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The Shendye-Fleming OBA Index for paper and paperboard, TAPPI Journal March 2022

ABSTRACT: We are proposing a new one-dimensional scale to calculate the effects of optical brightening agents (OBA) on the bluish appearance of paper. This index is separate from brightness and whiteness indices.In the paper industry, one-dimensional scales are widely used for determining optical properties of paper and paperboard. Whiteness, tint, brightness, yellowness, and opacity are the most common optical properties of paper and paperboard. Most of the papers have a blue cast generated by addition of OBA or blue dyes. This blue cast is given because of the human perception that bluer is whiter, up to a certain limit. To quantify this effect, it is necessary to determine how much blue cast paper and paperboard have. As the printing industry follows the ISO 3664 Standard for viewing, which has a D50 light source, this also plays a very important role in showing a blue cast. Color perception is based on light source and light reflected from an object. The ultraviolet (UV) component in D50 interacts with OBA to provide a reflection in the blue region of the visible spectrum. Use of a UV blocking filter results in measurements without the effect of emission in the blue region. This difference is used in determining the OBA effect in the visible range of the paper. This equation is known as the Shendye-Fleming OBA Index.

Wood Market Dynamics and the Impact on Pulp and Paper: Current Trends, November/December 2023 Paper360º

Paper Mill HMI Displays Benefit from Integrated KVM Extenders, November/December 2023 Paper360º

The role of hornification in the deterioration mechanism of physical properties of unrefined eucalyptus fibers during paper recycling, TAPPI Journal February 2024

ABSTRACT: Physical properties of cellulosic paper deteriorate significantly during paper recycling, which hinders the sustainable development of the paper industry. This work investigates the property deterioration mechanism and the role of hornification in the recycling process of unrefined eucalyptus fibers. The results showed that during the recycling process, the hornification gradually deepened, the fiber width gradually decreased, and the physical properties of the paper also gradually decreased. After five cycles of reuse, the relative bonding area decreased by 17.6%, while the relative bonding force decreased by 1.8%. Further results indicated that the physical property deterioration of the paper was closely related to the decrease of fiber bonding area. The fiber bonding area decreased linearly with the reduction of re-swollen fiber width during paper recycling. Re-swollen fiber width was closely related to the hornification. Hornification mainly reduces the bonding area of unrefined eucalyptus fiber rather than the bonding force. The work elucidates the role of hornification in the recycling process of unrefined eucalyptus fibers and the deterioration mechanism of paper physical properties, which will be helpful to control the property deterioration of paper and achieve a longer life cycle.

Paper Mills Forge Ahead with Complex Forged Parts, Paper360º May/June 2024

How Much Can Your Mill Save Through Optimization?, Paper360º March/April 2024

Are Pulp and Paper Companies Sitting on a Hidden Goldmine?, Paper360º January/February 2024

Convolutional neural networks enhance pyrolysis gas chromatography mass spectrometry identification of coated papers, TAPPI Journal August 2024

ABSTRACT: In the evolving paper industry, accurate identification of coated paper components is essential for sustainability and recycling efforts. This study employed pyrolysis-gas chromatography mass spectrometry (Py-GCMS) to examine six types of coated paper. A key finding was the minimal interference of the paper substrate with the pyrolysis products of the coatings, ensuring reliable analysis. A one-dimensional convolutional neural network (1D-CNN) was employed to process the extracted ion chromatograms directly, simplifying the workflow and achieving a predictive accuracy of 95.2% in identifying different coating compositions. Additionally, the study high-lighted the importance of selecting an optimal pyrolysis temperature for effective feature extraction in machine learning models. Specific markers for coated papers, including polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polybutylene succinate (PBS), polylactic acid (PLA), and waterborne polyacrylates (WP), were identified. This research demonstrates a novel approach to coated paper identification by combining Py-GCMS with machine learning, offering a foundation for further studies in product quality and environmental impact.

Valve Actuators Built for Longevity, Reliability, Paper360º July/August 2024

Summaries from TAPPI Journal, Paper360º July/August 2024