Value biomass composition

Correlations have been developed to obtain each one of the above candidate values, as functions of the biomass compositions and are detailed in previous publications [4-7J. The correlations used for this purpose arc presented here for the sake of completeness. Based on these, the stepwise procedure to calculate the different rating indices can be laid down as follows ... 

Photobioreactor engineering addresses optimization of the volumetric rate of biomass production, the surface rate of biomass production (with respect to the solar-energy collecting surface), and thermodynamic efficiency of the process as well as biomass composition (ie, the biomass quality). Hereafter, we mainly focus on construction of a predictive model for the volumetric rate (eg, expressed in kg, or moles of dry biomass per second and per m of the reaction volume). This is the main difficulty with assessing performance of a photobioreactor because most of the other parameters of interest can be deduced only from the value of < >, in a... 

PP is very suitable for filling, reinforcing and blending. Compoimding PP with fibrous natural polymers of biomass origin is one of the most promising routes to create natural-synthetic polymer composites. The key question is whether they are cheap filled compounds or high value, reinforced composites. It depends on the cohesion, cooperation between the synthetic PP matrix and the natural fiber. 

ER is typically between 0.2 and 0.4 for biomass gasification at lower values large amounts of tars are produced, and at higher values the composition of the gas produced is too poor in syngas (Narvaez et al., 1996). The steam/biomass ratio is limited by the stoichiometry of the gasification reaction. Over this limit, H2O is present in the product gas, which negatively affects the energy efficiency of the whole process (De Lasa etal., 2011). 

Table 8. Composition and Heating Value of Biomass, Wastes, Peat, and Coal...

The qCC>2 is often used as an indicator of whether the microbial biomass is under stress. In general, factors that decrease the size of the microbial biomass tend to increase qC02. That is, factors that cause stress to the microbial community tend to reduce its size. Other factors could also contribute to an increased qC02. For example, bacterial communities are less efficient at converting substrate C into cellular C than fungi (Sakamoto and Oba 1994) so a change in the composition of microbial biomass can alter qC02 values. 

Certainly, calculation of the metabolic quotient can reveal trends very different from those of basal respiration. As shown in Fig. 1, for the 7 land uses, trends in basal respiration were broadly similar to those for microbial biomass C and organic C. However, when the metabolic quotient was calculated, trends with land use were very different. Values were greater under sugarcane, maize and to a lesser extent annual ryegrass, than the other treatments. This suggests that the microbial community under these arable land uses is under more stress and/or has a different composition to that under the others. The most likely microbial stress under these land uses is likely to be a shortage of available substrate C. 

Uncertainties with the availability and suitability of biomass resources for energy production are primarily due to their varying moisture content, and to a lesser degree to their chemical composition and heating value. As the moisture content of biomass increases, the efficiency of thermal conversion process decreases. At some point more energy may have to be expended to dry the biomass than it contains. Uncertainties can be reduced by conducting a detailed chemical and physical analysis of the biomass sources. 

Friedl, A., Padouvas, E., Rotter, H., Varmuza, K. Anal. Chim. Acta 544, 2005, 191-198. Prediction of heating values of biomass fuel from elemental composition. 

Varmuza, K., Liebmann, B., Friedl, A. University of Plovdiv Paisii Hilendarski —Bulgaria, Scientific Papers—Chemistry 35[5], 2007, 5-16. Evaluation of the heating value of biomass fuel from elemental composition and infrared data. 

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