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BioResources
Reeb, C. W., Venditti, R., Hays, T., Daystar, J., Gonzalez, R., and Kelley, S. (2015). "Environmental LCA and financial analysis to evaluate the feasibility of bio-based sugar feedstock biomass supply globally: Part 1. Supply chain analysis," BioRes. 10(4), 8098-8134.

Abstract

Chemical production from crude oil represents a substantial percentage of the yearly fossil fuel use worldwide, and this could be partially offset by renewable feedstocks such as woody biomass and energy crops. Past techno-economic and environmental analyses have been conducted for isolated feedstocks on a regional or national scope. This study encompasses complete supply chain logistics analysis, delivered cost financial analysis, national availability, and environmental life cycle assessment (LCA) for 18 selected cellulosic feedstocks from around the world. A biochemical conversion route to monomeric sugars is assumed for estimated sugar yields and biosugar feedstock cost analysis. US corn grain was determined to have the highest delivered cost, while rice hulls in Indonesia resulted in the lowest cost of the feedstocks studied. Monomeric sugar yields from literature ranged from 358 kg BDMT-1 for US forest residues to 700 kg BDMT-1 for corn syrup. Environmental LCA was conducted in SimaPro using ecoinvent v2.2 data and the TRACI 2 impact assessment method for mid-point impacts cradle-to-incoming biorefinery gate. Carbon absorption during biomass growth contributed most substantially to the reduction of net global warming potential. Rice hulls and switchgrass resulted in the highest global warming potential, followed closely by corn and Thai sugarcane bagasse. Contribution analysis shows that chemical inputs such as fertilizer use contribute substantially to the net environmental impacts for these feedstocks.



Full Article

Environmental LCA and Financial Analysis to Evaluate the Feasibility of Bio-based Sugar Feedstock Biomass Supply Globally: Part 1. Supply Chain Analysis

Carter W. Reeb,a Richard Venditti,a,* Tyler Hays,a Jesse Daystar,b Ronalds Gonzalez,a and Stephen Kelley a

Chemical production from crude oil represents a substantial percentage of the yearly fossil fuel use worldwide, and this could be partially offset by renewable feedstocks such as woody biomass and energy crops. Past techno-economic and environmental analyses have been conducted for isolated feedstocks on a regional or national scope. This study encompasses complete supply chain logistics analysis, delivered cost financial analysis, national availability, and environmental life cycle assessment (LCA) for 18 selected cellulosic feedstocks from around the world. A biochemical conversion route to monomeric sugars is assumed for estimated sugar yields and biosugar feedstock cost analysis. US corn grain was determined to have the highest delivered cost, while rice hulls in Indonesia resulted in the lowest cost of the feedstocks studied. Monomeric sugar yields from literature ranged from 358 kg BDMT-1 for US forest residues to 700 kg BDMT-1 for corn syrup. Environmental LCA was conducted in SimaPro using ecoinvent v2.2 data and the TRACI 2 impact assessment method for mid-point impacts cradle-to-incoming biorefinery gate. Carbon absorption during biomass growth contributed most substantially to the reduction of net global warming potential. Rice hulls and switchgrass resulted in the highest global warming potential, followed closely by corn and Thai sugarcane bagasse. Contribution analysis shows that chemical inputs such as fertilizer use contribute substantially to the net environmental impacts for these feedstocks.

Keywords: Biomass supply feasibility; Supply chain analysis; Life cycle assessment; Delivered cost

Contact information: a: Department of Forest Biomaterials, North Carolina State University, Campus Box 8001, NCSU Campus, Raleigh, NC 27695 USA; b: Center for Sustainability and Commerce, Nicholas School of the Environment, Duke University, Durham, NC 27708;

