Fibers | Free Full-Text | Pulp Particle Classification Based on Optical Fiber Analysis and Machine Learning Techniques

1. Introduction

The development of machine learning (ML) has accelerated, and its industrial applicability for, e.g., the pulp and paper industry, has increased dramatically during the last few years. These advancements, in combination with the possibility to automatically measure geometrical data from micrographs for large sets of particles, enable improved quality control, reduced global footprint and lowered manufacturing costs of materials comprising cellulose fibers [1,2].

Traditionally, pulp quality in the pulp and paper industry is assessed by manual preparation and testing of hand sheets. Although these laboratory methods are valuable, they are time consuming and cost ineffective. Furthermore, due to the time lag of such testing, it does not provide real-time quality control of production. This means that substandard production is observed and adjusted with substantial delay. Although laboratory testing of pulp will also be necessary in the future, there is a need for complementary, automated quality control systems which provide real-time pulp quality feedback. Online imaging systems for fiber distribution analysis, eventually combined with ML software, can be used to meet those requirements [1,3,4].

Pulp particle classes such as shives, fibers, vessel cells, and fine particles are established concepts in pulping and papermaking when discussing pulp quality. Indeed, size-based partitioning of pulp particles into classes is implemented in commercial optical fiber analyzers today. In this work, we set out to refine and automate pulp particle classification using ML techniques and thereby enhance the alignment between pulp particle classification of engineer users and analyzers while also supporting classification of a greater number of particle classes.

Image analysis is a technique used to extract valuable information from digital images. The process typically unfolds in several stages. It begins with the capture of high-quality digital images using a high-resolution camera, with subsequent image enhancement. The next step involves identifying and segmenting the subject of interest, such as fibers within digital images of pulp. The process concludes with the measurement of salient parameters of the specimen and interpretation of the resultant data [5].

As input to image analysis, digital images of cellulose fibers can be obtained by a variety of microscopy methods. These methods include scanning electron microscopy (SEM) and transmission electron microscopy (TEM) as well as microscopy based on optical light, X-rays, ultraviolet light (UV-vis), near-infrared light (NIR) and changes in polarized light (CPL) [3]. New advances in, e.g., computed tomography (CT) [5] have also enabled high-resolution three-dimensional images of fiber structures. The mechanical properties of the pulp are strongly affected by the complex microstructures of the fibers, which can be revealed with microscopy methods [6,7].

In this study, micrographs were obtained by optical light microscopy using fiber analysis equipment (L&W Fiber Tester plus, ABB). This equipment includes built-in image analysis software with five output parameters: fiber length, fiber width, fiber shape, area-based fibrillation and perimeter-based fibrillation [3]. These particle parameters provide pulp characterization in excellent detail to the human user. However, to exploit potentially untapped particle-level information that may be meaningful to ML algorithms, a complementary, in-house image analysis program was implemented with additional output parameters, e.g., light attenuation of the particles. ML algorithms were applied both with and without using the extra image parameters, thereby assessing whether the extra parameters could facilitate classification of different pulp particles.

ML, a subfield of artificial intelligence, is an important tool for automated analysis of big datasets. It can be seen as a collection of methods used to create algorithms which enable the computer to learn patterns from the input data [8]. The use of ML in the industry plays a major role in the advancement of the fourth industrial revolution ‘Industry 4.0’, which adopts such methods to increase efficiency in manufacturing and processing [9]. Advancements in ML have enabled a range of computational image analysis methods, which often surpass the human eye in a variety of fields [10]. With an automated characterization method, more precise and efficient quality control can be obtained. Combining ML with image analysis enables the use of online and offline measurements for improved, automated categorization of pulp and paper [1,3,11]. The potential cost reduction by implementing ML based on existing and future technologies in the paper and forest product industry is estimated to be 9.5% [12].

ML is often separated into supervised and unsupervised learning, where the former is currently more common [8]. The aim of supervised ML is to have the computer learn patterns from a labeled training set and use this to predict patterns for unlabeled data, whereas unsupervised learning considers unlabeled data. Therefore, supervised ML methods such as logistic regression, decision trees and linear model trees are often used to classify objects. Artificial neural networks (ANNs) are a type of deep learning methods which can be supervised or unsupervised. They mimic the biological nervous systems of the human brain, enabling models to process a large number of parameters and learn from experience [13]. Herein, we use different supervised MLs, including supervised ANNs, for pulp particle classification.

