Measurement of the Quality of Mechanical Pulp by Machine Vision Image analysis is able to characterize pulp quality. It provides an accurate measurements for controlling pulp quality in the manufacturing of high-quality printing papers.
Highly enlarged part of a fiber. Blue lines indicate the center line and the center lines of the measurement lines. Green lines indicate outer side if the fiber wall and red lines correspond-ingly the inner side of the fiber wall. In the refining pulp, fibers are applied mechanical treatment so that their physical and chemical structure is changed which leads to a significant improvement in their paper-making potential. In the refining, fiber is less or more delaminated and fibrillated, depend-ing on the energy applied and refining conditions. Especially in the case of mechanical pulps, the control of pulp quality is necessary for producing high-quality printing papers. Better pulp quality control also leads to a more stable paper-making process with a decreased number of web breaks on the paper machine. This automatically means a decrease in the energy consumption, savings in the raw materials and a decrease in the effluent discharge from the paper mill. Thus, the better pulp quality control has multiplied effects on several factors in the society in terms of savings in the natural resources and of environmental load. Microscopy and image analysis have been applied on measuring pulp quality in research laboratories. Mostly the studies have concentrated on measuring some specific feature and efforts have been made to correlate these measurement with some process data or paper quality. The results have not been very convincing due to the small amount of data obtainable by ordinary microscopy. A step towards on-line measurements was taken by VTT Automa-tion, Laboratory of Paper Technology at Helsinki University of Technology, Valmet Automation Kajaani Ltd., and UPM-Kymmene in years 1995-1996, when a novel image analysis equipment and method for characterizing mechanical pulp fines were developed. The system can, in principle, be installed on-line and produce fines quality measures every ten minutes. Today there are few feasible methods for on-line measurement of pulp. Some laboratory methods are based on manual fiber imaging, which is laborious and do not give a sufficient number of images for statistical consideration in a reasonable time. Thus, it is obvious that an automatic imaging device should be developed. In addition, the image processing must be fast enough so that the apparatus can offer real-time information (i.e. 5-15 min). An automatic device for pulp quality would be a valuable tool for controlling the whole paper-making process, including pulp treatment, energy cost and end product quality. Image acquisition and processing.
The image processing sequence is the following: First the uneven illumination is corrected, secondly, depending on the fraction, all material or fibers are. In case of short fiber fraction the material is divided into two classes, one containing thicker flake-like particles an the other containing the rest. We call this part fibrillated mate-rial. In case of long fibres the fibers are detected and various measurement lines are fund as shown in Figure 1. The found lines are then used in the final fibre quality measurement. Results Altogether 15 pulps were fractionated. From each pulp and each fraction test sheet were made. All relevant quality measurement for these sheets were made. From each fraction 1000 images were captured and stored for the purpose of algorithm development. The development work was started with the short fiber fraction due to the good earlier results in the characterization the fines. The next figure shows the tensile index of test sheets made from the corresponding fractions as function of the fibril content calcu-lated from the images obtained from the same fraction. The corre-lation coefficients within pulp types (TMP and PGW) shown in the figure are very high.
Tensile index of test sheet made form short fiber fraction as function of fibril content calculated from images. In the next figure the tensile indexes of the test sheet made from the whole pulp are depicted against the fibril content measured from the images of short fiber fraction of the same pulp. The figure shows that within same pulp type the tensile index of can be predicted from images. This is particularly true for PGW pulp where the correlation coefficient is as high as 0.942:
Tensile index of test sheet made from whole pulp as the func-tion of fibril material calculated from images. Next figure shows modification index distributions calculated for four long fiber fractions. The figure shows that image processing can detect the effect of the treatment applied to fibers. The inter-pretation of these distributions is going on.
Modification index distributions of four long fiber fractions Conclusions Today, paper-making process is largely automated, but the pulp production and especially its quality control are still lacking of good on-line measurement equipment. Each step forward that leads to a better fiber quality control, leads also not only to the better pulp and paper quality, but also more efficient paper-making economy, and paper-making process with a decreased number of web breaks on the paper machine. This automatically means a decrease in the energy consumption, savings in the raw materials and a decrease in the effluent discharge from the paper mill. Contact information VTT AUTOMATION
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