UBC Theses and Dissertations
Property prediction with Raman spectroscopy in the pulp and paper industry : a chemometric approach Bain, Alison
The use of online spectroscopic analysis to predict final properties of in-process pulp has the capacity to revolutionize the paper industry saving time and money in process control and quality assurance. Water has a very low Raman scattering cross section, which makes this spectroscopic probe ideal for classification typical pulps with high and varying water content. Work described in this thesis aims to develop new methods to predict pulp end product properties from the Raman spectra of in process pulps. We predict breaking length of wet and dry pulps from their Raman spectra using a laboratory Raman probe. Pulp samples were refined to five levels of refining energy by Canfor Pulp Ltd. We determined that to accurately predict breaking length, pulp spectra must first, before modelling, be sorted into groups based on refining energy. Dry pulps yield better prediction models then wet pulps. Breaking length can be determined within 10% error after discrete wavelet transform (DWT) and template oriented genetic algorithm (TOGA) preprocessing, however, industry standard is 5%. Principal component analysis (PCA) suggests that Raman measurements made on the side edge of a pulp sheet are similar to those made on the top surface. We designed and built a Raman probe set-up for the investigation of industry-standard brightness pads in a modified PulpEye sample chamber to mimic online monitoring. We fashioned a 5-axis stage, from optical positioning components, to hold an INNO spectrometer for easy alignment through a small sapphire widow in the sample holder. Spectra acquired with the INNO-PulpEye set-up predict breaking length with an accuracy similar to the spectra taken with the laboratory set-up. Utilizing DWT for background suppression and noise reduction as well as TOGA for feature selection we were able to predict breaking lengths with 6-10% error using both the lab and PulpEye sample chamber Raman probe systems. We have also investigated the possibility of predicting pulp viscosity from its Raman spectrum. We were able to predict viscosity over the full range with errors below 6% and 1% for pulps with viscosities between 500-560 mL/g.
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