Even though ML combined with automated image analysis has the potential to reduce both cost and energy usage in the pulp and paper industry, this combination has not, to the best of our knowledge, been previously used to systematically classify cellulose fibers and debris in pulp, even though ML has recently been used for many other pulp and paper processing applications. Devi et al. described ways to utilize ML techniques for improving paper quality by optimizing process parameters [14], Nisi et al. applied multi-objective optimization techniques on pulp and paper processes [15], Jauhar et al. used neural network methods to improve the efficiency of Indian pulp and paper industries [16], and Narciso et al. wrote an review paper on ways to use ML tools to reduce energy consumption in industries [17]. Othen et al. described how to use ML techniques for predicting cardboard properties [18] and Parente et al. showed the ways in which Monte Carlo techniques can be used to pre-process data intended for ML-based “fault detection and diagnosis” models for pulping industries [19]. Talebjedi et al. used deep learning ML techniques to analyze the effect of different process parameters in TMP pulp mills [20] and to develop optimization strategies for energy saving in such facilities [21]. However, none of the aforementioned papers apply ML techniques directly on fiber data.

In this study, four different ML methods were assessed by comparing their ability to classify fibers and other pulp components. Pulp suspension micrographs were obtained using an optical fiber analyzer, and the micrographs were analyzed using the built-in software and an in-house image analysis software, which includes additional parameters such as the light attenuation of particles to uncover whether the extra parameters could further improve the accuracy of the classification. ML methods can be used improve the results obtained by optical fiber analysers, but do not eliminate the necessity of using such hardware.

2. Experimental Section

Thermomechanical pulp samples were extracted in Holmen Braviken pulp mill (Holmen, Stockholm, Sweden), where the raw material was a mix of 70% roundwood chips and 30% sawmill chips, both from Norway spruce (Picea abies). A chip refiner of type RGP68DD from Valmet was used and run at a specific energy of 1060 kWh/t (dry pulp). Pulp samples were extracted from the latency chest after the refiner, at a consistency of 4%, dewatered on a Büchner funnel with a 100-mesh wire, and the filtrate was recirculated once before freezing. The pulp samples were removed from the freezer one day before testing to defrost at room temperature, followed by hot disintegration according to ISO 5263-3:2023 [22].

The pulp samples were analyzed in a L&W Fiber Tester plus from ABB [23]. Fiber Tester follows relevant TAPPI/ISO standards, such as ISO-16065-2:2014 [24]. In accordance with recommendations, 100

m

L beakers of 0.100% consistency pulp suspension were used. The pulp was further diluted to 20 ppm in the analyzer. Inside the machine, the suspension was pumped through a narrow rectangular cross-section where grayscale images were captured using a digital camera with a stroboscope flash. The images were analyzed using built-in and in-house software (Section 3.1).

The built-in image analysis was set to capture all objects longer than 100

μ

m

and thinner than 75

μ

m

, and the instrument had a pixel resolution that enables detection of objects down to 6.6

μ

m

. The setting with a minimum fiber length of 0.1

m

m

was in agreement with, e.g., TAPPI-standard 271 [25]. Each identified object was exported as a separate image, and the five measured parameters (contour length, width, shape factor, perimeter-based fibrillation, and area-based fibrillation) of each particle were saved in one data file. Particle contour length

L_{c}

and width W were calculated from the measured particle area A and perimeter P. Fibers with approximately band-shaped geometry were assumed, so that

Shape factor S, which measures the straightness of the particle, was calculated as

where $L_{p}$ is the Euclidean distance between the two most distant points (endpoints) of the particle. Perimeter-based fibrillation $P_{F}$ and area-based fibrillation $A_{F}$ both correlated to the number of fibrils on the particle calculated from the light gray halo surrounding each fiber [26].

