A typical biomass supply chain is shown in Figure 2.4. Maximising renewable energy utilisation requires a balanced mix of primary resources, including biomass, integrated in combined supply chains with food production and waste management (Junginger et al., 2001; Raven and Gregersen, 2007). Biomass supply chains deal with harvesting, densification, drying, storage, and transportation activities. Shah and his group (Dunnett et al., 2007) presented a systems modelling framework for the simultaneous design and operations scheduling of a biomass to heat supply chain. They proposed an efficient scheduling system that is affected by the harvest yield, crop moisture content, ambient drying rates and seasonal demand. Rentizelas et al. (2009) focus on the logistics issue of biomass utilisation, especially the storage and multi-biomass supply chain optimisation. The scope of those papers involves only harvesting, transportation and storage of biomass and does not integrate the energy conversion processes into the
Primary energy carrier Secondary energy carrier
Collection and distribution point & storage
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Figure 2.4 Biomass flow in the energy supply chain (Lam et al., 2008)
There are also several biomass supply chain case studies presented such as:
i A case study of Far West Texas regional power generation systems has been presented by Becerra-López and Golding (2007). In this work the array of participating technologies has been optimised within a sustainability framework, which translates into a multi-objective optimisation problem. The problem is formulated and solved to determine supply shares for some chosen technologies based on both renewable power conversion and natural gas use. The cost based on the exergy and economic evaluations is established as primary competing factors. The deployment of renewable power technologies hypothetically follows the Gompertz Growth Model, which is constrained by exergy self sustenance.
The solution is given as a Pareto trade-off front for arrays of optimal technologies and capacities. Additionally, the sustainability of these arrays is analysed through indicators, and the current goal for renewable power technologies is discussed.
ii Junginger et al. (2001) proposed a fuel supply strategies for large-scale bio-energy projects – the electricity generation from agricultural and forest residues in North-eastern Thailand. The scope of study presented a methodology to set up fuel supply strategies for large-scale biomass conversion units (between 10 and 40 MWe), and to determine the connected risks and to minimize them. The methodology focuses (amongst others) on variations in residue quantities produced, limited accessibility of residues, utilization by other competitors and logistical risks. For each risk, possible ranges are determined and incorporated in different fuel supply scenarios which indicate how biomass quantities and prices may vary under different circumstances.
iii Freppaz et al. (2004) demonstrated a decision support system, which aim to optimise forest biomass exploitation for energy supply at a regional level. The
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geographic information system based techniques are integrated with mathematical programming methods to yield a comprehensive system that allows the formalisation of the problem, decision taking, and evaluation of effects.
The aim of this work is to assess the possibility of biomass exploitation for both thermal and electric energy production in a given area, while relating this use to an efficient and sustainable management of the forests within the same territory.
iv Berndes et al. (2003) gave several case studies of the contribution of biomass in the future global energy supply. The question how an expanding bioenergy sector would interact with other land uses, such as food production, biodiversity, soil and nature conservation, and carbon sequestration has been insufficiently analyzed in the studies. A refined modelling of interactions between different uses and bioenergy, food and materials production - i.e., of competition for resources, and of synergies between different uses—would facilitate an improved understanding of the prospects for large-scale bioenergy and of future land-use and biomass management in general.
v A study of Mississippi region with the supply chain design for biomass-to-ethanol industry has been presented by Ambarish and his group (2008). This paper models the in-bound supply chain of a biorefinery as a network design problem with additional constraints. This model takes as an input the distribution and supply of biomass (corn and corn stover), and identifies the number and size of biorefineries needed to make use of the available biomass.
vi An Integrated Biomass Supply and Logistics (IBSAL) Model has been proposed and presented by Shahab et al. (2008). IBSAL is a powerful tool for evaluating the biomass supply chain from field to biorefinery. IBSAL consists of a series of equations that calculate the collectible fraction of biomass, while tracking biomass moisture during harvest and storage, machinery performance, compositional changes, and dry matter losses. The model analyzes the effects of
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variations (annual weather patterns, variations in yield and moisture, variable biomass composition) associated with the feedstock supply.
vii Iakovou et al. (2010) gave an overview of the generic system components and then the unique characteristics of Waste biomass-to-energy supply chain management that differentiate them from traditional supply chains. Their work proceeds by discussing state-of-the-art energy conversion technologies along with the resulting classification of all relevant literature. It followed by the natural hierarchy of the decision-making process for the design and planning of waste biomass supply chain and provide a taxonomy of all research efforts as these are mapped on the relevant strategic, tactical and operational levels of the hierarchy.
The critical synthesis demonstrates that biomass-to-energy production is a rapidly evolving research field focusing mainly on biomass-to-energy production technologies.
There are several papers discussed and presented the implementation of regional clustering approach in the biomass production networks:
i Williams et al. (2008) presented a quantitative analytical method to provide delineation of agro-ecoregions in a more objective and reproducible manner, and with use of generalized crop-related environmental inputs offers an opportunity for delineation of regions with broader application. A raster (cell-based) environmental data at 1 km scale were used in a multivariate geographic clustering process to delineate agro-ecozones. Environmental parameters included climatic, edaphic and topographic characteristics hypothesized to be generally relevant to many crops. Clustering was performed using five a priori grouping schemes of 5–25 agro-ecozones.
ii Aguilar et al. (2009) utilized geo-referenced data on the location of primary wood products manufacturers in the US South to examine spatial clustering within this
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industry. A marginal analysis indicated that counties with adequate transportation infrastructure and presence of related industries were most likely to attract new primary forest products manufacturers.
iii A two levels general Bioenergy Decision System (gBEDS) for bioenergy production planning and implementation was developed by Ayoub et al. (2007). It also includes a scenario database, which is used for demonstration to new users and also for case based reasoning by planners and executers. Based on the information base, the following modules are included to support decision making:
the simulation module with graph interface based on the unit process (UP) definition and the genetic algorithms (GAs) methods (Ayoub et al., 2007) for optimal decisions and the Matlab module for applying data mining methods (fuzzy C-means clustering and decision trees) to the biomass collection points, to define the location of storage and bioenergy conversion plants based on the simulation and optimization model developed of the whole life cycle of bioenergy generation.
iv Kajikawa and Takeda (2008) demonstrated their work in a citation network analysis of scientific publications to know the current structure of biomass and bio-fuel research. By clustering and visualizing the network, these revealed their taxonomic structure. Emerging technologies are detected by analyzing the average publication year of clusters. The paper also analyzed the position of each cluster in the global structure of research.