Dalarna University's logo and link to the university's website

Cellulose fibers come in a wide range of shapes and sizes. The heterogeneity of the fiber length, width, wall thickness, curl and external fibrillation is detrimental to the mechanical performance of products such as paper and paperboard. Although micro-mechanical models of these materials sometimes incorporate features of this heterogeneity, so far there is no standardized method of fully incorporating this. We examine a large number of industrial mechanical fiber pulps to determine what information such a standardized method would have to have. We find that the method must allow for both non-Gaussian distributions and dependence between the variables. We present a method of characterizing mechanical pulp under these conditions that views the individual fiber as outcome of a sampling process from a multivariate distribution function. The method is generally applicable to any dataset, even a non-Gaussian one with dependencies. Using a micro-mechanical model of a paper sheet the proposed method is compared with previously presented methods to study whether incorporating both a varying fiber size and dependencies is necessary to match the response of a sheet modeled with measured characterization data. The results demonstrate that micro-mechanical models of paper and paperboard should not neglect the influence of the dependence between the characteristic shape features of the fibers if the model is meant to match physical experiments. © 2022 The Authors

    Cellulose fibers exhibit a wide range of shapes and sizes. This variation influences the mechanical performance of paper and paperboard by affecting the stress distribution inside the network and the degree of fiber-to-fiber bonding which is possible at a given density. However, the methods used to characterize the distribution of fiber sizes in the pulp neglect that the characteristic features of a fiber are generally not independent.

    Here, we resolve this shortcoming by fitting the fiber population to a multivariate distribution without enforcing normality or independence between the properties. The high-dimensional multivariate function is recast as a set of univariate distribution functions and a series of bivariate distributions connected by a canonical vine. 

    Using a micro-mechanical model of a paper sheet the influence of this improved characterization is investigated. Reasonable margins and a description of the dependency is shown to be superior to assuming independence even for perfectly preserved marginal distributions. This result demonstrates that micro-mechanical models of paper and paperboard cannot by assumption neglect the influence of the interdependence between the characteristic features of fibers. 

Black liquor gasification–combined cycle (BLGCC) is a new technology that has the potential to increase electricity production of a chemical pulping mill. Increased electricity generation in combination with the potential to use biomass (e.g. bark, hog fuel) more efficiently can result in increased power output compared to the conventional Tomlinson-boiler. Because the BLGCC enables an integrated pulp and paper mill to produce excess power, it can offset electricity produced by power plants. This may lead to reduction of the net-CO2 emissions. The impact of BLGCC to offset CO2 emissions from the pulp and paper industry is studied. We focus on two different plant designs and compare the situation in Sweden and the US. The CO2 emissions are studied as function of the share of recycled fibre used to make the paper. The study shows that under specific conditions the production of “CO2-free paper” is possible. First, energy efficiency in pulp and paper mills needs to be improved to allow the export of sufficient power to offset emissions from fossil fuels used in boilers and other equipment. Secondly, the net-CO2 emission per ton of paper depends strongly on the emission reduction credits for electricity export, and hence on the country or grid to which the paper mill is connected. Thirdly, supplemental use of biomass to replace fossil fuel inputs is important to reduce the overall emissions of the pulp and paper industry.

Particles in mechanical pulp show a wide variety but are commonly described using averages and/or collective properties. The authors suggest using distributions of a common bonding factor, BIND (Bonding INDicator), for each particle. The BIND-distribution is based on factor analysis of particle diameter, wall thickness, and external fibrillation of several mechanical pulps measured in an optical analyser. A characteristic BIND-distribution is set in the primary refiner, depending on both wood and process conditions, and remains almost intact along the process. Double-disc refiners gave flatter distributions and lower amounts of fibres with extreme values than single-disc refiners. More refining increased the differences between fibres with low and high BIND. Hence, it is more difficult to develop fibres with lower BIND. Examples are given of how BIND-distributions may be used to assess energy efficiency, fractionation efficiency, and influence of raw material. Mill scale operations were studied for printing-grade thermomechanical pulp (TMP), and board-grade chemi-thermomechanical pulp (CTMP), both from spruce.

This thesis presents a method of measuring the distribution of fiber bonding ability in mechanical pulp furnishes. The method is intended for industrial use, where today only average values are used to describe fiber bonding ability, despite the differences in morphology of the fibers entering the mill. Fiber bonding ability in this paper refers to the mechanical fiber’s flexibility and ability to form large contact areas to other fibers, characteristics required for good paper surfaces and strength.