The Technical Association of the Pulp and Paper Industry (TAPPI) and the International Organization for Standardization (ISO) have defined standards for measuring many fiber properties. For instance, the measurement of average fiber length can be performed manually using TAPPI standards 232 (Fiber length of pulp by projection) [27] and 233 (Fiber length of pulp by classification) [28] or automatically using TAPPI 271 (Fiber length of pulp and paper by automated optical analyzer using polarized light) [25], ISO 16065-1 (Pulps—Determination of fiber length by automated optical analysis—Part 1: Polarized light method) [29] and ISO 16065-2 (Pulps – Determination of fiber length by automated optical analysis—Part 2: Unpolarized light method) [24]. It can be noted that many of the traditional standards for measuring fiber length, such as TAPPI 232 [27] and 233 [28], focus on calculating the (weighted) average fiber length rather than the lengths of the individual fibers, which is performed in this paper. Since our fiber tester is a device for automated optical analysis of individual fibers using non-polarized light, it follows ISO 16065-2. Both the built-in image analysis program and the new image analysis software fulfill the criteria of this standard, and since machine learning algorithms utilize ISO-certified data, the results are produced in accordance with current standards. Additional ISO standards for machine learning techniques will, however, probably be introduced in the future.

5. Discussion

5.1. Image Analysis and Data Processing

Initially, Dataset 1 had no objects wider than 75 $μ$ $m$ and no objects traversing the edges of the original pulp suspension micrographs. However, 978 of 3332 objects or 29% were classified as cropped and filtered out; these were predominantly particles with low contrast against the background, i.e., fiber wall ribbons and fines. In comparison, none of the 1391 objects in Dataset 2 were cropped. This is likely to introduce sampling bias toward dark objects in Dataset 1. Furthermore, the sample size difference causes selection bias; all available data points were used in Dataset 1, whereas Dataset 2 was the result of stratified sampling. Considering these biases in conjunction with the natural composition of the pulp, the predictors in Dataset 1 were expected to be highly skewed, which was confirmed by a significant increase in accuracy after the Yeo–Johnson transformation.

Figure 2 implies two sub-populations of shives when considering particle width distribution: one that is normally distributed around a regression line just below

W = 75 μ m

, and one smaller group of randomly scattered wider particles. The L&W Fiber Tester plus automatically discards objects with

W > 75 μ m

as shives [3]. This likely reduces the sensitivity of the shive category in Dataset 1, since the widest and most easily characterized particles were removed.

Due to the relatively small size of Dataset 2, some additional shives were included to facilitate ML, which somewhat skewed the distributions of W, $\bar{ρ}$ and F. Since the bulk of this dataset is the product of equally allocated stratified sampling, it should have few other sources of skewness, which is supported by the somewhat ambiguous results of the Yeo–Johnson transformation.

Binary encoding alters the distribution of $L_{c}$ , eliminating the perfect multicollinearity involving C, $L_{c}$ and $L_{p}$ , yielding an additional independent predictor. It also reduces bias in $\bar{ρ}$ , stemming from over-allocation of shives in stratified sampling. Finally, it reduces noise, which makes data easier to interpret and generates better and more uniform results across different types of models. A classification strategy with a stronger focus on quantitative category limits could arguably further facilitate encoding of predictors on binary or ordinal scales while improving consistency and repeatability. However, such a strategy is only recommended for pulps for which marginal distributions exhibit local minima.

5.2. Machine Learning

All four ML techniques were robust and were able to predict the desired classifications at a high accuracy. ML algorithms achieved higher accuracy when light attenuation predictors,

{\bar{ρ}}^{'}

\bar{ρ}

, were included. With transformed data, i.e., after preprocessing of Dataset 2, all methods produced better results by including light attenuation predictor

{\bar{ρ}}^{'}

. With non-transformed data, three out of four methods produced better results by including the corresponding light attenuation predictor

\bar{ρ}

. Only RNN deviated slightly from the observed positive trend, which is probably because it is more sensitive to outliers such as those observed for shives in Figure 2. However, for both RNN and FFNN, the effect of

\bar{ρ}

on non-transformed data was so small that it cannot be ruled out as a coincidence. The influence of light attenuation was only assessed for Dataset 2, since these parameters were only included in the in-house image analysis program.

For all ML methods, the general trend was that both the accuracy and the sensitivity increased when the Yeo–Johnson transformation was applied on Dataset 1 (Table 2). According to Figure 4, the methods that yielded largest improvements in accuracy when applying Yeo–Johnson transformations on Dataset 1 with light attenuation were SVM and FFNN methods. However, this trend cannot be seen for Dataset 2, for which SVM yielded lower accuracy after transformation than before. More exceptions could be observed for the RNN method and for shive and other categories, which sometimes showed a decrease in sensitivity. This trend can be correlated to the shapes of marginal distribution functions for shives and other in Appendix A (Figure A1, Figure A2 and Figure A3). These two categories exhibit more than one peak, whereas the other categories generally only have one. This indicates that shive and other were not favored by being treated as one population, whereas the remaining categories were. The RNN method, which sometimes produced results opposite to the general trend, could eventually be more sensitive to such tendencies. This unconfirmed hypothesis is corroborated by the observation that such deviating trend was not observed when (binary) transformations were applied on Dataset 2.