*Corresponding author: Richard_Venditti@ncsu.edu

INTRODUCTION

Bio-based chemicals are poised to play an integral role in the chemical industry at large and to contribute to decreasing the net climate change impacts through reductions in the use of petroleum feedstocks for chemical production. Production of non-fuel chemicals from crude oil currently represents roughly 5.5% of petroleum use in the U.S. (EIA 2015). A few key factors to consider when commercializing the products of such a chemical production system include chemical product choice, conversion pathway, the location of the biorefinery, biorefinery scale, and feedstock choice. These scenario conditions are instrumental to the feasibility of the modeled production system and competitiveness of a bio-based product entering an existing market. Many academic and industry studies have analyzed end product choice (Jang et al. 2012; Liao and Hu 2012), compared conversion pathways (Baskar et al. 2012; Shabbir et al. 2012; Tay and Ng 2012), optimized biorefinery location (Stephen et al. 2013), and supply chain logistics (Akgul et al. 2012; Awudu and Zhang 2012; Čuček et al. 2012), and have determined the most appropriate biorefinery scale (Argo et al. 2013). Other biomass-to-bioproducts studies have been conducted (Kim et al. 2011), though typically for a single country (Gonzalez-Garcia et al. 2009; Yu and Tao 2009; Stephen et al. 2010; Gonzalez et al. 2011; Daystar et al. 2014), for limited feedstock options (Giarola et al. 2011; You et al. 2012), or for other than a biorefinery scale (U.S. DOE 2001).

In the literature, there is an explicit gap in models that practically and objectively compare biomass feedstocks in an integrated manner, including technical, financial, and environmental concerns for a bio-based chemical refinery across multiple continents, irrespective of conversion pathway. This research would be helpful for those intending to construct and operate a biomass-to-monomeric sugar biorefinery to understand the impact of biomass type, biorefinery scale, location, and other parameters on the feasibility of successful biosugar commercialization. Additionally, a complete financial analysis would identify major cost drivers and a single feedstock delivered cost per bone-dry metric tonne (BDMT) for pertinent biomass feedstocks, biorefinery locations (country), and biorefinery scales.

Herein, we compared 18 biomass feedstocks from three different continents, calculated the delivered cost and environmental impacts per BDMT, provided estimated feedstock-specific monomeric sugar yields assuming a biochemical conversion process, and estimated the regional biomass availability. While the range of biomass feedstocks surveyed herein is by no means exhaustive, this study includes those feedstocks most commonly explored in the literature. The comparison of these biomass feedstocks using such measures enabled the authors to compare feedstocks objectively, to identify the parameters of each feedstock supply chain that could be optimized, and to discuss the realistic feasibility of commercialization of a bio-based economy.

METHODS

Biomass feedstock supply chain models were developed to determine the techno-economic and environmental feasibility of supplying a centralized biorefinery with biomass for a biomass-to-sugar production platform. Feedstocks analyzed were chosen based on preliminary research that indicated high potential availability and adequacy for conversion (Table 1). A biorefinery production scale of 500,000 BDMT yr-1 was chosen for analysis (Daystar et al. 2014; Reeb et al. 2014). The time horizon for supply chain and financial analysis was thirty project years and environmental impacts were analyzed on a 100-year time frame. US dollar values are presented as inflation-adjusted 2014 dollars.

The technical results include the form of delivery, biomass density, embodied energy content, chemical composition, and moisture content. The transport distances were calculated based on estimates of covered area and feedstock yield per hectare. The delivered cost includes the cost of biomass purchase or production, the cost of loading, transport to the biorefinery, and storage, as well as yield losses due to biomass degradation during storage. Environmental impacts of the cradle-to-gate feedstock supply chain were calculated by modeling necessary chemical, fuel, fertilizer, herbicide, pesticide and irrigation inputs and wastes for the establishment, maintenance, harvest, collection, loading, transport, and storage of the biomass feedstocks prior to the biorefinery gate (Khanchi 2012). The system boundary includes upstream and downstream impacts of mass and energy inputs, but no infrastructure impacts. Mass allocation was used for all scenario co-products (Appendix Table A3, Reeb et al. 2014).

