Machine vision methods for process measurements in pulping
Strokina, Nataliya (2013-11-15)
Väitöskirja
Strokina, Nataliya
15.11.2013
Lappeenranta University of Technology
Acta Universitatis Lappeenrantaensis
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-265-494-6
https://urn.fi/URN:ISBN:978-952-265-494-6
Tiivistelmä
The papermaking industry has been continuously developing intelligent solutions to characterize
the raw materials it uses, to control the manufacturing process in a robust way, and to guarantee
the desired quality of the end product. Based on the much improved imaging techniques and
image-based analysis methods, it has become possible to look inside the manufacturing pipeline
and propose more effective alternatives to human expertise. This study is focused on the development
of image analyses methods for the pulping process of papermaking. Pulping starts with
wood disintegration and forming the fiber suspension that is subsequently bleached, mixed with
additives and chemicals, and finally dried and shipped to the papermaking mills. At each stage of
the process it is important to analyze the properties of the raw material to guarantee the product
quality.
In order to evaluate properties of fibers, the main component of the pulp suspension, a framework
for fiber characterization based on microscopic images is proposed in this thesis as the first
contribution. The framework allows computation of fiber length and curl index correlating well
with the ground truth values. The bubble detection method, the second contribution, was developed
in order to estimate the gas volume at the delignification stage of the pulping process based
on high-resolution in-line imaging. The gas volume was estimated accurately and the solution
enabled just-in-time process termination whereas the accurate estimation of bubble size categories
still remained challenging. As the third contribution of the study, optical flow computation
was studied and the methods were successfully applied to pulp flow velocity estimation based on
double-exposed images. Finally, a framework for classifying dirt particles in dried pulp sheets,
including the semisynthetic ground truth generation, feature selection, and performance comparison
of the state-of-the-art classification techniques, was proposed as the fourth contribution. The
framework was successfully tested on the semisynthetic and real-world pulp sheet images. These
four contributions assist in developing an integrated factory-level vision-based process control.
the raw materials it uses, to control the manufacturing process in a robust way, and to guarantee
the desired quality of the end product. Based on the much improved imaging techniques and
image-based analysis methods, it has become possible to look inside the manufacturing pipeline
and propose more effective alternatives to human expertise. This study is focused on the development
of image analyses methods for the pulping process of papermaking. Pulping starts with
wood disintegration and forming the fiber suspension that is subsequently bleached, mixed with
additives and chemicals, and finally dried and shipped to the papermaking mills. At each stage of
the process it is important to analyze the properties of the raw material to guarantee the product
quality.
In order to evaluate properties of fibers, the main component of the pulp suspension, a framework
for fiber characterization based on microscopic images is proposed in this thesis as the first
contribution. The framework allows computation of fiber length and curl index correlating well
with the ground truth values. The bubble detection method, the second contribution, was developed
in order to estimate the gas volume at the delignification stage of the pulping process based
on high-resolution in-line imaging. The gas volume was estimated accurately and the solution
enabled just-in-time process termination whereas the accurate estimation of bubble size categories
still remained challenging. As the third contribution of the study, optical flow computation
was studied and the methods were successfully applied to pulp flow velocity estimation based on
double-exposed images. Finally, a framework for classifying dirt particles in dried pulp sheets,
including the semisynthetic ground truth generation, feature selection, and performance comparison
of the state-of-the-art classification techniques, was proposed as the fourth contribution. The
framework was successfully tested on the semisynthetic and real-world pulp sheet images. These
four contributions assist in developing an integrated factory-level vision-based process control.
Kokoelmat
- Väitöskirjat [1037]