1This article presents a computational model and some simulation results for fibrous materials such as paper. To obtain a better understanding of the influence of fibre properties on the paper structure a novel paper model was developed. This is a physically based model where paper is formed by the sequential deposition of individual fibres. The model intends to capture key papermaking fibre properties like morphology, flexibility, and collapse and process operations such as fibre deposition, network forming or densification. This model is a step forward in transverse paper modelling. In fact, it is a three dimensional model that includes the fibre microstructure, that is, lumen and fibre wall thickness, with a resolution up to 0.05 lm. To test the model validity and predictive capability, laboratory hand sheets were used to study the network formation of an office paper, mainly produced from Eucalyptus globulus bleached Kraft pulp. This paper was characterized via an experimental design that included factors such as raw material and beating degree. The resulting porous structure was characterized and the mechanical performance was assessed. The computational simulation was used to investigate the relative influence of fibre properties such as fibre flexibility, dimensions and collapsibility. The developed multiscale model gave realistic predictions and enabled us to link fibre microstructure and paper properties.Keywords: Three dimension modelling / fibre modelling / cellulosic fibre materials modelling / fibre flexibility and collapsibility / office paper case study / Schlüsselwörter: Dreidimensionale Modellierung / Fasermodellierung / Modellierung zelluloser Fasermaterialien / Flexibilität und Kollabierneigung der Fasern / Untersuchung von Büropapier /

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Generalized least-squares and maximum likelihood approaches for parameter estimation in multivariate response models have been prevalent in the chemical kinetics literature to date. In contrast, robust alternatives have received considerably less attention. These methods safeguard against possible deviations from the assumptions, such as the presence of outliers or non-normality of the random errors. We compare, through Monte Carlo simulation, the performance of the classical Box–Draper determinant criterion (ML) to those of two robust estimators: the multivariate Huber’s M-estimator and the multivariate least-trimmed squares estimator (MLTS). Although the results are not entirely conclusive, overall, we find no compelling evidence for preferring any one of the two robust methods over the conventional ML estimates. At the same time, it was unexpected to find that ML is still reasonable under mild outlier contamination and mild deviations from normality. This notwithstanding, one loses nothing by comparing ML together with MLTS to cross-check each other as a safety measure.