- Email: [email protected]
Techno-economic analysis and life cycle assessment of heterotrophic yeast-derived single cell oil production process
Fuel 264 (2020) 116839
Contents lists available at ScienceDirect
Fuel journal homepage: www.elsevier.com/locate/fuel
Full Length Article
Techno-economic analysis and life cycle assessment of heterotrophic yeastderived single cell oil production process
T
Nikolaos Bonatsosa, Constantina Maraziotia,c, Eleni Moutousidia,c, Angeliki Anagnostoua, ⁎ Apostolis Koutinasb, Ioannis K. Kookosa,c, a
Department of Chemical Engineering, University of Patras, Rio, 26504 Patras, Greece Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece c INVALOR: Research Infrastructure for Waste Valorization and Sustainable Management, Caratheodory 1, University Campus, Patras GR-26504, Greece b
A R T I C LE I N FO
A B S T R A C T
Keywords: Single cell oil Optimization Techno-economic evaluation Bioconversion Life cycle assessment
The widespread application and utilization of vegetable oils and fats has led to a significant increase of their annual production. Fats and oils are mainly consumed as food and as animal feed, as raw material in the chemical industry and more recently as raw material for biofuel production. In this work the economics of the biochemical production of microbial oil is investigated and the life cycle assessment is performed for first time in the open literature. The production process is simulated using commercial simulators to perform accurate material and energy balances and all necessary data are collected from the open literature. Following the process simulation step a detailed technoeconomic analysis is performed and the fixed capital investment is estimated for a production that varies from 2 to 40 kt of microbial oil per year. Having completed the technoeconomic analysis the Life Cycle Inventory Analysis is performed, and the environmental impacts are estimated, using the comprehensive Life Cycle Assessment methodology.
1. Introduction The environmental impact of human activity combined with the intensive extraction and use of nonrenewable fossil resources have resulted in a significant increase in greenhouse gases with direct consequence of increased radiation absorption, global warming, air pollution and acid rain. Thus, efforts are being made to introduce “green” or sustainable, environmentally friendly processes that are designed to minimize these effects by using renewable resources and sustainable production technologies. One of these efforts is the transition toward a biobased economy, where innovative use of primary or residual biomass for the production of biobased products and bioenergy will be driven by well-developed biorefining systems. Biorefining is the sustainable processing of biomass into a spectrum of marketable products and energy. The biorefinery concept embraces a wide range of technologies able to separate biomass resources (wood, grasses, corn) into their building blocks (carbohydrates, proteins, triglycerides) which can be converted to value added products, biofuels and chemicals. A biorefinery is a facility (or network of facilities) that integrates biomass conversion processes and equipment to produce transportation biofuels, power and chemicals from biomass. This concept is analogous to today’s petroleum, wheat or corn refineries which ⁎
produce multiple products from a common raw material and achieve significant economic and environmental benefits by the complete material utilization and process integration [1,2]. In recent years the production of chemicals and fuels through microbial bioconversion technologies using renewable resources has attracted significant attention from both industry and academia. Waste and by-product streams from existing industries can be used as renewable energy sources for the development of bio-refineries or as raw materials to produce chemicals [3]. Any co-product stream of a food or agricultural industrial activity can potentially be used, with or without prior treatment, as a carbon or nutrients source for a great variety of biotechnological processes. One of the most important groups of raw materials used in these ‘green’ technologies is the oils and fats, although the biggest amount of their quantities produced annually is used to satisfy nutrition needs. Scientists have turned their interest on Microbial Oils (MO) or Single Cell Oils (SCO), which can also be produced through fermentation by many different oleaginous microorganisms, using a variety of renewable sources [4]. In The last 60 years many studies have been conducted for Single Cell Oil production. These investigations include the use of a wide variety of raw materials used as a carbon source and different types of microorganisms and bacteria [5]. Microorganisms that can
Corresponding author at: Department of Chemical Engineering, University of Patras, Rio, 26504 Patras, Greece. E-mail address: [email protected] (I.K. Kookos).
https://doi.org/10.1016/j.fuel.2019.116839 Received 8 August 2019; Received in revised form 24 November 2019; Accepted 5 December 2019 Available online 14 December 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Fig. 1. Process flow diagram of the Single Cell Oil production process.
