Title

GOALI/IUCP: Prediction of Wood Pulp K-Number with Neural Networks

Investigators

Mohamad T. Musavi, Principal Investigator; University of Maine, OronoFollow
Daniel Coughlin, Co-Principal Investigator; University of Maine, Orono

Additional Participants

Graduate Student

Miranda Jon
Junxu Li
Wenxun Huang

Undergraduate Student

Wayne Slade

Partner Organization

S. D. Warren Company

Project Period

April 1, 1996-March 31, 1999

Level of Access

Open-Access Report

Grant Number

9522981

Submission Date

6-17-1999

Abstract

Lignin holds wood fibers together, and must be removed to produce high strength pulp for kraft paper. The Kappa- or K-number indicates the degree of lignin removal by a pulping process, and is probably the key variable for measuring quality in this process. A difficulty is that it is an off-line measurement. More importantly, there is usually a four hour process delay between when raw materials enter a pulping digester and when the K-number is measured. This makes modeling and control difficult. This Grant Opportunity for Academic Liaison with Industry project uses neural network models to predict K-number as a function of a number of more readily available process parameters. This is a first step in improving the control and responsiveness of this process to changes in chip feed stock. The research team from the University of Maine and S.D. Warren Company will develop characterization and prediction models using data from an operating plant, and compare their long term predicative capability when integrated into digester operations. Throughout, seminar and workshops are part of the technology transfer and model improvement. The impact of this research will be more uniform quality of pulp, even with variable feed stock, and more uniform quality in subsequent bleaching and papermaking processes.

Rights and Access Note

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Recommended Citation

Musavi, Mohamad T. and Coughlin, Daniel, "GOALI/IUCP: Prediction of Wood Pulp K-Number with Neural Networks" (1999). University of Maine Office of Research Administration: Grant Reports. 178.
https://digitalcommons.library.umaine.edu/orsp_reports/178