Statistical Modelling of Temperature and Moisture Uptake of Biochars Exposed to Selected Relative Humidity of Air.

New experimental techniques, as well as modern variants on known methods, have recently been employed to investigate the fundamental reactions underlying the oxidation of biochar. The purpose of this paper was to experimentally and statistically study how the relative humidity of air, mass, and particle size of four biochars influenced the adsorption of water and the increase in temperature. A random factorial design was employed using the intuitive statistical software Xlstat. A simple linear regression model and an analysis of variance with a pairwise comparison were performed. The experimental study was carried out on the wood of Quercus pubescens, Cyclobalanopsis glauca, Trigonostemon huangmosun, and Bambusa vulgaris, and involved five relative humidity conditions (22, 43, 75, 84, and 90%), two mass samples (0.1 and 1 g), and two particle sizes (powder and piece). Two response variables including water adsorption and temperature increase were analyzed and discussed. The temperature did not increase linearly with the adsorption of water. Temperature was modeled by nine explanatory variables, while water adsorption was modeled by eight. Five variables, including factors and their interactions, were found to be common to the two models. Sample mass and relative humidity influenced the two qualitative variables, while particle size and biochar type only influenced the temperature.

Effect of torrefaction pretreatment on the pyrolysis of rubber wood sawdust analyzed by Py-GC/MS.

The aim of this study was to investigate the effect of torrefaction on the pyrolysis of rubber wood sawdust (RWS) using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Three typical torrefaction temperatures (200, 250, and 300 °C) and pyrolysis temperatures (450, 500, and 550 °C) were considered. The results suggested that only diethyl phthalate, belonging to esters, was detected at the torrefaction temperatures of 200 and 250 °C, revealing hemicellulose degradation. With the torrefaction temperature of 300 °C, esters, aldehydes, and phenols were detected, suggesting the predominant decomposition of hemicellulose and lignin. The double-shot pyrolysis indicated that the contents of oxy-compounds such as acids and aldehydes in pyrolysis bio-oil decreased with rising torrefaction temperature, implying that increasing torrefaction severity abated oxygen content in the bio-oil. With the torrefaction temperature of 300 °C, relatively more cellulose was retained in the biomass because the carbohydrate content in the pyrolysis bio-oil increased significantly.

Understanding and reducing variability of SOM neighbourhood structure.

The self-organizing map (SOM) is a nonlinear unsupervised method for vector quantization. In the context of classification and data analysis, the SOM technique highlights the neighbourhood structure between clusters. The correspondence between this clustering and the input proximity is called the topology preservation. We present here a stochastic method based on bootstrapping in order to increase the reliability of the induced neighbourhood structure. Considering the property of topology preservation, a local approach of variability (at an individual level) is preferred to a global one. The resulting (robust) map, called R-map, is more stable relatively to the choice of the sampling method and to the learning options of the SOM algorithm (initialization and order of data presentation). The method consists of selecting one map from a group of several solutions resulting from the same self-organizing map algorithm, but obtained with various inputs. The R-map can be thought of as the map, among the group of solutions, corresponding to the most common interpretation of the data set structure. The R-map is then the representative of a given SOM network, and the R-map ability to adjust the data structure indicates the relevance of the chosen network.

Enhancing the combustible properties of bamboo by torrefaction.

Bamboo has wide range of moisture content, low bulk energy density and is difficult to transport, handle, store and feed into existing combustion and gasification systems. Because of its important fuel characteristics such as low ash content, alkali index and heating value, bamboo is a promising energy crop for the future. The aim of this study was to evaluate the effects of torrefaction on the main energy properties of Bambusa vulgaris. Three different torrefaction temperatures were employed: 220, 250 and 280°C. The elemental characteristics of lignite and coal were compared to the torrefied bamboo. The characteristics of the biomass fuels tend toward those of low rank coals. Principal component analysis of FTIR data showed a clear separation between the samples by thermal treatment. The loadings plot indicated that the bamboo samples underwent chemical changes related to carbonyl groups, mostly present in hemicelluloses, and to aromatic groups present in lignin.