Category Archives: Bioenergy from Wood

Managing the Value Chains

A supply chain management approach is applicable to biomass to bioenergy con­version processes. The value chain approach allows sustainable linkages between individual solutions in the value chain such as the availability of resources in time and space and transport and conversion techniques to a sustainable con­cept (Fig. 1.5). As a result the value chain approach provides the basis for the comprehensive analysis of the economic and ecologic consequences of bioenergy production from wood.

The value chain illustrated in Fig. 1.5 shows the bioenergy concept of supply chain management. In this case the chain is initiated with the assessment of the current and future availability of a resource. It goes on to include sound sustainable resource management to maintain production, harvesting and logistics planning and ends with characterisation of biomass and processing. Undelaying the value chain are aspects concerning economic sustainability, socio-economic considerations and environmental impact assessments at both local and global levels (Fig. 1.6).

image006Global ecologic
life cycle

Fig. 1.5 Value chain approach for sustainable biomass production systems



Подпись: technically available

Подпись: Local ecologic impacts
Подпись: Harvesting and logistics
Подпись: Modelling & Simulation Подпись: Quality aspects of biomass


Подпись: conversion available for socio-economically available


Fig. 1.6 Different constraints for the availability of biomass

In cases where the total supply chain is not taken into account with its technical, environmental and socio-economic constraints in the planning of bioenergy projects the available resource is frequently overestimated. Another challenge to the system is the choice of the conversion processes to match the biomass supply in particular to ensure efficient utilisation of biomass for an end-purpose in mind. Most conversion plants entail large capital investment and stationary biomass is sensitive to transport
costs. As such spatial resource localisation planning is a priority at the entry point to any bioenergy project to ensure sustained fuel supply.

The choice of the best method to convert the available biomass and the scale of the operation are often decisive for the success of a project and depend on available biomass. This biomass may be different from region to region as regards quantity and quality. In addition forest based biomass may only be one part of a larger initiative to feed a bioenergy plant. On the other hand the scale of the biomass processing plant is also relevant and must be matched to the potentially available biomass. Another factor to take into account is current and future market segmentation. This concerns more traditional markets for biomass in terms of pulp and paper or particle board, which may inhibit local wood markets.

Ensuring sustainable use of woody biomass for bioenergy strongly relies on a value-chain approach to supply solutions. It is clear from the above that any bioenergy project must involve a multidisciplinary approach from remote sensing and growth and yield modelling to ascertain the extent of the resource; silviculture in terms of biological production; forest engineering and logistics to make the biomass in question available and delivered; environmental aspects and maintenance of biodiversity; socio-economics to consider communities and the financial viability and process engineering for the development of the envisaged products, and finally all relevant step analysed through a life cycle assessment of the system.

Simulation of Biomass in Growth Models

Simulation of biomass in individual tree growth models adds a level of complexity to the modelling steps described above. A seamless integration of biomass models in the simulation environment is required and one of the major objectives is to project biomass consistently with other outputs like volume. This means, for example, that simulated stem biomass should be consistent with the product of simulated stem volume and wood density. The application of allometric equations to estimate biomass from diameter and height usually leads to inconsistencies with taper and volume models as far as stem biomass is concerned. The reason is that biomass models are naturally parameterised on much smaller data sets because of the huge effort it takes to gather biomass data. In addition, the data sets are frequently not even a sub-sample of the trees sampled for to establish the taper and volume functions.

Three different avenues could be followed to achieve a consistent estimation of biomass: (1) The use of expansion factors, (2) the application of stem biomass models, or (3) the combination of taper and density models.

A straightforward method to integrate biomass in growth simulators is based on expansion factors. Biomass expansion factors (BEF) serve as multipliers to convert timber volume to biomass and are widely applied in tropical and subtropical regions (e. g. Brown et al. 1989; Chhabra et al. 2002; Dovey 2009). Since the stem is the main contributor to tree biomass this approach usually produces plausible results. However, expansion factors linked to the stem volume only are constrained to the use within the same silvicultural treatment of the parameterisation data. They are not particularly suited to adapt to changes in the relationship between stem volume and aboveground tree biomass. Examples of such a situation are trees in the understorey, that have overproportionally more stem biomass as a result of suppressed crowns or also trees growing with single-sided competition that might branch earlier and have substantially more branch biomass (Ackerman et al. 2013a). This is the reason why the use of constant BEFs without further adaptations has been criticised (Soares and Tome 2004).

