Handbook of Bioenergy

Energy production from biomass is an alternative and additive way to fossil fuel based energy production to reduce the dependency on limited fossil fuel sources and mitigate the harmful environmental impacts of these systems. One of the major challenges in establishing efficient renewable energy systems is the complex supply chain structure in an uncertain decision environment, various decisions to be made and different conflicting criteria/objectives. This study describes the key issues in decision making for biomass to energy supply chains such as decision levels, uncertainty and sustainability concepts. It also provides a comprehensive review and systematic classification of the current literature on decision making approaches for design, management and operation of biomass to energy supply chains. This study allows readers to identify the decision making methods that satisfy the problem specific requirements and offer a clear vision of the advances in the field.

In this chapter, we present a new model for optimal strategic and tactical planning of the bioenergy supply chain under uncertainty. We discuss specific challenges, characteristics and issues related to this type of model. The technological details, variability in supply and demand, and uncertainty in virtually all aspects of the supply chain require advanced modeling techniques. Our model provides a broad modeling approach that addresses the entire supply chain using an integrated perspective. The broad applicability of the approach is illustrated by the two cases discussed at the end of the chapter. The first case presents a forest to bioenergy supply chain in a region of the Norwegian west coast. The second case presents the miscanthus supply chain to a transformation plant in Burgundy, France and takes into consideration uncertain final demand.

The economic potentials of producing cellulosic ethanol from biowaste resources as an alternative to corn ethanol is explored in this paper. A portfolio of eight types of biowastes, including crop residues, municipal wastes, and forest residues, is considered and a multilayer biofuel supply chain system is developed based on a systems optimization technique. A case study of converting lignocellulosic biomass to biofuel in California is presented. The biowaste resources can produce up to 900 million gallons of ethanol per year. Through smart modeling of the biofuel supply chain in an integrative manner, a low delivered ethanol cost can be achieved at $1.85 for the use of near-term (2015) conversion technology and $1-1.1 per gallon for the use of mid-term (2015–2025) conversion technology.

In this research, the logistics alternatives of Finnish forest industry under increasing imports of Russian wood are discussed. The paper show that the transportation strategy of a third party logistic provider (3PLs) used in the railway has large implications for the logistics of imported wood from Russia. In the primary data used in this research, the basic scenario described the railway transportation strategy of 3PLs at 2011, before Russia’s World Trade Organization (WTO) membership. The alternative scenarios described the strategies of 3PLs for changed wood transportation and storing needs of imported Russian wood under WTO. In the alternative scenarios also the railway wagon rotation was adjusted to reach the global optimum strategy for more efficient logistics of plants. After adjusting merely 3PLs’ wagon traffic flow, the global optimum was not reached, since the reduction of empty wagons caused severe shortages of wood flow and later a need to cut down storage inventory at districts and plants increasing total logistics costs. As the conclusion, the model based on the dynamic multiple objective linear programming (DMOLP) is recommended to the supply chain parties, as the adaptation of Russian wood import to the global railway transportation environment would require collaborative logistics and integrated optimizations of wood and the wagon-traffic flows to be carried out.

A GIS-based methodology to identify the optimal locations for biomass extraction plants and biomass power plants is presented. Both agricultural land suitability criteria and logistic criteria were taken into account to select the optimal locations. Agricultural land suitability criteria were included as several independent variables of edaphic and climate conditions. A generalized additive model (GAM) was developed for estimating crop yield by using those edaphic and climate independent variables in potential zones where crops of interest are not currently grown, planted or seeded. Logistic criteria were incorporated in the model via network analysis of the available roads for the accessibility of each zone. Using a saturation approach of candidate locations, it was possible to generate a ranked list of sites for the project development. This list can be sent as input to an energy supply chain optimization model

This study presents a mathematical model that aids the design and management of a biofuel supply chain network based on the treatment of wastewater sludge generated at Pulp and Paper plants. The model presented here analyzes the supply chain performance under different biomass conversion processes, different requirements for plant location and capacities. We present a case study using data from the states of Alabama, Louisiana and Mississippi. The modes of transportation considered in this analysis are truck, rail and barge. The model identifies the location of biocrude plants, the amounts of glycerol and wood chip to purchase, the amounts of products to deliver, the amount of biodiesel to produce, the modes of transportation and the suppliers to use in order to minimize the system wide cost. Numerical analysis identifies Lincoln, Mississippi and Naheola, Alabama as two potential locations for biocrude plants.

The scarcity of fossil fuels and the environmental implications of their use has drawn increasing attention to the production of bioenergy from nonfood sources. To validate the progressive experimental research in this field, we require a credible tool that can quantify various impacts of potential biorefining processes. This chapter will demonstrate a novel decision support model that can provide comprehensive techno-economic results to various stakeholders. The framework integrates process optimization, supply chain optimization and discrete event simulation (DES) capabilities to provide a comprehensive and multi-disciplinary tool for bioenergy supply chain design following an iterative process. The tool is further enhanced by the incorporation of supply chain risk modeling to capture various uncertainties. A proof of concept case study is presented to illustrate the applicability of this framework to any given geographic region.

Co-firing biomass with a primary fuel in existing power plants is a cost effective environmental strategy. Co-firing implementations are carried out considering the impact of emissions reduction, logistic-related costs, plant efficiency and incentive savings. The literature on mathematical programming models for co-firing can be grouped in two categories: supply chain network design and determination of optimal fuel combination. Our literature review reveals that there is a need for models to show the effect of uncertainties in both of these categories. In this chapter, we carry out a traditional sensitivity analysis and propose a stochastic mixed-integer linear programming model within a single-period planning framework for a supply chain design problem which integrates biomass co-firing in existing coal plants. Purchase costs of coal, biomass and the amount of available biomass are considered as the uncertain parameters. The performance of the proposed model is evaluated by computational tests using data from the State of Mississippi. The computational results reveal that the benefit of the two-stage stochastic programming approach is marginal although a considerable uncertainty is taken into account in the model, while our sensitivity analysis shows that strategies making the co-firing option profitable under uncertainties can be revealed by the model.

