Mid Sweden University

Continuous kraft cooking digesters face challenges affecting product quality, making it valuable to improve control through advanced techniques like near-infrared (NIR) spectroscopy, model predictive control, and machine learning models. The primary goal of this study was to use NIR spectra to predict the amount of lignin in hardwood and softwood samples. This study investigated the correlation of NIR derivative spectra with the amounts of lignin relative to other constituents, namely cellulose, hemicellulose, and water, in wood chip samples of varying chip sizes and shapes from six Nordic wood species. It employed partial least squares regression (PLSR) on the NIR data to construct a model that predicted the lignin fraction and the relative fraction of acid-soluble lignin. When trained on a group of five wood species, the model achieved a satisfactory predictive ability, striking a balance between a wide range of lignin content and a consistent chemical environment. The accuracy increased further when the model was restricted only to spruce and pine, reflecting the benefits of a more homogenous dataset. Additionally, the optimal number of latent variables was identified as two, indicating that three distinct chemical components - cellulose, lignin and water - can be effectively differentiated using NIR. Application: This NIR-based methodology is designed to be robust and applicable for pulp mills that alternate between softwood and hardwood campaigns, aiming to create models that perform well in industrial environments across various wood species.

In this study, a two-stage, low-consistency (LC) refining process at the Holmen Braviken paper mill in Sweden was examined to evaluate the relationship between energy input, fibre distributions, and pulp properties, including handsheet properties. The LC refiners used thermo-mechanical pulp based on 100 % Norway spruce, with two specific energy levels: "low" (approximate to 80 kWh/adt) or "high" (approximate to 100 kWh/adt). All four permutations of these settings were examined. Overall, higher refining efficiency (measured by the increase in tensile index per applied energy) was observed in the first LC refiner stage than in the second. To further explore the impact of LC refining, pulp particle distributions were investigated. Samples from before, between and after the two LC stages were analysed using an optical fibre analyser, which provided detailed data on length-width-curl-fibrillation distributions. The impact of LC refining on these distributions was quantified using Kolmogorov-Smirnov statistics, highlighting statistically significant changes observed in the length and curl distributions. We investigated the correlation between energy input into the LC refiners and the impact on fibre distributions and handsheet properties. These insights underscore the effectiveness of our analytical approach and its potential for refining process control in mechanical pulping, offering a method for more targeted and efficient adjustments.

We propose a fracture mechanics based method for determination of equivalent initial damage size (EIDS) distribution in as-built additively manufactured (AM) Ti-6Al-4V notched geometries. The crack growth model is shown to correctly capture the effect of the stress raisers and load ratio on the fatigue life of notched specimens. Results of constant amplitude fatigue tests on notched round bar specimens, with two different stress concentration factors and at multiple load ratios, are fitted to a three-parameter Weibull distribution. Based on the surface roughness measurements performed in this study and in the literature, maximum surface valley depth is found to be a reasonable estimation of the median EIDS. 

This study evaluates the Ottosen–Stenström–Ristinmaa (OSR) incremental fatigue damage model for predicting fatigue life in powder bed fusion with laser beam (PBF-LB) Ti6Al4V notched specimens. To fit the OSR model, we conduct constant-amplitude tension-compression fatigue tests on PBF-LB Ti6Al4V specimens with as-built surface. Our results highlight a relatively low scatter in fatigue life data for PBF-LB Ti6Al4V across different studies, a critical factor for reliable design against fatigue failure. The study suggests that the stress gradient effect is influenced by the as-built surface, which carries load differently from the target build geometry due to surface undulations. The OSR model effectively captures the characteristics of Wöhler curves for various notch geometries and stress ratios. We validate the OSR model with out-of-phase tension-torsion tests, demonstrating that it provides safe fatigue life predictions for nonproportional loads. Overall, our findings show that the OSR model offers conservative fatigue life predictions for PBF-LB Ti6Al4V, underscoring its practical utility and reinforcing the suitability of PBF-LB Ti6Al4V for aircraft applications. 

An incremental high-cycle fatigue damage model is combined with topology optimization to design structures subject to non-proportional loads. The optimization aims to minimize the mass under compliance and fatigue constraints. The fatigue model is based on the concept of an evolving endurance surface and a system of ordinary differential equations that model the local fatigue damage evolution. A recent model extension that uses a quadratic polynomial endurance function to enhance the accuracy and extrapolation capabilities, especially for non-proportional loads, is used. To enable computationally efficient design updates, an adjoint sensitivity analysis that is consistent with the state solution, requiring only a few linear solves involving the stiffness matrix is derived. Furthermore, a new compliance constraint is developed for uncorrelated, stochastic force components to take worst-case force combinations into account. Numerical examples in both 2D and 3D demonstrate that the proposed framework is able to design structures subject to non-proportional loads. 

