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Catalytic torrefaction using potassium carbonate (K2CO3) impregnation is a pretreatment method demonstrated to catalyze wood powder’s thermal degradation for energy use. In this study, beech wood boards were impregnated with K2CO3, with the aim to scale up from the studies on wood powder found in the literature. The beech boards were impregnated with five different concentrations and torrefied at 300◦C for four durations (5–60 min). The impregnation procedure was successful with a linear increase of K content in wood from 0.103 wt% for raw to 0.207 wt% for the 0.012 M sample. The weight loss during torrefaction increased with the increasing potassium (K) content in wood, reaching a maximum increase of 27.17% between 0.012 M and washed (no K2CO3) after 30 min. For the longest duration, the extent of the catalytic action of K decreased, similar to what is observed in wood powder. After 60 min torrefaction, potassium increased the torrefaction severity index by up to 10% and the higher heating value (HHV) by up to 55%. Potassium efficiently increasedthe fixed carbon and decreased the volatile matter to values comparable to coal by catalyzing the devolatilization during torrefaction. The atomic H/C and O/C ratios shifted to similar ratios as coal. The energy yield (EY) was above 80% for the shorter durations but dropped drastically at 30 and 60 min torrefaction. The prediction of the solid yield (SY), energy yield (EY), and enhancement factor of the HHV (EF) through an artificial neural network was robust with a fit quality R2≥0.999. The proposed method for catalytic torrefaction on wood boards was efficient and could be used prior to grinding and transportation for bioenergy production. This process could decrease the production costs of biomass fuel to compete with fossil fuels. 

Unsupervised learning makes manifest the underlying structure of data without curated training and specific problem definitions. However, the inference of relationships between data points is frustrated by the “curse of dimensionality” in high dimensions. Inspired by replica theory from statistical mechanics, we consider replicas of the system to tune the dimensionality and take the limit as the number of replicas goes to zero. The result is intensive embedding, which not only is isometric (preserving local distances) but also allows global structure to be more transparently visualized. We develop the Intensive Principal Component Analysis (InPCA) and demonstrate clear improvements in visualizations of the Ising model of magnetic spins, a neural network, and the dark energy cold dark matter ( Λ CDM ) model as applied to the cosmic microwave background.