A second, widely used modelling approach is based on stem biomass-models, which are constructed according the established technique of taper functions. An appropriate modelling method was introduced by Parresol and Thomas (1991) and further refined by Jordan et al. (2006). A definite advantage of such an approach is that it is easy to keep it consistent with existing taper functions and volume models. Another advantage is that stem biomass can be predicted for any cut-off diameter. But one disadvantage is that the wood density is fixed in the model and cannot be adapted easily to new growing conditions of the tree which might limit the generality of the model. It must also be noted that the crown biomass is not covered by the model and has to be modelled additionally, e. g. with allometric models.

A third alternative is the modelling approach based on linked taper and density models (Seifert et al. 2006). Stem biomass is simulated spatially based on taper — equations for volume determination and a separate wood density model, which predicts basic wood density as a function of tree ring growth. While the disadvantage is that the taper and density models might originate from different data sets this model can adapt to different silvicultural treatments and extreme growth conditions can be simulated reliably as well. However, this approach does also not cover crown biomass. A possible solution, also applicable to stem biomass models as well, is to develop a model for the estimation of biomass proportions, e. g. the ILR-transformed model described earlier, and simulate foliage and branch biomass according to the obtained proportions simply by multiplying the obtained stem biomass with the proportion. All other biomass fractions can then be determined because an absolute value for the stem biomass is fixed and the proportions are known. This way it is possible to establish a fully consistent modelling system for the simulation of volume, biomass and biomass composition with all the advantages of flexibility with respect to silvicultural treatment (including wood density changes) and flexible cut­off diameters in the tree harvesting process.

3.4 Conclusions

An important observation in biomass modelling is the manifold of different approaches. Despite numerous efforts no real or de facto standards for sampling and modelling have been established so far, which complicates a comparison of studies and might confuse people wanting to inform themselves about biomass modelling. This point is particularly valid for error budgeting. Different methods and descriptors of error are prevalent. It is no wonder that Cunia (1990) warned from accepting existing methods of error budgeting without prior reflexion. However, a diversity of methods also gives evidence for a dynamic field of science that is still in its evolution. Many aspects, such as the error budgeting and the seamless integration of models in growth and yield simulators still have scope for future improvement.

This chapter is intended to act as a structured guideline for interested researchers and practitioners through the quagmire of biomass modelling and will hopefully be a valuable support despite its many places where explanatory text could not be extended by examples but had rather to be based on references.

Biomass Sources and Harvesting Systems

This section discusses the utilization of the most economically accessible biomass resources arising from plantation forestry that include:

• Early thinnings, or dedicated energy roundwood.

• Harvesting residues (i. e., branches, tops and off-cuts).

• FT or salvaging from calamities (i. e., insects, wind, forest fires).

• Stumps and other sources.

Probably the most fundamental issue of biomass supply systems is that they con­sist of a number of stage-state steps between the standing tree and the boiler grate. Each step requires an action or selection of a method or machine that has implica­tions for the whole downstream chain, and which cannot be reversed. The choice of felling method reduces options for processing or extraction. A decision to do in-field chipping eliminates the option of conventional forwarding and transport. There are a larger number of stage-state combinations, alternating between change of location and change of form, and only the most predominant are discussed in this section.

Physical Pretreatments

Mechanical comminution (see Chap. 8) is a requirement for the biochemical conversion of biomass to alter the recalcitrance of lignocelluloses by reducing particle sizes and the degree of polymerization of the celluloses (Vidal et al. 2011). The reduction of particle size favours heat and mass transfer during pretreatment, increasing the susceptibility of biomass to hydrolysis. The ultimate particle size is determined by the feedstock type and the pretreatment method applied. In the case of woody biomass, the reduction of particle size to less than 3 mm (based on screen openings used for biomass fractionation) does not impact the digestibility further. Furthermore, the excessive reduction of particle size can have a detrimental effect on the digestibility of softwoods (Cullis et al. 2004). Different pretreatments tolerate different particle sizes: steam explosion allows greater particle sizes (large, >10 mm) followed by liquid hot water (intermediate, 1-15 mm), in turn followed by dilute acid and base pretreatments (low, <3 mm). In the case of biogas production, particle reduction increases digestibility by 5-25 % as well as reducing hydrolysis time by 23-59 % (Kratky and Jirout 2011).