literature). The aim of this study is to revise and update a previous technoeconomic analysis (TEA) by the same authors on the SCO production using oleaginous yeasts [8] in order to incorporate several developments in the bioprocess design such as the optimal design of the bioreactors and the availability of recently developed equations for estimating the production cost of bioprocesses. More importantly, our aim is to undertake a comprehensive “cradle to gate” Life Cycle Inventory (LCI) and LCA of the SCO using oleaginous yeasts cultivated in conventional stirred tank bioreactors. Therefore, an integrated study is described that evaluates the economic and environmental performance of the underlying technology so as to evaluate the potential for its industrial implementation, identify the elements that may be the sources of risk and concern and finally contribute towards identifying the issues that are critical to a successful commercialization of this important and promising technology.
produce lipids at a minimum percentage of 20% of their biomass are recognized as oleaginous species [5,6]. A new, promising, way to produce microbial lipids with the lowest cost is the use of waste materials of agro-industrial processes [3]. These fats, can be converted to a great variety of high added value products, such as lubricants, surfactants, pharmaceuticals and cosmetics, and a polymer additive, but also as a raw material for the emerging biofuels industry. The utilization of oleaginous microorganisms for the industrial, large-scale production of oils and fats presents a great challenge and has attracted industrial interest. The production costs of SCOs can be high (in comparison with the traditional or common oils and fats) due to the need of maintaining aseptic conditions. However, alternative options for large-scale production of SCOs can be considered and the production cost must be compared with that of various naturally occurring lipids and fats of the plant and animal kingdom as the price of the latter vary tremendously (from 0.3 to up to 100 $/kg) [4,6]. Identification of microorganisms capable of overproducing lipids with structure, composition and applications similar to that of high-value fats can be of enormous financial interest, especially when use is made of wastes or industrial by-products. Parsons et al. [7] have recently presented a thorough review of the life cycle assessment (LCA) studies available in the open literature concerning the environmental performance of heterotrophic algae and yeast-derived SCOs. They conclude that the impact assessment presented in these studies is restricted to (non-renewable) energy use and climate change (global warming potential). The authors stress the need for further LCA studies to answer wider implications questions as they consider them vital in achieving public acceptance of the technology so as to ensure sustainability and commercial viability. From this comprehensive study it also follows that there is lack of any published study on the LCA of heterotrophic yeast-derived oils which have, according to many authors, significant advantages over the algae oils (while a significant number of LCA studies for the latter are available in the open
2. Techno-economic analysis 2.1. Description of the heterotrophic yeast-derived SCO process The conditions and parameters on which the design of the process for the yeast-derived SCO is based are obtained from the open literature and are mainly based on experimental work reported by Li et al. [9] as discussed in detail in Koutinas et al. [8]. The microorganism used is Rhodosporidium toruloides Y4 grown on glucose as a carbon source to produce microbial oil. For the fermentation process a fermentation time of 134 h is reported with a final concentration (titer) of 71.9 g SCO/L and 106.5 g/L of microbial cells (67.5% wt in SCO) corresponding to a SCO volumetric productivity of 0.54 g/(L⋅h). The designed plant is assumed to operate for approximately 95% of the year (8,300 h). Six different SCO annual production capacities are considered: 2, 5, 10, 20, 30 and 40 kt/y. 2
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Fig. 2. Process flow diagram of the Single Cell Oil extraction unit.
The typical process flow diagram for the biotechnological process for yeast-derived SCO is presented in Fig. 1 and Fig. 2. The process can be divided into two areas: Area 100 (Fig. 1, the bioreaction area) and Area 200 (Fig. 2, the SCO recovery and purification area). The carbon source and the nutrients are mixed in vessel MT-101 and then sterilized in heat exchangers E-101, E-102 and holding tube (HT-101). The sterilized mixture is fed to the bioreactor (R-101) together with the inoculum. The broth is then constantly aerated (C-101), stirred (A-101) and cooled while operated in a fed-batch mode until the completion of the SCO production and then the broth is heated (heat exchanger E103) to deactivate the enzymes. Area 100 is completed by a rotary vacuum filter (RF-101) where the microbial mass is recovered and then dried in a fluidized bed drier (FBD-101). The dried microbial mass is then mixed with hexane in the mixing tank V-201 and fed to a homogenizer (HG-201) that breaks down the cells and makes oil amenable to the solvent. A centrifugal separator (DS-201) is used to remove the residual microbial mass and the process is finally completed by performing phase separation in a tank (V-202) where the volatile solvent is (thermally) recovered and recycled while the SCO is obtained in high purity. Details about the specific process conditions selected and process performance can be found in our previous publication [8].
