Abstract Steam explosion (STEX) of woody biomass is an efficient pretreatment method in the production of water-resistant wood pellets. The STEX process also generates an aqueous condensate stream containing dissolved organic compounds, with furfural as the most abundant and valuable component. An industrial-scale recovery process for furfural and other organic by-products is therefore in the process of being developed and built. One challenge in the process has turned out to be the formation of solid particulate matter that can clog filters in the process unit. We have analyzed both the solid deposits and the fluids present at different points in the process unit to try to identify the origin of the particles using spectroscopic and chromatographic analysis, elemental analysis, and scanning electron microscopy.The aqueous fluids deriving from condensed steam contain furfural and other small organic molecules, with a separate low-density organic layer occurring at some points. This layer largely consists of wood extractives, typically terpenoids. In addition, a heavy organic phase comprising mostly furfural was found at one sampling point. The particles comprise a black, largely insoluble material with a H/C ratio of 0.88 and an O/C ratio of 0.26 and a very low ash content. IR spectra show a low content of C–H functional groups, and chromatographic analysis supports an interpretation that the particles are dominantly furfural-sourced humin-like polymers with adsorbed or co-polymerized terpenoids. Particle formation has been reproduced in a laboratory setting with conditions similar to those in the full-scale process.

Steam explosion breaks down the polymeric matrix and enables the recovery of valuable compounds from lignocellulosic feedstock. In the steam explosion process, biomass is treated with high-pressure steam which subsequently generates large quantities of a condensed aqueous liquid (process effluent) and a filtered aqueous liquid (filtrate) that contain furfural, 5-hydroxymethylfurfural, 5-methylfurfural, methanol, and acetic acid as major constituents. This study addresses the identification and quantification of value-added chemicals in the aqueous product streams using quantitative analytical nuclear magnetic resonance spectroscopy with water suppression. This work reports a screening study for two different types of sawdust (Norway spruce and birch) at two different scales (4 L and 10 L reactors) using different reaction temperatures (190–223 °C) and corresponding pressures (13–24 bar), with and without the addition of SO2 gas. The duration of all experiments was 8 min. The process effluents contained acetic acid, methanol, formic acid, 5-methylfurfural, and furfural. Acetic acid (0.5 g/kg dry input biomass) and furfural (1.0 g/kg dry input biomass) were more abundant than methanol, formic acid, and 5-methylfurfural for both feedstocks. The addition of SO2 increased the furfural yields, indicating more efficient hydrolysis of hemicelluloses under acidic conditions. Filtrate samples also contained 5-hydroxymethylfurfural, with the highest concentrations (5.7–6.0 g/kg dry input biomass) in the filtrates from spruce. The different feedstocks and steam explosion temperatures strongly influenced the overall yields of the target compounds, in some cases tripling the concentrations. The results can be used to improve the profit margins in a pellets and chemicals biorefinery, as demonstrated in the ArbaOne pellets plant.

Traditional claim estimation in general insurance works with accident year cohorts and development patterns. For the impending International Financial Reporting Standard (IFRS) 17 Insurance Contracts and often for reinsurance purposes, claim estimates must be split by contract year. This paper proposes to add contract year as a cohort classifier and to adjust the development patterns accordingly. To this end, we use the continuous time models of Hesselager and Norberg. Having contract year as an additional cohort classifier, display of claim estimates by contract year and/or accident year becomes a simple matter of summation across the appropriate dimensions. The continuous time model also enables us to derive mutually consistent development patterns for discrete time intervals of different length, such as years and quarters. In addition to delivering consistent development patterns in discrete time, continuous time modelling offers the advantage of requiring only a fixed number of model parameters. Although most of the derivations in this paper are explained in terms of claim numbers, the mechanics can also be applied to claim payments.