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Journal articles
Application of AI-based approach to control the papermaking process, TAPPI Journal March 2025
ABSTRACT: This paper explores AI’s role in revolutionizing the pulp and paper industry, and specifically in predicting wet tensile strength (WTS) for specialty-grade papers. Leveraging eLIXA technology, a 90-day study achieved a 15% reduction in chemical dosage and an 80% decrease in wet tensile standard deviation. The real-time dosage prediction led to optimizing the wet strength resin (WSR) consumption and improved process reliability. The self-learning models exhibited adaptability to changing variables, ensuring their robustness. Overall, this study highlights AI’s transformative impact on efficiency, cost savings, and product quality within the dynamic landscape of papermaking. The approach used for wet strength optimization has been used to optimize other aspects of pulp and paper production.
Journal articles
Magazine articles
Editorial: Agility and adaptation in a dynamic business world, TAPPI Journal January 2021
ABSTRACT: As we move into 2020, it's interesting to look back at the research topics that were covered in TAPPI Journal (TJ) the previous year. Members of the TJ editorial board organized diverse special issues on lignin, coating ,forming, and diverse papermaking and biorefinery topics, which are discussed in the following sections.
Journal articles
Adsorption of anionic dissolved and colloidal substances onto calcium carbonate fillers, TAPPI JOURNAL, July 2000, Vol. 83(7)
Adsorption of anionic dissolved and colloidal substances onto calcium carbonate fillers, TAPPI JOURNAL, July 2000, Vol. 83(7)
Journal articles
A laboratory method for biaxial straining of paper during drying, TAPPI JOURNAL, July 2000, Vol. 83(7)
A laboratory method for biaxial straining of paper during drying, TAPPI JOURNAL, July 2000, Vol. 83(7)
Journal articles
Impact of the cluster rule on the cost competitiveness of the u.s. papermaking industry in the global market, TAPPI JOURNAL, September 2000, Vol. 83(9)
Impact of the cluster rule on the cost competitiveness of the u.s. papermaking industry in the global market, TAPPI JOURNAL, September 2000, Vol. 83(9)
Journal articles
89APR177
MD microstriations in paper: a two-sided shrinkage phenomenon?, TAPPI JOURNAL April 1989
Journal articles
Use of enzymes for reduction in refining energy - laboratory
Use of enzymes for reduction in refining energy - laboratory studies, TAPPI JOURNAL, November 2006
Journal articles
Predictive advisory solutions for chemistry management, control, and optimization, TAPPI Journal March 2025
ABSTRACT: Process runnability and end-product quality in paper and board making are often connected to chemistry. Typically, monitoring of the chemistry status is based on a few laboratory measurements and a limited number of online specific chemistry-related measurements. Therefore, mill personnel do not have real-time transparency of the chemistry related phenomena, which can cause production instability, including deposition, higher chemical consumption, quality issues in the end-product and runnability problems. Machine learning techniques have been used to establish soft sensor models and to detect abnormalities. Furthermore, these soft sensors prove to be most useful when combined with expert-driven interpretation. This study is aimed at utilizing a hybrid solution comprising chemistry and physics models and machine learning models for stabilizing chemistry-related processes in paper and board production. The principal idea is to combine chemistry/physics models and machine learning models in a fashion close to white box modeling. A cornerstone in the approach is to formulate explanations of the findings from the models; that is, to explain in plain text what the findings mean and how operational changes can mitigate the identified risks. The approach has been demonstrated for several different applications, including deposit control in the wet end, both raw water treatment and usage, and wastewater treatment. This approach provides mill personnel with knowledge of identified phenomena and recommendations on how to stabilize chemistry-related processes. Instead of using close to black box machine learning models, a hybrid solution including chemistry/physics models can enhance the performance of artificial intelligence (AI) deployed systems. A successful way of gaining the trust from mill personnel is by creating a plain text explanation of the findings from the hybrid models. The correlation between the likelihood of a phenomena and disturbance and the explanations are derived and validated by application and chemistry and physics experts.
Journal articles
Research on an energy model for X-ray measurement of paper ash content using COMSOL, TAPPI Journal May 2025
ABSTRACT: Ash content is one of the critical quality parameters in papermaking production. Traditional 55Fe radioactive sources used for online ash content measurement have a short lifespan and high costs, while offline methods such as the combustion method or chemical analysis are time-consuming. Using an X-ray tube as the radiation source, continuous X-ray measurement offers advantages such as being rapid, non-destructive, and cost-effective. In this study, COMSOL software was employed to simulate the measurement process and establish an energy attenuation model for X-ray measurement of paper ash content. The model simulates the energy attenuation of X-rays before and after transmission through four materials: calcium carbonate (CaCO3), titanium dioxide (TiO2), wood-based plant fibers, and paper samples filled with CaCO3. The absorption coefficients of paper samples with varying ash content were investigated using the model and compared with experimental results obtained from continuous X-ray measurements. The results indicate that the proposed energy simulation model can reduce the measurement error of paper ash content by 1%, significantly enhancing the reliability and accuracy of ash content measurement.
Journal articles
Forming fabric weave-scale variations in paper filler content, TAPPI JOURNAL November 2013
Forming fabric weave-scale variations in paper filler content, TAPPI JOURNAL November 2013