Biomass yield variables

Biomass yield variables—those that affect the mass per acre delivered to the biorefinery—are particularly important since they reside at the front end of the feedstock supply chain and thus have broad impacts that extend through the entire supply chain. In fact, biomass yield affects many of the other variable categories identified in the analysis, including machinery performance and system variables (harvest window and transportation distance). Accordingly, uncertainties of biomass yield variables—particularly collection efficiency and storage dry matter losses—are the focus of the discussion that follows. 

The last thing that I want to say about competition is that it might be rewarding to consider models of it that take into account some of the non-ideal factors that frequently complicate growth of microorganisms. By non-ideal factors I mean such things as the occurrence of maintenance, variability of biomass yield, time lag of metabolic process rates in response to changes in... 

Where yield coefficients are constant for a particular cell cultivation system, knowledge of how one variable changes can be used to determine changes in the other. Such stoichiometric relationships can be useful in monitoring fermentations. For example, some product concentrations, such as CO2 leaving an aerobic bioreactor, are often the most convenient to measure in practice and give information on substrate consumption rates, biomass formation rates and product formation rates. 

A major issue for biomass as a raw material for industrial product manufacture is variability. Questions of standardisation and specifications will therefore need to be addressed as new biofuels, biomaterials and bioproducts are introduced onto the market. Another major challenge associated with the use of biomass is yield. One approach to improve/modify the properties and/or yield of biomass is to use selective breeding and genetic engineering to develop plant strains that produce greater amounts of desirable feedstocks, chemicals or even compounds that the plant does not naturally produce (Fernando et al., 2006). This essentially transfers part of the biorefining to the plant (see Chapter 2 for some example of oils with modified fatty acid content). 

Although the yields and total growth rate are useful parameters, it is the state variables like the concentrations of biomass, substrate and product, and the culture parameters like the specific rates of growth, substrate uptake, etc., that provide a complete description of the bioreactor. One attempt in estimating these variables from R and the yields consisted of integrating the governing differential equation with known initial conditions and the measured values for R and the yields (9). For a batch reactor, for example, b was estimated by integrating... 

To implement the above equations in the specific problem of estimating the state of a bioreactor employed for the propagation of a pure culture, the state and measured variables need first be identified. Of the state variables, one would certainly like to monitor the biomass and substrate concentrations, b and s, but also the specific growth rate, u, and yield with respect to the substrate, Y, which are culture parameters. Since it is not desirable to use a model for the dependence of u and Y on b and s, both of them will have to be treated as state variables. The state vector then will comprise four variables, namely, b, s, u and Y. ... 

Cell populations are normally viewed as unstructured and unsegregated their only property is to have mass. We also abbreviate biomass with X - the great unknown. Substantial losses occur in the bioprocess industry each year due to this unknown factor, specifically, the variability in inoculum cultures and, hence, in yields and productivities of production cultures [455]. 

Compositional variability can have a significant impact on biomass conversion process economics. The large effect (i.e., at least 0.30/gal ethanol) of observed compositional diversity on process economics is shown in Fig. 33.19 and is primarily due to the fact that the maximum theoretical product yield is proportional to feedstock carbohydrate content (Fig. 33.20).131 Yield is the major economic driver for the technoeconomic model used to assess the economic impact of composition on minimum product selling price,130 as can be seen from the data in Fig. 33.21. 

Both analyses merit closer scrutiny. Joos et al. (2002) have pointed out, e.g., that the relationship between forest age and wood volume (or biomass) is too variable to constrain the enhancement of growth to between 0.001% and 0.01% per year, as Caspersen et al. claimed. An enhancement of 0.1 % per year fits the data as well. Furthermore, even a small enhancement of 0.1 % per year in NPP yields a significant sink ( 2PgCyr ) if it applies globally (Joos et al., 2002). Thus, Caspersen et al. may have underestimated the sink attributable to enhanced growth. 

The reaction temperature is one of the most influential variables in thermochemical biomass conversion. Pyrolysis, steam gasification and CO gasification are endothermic processes, and an input energy is required. Therefore, a decrease in the operating temperature allows a significant saving in energy and a consequent decrease in costs. However, this temperature decrease causes lower gas yields and more tar production. 

In wood pyrolysis, it is known that several parameters influence the yield of pyrolytic oil and its composition. Among these parameters, wood composition, heating rate, pressure, moisture content, presence of catalyst, particle size and combined effects of these variables are known to be important. The thermal degradation of wood starts with free water evaporation. This endothermic process takes place at 120 to 150 C, followed by several exothermic reactions at 200 to 250°C, 280 to 320 C, and around 400 C, corresponding to the thermal degradation of hemicelluloses, cellulose, and lignin respectively. In addition to the extractives, the biomass pyrolytic liquid product represents a proportional combination of pyrolysates from cellulose, hemicelluloses. 

The liquid yields calculated by using Eq. 2 are also given in Fig. 2b. Equation 2 represents the correlation obtained by means of regression analysis. The correlation coefficient is 0.9358. However, Eqs. 1 and 2 are only valid for the experiments within the extreme range of the heating rates between 5°C/min and 140°C/min while maintaining other variable parameters to be constant, including reactor type and dimensions, biomass conditions, etc. 

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