Table 1. Overview of Biomass Feedstocks Chosen for Analysis, the Country Assumed for Each Biomass Type, and the Primary Literature Sources Used for Data Collection

*Indicates a residue co-product biomass type

Feedstock Supply Chains

Excel-based feedstock supply chain models were used to systematically model biomass production inputs, feedstock characteristics, and supply chain parameters. All data were collected from literature as referenced in Table 1. In order to facilitate more objective comparisons between scenarios, feedstock supply chains were separated into life cycle stages, including: land use change, establishment, maintenance, harvest, transportation, and storage. Feedstocks classified as ‘residues’ do not include land use change, establishment, maintenance or harvest life cycle stages as these impacts and costs are allocated to the main product of biomass production, though collection of the residues was modeled. The life cycle stages, major inputs and outputs to the system, and system boundary for each feedstock are outlined in Fig. 1.

Fig. 1a. Supply system scope and boundary for corn grain, corn syrup, corn stover, and Genera corn stover

Fig. 1b. Supply system scope and boundary for softwoods, US eucalyptus, and Brazilian eucalyptus. Adapted from Daystar et al. (2014)

Fig. 1c. Supply system scope and boundary for unmanaged hardwoods, forest residues, and Indonesian rice hulls. Adapted from Daystar et al. (2014).

Fig. 1d. Supply system scope and boundary for switchgrass, sweet sorghum, Genera biomass sorghum, and Genera biomass sorghum. Adapted from Daystar et al. (2014).

Fig. 1e. Supply system scope and boundary for Malaysian empty fruit bunches

Fig. 1f. Supply system scope and boundary for Thai sugarcane bagasse

Fig. 1g. Supply system scope and boundary for Brazilian sugarcane and Brazilian sugarcane bagasse

Delivered Cost

The major outputs of the supply chain analysis include delivered cost and the feedstock production life cycle inventory (all material and energy consumption and production along with emissions). Delivered cost was calculated as the sum of establishment, maintenance, harvest, biomass purchase, loading and transportation, as applicable for each feedstock. The bases of feedstock cost were US$ per BDMT of biomass delivered, per metric tonne of carbohydrates delivered, per million British Thermal Units (MBTU) delivered, and per metric tonne of monomeric sugars subsequently produced. A more complete discussion of the methodology used for calculating the delivered cost is provided by Daystar et al. (2014) and Reeb et al. (2014).

Life Cycle Assessment

Greenhouse gas (GHG) accounting was accomplished through the use of a carbon balance and reported using a carbon dioxide equivalency (CO2-eq.) based upon the Inter-governmental Panel on Climate Change (IPCC 2013) 100-year timeframe characterization factors for equivalency between CO2 and other GHG molecules to the CO2 baseline impact factor of 1.00. With respect to GHG accounting, plant growth was treated as a negative emission based on the proximate and ultimate analysis of each biomass type, and assuming a 3.667 carbon to CO2 stoichiometric balance (Daystar et al. 2014; Reeb et al. 2014). The bases of analysis for GHG accounting and for life cycle assessment include a mass basis (per BDMT), a carbohydrate basis (per MT carbohydrates), and a biosugar basis (per MT monomeric sugar).

In addition to GHG accounting for the cradle-to-gate biomass feedstock life cycles, a full life cycle inventory (LCI) was developed and the life cycle impact assessment (LCIA) was conducted using SimaPro 7.3 (PRé 2013), ecoinvent v2.2 (Frischknecht et al. 2005), and the LCA methodology outlined by the International Organization for Standardization (ISO 2010). In order to maintain a basis for comparison between the feedstocks analyzed, the Tool for the Reduction and Assessment of Chemical and other Environmental Impacts 2, version 3.01 (TRACI, Bare et al. 2002) was used to consistently calculate mid-point environmental impacts for the different biomass types (Table 2).

Table 2. Table of TRACI Impact Categories and Acronym Used

TRACI was used for all LCAs because it is of great importance to compare feedstocks consistently, though not all feedstocks are produced in the US and non-US LCI data was used for non-domestic feedstock supply models. Details about the GHG accounting method used and the LCA method, TRACI impact assessment method, and other parameters of the environmental assessment were outlined in detail by Reeb et al. (2014). Mass allocation data for coproducts are described in Appendix Table A3 and Appendix Figure A1.

