weights of 7–10 g/m2. The use of filler materials such as kaolin improved the vapor barrier properties of the coating, but this was not critical to the liquid water barrier properties.

Effects of Neutralization of Recovery Boiler Ash in Ash Leaching, 2008 Engineering, Pulping and Environmental Conference

Effects of Neutralization of Recovery Boiler Ash in Ash Leaching, 2008 Engineering, Pulping and Environmental Conference

Sodium and Sulfur Process Chemistry in Black Liquor Gasification and Combustion - Review and Reevaluation of Basic Thermochemical Data, 2004 International Chemical Recovery Conference

Sodium and Sulfur Process Chemistry in Black Liquor Gasification and Combustion - Review and Reevaluation of Basic Thermochemical Data, 2004 International Chemical Recovery Conference

Books
Kraft Recovery Boilers, Third Edition Now Available From TAPPI Press

The much-anticipated update of bestseller provides rapid access to recovery boiler technology

Journal articles
Open Access
Assessing lignin content in Nordic hardwood and softwood species using models based on near-infrared (NIR) spectral data and partial least squares regression (PLSR), TAPPI Journal September 2025

ABSTRACT: Continuous kraft cooking digesters face challenges affecting product quality, making it valuable to improve control through advanced techniques like near-infrared (NIR) spectroscopy, model predictive control, and machine learning models. The primary goal of this study was to use NIR spectra to predict the amount of lignin in hardwood and softwood samples. This study investigated the correlation of NIR derivative spectra with the amounts of lignin relative to other constituents, namely cellulose, hemicellulose, and water, in wood chip samples of varying chip sizes and shapes from six Nordic wood species. It employed partial least squares regression (PLSR) on the NIR data to construct a model that predicted the lignin fraction and the relative fraction of acid-soluble lignin. When trained on a group of five wood species, the model achieved a satisfactory predictive ability, striking a balance between a wide range of lignin content and a consistent chemical environment. The accuracy increased further when the model was restricted only to spruce and pine, reflecting the benefits of a more homogenous dataset. Additionally, the optimal number of latent variables was identified as two, indicating that three distinct chemical components — cellulose, lignin and water — can be effectively differentiated using NIR.