Open this publication in new window or tab >>2019 (English)In: BioResources, E-ISSN 1930-2126, Vol. 14, no 3, p. 5451-5466Article in journal (Refereed) Published
Abstract [en]
Frequently sampled process data from a conical disc refiner and infrequently sampled pulp data from a full scale chemi-thermomechanical pulp (CTMP) mill were evaluated to study autocovariance with aspects of potential dynamic modelling applicability. Two trial measurements with an online pulp analyzer at decreased sampling intervals were performed. For variability analysis, time-series containing up to one day of operational data were used. At the chip refiner, the clearest significant autocovariance was identified for the specific electricity consumption, based on the longer sequences. Most of the estimated pulp properties indicated low or non-significant autocovariance, limiting applicability of a specific dynamic model. A mill trial was conducted to investigate the impact from an increase in the conical disc gap on the specific electricity consumption and the resulting freeness. The response time from the gap change in the refiner to measured change in freeness was estimated at 19 min, which was approximately the hydraulic residence time in the latency chest. The relevance of this study lies in applicability of mill-data-driven modelling to capture the dynamics of a specific refining process. Through mill trials the sampling speed of pulp properties was more than doubled to gain insights into short term systematic variations by applying time-series-analysis.
Place, publisher, year, edition, pages
North Carolina State University, 2019
Keywords
Chemi-thermomechanical pulp (CTMP); Freeness; Dynamic modelling; conical disc refiner; Specific electricity consumption; Energy efficiency; Autocovariance
National Category
Paper, Pulp and Fiber Technology
Research subject
Environmental and Energy Systems
Identifiers
urn:nbn:se:kau:diva-65734 (URN)000473204700036 ()
Funder
Knowledge Foundation
Note
Artikeln ingick som manuskript i Ekbåges licentiatuppsats (2018) Process modelling based on data from an evaporation and a CTMP process
DOI 10.15376/biores.14.3.5451-5466
2018-01-292018-01-292024-07-04Bibliographically approved