RESULTS AND DISCUSSION

Supply Chain Analysis

The supply chain logistics for eighteen biomass feedstocks of interest for the potential bio-based economy were modeled at commercial scale. The characteristics of the selected biomass types that contribute to their selection include high carbohydrate content, relatively high yield, low cost, and sufficient availability (existing or projected) for the proposed biorefinery scale of 500,000 bone-dry metric tonnes (BDMT). Relevant assumptions about the analyzed biomasses and the modeled supply chains are further detailed in Table 3. A breakdown of the feedstock dry-mass composition is provided in Fig. 2 and in the Appendix (Table A2). These supply chain assumptions are important to take into account when comparing the biomass feedstocks because differences in delivered cost between feedstocks can likely be explained by yield differences, transport distances, required storage due to harvest window differences, covered area, and other factors.

Another factor which may impact the appropriateness of a biomass type for commercial-scale biorefinery feedstock supply is the availability of this feedstock within a financially-feasible transportation distance. In the case of some North American feedstocks the covered area is 10%, which, when coupled with low yields such as for unmanaged hardwoods, can contribute to very high maximum transportation distances. Assumptions about covered area and transport distance can be found in Table 3 and the results of the availability study in Table 4.

Fig. 2. Biomass feedstock composition on a dry-mass basis for the biomass types analyzed

Composition and supply chain logistics vary greatly for the various biomass types, as shown in Table 3 and Fig. 1. Other important factors and assumptions drawn from the literature include transportation distance (Gonzalez et al. 2011; Daystar et al. 2014; Reeb et al. 2014), a 1.31 tortuosity factor (Ravula 2007; Sultana and Kumar 2014), compositional analysis (Reeb et al. 2014; Daystar et al. 2015), and moisture content (Daystar et al. 2013). National biomass feedstock availability was estimated from literature and national agricultural production databases for each feedstock in each country of analysis (Table 4). Other important sources used throughout this study include: Allan et al. (2005), Rausch and Belyea (2006), Nguyen and Gheewala (2008), Lois-Correa et al. (2010), Couto et al. (2011), Prasera-A and Grant (2011), Shinners et al. (2011), Thao et al. (2011), Bolin (2012), Cavalett et al. (2012), Sakdaronnarong and Jonglertjunya (2012), Shafie et al. (2012), Munoz et al. (2013), Stephen et al.(2013), Vadas and Digman (2013), Daystar (2014), Daystar et al. (2014), and Reeb et al. (2014). Primary data regarding the three Genera feedstocks (corn stover, switchgrass, and biomass sorghum) were collected through personal communication with Genera Energy (Tiller 2015).

Table 3. Overview of Biomass Feedstock Options Chosen for Analysis and Relevant Feedstock Characteristics

Table 4. National Annual Availability Estimate for Each Biomass Feedstock Type Analyzed

Delivered Cost

The delivered cost can be defined as the sum of land preparation, planting, maintenance, harvesting, loading, and transport costs for feedstocks that are a primary product in their system. Alternatively, delivered cost can be defined as the sum of biomass purchase price in the “field,” cost of collection, loading costs, and transport costs for feedstocks which are a waste co-product of their system. Values for chemical use, yield, irrigation, harvest activities, transport distance, and other cost drivers were calculated using the methods more extensively outlined by Daystar et al. (2014) and Reeb et al.(2014). Table 5 gives a breakdown of costs by life cycle stage and the aggregate delivered cost per metric dry tonne of biomass and per metric tonne of carbohydrates. Cost data per metric tonne of carbohydrates and the total annual carbohydrate delivery potential for each feedstock within each studied country are provided below (Table 5 and Fig. 3).

Table 5. Total Delivered Cost Per BDMT, Per Metric Tonne (MT) of Carbohydrates and Per Million British Thermal Units (MBTU) Embodied Energy for Each Biomass Feedstock Type by Life Cycle Stage