Effect of thermal pretreatment on equilibrium moisture content of lignocellulosic biomass
Introduction
The moisture content of lignocellulosic biomass is of significant interest, when considering thermochemical conversion to fuels, e.g., by gasification or pyrolysis (Tester et al., 2005, US Department of Energy (DOE), 2008). In pyrolysis, the presence of moisture increases the yield of char, but for tar formation moisture has a dual role suppressing or enhancing depending on the pyrolysis temperature and ash content (Gray et al., 1985). A limited amount of moisture is beneficial in gasification, as the steam generated can influence the chemistry (by water gas shift reactions), increasing the Hydrogen content of the product synthesis gas. On the other hand, too much moisture increases the costs of thermochemical conversion, (Singh, 2004), since the moisture is first evaporated, rendering the process as an expensive dryer. A clear understanding of the factors influencing biomass equilibrium moisture content (EMC) will aid the commercialization of biomass conversion processes.
In general, moisture plays a fundamental role on the properties and behavior of any biologically derived material (Vasquez and Coronella, 2009). Dry biomass is much more stable and exhibits reduced rates of biological deterioration. It is well established that a change in a material’s moisture content is influenced by a change in the relative humidity surrounding the material at constant temperature (Gronvall, 2006). EMC of biomass is defined as the moisture content in the biomass which is in thermodynamic equilibrium with the moisture in the surrounding atmosphere at a given relative humidity, temperature, and pressure.
Although the temperature and relative humidity of the surrounding air are the main factors controlling EMC, it may also be affected by the variety and composition of biomass, mechanical handling and previous moisture history (Silakul and Jindal, 2002). Knowledge of the relationship between the air relative humidity and moisture content of the lignocellulosic material is essential (Cordeiro et al., 2006). Understanding moisture content is important to design and control thermochemical conversions processes and control the final quality of the products (Bellur et al., 2009).
Throughout the biofuels literature, many papers have been written on biomass pretreatment. Most have the focus of producing soluble sugars from biomass, for subsequent biochemical conversion to fuels. Mosier et al. (2005) review those technologies, including steam explosion, lime pretreatment, acid pretreatment, and ammonia fiber explosion (AFEX). However, the needs for thermochemical conversion are different, and the emphasis is on producing a solid fuel that is relatively homogenous, regardless of origin, and one with increased fuel density, in order to enhance feedstock logistics. Torrefaction is a process for pretreating biomass well suited for subsequent thermochemical conversion (Bergman et al., 2005, Funke and Ziegler, 2009, Yan et al., 2009a). The effects of two distinct thermal pretreatment technologies, i.e., wet torrefaction and dry torrefaction, on EMC are investigated in this study.
Wet torrefaction of loblolly pine was performed in hot compressed water at temperatures ranging from 200 to 260 °C. The reactor pressure was not controlled but indicated by a pressure gauge and ranged from 200 to 700 psi, approximately in accord with the water vapor pressure (Yan et al., 2009a). On the other hand, dry torrefaction, sometimes called low-temperature pyrolysis, is a process in which the biomass is heated in an inert gas environment (usually nitrogen) at temperatures ranging from 200 to 300 °C (Bergman et al., 2005). In both cases, previous researchers (Bergman et al., 2005, Kobayashi et al., 2009) indicated that the solid product has increased hydrophobicity relative to the feedstock.
In this work, we experimentally determine the EMC at relative humidities ranging from 11% to 97% at a single temperature (30 °C). The measured EMC were correlated with a recent model (Vasquez and Coronella, 2009). This particular model is based on physical and chemical principles, and offers an approach different from many other EMC models which are generally empirical. Fitting the measured data to the model gives some insight into water activity in pretreated biomass.
Section snippets
Methods
The static desiccator technique (Bellur et al., 2009) was used to measure the EMC of raw and pretreated lignocellulosic biomass (raw loblolly pine) by exposing the solid samples to constant relative humidities maintained by saturated salt solutions.
EMC modeling
Various kinds of models have been proposed, both semi-empirical and empirical. Van den Berg and Bruin (1981) report in a literature review a list of 77 isotherm models that have been applied to biological materials, including wood and other fibrous materials. They classified these into four general categories: localized monolayer sorption models, multilayer sorption models, sorption models used in polymer science, and empirical models. Generally, sorbed water is said to consist of two phases:
Results and discussion
The EMC of a sample at a particular relative humidity was calculated as follows:where We is the weight of the sample at equilibrium and Wd is the weight of bone dry sample weight. In this study, equilibrium was attained in 9–15 days. EMC measurements of raw biomass and of wet-torrefied biomass at HR = 11.3% and HR = 83.6% were duplicated, to test for reproducibility. The results are shown in Table 1. Generally, the EMC measurements are reproducible within ±0.5%.
Fig. 1 shows the
Conclusions
The EMC of pretreated biomass at 30 °C and relative humidities in the range of 11–85% were measured. The EMC of solids produced by either wet or dry torrefaction processes is decreased from that of raw biomass. The EMC of pretreated biomass by wet carbonization decreases with increasing pretreatment temperature, and the wet torrefaction process produces a more hydrophobic solid than the one produced by dry torrefaction. An EMC model was used to analyze the measured data. The physical
Acknowledgements
The authors acknowledge financial support from the US Department of Energy (Award Number: DE-FG36-01GO11082). The authors acknowledge assistance of Jason Hastings at UNR, who conducted most of the torrefaction experiments and Orion Hanbury for helping with the modeling work.
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