An enlightening and comprehensive discussion of these issues is presented in Douglas [10] and Peters et al. [11]. Following the completion of the material and energy balances rules of thumb and prior experience are used in order to estimate the characteristic size(s) of major process equipment. A preliminary estimation of the equipment purchase cost is performed using historical data available in the literature [11,12]. The purchase cost of the major process units is then used to determine the fixed capital investment. The cost of utilities and the direct labor cost are also estimated using the energy balances and historical data. Finally, based on the material balances and process information the raw materials consumed are estimated and their cost is determined. All these cost elements are combined using appropriate coefficients so as to determine the cost of manufacture and the cash flows for the time horizon of the project examined. This offers the opportunity to estimate several indices of economic performance such as the net present value (NPV) of the project or the internal rate of return (IRR) achieved. The analysis is, in some cases, accompanied by a classical sensitivity analysis or evaluation of the financial risks associated with the project under investigation.
2.2. Techno-economic analysis
The calculations of the equipment purchase cost and installed equipment cost for an annual production capacity of 10 kt/y are summarized in Table 1. In the same table the utilities consumption is also summarized. The installed equipment cost is estimated to be M$22.74 and the Fixed Capital Investment (FCI) is then estimated to be FCI = M $27.29. The utilities cost (CUT) can be obtained by assuming a value for the electricity (0.06 $/kWh) and low-pressure steam (30 $/t) and the data of Table 1 (CUT = M$3.97). The direct operating labor cost (COL) is then determined based on the estimated number of workers (26 workers) COL = M$1.17. To determine the cost of raw material, it is noted that the glucose consumed is 4.35 kg per kg of SCO produced and the consumption of yeast extract 0.42 kg per kg of SCO produced. The raw materials cost is calculated for a unit price of glucose of 0.4 $/kg and a unit price of extract of 1.5 $/kg. Peptone and hexane have also been considered with a unit price of 1.5 $/kg and 0.4 $/kg, respectively. So, it follows that the raw materials cost is CRM = M$23.76. The total annual cost of manufacture (without depreciation COMwoD) is finally determined using the following equation proposed by Turton et al. [13] :
2.3. SCO production cost estimation and minimum selling price estimation
The term technoeconomic analysis (TEA) is broadly used in process systems engineering to denote the assessment and evaluation of the economic performance of a project that is relevant to the chemical/ petrochemical industry. This is normally performed at several levels of detail depending on whether the project is just an interesting and promising investment idea or close to potential commercialization. The present work belongs to the former case where a preliminary examination is normally performed, and the results are accurate within ± 30%. A preliminary TEA is based on the synthesis of a process flow diagram that incorporates the most important processing units for which material and energy balances are performed. Needless to say, that irrespectively of whether a commercial simulator is used or not, the number of assumptions that are normally needed to complete the balances is extensive as in most cases vital qualitative and quantitative information is missing. This classifies the TEA as an art rather than as a science. Experience is a key element in the completion of the material and energy balances and the subsequent equipment sizing and costing. 3
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Standards Organization ISO 14,040 series) standards LCA is a technique for assessing the potential impacts associated with a product or a process, by compiling an inventory of inputs and outputs and then evaluate the potential impacts associated with those inputs and outputs; and finally interpreting the results of the Inventory Analysis in relation to the study’s objective(s). The present work follows the recommendations of the ISO standards. Application of the LCA methodology generally follows four steps that are defined in several standards according to Hauschild [17]: goal and scope definition, life cycle inventory, life cycle impact assessment and interpretation. In the Goal and Scope the reason(s) for