Value chain optimization of forest biomass for bioenergy production: A review
Introduction
Increasing the contribution of biomass in energy generation is considered as an important step in developing sustainable communities and managing greenhouse gas emissions effectively [1]. Although the conversion and transportation of forest biomass for energy generation affect the air quality negatively [2], energy generation from forest biomass has the potential to decrease carbon emissions significantly when it substitutes fossil fuels [3], [4], [5], [6]. Converting forest biomass to energy also has the potential to recover the waste that would otherwise be disposed to landfills or be incinerated, create jobs, provide local and sustainable energy for communities, and decrease their dependency on the international fuel market. Compared to other renewable energy sources such as wind or solar, the advantage of using forest biomass for energy generation is that it can be stored and used on demand [2], [7].
Despite these advantages, there are several barriers in utilizing forest biomass as an energy source. One challenge is the complexity of forest bioenergy supply chains which is partly resulted from complexity in forest industries supply chain in general. Forest industries consist of different interrelated and interconnected sectors, products and markets [8]. As an example, the raw material of the bioenergy facilities can be supplied from final products or by-products of some other plants such as sawmills, and pulp and paper mills. Therefore, the economic situation and the production mix of these plants can directly impact the raw material availability and its quality for bioenergy production. Moreover, unlike fossil fuels, forest biomass is usually speared over large areas rather than being concentrated. Also, forest biomass is a bulky material with relatively low density (400 and 900 kg/m3 [7]), and high moisture content [3]. These characteristics contribute to high cost and complexity of forest biomass supply logistics [7]. The transportation cost could account for 50% of the total delivery cost of biomass in some cases [9] and the logistics system could comprise large number of equipment pieces and different transportation modes [2]. It is usually needed to convert forest fuel into chips before delivering it to the customer [10]. Inaccessibility of forests in some months during the year, when energy demand is quite high, raises concerns about the secure supply of biomass to energy plants. Therefore, storage, which affects the quality of material [11], is also important in this supply chain. Comminuting and storing residues can be done either in the forest, at the plant or at an intermediate point. The location of an energy plant also plays a key role in the economic performance of energy generation from forest biomass. There is variability and uncertainty in forest bioenergy supply chains due to several other factors, such as market instability, natural disasters, and policy and climate changes as well as the nature of the industry (for example heterogeneous raw material and unpredictable quality [3]). Uncertainty makes this supply chain volatile and risk vulnerable, which in turn makes the proper planning difficult. All these challenges contribute to high cost of forest bioenergy compared to other sources of energy. Utilizing more advanced technologies, for example to improve raw material quality or system efficiency, is one way to deal with some of these challenges. Another solution for improving the performance of a forest bioenergy supply chain is to optimize its design and management.
Mathematical programming models can be used to optimize these supply chains. These models are effective particularly when different parts of the supply chain, such as procurement, production, transportation, and distribution, and different decision levels, such as strategic, tactical and operational levels, are integrated. Usually, decisions regarding the supply chain design such as location, technology and capacity are made in the strategic level, while decisions related to the flow of material and production planning can be made in tactical and operational levels.
Optimization techniques have been employed for modeling supply chains in different industries including forest industries [8]. In addition to modeling the supply chain, optimization has the advantage of providing the optimum solution based on the objective function(s) defined in the model. Using optimization techniques in designing and management of forest bioenergy supply chain can result in better performance which helps making this energy source economically viable [12]. In several previous studies, optimization techniques have been employed to manage the forest bioenergy supply chain for heat, electricity and biofuels production from strategic, tactical and operational point of views. Most of these studies were deterministic and ignored uncertainty, while there are examples that included uncertainty in the supply chain models especially during the past few years. This paper reviews all of these studies.
A number of papers reviewed the literature related to different parts of the bioenergy supply chain. Jebaraj and Iniyan [13] reviewed energy models and allocated a small section of renewable energies in their paper to biomass and a section to optimization models in energy systems in general. Baños et al. [14] reviewed studies that used optimization methods in renewable and sustainable energies and dedicated a section to the bioenergy industry. These studies were not comprehensive and only provided examples from the bioenergy supply chain. Wang et al. [15] and Scott et al. [16] reviewed the multi-criteria decision making methods applied in sustainable energy decision-making and bioenergy systems, respectively. These studies did not focus on optimization and supply chain design and management problems. Johnson et al. [17] studied methods and literature on optimum location of forest biomass–biofuel facilities and did not consider other decisions in the supply chain. The potential application of geographic information system (GIS) in evaluating the feasibility of bioenergy projects was studied by Calvert [18]. As noted by the author, geographical aspects are important factors affecting the feasibility of such projects. The use of spatial data and GIS in modeling of the value chains could be helpful in location analysis, transportation cost estimation, and actual (vs. potential) feedstock availability quantification. Awudu and Zhang [19] reviewed studies that considered uncertainty in biofuel supply chains. The authors only focused on the biofuel industry and did not include deterministic cases or those related to heat and energy plants. Moreover, they considered both agricultural biomass and forest biomass which have different origin and different supply chains. The present paper is more comprehensive compared to the above mentioned review papers since it covers previous studies on optimization of forest bioenergy supply chain design and management, including those for heat, electricity and biofuels production. It also discusses the issue of uncertainty in these supply chains, its sources and the methods used for dealing with it. This paper gives a comprehensive review on the deterministic and stochastic models used for forest bioenergy supply chain in literature. The studies are categorized in two groups: (1) studies that used deterministic mathematical programming for modeling biomass supply chain in generating heat and/or electricity (mostly in district energy systems) and biofuels, and (2) studies that incorporated uncertainty in their modeling of forest bioenergy supply chain. The main focus of the reviewed studies was on optimizing the supply chain profit, while other objectives related to environmental and social issues should be considered in future studies.
Section snippets
Deterministic optimization models
The wood flow in the forest products supply chain starts from harvest areas to value added mills such as sawmills, pulp and paper mills, secondary wood products, wood pellet mills, and eventually bioenergy plants [8]. These mills produce different products, and may generate co-products (demand-driven) or by-products (secondary results of production). Forest biomass for the purpose of energy generation can be supplied from forest residues including branches and tops left in the harvest areas,
Uncertainty in the forest biomass supply chains
Uncertainty is referred to lack of information or lack of degree of belief in the validity of the information about the existing or future state of a system [63]. Uncertainty can result from measurement errors and ignorance, which is to some extent inevitable and might be reduced by further studies or investing in improved technology to acquire high quality data [64], [65]. Uncertainty may result from variability in random future events due to their inherent nature (such as feedstock
Conclusions
Energy generation from forest fuel has several advantages, most importantly in terms of mitigating adverse environmental impacts of fossil fuels. It helps societies diversify their energy sources by providing local energy for communities and potentially sell bioenergy products in the energy market. Commercialization of this energy source, however, is still under consideration. Characteristics of the forest biomass supply chain for energy production, such as unpredicted raw material quality and
Acknowledgment
The authors would like to thank the Natural Sciences and Engineering Council of Canada (NSERC) for providing the funding for this research.
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