During extrusion, materials are exposed to friction, heat, mixing and shearing, which results in chemical and physical modifications as the material moves through the extruding device. Moreover, the fact that extrusion is a continuous treatment supports its industrial application and subsequent commercialisation. Extrusion induces depolymerisation of cellulose, hemicellulose, lignin and protein which enhances lignocellulose conversion and the yields of biogas or alcohols (Hjorth et al. 2011). This pretreatment has been tested using pine wood chips, resulting in similar sugar recovery to conventional pretreatments but with no by-product formation (Karunanithy et al. 2012).

Thermochemical Conversion: Combustion, Torrefaction, Pyrolysis and Gasification

Ideally the ash content of biomass should be as low as possible for thermochemical conversion, as it poses many practical problems in the conversion process, ranging from slagging to corrosion of the reactor. A high nitrogen content in the biomass will lead to increased NOx emissions during combustion. Particularly undesirable in ash are Silicon (Si), Chlorine (Cl), Potassium (K) and Sulphur (S), as they form silicates, sulphates and alkali chlorides. For example, high potassium and chlorine

Table 8.6 Biomass requirements of various conversion techniques










Ash (%)


Fixed grate

Chunks, briquettes 0 < 50 mm

As high as possible


As low as possible



Sawdust, small shavings 0 < 10 mm




FB or CFB boiler

Sawdust, small shavings, low alkali

0 < 50 mm





Fixed bed

Wood chunks, briquettes etc. not too small 0 < 100 mm




Moving bed

Chips, pellets 0 < 50 mm






0 < 50 mm






Chips, pellets 0 < 70 mm










Chips, pellets 0 < 5 mm Wet chips; never dried material




As low as possible



Single stage

Chips, residues



> 50%


contents can cause slagging and corrosion in the combustion unit (Skrifvars et al. 2004). For an efficient use of biomass for power generation, the amounts of K and Cl should thus be as low as possible. The ash content is typically also inversely related to the calorific value, making biomass with high ash content undesirable for combustion and gasification. For pyrolysis, solvent-leaching can be used as a de — ashing step for either the biomass feedstock or the char product, to increase the char adsorption properties (Carrier et al. 2012). These changes in char adsorption due to de-ashing are directly related to the pyrolysis rate and volatile yield (Raveendran and Ganesh 1998; Carrier et al. 2012).

Thermochemical conversion processes are well suited to uniform, densified feedstock, such as pyrolysis products or pellets (Stephen et al. 2010). Torrefaction is an example of such a pre-treatment step to decrease the MC of the biomass, while at the same time increasing the energy density and CV. Subsequent size reduction, such as grinding, before feeding the reactor is easier with torrefied biomass, as it is more brittle.

The Lignin content has a significant impact on the suitability of biomass for thermochemical conversion. Although the high CV of lignin makes it desirable for combustion and gasification, an increased lignin content also affects the rate of thermal degradation. As a result, biomass with a higher lignin content will pyrolyse more slowly, while wood with high cellulose content can be pyrolysed faster (Gani and Naruse 2007). The positive impact of lignin on the CV, however, generally outweighs the negative effects of reduced reaction rate.

In the case of vacuum pyrolysis of wood, extractives showed an inhibiting effect on the oil yield, as they inhibited the levoglucosan formation. Removal of extractives did modify the hemicelluloses composition significantly, as reflected by the similar acetic acid yield derived from wood and extractive-free wood material (Roy et al. 1990).

Goal and Scope Definition

The definition of goal and scope represents the foundation of life-cycle assessment. As stated in the ISO standard 14041 (1998), the definition of goal refers to; ‘unambiguously state the intended application, the reason for carrying out the study and the intended audience’, i. e. to whom the results of the study are intended to be communicated. The definition of the scope of the LCA sets the boundaries of the assessment, which includes the functional unit to be used, the product system to be studied and the product system’s boundaries.