conducting the study and its intended audience are specified and the system boundaries are chosen. Based on the above elements the data requirements and the limitations of the study (including the time and geographic reference) are identified and acknowledged. In the Inventory Analysis the aim is to collect and aggregate all input and output data so as to quantify material use, energy use, and environmental emissions/ discharges associated with each stage of the process. The Impact Assessment step that follows includes the selection of impact categories and several multifaceted methodologies with the aim to translate the raw data into potential impact on human health and/or the environment. In this study, a “cradle to gate” LCA study for the production of 1 t of microbial oil, which is the functional unit considered, is presented. The system boundaries include the production of agricultural crops, their pretreatment to produce the monosaccharide (glucose monomers on which the yeast is grown), the sterilization section and fermentation process that includes the cells recovery and drying process (process shown in Fig. 1) and the oil extraction and purification of the final product (process shown in Fig. 2). Emissions that take place during the construction of the production facilities and the machinery and emissions due to administration, maintenance and supervision of their operation are excluded. In the present LCA studies for the heterotrophic yeast derived SCOs production either corn derived glucose or sugar cane derived glucose are considered as fermentation feedstock (carbon source). Several impact categories are considered and the environmental performance of SCOs is compared with that of representative vegetable oils. The inventory of the “cradle-to-gate” process, using glucose from sugarcane as a raw material, is summarized in Table 2. The functional unit selected is 1 t of heterotrophic yeast-derived SCO. The inventories for the agricultural production stage and the crop fractionation are obtained from Renouf et al. [18] and refer to the cases of raw material production from sugar cane and corn. Note that for the biotechnological process for SCO production the main incoming materials flows are the monosaccharide used as carbon source (4.35 t per t of SCO), yeast extract (0.42 t per t of SCO) and water (estimated to 10 t of water per t of SCO). The incoming energy flows are in the form of electrical energy demand (5,193 kWhe per t of SCO) and low-pressure steam (2.84 t of steam or per t of SCO). There are also direct emissions to the atmosphere that are mainly CO2 (4.08 t biogenic CO2 emissions per t of SCO) generated from the cells metabolism and hexane (2.8 kg of hexane per t of SCO) due to fugitive emission in the solvent recovery and storage system. It is important to note at this point that the CO2-uptake in the cultivation stage that ends up as stored carbon in the SCO is approximately 2.82 t biogenic CO2/t SCO. Residual microbial biomass (0.48 t biomass per t of SCO) is the main co-product of the production process (an additional 0.88 t of CO2 captured at the agricultural stage is stored in the residual biomass produced per t of SCO). In both cases examined, the production of the monosaccharide results in different co-products. In the first case, low pressure steam (LPS at 120 °C) is the co-product that is produced at the crop processing step at the significant amount of 18.2 t LPS/t SCO. Correspondingly, in the case of glucose production from corn, the co-products are corn gluten feed, corn gluten meal and corn oil at amounts of 1.166 t, 0.35 t and 0.12 t per t of SCO, respectively.
Table 1 Summary of fixed capital investment (FCI) estimation and utilities consumption for the SCO production (10 kt SCO/y). Unit
Short description
Cp (M $@ 2017)
#
AREA 100: R-101–6 A-101–6 C-101–6 MT-101 A-107 E-101 E-102 HT-101
bioreaction and cell drying 594.4 m3, SS304 0.566 6 530 kW, SS316 0.557 6 456 kW, CS 0.221 6 3 600 m , SS304 0.382 1 530 kW, CS 0.557 1 550 m2, SS316 0.203 1 32 m2, SS316 0.032 1 0.18 m × 120 m, 0.103 1 SS316 Subtotal (increased by 10% to account for seed fermentors) 0.005 1 E-103 6.1 m2, SS316 0.031 1 E-104 30 m2, SS316 RF-101 57 m2, CS 0.382 3 C-107 795 kW, CS 0.289 1 3 FBD-101 20 m , CS 0.281 1 Total area 100 AREA 200: SCO recovery and purification V-201 13.75 m3 0.070 1 HG-201 10.96 m3/h 0.188 1 DS-201 10.96 m3/h 0.124 1 V-202 5.5 m2 0.126 1 Total area 200 Total
Finst
Cinst (M$@ 2017)
106 kWh/ y
LPS (t/y)
2 1.5 1.6 1.8 1.5 2.2 2.2 2.2
6.792 5.013 2.122 0.687 0.835 0.447 0.070 0.227