According to Figure 4, the binary transformation of Dataset 2 yielded highest accuracies for all four ML methods (with

{\bar{ρ}}^{'}

), reaching an accuracy of 96% with Lasso regression. For both the binary and the Yeo–Johnson transformation of Dataset 2 (with

\bar{ρ}

), the greatest improvement in accuracy was observed for FFNN.

Without transformations, the neural network methods (RNN and FFNN) performed better than the linear models (SVM and Lasso) on Dataset 1 but worse on Dataset 2. We believe that this tendency is due to the removal of objects wider than 75 $μ$ $m$ from the former and the addition of such objects to the latter dataset. After binary transformations of $\bar{ρ}$ and $L_{c}$ , this effect disappears.

The optimal kernels for Dataset 1, with and without the Yeo–Johnson transformation, were the linear kernel and the Gaussian RBF kernel, respectively. This was a consequence of the skewness of the data, as observed in Table 4.

When comparing the accuracy without the light attenuation parameter (cyan bars in Figure 4), it is a reasonable assumption that the difference observed between Datasets 1 and 2 cannot only be explained by the number of independent predictors, nor can this difference be explained by the amount of data or by the models themselves. It is rather data quality in conjunction with the number of predictors that contribute to the higher accuracy obtained with Dataset 2. At the cost of computational efficiency, the in-house image analysis model generates a larger and less skewed population of images, which allows for random, stratified sampling. In addition, the light attenuation predictor lends itself to a binary transformation, which seems to be highly conducive to ML.

5.3. Future Investigations

A suggestion for improving in-house image analysis is to adapt the polynomial functions to detect more objects and minimize the fraction of cropped images. Further investigation of adaptation could be conducted by selecting a polar coordinate system for the polynomial function in cases of re-entrant structures, or to split the polynomial into multiple polynomial segments when the variance becomes greater than some threshold value.

The category ’other’ contains objects that are of importance for the pulp and paper industry, and can therefore be divided into other industry informative categories such as fiber flake, ray cell, fibril, and pore. This would likely improve accuracy in ML, since the skew in the input would be reduced. Such subdivision would, however, require a much greater set of training data. It could also be useful to split the category shive into smaller categories in order to increase its sensitivity and thereby reach a level comparable to that of the other categories.

For all algorithms, the highest sensitivity values were obtained for Dataset 2 with binary transformations. These results indicate that methods where binary characteristics are assigned as parameters instead of numerical values, for example, multi-view clustering or crossed categorization, could improve ML for pulp and paper characterization. The possibility to use binary transformations, however, hinges on the presence of bimodal features in the marginal distributions of individual classes.

Furthermore, it could also be useful to apply ML directly to the pictures, since this would eliminate the requirement of using a separate image analysis program to process and calculate object parameters. This could be achieved using the four tested ML methods, but other techniques could also be utilized, such as the k-nearest neighbors (KNN) ML method.

6. Conclusions

All four assessed ML techniques performed very well and reached accuracies in the range of 94% to 96% when optimal settings and input data from the in-house image analysis program were used. These results confirm that (on-line) image analysis of fiber suspensions, combined with modern ML techniques, can become a fast and cost-efficient tool for the industry to assess improved quality control of fiber materials.

Data obtained with the in-house image analysis software, which included an important light attenuation parameter, generally offered improved ML accuracy compared to the dataset with conventional pulp particle characterization intended for engineer users. This is to a large extent due to the extended parameter set provided by the in-house software, which should be straightforward to include in commercial fiber analyzers to provide robust, automated particle classification.

Preprocessing of the data with either the Yeo–Johnson transformation or binary transformations also improved the accuracy for all ML algorithms and datasets. After transformation, the FFNN method displayed the highest accuracy, 81%, for the datasets obtained with the built-in software of the fiber analyzer, whereas Lasso regression showed the best accuracy, 96%, for the in-house image analysis software.

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