Since LCA is an iterative technique, the scope of the study may need to be modified while conducting the study as additional information is collected.

Remote Sensing Methods Estimating Bulk Biomass

2.4.1 RADAR

Several satellite-based RADAR sensors are currently available for biomass studies. There are many papers available on this subject and the reader is advised to study the references provided to gain more insight into the application of RADAR data in biomass studies. Past studies showed that RADAR backscatter does not correlate well with stand parameters like DBH, height or even basal area (Hyyppa et al. 2000). This section thus provides an overview of the technology and application to bulk biomass estimates.

Theory — confirmed by studies — indicate that backscatter signatures at different RADAR frequencies, as well as polarisations of backscatter, result from scattering from different portions of the tree canopy and the ground surface, while slope and aspect also affect backscatter. The portion of the tree with which the RADAR energy interacts is a function of wavelength. Wavelength is described in bands: L-band SAR with 24 cm wavelength (e. g., JERS-1 satellite, HH polarisation), C-band SAR with 5.6 cm wavelength (e. g. ERS-1 and ERS-2 satellites, VV polarisation), and P-band SAR (airborne sensors) are the bands used in remote sensing applications. Longer wavelengths, like L-band and P-band, penetrate deeper into the vegetation canopy, and scattering of the radiation originates from trunks and large branches. At shorter wavelengths, like C-band, scattering occurs in the upper layers of the canopy from leaves and small branches.

SAR interferometry (InSAR) provides information on the topography of the surface and on temporal changes in certain land surface properties. This technique can retrieve both structural information of natural targets, which in some cases can be converted to biophysical parameters, and digital elevation models (DEM), which can be used for geocoding and radiometrically correcting SAR backscatter imagery. Interferometry is based on the principle that two SAR sensors image an area with the same sensor characteristics from different viewing positions. The two sensors are separated by a spatial baseline. From the two signals an interferogram can be computed from which two parameters can be derived: (i) the interferometric coherence as a measure of the correlation between the two signals; and (ii) the interferometric phase, which is related to topographic height (Balzter et al. 2002).

Kasischke et al. (1995) provide detailed discussions on scattering in SAR and the components of woody biomass. They summarise that the variation in stem biomass accounts for more of the variability in RADAR signature in the P-band HH polarisation, while highest correlation in VV polarisation occurred in the case of total biomass in the canopy layer. The authors also concluded that, as RADAR frequencies increases, overall sensitivity to variations in biomass decrease, which is from L-band to P-band to C-band. L-band exhibits similar biomass scattering in HH and VV polarisation to P-band. They conclude that multi-polarisation C-, L-, and P-band SAR data can be used to estimate biomass in pine forests with total aboveground biomass up to 40 kg m“2.

Carreiras et al. (2013) reported that their study used a machine learning algorithm to establish a relationship between in situ forest aboveground biomass (AGB) in Miombo woodlands in Mozambique and L-band Synthetic Aperture Radar (SAR) backscatter intensity (gamma nought, y°) data obtained from the Phased Array L-band SAR (PALSAR) sensor, on-board the Advanced Land Observing Satellite (ALOS). The algorithm used, unique bagging stochastic gradient boosting (BagSGB), as it also allows the production of spatially explicit estimates of prediction variability and an indication of the importance of each predictor variable. Estimates of forest AGB with a root mean square error (RMSE) of 5.03 Mg ha_1, based on a tenfold cross validation, were produced with their modelling approach. Also, the coefficient of correlation (r) between the observed and predicted forest AGB value was 0.95, again based on tenfold cross validation. The variable contributing the most to this model was the mean backscatter intensity for the HH polarisation, which was explained by the low tree canopy cover characterising Miombo savannah woodlands, thus invoking scattering mechanisms associated with this polarisation (e. g., trunk-ground scattering).

Saatchi et al. (2007) used RADAR remote sensing data to map biomass distribution of the Amazon basin. The RADAR data were combined with forest inventory plot data and optical remote sensing at 1 km resolution and ranges up to 400 Mg ha_1. Sun et al. (2003) also reported results from the fusion of LiDAR and RADAR data, which hints at more accurate measurement of biomass distribution on a worldwide scale. Studies by Santoro et al. (2002) concluded that stem volume retrieval was possible up to 350 m3 ha_1 in boreal forests, but that forest density still presents a challenge and that this was not possible at the stand level.