This study focuses on the development of a compact model with improved interpretability compared to similar approaches, relating thermomechanical pulp (TMP) properties, quantified using a fiber analyzer, to Canadian standard freeness and handsheet properties. The data used in this study are obtained from TMP produced by a conical disc refiner. Utilizing the LASSO-regularized Latent Variable Regression (LASSO-LVR) model, we identified three key latent variables – representing shives content, fibrillation, and slender fines content – that accurately predict eight distinct handsheet properties. In a subsequent analysis, we investigated the linkage between refiner settings and Specific Refining Energy (SRE) to these key analyzer readings and, consequently, to handsheet properties. The inclusion of SRE as an internal state variable in the model significantly enhanced predictive accuracy, providing a foundation for more precise and energy-efficient control strategies in refining processes. 

Nanocellulose has emerged as a widely utilized building block in nanostructured materials due to its availability, sustainability, large surface area, and high stiffness and aspect ratio. The wet or dry elastoplastic properties of these materials are determined by the fibrils' stiffness, chemical properties, hemicellulose content, and the number of fibril contacts. However, the specific contributions and relative importance of each factor remain unclear. Therefore, this work was devoted to systematically comparing the material properties of gels, aerogels, and wet and dry sheets prepared from CNFs with different aspect ratios, chemical functionality, and hemicellulose content. The fibrils were prepared by chemical and mechanical processing of different pulps. By preserving the native structure as much as possible, higher aspect ratio fibrils can be obtained, which allows for the development of more mechanically robust materials. The results demonstrate that higher aspect ratios lead to more interconnected networks at a lower solids concentration, resulting in a more evenly distributed stress and longer-range stress transfer, yielding stiffer and more ductile materials. The most important finding was that the aspect ratio influences the network formation, resulting in different network topologies. The results were also compared to earlier published data and integrated into a theoretical beam-bending model for a complete elastoplastic description of the network properties, including the influence of fibril aspect ratio and chemical functionality. This information improves our understanding and description of nanofibril networks for which general models have been missing. It can be used to optimize nanofibril preparation and, hence, the resulting eco-friendly materials. 

We present a method for pulp particle characterization based on a truncated lognormal mixture (TLM) model, as motivated by size statistics of organisms. We use an optical fiber analyzer to measure the length–width distribution of kraft-cooked roundwood or sawmill sources, of chemi-thermomechanical pulp (CTMP) samples from roundwood or sawmill sources, and the same CTMP samples after kraft post-processing. Our results show that bimodal TLMs capture salient features of the investigated pulp particle distributions, by decomposition into a large-particle and a small-particle fraction. However, we find that fibers from sawmill sources, which have not undergone mechanical treatment, cannot be described by TLM, likely due to non-random sampling. Within the confines of our dataset, the TLM characterization predicts laboratory sheet properties more effectively than conventional averaging methods for pulp particle size distributions. The TLM characterization is intended as a tool for controlling the pulp production process towards higher product quality, uniformity, and energy efficiency, pending further mill trials for validation. 

In the pulp and paper industry, pulp testing is typically a labor-intensive process performed on hand-made laboratory sheets. Online quality control by automated image analysis and machine learning (ML) could provide a consistent, fast and cost-efficient alternative. In this study, four different supervised ML techniques—Lasso regression, support vector machine (SVM), feed-forward neural networks (FFNN), and recurrent neural networks (RNN)—were applied to fiber data obtained from fiber suspension micrographs analyzed by two separate image analysis software. With the built-in software of a commercial fiber analyzer optimized for speed, the maximum accuracy of 81% was achieved using the FFNN algorithm with Yeo–Johnson preprocessing. With an in-house algorithm adapted for ML by an extended set of particle attributes, a maximum accuracy of 96% was achieved with Lasso regression. A parameter capturing the average intensity of the particle in the micrograph, only available from the latter software, has a particularly strong predictive capability. The high accuracy and sensitivity of the ML results indicate that such a strategy could be very useful for quality control of fiber dispersions. 

Highly anisotropic cellulose nanofibrils can solidify liquid water, creating self-supporting structures by incorporating a tiny number of fibrils. These fibrillar hydrogels can contain as much as 99.99 wt% water. The structure and mechanical properties of fibrillar networks have so far not been completely understood, nor how they solidify the bulk water at such low particle concentrations. In this work, the mechanical properties of cellulose fibrillar hydrogels in the dilute regime from a wt% perspective have been studied, and an elastoplastic model describing the network structure and its mechanics is presented. A significant insight from this work is that the ability of the fibrils to solidify water is very dependent on particle stiffness and the number of contact points it can form in the network structure. The comparison between the experimental results and the theoretical model shows that the fibrillar networks in the dilute regime form via a non-stochastic process since the fibrils have the time and freedom to find contact points during network formation by translational and rotational diffusion. The formed, dilute fibrillar network deforms by sliding fibril contacts upon straining the network beyond its elastic limit. Our results also show that before macroscopic failure, the fibril contacts are restored once the load is released. The exceptional properties of this solidified water are exploited to host fluidic channels, allowing directed fluid transportation in water. Finally, the microfluidic channels formed in the hydrogels are tailored by the layer-by-layer technique to be interactive against external stimuli, a characteristic envisioned to be useful in biomedical applications.