The ever increasing concerns such as energy security and climate change calls for a wide range of alternate renewable sources of energy. Bioethanol produced from lignocellulosic feedstock show enormous potential as an economically and environmentally sustainable renewable energy source. In recent years considerable research has focused on the economic feasibility of lignocellulosic-based biofuel supply chains while analytical understanding of land-usage for biomass cultivation has remained limited. Switchgrass is considered as one of the best lignocellulosic feedstock for bioethanol production that can be cultivated on both marginal land with arid soil and crop land. Switchgrass cultivated on crop land normally gives twice the yield when compared with marginal land: however, the higher yield is obtained due to higher input costs. Crop lands are a finite resource and their widespread use for growing energy crops rather than food crops like corn and wheat has resulted in land-use issues such as food versus fuel debate. Therefore, cultivation of switchgrass on marginal land is being studied intensively to minimize the use of crop land for biomass cultivation. This work proposes a novel dual-objective stochastic optimization model to maximize the expected profit and simultaneously minimize usage of crop land to cultivate switchgrass for a lignocellulosic-based bioethanol supply chain (LBSC) under uncertainties of biomass supply, bioethanol demand and bioethanol sale price. The model determines the optimum allocation of marginal land and crop land for switchgrass cultivation, biorefinery locations, and biomass processing capacity of biorefineries. The e–constraint method is applied to trade-off among the competing objectives of profit maximization and land-use minimization. In order to solve the proposed stochastic model efficiently and effectively, the Sample Average Approximation method is utilized. A case study based in the state of Alabama in the U.S. illustrates the application of the proposed stochastic model. In addition, sensitivity analyses are conducted to provide insights on the important factors that impact on the profitability and land usage in the LBSC.

The biofuel production and distribution system is supported by a number of infrastructure subsystems, including agriculture, transportation, water supply, etc., that are interdependent on one another. Transportation of the bulky, low energy density biomass feedstock (and bioethanol) incurs one of the major operational costs in the biofuel supply chain. The large volume of shipping trucks imposes additional pressure on the already congested and aging transportation infrastructure, causing traffic congestion and pavement damage especially in local areas with biorefineries. The resulting transportation impacts and environmental issues may raise community resistance, and could in turn influence refinery location choice and supply chain efficiency. Therefore, the planning of biorefinery locations and biofuel logistics should be made cautiously with a long term objective of establishing a sustainable bioenergy production and distribution system. This chapter extensively discusses the interdependencies between biofuel supply chain and its supporting transportation infrastructure. We will review the methodologies that quantify the bidirectional impacts and incorporate the transportation externalities into integrated system optimization. Insights are drawn upon the studies of sustainable system design.

Calculations of energy, greenhouse gas emissions, and other environmental impacts from biofuel production often allocate impacts between biofuel and its co-products by calculating that co-products substitute for other products. We illustrate the issues of co-product allocation with a case study of corn-derived ethanol, and show that the choice of allocation procedure and parameters can significantly influence the results. This is an open issue in environmental lifecycle assessment methodology; there are research opportunities to determine co-product allocation values by using data that relate co-product utilization to land use or market changes.

This study conducts a life-cycle assessment (LCA) for a gasification process that converts wood biomass into various biofuels. The analysis is based on the mass balance and input/output of the two steps of the 10-ton biofuel production per day. By using OpenLCA, an open-source licensed LCA software package, and the NREL and databases, this LCA shows that the system producing the syngas from biomass has very similar emissions to natural gas and the gasification process is the one with the major impact potential.

Questions related to energy supply, security, environmental sustainability, and possible alternative sources are of a growing concern due to population growth and increased energy demand. The main goal of this Chapter is to present different aspects related to the availability and recovery of woody biomass as a feedstock for bioenergy production in the southern United States. To facilitate growth of an emerging bioenergy industry it is important to identify feedstocks that are appropriate for bioenergy production. Common feedstocks include logging residues, small-diameter trees, mill waste, and urban wood waste. In addition, there are many factors affecting the estimation of woody biomass feedstock available for processing. They include the intensity and frequency of thinning operations, woody biomass accessibility and recovery, soil nutrient compensation as well as availability and type of existing forest inventory data. Other potential constraints that can affect utilization of woody biomass feedstocks for bioenergy include logging and transportation costs, landowner willingness to produce and sell woody biomass, feedstock storage issues, and mill processing capacity.

Biofuel can play a crucial role in achieving energy independence goals. The success story of the Brazil’s ethanol program started in 1976 and during the past four decades it has gained remarkable achievements in terms of economic development and energy independence. The aim of this study is to show how Brazil has achieved its goal of energy independence by implementing and updating its renewable fuel policies over the past 40 years. We adopt an evolutionary policy framework to study the emergence of ethanol as an alternative fuel in Brazil. We propose a dynamic framework for technology diffusion policies that can explain why Brazil chose ethanol as an alternative fuel and how this policy affected the country’s energy market. We argue that the success of the Brazilian ethanol program is due to its flexibility and adaptability over the past four decades. In addition to this, we note that other domestic and international forces such as oil price and domestic politics have been largely responsible for the success or failure of these policies. Lastly, we discuss the key lessons from Brazil which can help the policy makers in other countries in devising a sustainable energy policy in the future.