Life cycle analysis of a large-scale limonene production facility utilizing filamentous N2-fixing cyanobacteria
Introduction
Developing renewable, sustainable sources of biofuels is necessary to decrease the environmental burden created by the extensive use of fossil fuels. Fossil fuel reserves are finite and the adverse effects of fossil fuel-generated greenhouse gases are well established [1], [2], [3]. Biofuels can be categorized based on the type of feedstock used and/or the type of fuel produced. Each new generation of biofuel has been developed to overcome limitations or disadvantages of prior generations. This categorization has led to 4 generations of biofuels being defined [4]. While each biofuel type has its own advantages and disadvantages, together they have begun to decrease the burden of global fossil fuel consumption.
First generation biofuels were developed in the 1970s and 80s and consist of either: 1) ethanol produced via fermentation of sugar (primarily from sugar cane) or hydrolyzed starch (primarily from corn), or 2) biodiesel produced via trans-esterification of vegetable oil (primarily soybean oil or animal fats). The fuel ethanol process is well established and consists of feedstock pretreatment (milling, crushing, and solubilizing in water), saccharification (converting starch into sugars for the corn ethanol process), fermentation, distillation, and co-product recovery [5], [6]. First generation biofuels have three major disadvantages: production costs, market access, and competition for arable land with food crops. Because 1st generation biofuel feedstocks are also used for food, the feedstock usually accounts for more than 33% of total production costs, and this situation is unlikely to change as the world population and food demand continue to rise [7]. Second generation biofuels are typically defined as ethanol or other biofuels produced from lignocellulosic biomass, and includes a diverse range of by-products, wastes, and dedicated feedstocks [8]. The sustainability of 2nd generation biofuels is limited by land availability and competition for land use [9], [10], [11], [12]
Due to the drawbacks associated with 1st and 2nd generation biofuels, 3rd and 4th generation biofuels have been developed. These are fuels derived from the fixation of CO2 by photosynthetic algae and cyanobacteria [13], where the photosynthetic organism serves as both the photocatalyst and producer of biofuel [14]. At this time, algae and cyanobacteria appear to be the only sources of biofuel capable of meeting the global demand for transportation fuel [15], [16], [17], [18]. While algal oil can potentially be used directly as a fuel, in most cases the oil is subsequently processed through traditional oil refinery or biodiesel technologies into biofuels [19], [20], [21]. Therefore, many researchers now suggest that the definition of 3rd generation biofuels be altered to photoautotrophic conversion of CO2 into oil or algal biomass that is subsequently converted into biofuels [22]. This conversion step is a limitation to 3rd generation biofuels that does not exist with 4th generation biofuels. Fourth generation biofuel is the term used for the production of ‘drop-in’ biofuels directly from genetically engineered algae or cyanobacteria [19], [20], [21]. The benefit of using drop-in biofuels is that they can be mixed with crude derivatives without the need to develop new fuel infrastructures [23].
Heterocyst-forming filamentous cyanobacteria have the ability to fix atmospheric nitrogen, meaning that the cultivation medium does not need a combined nitrogen source, which is a considerable expense. This is one of the reasons that industrial microbiologists have focused on engineering filamentous N2-fixing cyanobacteria to produce next-generation biofuels and high-value chemicals [24], including limonene [25], farnesene [26], myrcene [27], and linalool [28].
The biofuel producing strain of filamentous cyanobacteria evaluated in this study was previously described by Halfmann et al., [25]. In that study, a genetically engineered Anabaena sp. PCC 7120 (herein referred to as LimS-DXP Anabaena) produced limonene (Fig. 1) photosynthetically and it was postulated that this strain could be used for the large-scale production of limonene as a next-generation biofuel. However, production would need to be increased substantially before an economically feasible process could be achieved. Previously, our research group [29] performed an economic feasibility analysis on a theoretical limonene production facility which used the genetically engineered filamentous cyanobacteria as the limonene producer. This study showed that an economically feasible process is currently not possible due to the low limonene productivity of the cyanobacterial strain. However, if productivity was increased, an economically feasible process would be possible. Data from the Halfmann et al., [25] study and our previous study are [29] were used as the basis for substantial parts of this LCA study.
The aim of this study was to evaluate the environmental profile of a hypothetical, next-generation biofuel production facility that uses genetically engineered cyanobacteria to produce limonene [29]. To evaluate the environmental profile of the theoretical production facility, an LCA was conducted. LCA is a method commonly used to evaluated the environmental impacts of a process by quantifying resource demands, energy demands, and the resulting emissions [30]. LCAs have been commonly used to evaluate environmental profiles of algal and cyanobacterial chemical production facilities [30], [31], [32]. In this study, a cradle-to-gate strategy was applied to define the systems boundaries. Scenario 1 was defined based on a hypothetical production facility described by Johnson et al., in which 32,727 L/yr limonene was produced [29]. Scenario 2 was based on a hypothetical facility described by Halfmann et al., that produced 1,000,000 L/yr of limonene [25]. The only difference between Scenario 1 and Scenario 2 was the total annual limonene production, due to different limonene productivities of the engineered cyanobacteria. The environmental profiles of both scenarios were then compared to the conventional production of fossil-based diesel environmental profile. The goal of this study was to provide evidence as to what the expected environmental effect of increased limonene production by the theoretical facility described below will be. Minimizing the negative environmental impact of this facility while maintaining economic feasibility will be essential.
Section snippets
Production system overview
A process flow diagram of the limonene production process is shown in Fig. 2, while Table 1 lists process inputs and parameters for both scenarios. The process includes photobioreactors (PBRs), clean-in-place units (CIPs), a Limonene Recovery Unit, a biomass harvesting unit, an anaerobic digestion (AD) unit, and a wastewater treatment unit [33], [34], [35], [36]. The system requires a CO2 supply as both the primary carbon source for cyanobacteria (sodium bicarbonate from BG-11 also provides
Overall energetic analysis
Table 3 summarizes the overall energy demand for unit processes, co-product allocations, and net energy demand per FU of limonene. Overall energetics of the process can be described as total energy input, net energy input, net energy balance, and net energy ratio as described below:
Conclusions
The life cycle analysis described in this study showed that production of limonene by genetically engineered filamentous cyanobacteria is less energy intensive than both fossil fuel based diesel and 3rd generation biodiesel production. Algal biodiesel production is more energy intensive due to the additional steps of drying biomass, separation of lipids, and conversion of lipids into biodiesel. Scenario 1 was found to be more environmentally friendly compared to Scenario 2 because Scenario 1
Declaration of contributions
All authors of this manuscript were involved in the conception and design of the study, acquisition and interpretation of data, and drafting the article and revising it critically.
Acknowledgements
This work was supported by the South Dakota Agricultural Experiment Station under grant [SD00H398-11], by NASA under award [NNX11AM03A], and by the US Department of Energy through contract [DE-EE0003046] to the National Alliance for Advanced Biofuels and Bioproducts (NAABB) and Texas A&M Agrilife Research Hatch (6806).
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Both authors contributed equally to this work.