Silvicultural Management

The emphasis of silvicultural systems is on wood products, traditionally timber, but recently extended to incorporate other wood products such as firewood and poles of various sizes (Lowore and Abbot 1995; Chidumayo et al. 1996). However, very little research has been done on harvesting rates and designing management systems for non-wood products. The argument has been that products which are seasonally available such as fruits do not require harvesting limits, and that provided no damage is done to the trees during harvesting, the impact of fruit removal is minimal (see Shackleton and Clarke 2007). However, harvesting of bark for various products including medicine, rope fibre and for making beehives can be highly destructive and result in increased tree mortality (Chidumayo et al. 1996). A number of methods for reducing the negative impacts of bark harvesting have been proposed and tested, including obtaining bark from woody material that has already been cut for other purposes and improved harvesting methods that prevent ring barking and reduce fungal infestation; substitutions such as the use of leaves to obtain medicinal products rather than bark, and the provision of timber beehives (see Geldenhuys et al. 2006). Three basic silvicultural systems have been employed in harvesting extensively managed woodlands, especially miombo woodlands, namely; coppice with standards, selection system and complete coppice or clear cutting. Employing either of these systems requires that some management mechanisms are put in place to ensure high productivity. For example, adhering to optimum diameter classes within which particular species have high coppicing effectiveness would provide for enhanced coppicing ability for many woodland species. Handavu et al. (2011) observed that Brachystegia longifolia, B. spiciformis and Isoberlinia angolensis tend to have high coppicing ability in the diameter range of 15-36 cm DBH. Additionally, increased stump heights during woodland clearing have been observed to enhance the survival of stumps and coppicing. Grundy (1990) observed a reduction in coppices in lower stumps (<5 cm) compared to higher stumps (>1.3 m) in Brachystegia spiciformis. Shackleton (2001) made a similar observation in the study of indigenous savanna tree species (Terminalia sericea) for fuelwood production. According to Shackleton (2001), this may be attributed to lower potential impacts of browsers and fires. As such, the consideration of cutting heights will provide for marked effects on the resultant coppice number and regrowth rate, and hence harvest turnover time. However, too high stumps may result in instability of coppices as they develop.

Other factors that may be considered in enhancing productivity include plant age and surface area. Furthermore, thinning of regrowth stands to reduce competition for nutrients between often many coppices may result in increased survival and vigour of coppices. Lastly, management especially of young stands should also include protection against fires and drought.

Storage and Handling

For all production systems, biomass needs to be stored and handled a number of times between the stump and the conversion plant. Good supply chain theory suggests that raw materials be kept in their original form as far down the chain as possible. This is to minimize early investments in the form of processing costs, and to allow the “manufacturer” more freedom in utilizing the resource right up until final conversion. The same idea holds true for biomass, though also for biological reasons. Comminuting biomass into chips radically increases the surface area, which together with high moisture content, provides ideal conditions for microbial activity. Exothermic respiration heats up the chip pile and results in dry matter loss due to the breakdown of cellulose and hemi-cellulose and even spontaneous combustion. Dry matter loss transforms directly to a loss in calorific value, and economic erosion. In addition to this, there is a growing awareness of the risks to human health of the clouds of fungal spores that emanate from stored chip piles. People in close contact with these, e. g., truck drivers, should wear respiratory masks when handling chips.

Roundwood is stable and dry matter loss is minimal in the first year after felling. Storage can take place at the point of felling, in bundles on the strip road, in piles at the landing or at the conversion plant. Initially, storage equates to drying, and freshly felled timber can dry to around 40 % moisture content (wet basis) within a number of weeks, depending on the ambient climate. FT felled and left on the ground have a steep drying profile, accelerated by transpiration from the leaves or needles. In spruce, transpirational summer drying is enough to allow the needles and fine fractions to fall to the ground during chipping or handling. This reduces the off take of nutrients from the site and reduces concentrations of corrosive elements (e. g., chlorine) in the fuel (Chap. 5).