Five mechanical pulps (Pulps A-E), all produced in different processes from Norway spruce (Picea Abies) were fractionated in hydrocyclones with respect to the fiber bonding ability. Five streams were formed from the hydrocyclone fractionation, Streams 1-5. Each stream plus the feed (Stream 0) was fractionated according to fiber length in a Bauer McNett classifier to compare the fibers at equal fiber lengths (Bauer McNett screens 16, 30, 50, and 100 mesh were used).

Stream 1 was found to have the highest fiber bonding ability, evaluated as tensile strength and apparent density of long fiber laboratory sheets. External fibrillation and collapse resistance index measured in FiberLab^TM, an optical measurement device, also showed this result. Stream 5 was found to have the lowest fiber bonding ability, with a consecutively falling scale between Stream 1 and Stream 5. The results from acoustic emission measurements and cross-sectional scanning electron microscopy analysis concluded the same pattern. The amount of fibers in each hydrocyclone stream was also regarded as a measure of the fibers’ bonding ability in each pulp.

The equation for predicted Bonding Indicator (BIN) was calculated by combining, through linear regression, the collapse resistance index and external fibrillation of the P16/R30 fractions for Pulps A and B. Predicted Bonding Indicator was found to correlate well with measured tensile strength. The BIN-equation was then applied also to the data for Pulps C-E, P16/R30, and Pulp A-E, P30/R50, and predicted Bonding Indicator showed good correlations with tensile strength also for these fibers.

From the fiber raw data measured by the FiberLab^TM instrument, the BIN-equation was used for each individual fiber. This made it possible to calculate a BIN-distribution of the fibers, that is, a distribution of fiber bonding ability.

The thesis also shows how the BIN-distributions of fibers can be derived from FiberLab^TM measurements of the entire pulp without mechanically separating the fibers by length first, for example in a Bauer McNett classifier. This is of great importance, as the method is intended for industrial use, and possibly as an online-method. Hopefully, the BIN-method will become a useful tool for process evaluations and optimizations in the future.

In this paper, experiences are reported from our work of developing a method for characterizing fibers with respect to their distribution in fiber bonding ability. As a first step to develop a method, fibers from two commercial TMPs have been fractionated in a four stage hydrocyclone system. The feed pulp was separated into five streams. The fiber bonding ability of R16, P16/R30 and P30/R50 Bauer McNett fractions collected from each stream were analyzed. Five different ways of evaluating fiber bonding ability showed that the fibers were separated in the hydrocyclones according to bonding ability. It was found that both fibrillation and collapse resistance index (CRI) of the fibers are required in order to well predict tensile strength of handsheets made from fiber fractions. CRI was calculated from optical measurements of cell wall thickness and fiber width. We also propose how to describe the distribution in fiber bonding ability for mechanical pulps. A method to calculate fracture toughness of handsheets based on acoustic emission is also illustrated. A more rapid method for characterizing fibers in mechanical pulps with respect to their bonding ability distribution needs to be developed in the future.

Currently, the pulp and paper industry mainly uses average values of   mechanical pulp properties to characterize fibers, while printing paper   grammages keep decreasing, making every fiber more important for   strength, surface, and structure properties. Because fibers are   inhomogeneous, average values of the whole pulp may not be enough for   proper fiber characterization. This paper reports results from the   development of a method to measure the distribution of fiber bonding   ability in mechanical pulps.   Fibers from two commercial TMPs were fractionated into five   hydrocyclone streams, using a four-stage hydrocyclone system. The fiber   bonding ability of Bauer McNett fractions R16, P16/R30 and P30/R50   collected from each stream was analyzed. Five different methods of   evaluating fiber bonding ability all showed that fibers were separated   in the hydrocyclones according to their bonding ability.   Long fiber handsheets of the highest bonding fibers had up to 2.5 times   higher tensile strength for the P16/1330 fraction than handsheets from   the lowest bonding fibers. We also found that both the degree of   fibrillation and collapse resistance index (CRI) of the fibers obtained   from optical measurements are sufficient to predict quite accurately   the tensile strength of handsheets made from fiber fractions. Further,   we propose how to describe the distribution in fiber bonding ability   for mechanical pulps, by combining some of these five different   methods. A method to calculate fracture toughness of long fiber   handsheets based on acoustic emission is also illustrated.   A more rapid way to characterize fibers in mechanical pulps with   respect to their bonding ability distribution needs to be developed in   the future. It appears that it is time to move on from characterizing   pulp suspensions and handsheet properties using conventional approaches   based on average values.