Kraft pulp mills produce the main raw material for paper, while several waste products are generated in large quantities in the process. This review study addresses four of the main inorganic wastes formed by this industry, namely green liquor dregs (GLD), slaker grits (SG), lime mud (LM) and boiler fly ash (BFA), which are still mostly discarded in landfills. A brief overview of a typical industrial process was included to outline the waste generation points. The main chemical and physical properties are indicated for highlighting the most relevant characteristics to determine which applications may be considered in each case. An in-depth literature review allowed the identification of the main applications that have been tested mainly at the laboratory scale and some at an industrial scale. The applications are grouped into construction materials, geotechnical, environmental, agricultural and others. This assessment shows that the circular economy and the sustainable development goals of the UN are important issues for organizations in general, and the pulp mill in particular. In fact, this industry has managed to close the chemicals loops, recover energy and reduce water consumption in the process. However, the current situation of inorganic waste can still be improved if industrial applications are developed to avoid landfill.

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Multivariate methods such as partial least squares (PLS), interval PLS, and other variants are often the default option for prediction of lubricant properties based on FTIR spectra. However, other advanced analytical methodologies are also available that have not been properly tested and comparatively assessed so far. The present work focuses on the comparison of the predictive ability of four classes of analytical methods: regression with variable selection, penalized regression, latent variable regression, and tree-based ensemble methods. A data set of 62 lubricant samples for different applications was collected in Portugal. Assessed lubricant properties included kinematic viscosity (at 40 and 100 °C), viscosity index, density, total acid number (TAN), saponification number, and percentage of aromatics, naphthenics, and paraffinics. This work showed that there is no overall superior regression method and the choice is dependent on the predicted property. Density, percentage of aromatics, naphthenics, and paraffinics were well predicted (correlation between predicted and observed of 0.97–0.98). Elastic nets was the best method to predict naphthenics and density, but the former property was also well predicted by least absolute shrinkage and selection operator. Interval PLS was the method that provided better prediction of aromatics and paraffinics. TAN could be reasonably predicted by support vector regression but some clusters were observed. Saponification number and the properties related to viscosity were not satisfactorily predicted with any of the tested methods. Finally, it can be concluded that the adopted methodology is highly relevant in the field of prediction of lubricant oil properties.

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