FT, tree sections, tops or stemwood for energy can be stored in piles with or without cover. In Finland it is common practice to cover biomass piles with heavy duty paper sheeting as mentioned above. The benefit of doing this is dependent on the time of year the biomass is harvested, and for how long it will be stored. For a single summer, the drying profiles for covered and uncovered stacks are very similar, while biomass that is harvested late in the season will dry substantially faster under cover during autumn and winter (Filbakk et al. 2011).

Handling of biomass is accomplished with conventional forestry equipment as far as possible. Round wood for energy is no different from e. g., pulpwood. However, loose tops and branches are characterized by low densities and benefits can be gained from using adapted grapples that can handle high bulk loads. A residue grapple is made up of four separate grapple arms (tines) that are sharpened and easily penetrate a residue pile. In collecting harvesting residues, it is common to use forwarders with extendable loadbeds, or trailers with the capability to compress the load. For handling chips outside of a specialized terminal, either a front-end loader fitted with a large bucket or a bucket-grapple on a crane is commonly used. Due to the low bulk density, buckets can be over-dimensioned without the risk of exceeding the working capacity of the crane. At the conversion plant, a bunker below ground level allows for trucks to quickly tip a load that is subsequently evenly distributed or mixed with other forms of biomass with an over-head gantry, capable of operating continuously in two dimensions. In modern plants, these gantries operate autonomously, and also serve to feed chips into the boiler in-feed.

Chipping at the conversion plant offers considerable advantages, in that a large and powerful chipper can run consistently, and is well maintained by maintenance staff at the plant, resulting in very little downtime. The stationary chipper can be used to chip material of any size (bundles, stemwood, off-cuts, FT), and is fed and monitored by sophisticated systems, allowing it to operate around the clock. Also, the feedstock is stored in a natural and stable form, and only chipped on demand, reducing the need for covered or paved chip storage areas, and eliminating the risk of fire. If the plant is located near an urban area, noise pollution from centralised chippers can be experienced. Another disadvantage is the fact that all loose material needs to be transported to the plant for comminution. This can have implications on the potential extent of the procurement area.


Electricity production from lignocellulose, as well as retrieval of intermediate lignocellulose-derived bioenergy products can be performed at small-, medium — or large-scales, but smaller scales are invariably more costly. Production costs of combustion, gasification and fast pyrolysis technologies have been compared for biomass-derived electricity production in the range of 1-20 MW (Bridgwater et al. 2002). Although the costs of technologies differed significantly at small scales, pro­duction costs between technologies converged at larger scales. The abovementioned study recommended the use of pyrolysis bio-oil in diesel engines and gas turbines for direct production of electricity at small scales, although further development is required to address the corrosive effects of bio-oil on equipment. Community — level projects for the production of electricity from community-managed forests were investigated for the state of Madhya Pradesh, India (Dwivedi and Alavalapati 2008). Gasification technology was preferred for this application, and showed a five-fold reduction in electricity production costs from 5 to 100 MWe. The average cost of electricity at the consumer level produced using the largest capacity 100 kW gasifier was $0.15/kWh, which was greater than the $0.08/kWh price of electricity supplied from the grid. The study demonstrated significant differences in the hectare requirements for different types of biomass, based not only on the projected annual biomass increment per hectare, but also on the calorific value (energy content) of the various types of biomass (see Chap. 8). Such changes in biomass yields will also affect the environmental impact of bioenergy supply chains (see Chap. 11). Large-scale electricity production from forests showed an economic optimum in the range of 450-3,150 MW, indicating that a wide range of scales can be considered, rather than only a minimum production scale (Kumar et al. 2003). A similar plateau-effect in the capital investment for electricity production has been observed, showing increased costs at smaller scales (Uslu 2005; Uslu et al. 2008; Bridgwater et al. 2002). Although a minimum cost of electricity production can be achieved by increasing production scale, economic benefits should be balanced against increased biomass feedstock demand, which may increase production costs, as will be considered in the section below. As a result of limitations in biomass supply, a smaller scale of electricity production may need to be employed in certain instances. For large-scale biomass co-firing in coal-fired power plants, a location near a large deep-water harbour to the facilitate shipping of large quantities of feedstocks and bioenergy products is an important advantage for economic competitiveness (IEA ETSAP 2010).