– 20.71 17.80 – 0.93 – – –
– – – – – 2,788 –
17.506
43.38
3,067
2.2 2.2 2.32 1.6 2.2
0.011 0.068 2.658 0.463 0.618 21.630
– – 6.60 – 49.98
9,908 8,332 – – – 21,306
3.5 2.06 1.3 2.5
0.245 0.387 0.161 0.315 1.108 22.738
0.13 0.66 1.16 – 1.95 51.93
– – – 7,158 7,158 28,464
COMwoD = 0.18FCI + 2.73COL + 1.23(CRM + CUT + CWT )
(1)
The estimated COM is M$42.45 or 4.245 $/kg SCO. The minimum selling price (MSP) of the SCO is finally determined by setting the net present value of the project equal to zero. The assumption for performing the discounted cash flow (DCF) analysis are based on the 2011 NREL bioethanol production report [14] and are summarized as follows: 10% discount rate, 30 y plant lifetime, 100% equity financing, 7 years MACRS depreciation, 35% corporate tax rate, 3 years of construction time and working capital that is 5% of the FCI. The resulting MSP, for an annual capacity of 10 kt/y and a raw materials price of 400 $/t, is estimated to be 4.613 $/kg SCO. 3. Life cycle assessment The main goal of the present work is to present, for first time in the open literature, the “cradle-to-gate” life cycle inventory and life cycle assessment of the heterotrophic yeast-derived SCO. The evaluation of an innovative process for the sustainable production of a chemical, energy or fuel cannot be considered comprehensive without the careful assessment of its environmental performance (impacts). The widespread belief that all biotechnological processes are “by definition” environmentally friendly or “green” is proven to be wrong when specific cases are examined in detail (see for instance Kookos et al.) [15]. Life cycle assessment is a systematic method to evaluate the environmental impact related to a product, a process, or an activity by determining all energy and materials flows that take place across the boundaries of a well-defined system. According to Fava et al. [16], “the assessment includes the entire life cycle of the product, process, or activity, encompassing extraction and processing of raw materials, manufacturing and distribution, use/reuse/maintenance, recycling, and final disposal”. Typical types of systems boundaries include (among others): cradle-to-gate (i.e., from raw materials extraction to plant “gate”) and cradle-to-grave (i.e., from raw materials extraction through product use and final disposal). LCA can also be a useful technique when comparing two or more different processes according to their environmental impact is needed. According to the ISO (International 4
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Table 2 Summary of “Cradle to gate” inventory for the production of 1 t SCO from sugarcane.
Inputs Energy
Fertilizers
Chemicals and nutrients
Water Transportation
Emissions Emissions to air
Emissions to water
Material or energy
Unit
Agricultural production
Diesel Electricity from grid Electricity on-site Coal Low Pressure steam Nitrogen fertilizer as N Phosphorous fertilizer as P Potassium fertilizer as K Agrilime Pesticide (active ingredient) Lime (CaO) Phosphoric acid Flocculants Yeast Extract Hexane Irrigation water Process Water Transport/Shipping Transport/Agricultural truck Transport/Rigid truck Transport/Rail Transport/Road
L kWh kWh MJ kg kg kg kg kg kg kg kg kg kg kg m3 kg tkm tkm tkm tkm tkm
79.38 657.89
Direct emissions to air: N2O Direct emissions to air: CH4 Direct emissions to air: NOx Direct emissions to air: SOx Direct emissions to air: NH3 Direct emissions to air: NMVOC Direct emissions to air: CO2 biogenic Emissions to water: NO3 Emissions to water: P Emissions to water: pesticide
kg kg kg kg kg kg kg kg kg kg
Outputs (product and co-products) Product SCO Co-Products Residual microbial mass Low pressure steam
Processing of crop
SCO production unit
5,193 –
304.50 2,848
59.68 6.83 23.36 240.51 1.08 15.23 1.22 0.0035 420 2.80 1,869.42 10,500 1,898.90 84.12 8.99 516.97 136.25 6.18 1.04 14.02 0.47 1.62 2.34
0.335 0.756 6.240 2.608 0.007 4,080
16.82 0.86 0.0136
kg kg kg
1,000 480 18,270
Total
79.38 657.89 5,193.00 304.50 2,848 59.68 6.83 23.36 240.51 1.08 15.23 1.22 0.0035 420 2.8 1,869.42 10,500 1,898.90 84.12 8.99 516.97 136.25 6.52 1.80 20.26 3.08 1.62 2.35 4,080.00 16.82 0.86 0.0136 1,000 480 18,270
three years presents a big reduction. As mentioned in the latest Oil Crop Yearbook report, vegetable oils prices stay at low levels, and mark the lowest level since 2007. The trend of the prices over the last years are shown on Fig. 4. In accordance with this report, the prices of the main vegetable oils sunflower, rapeseed, palm and soybean, in 2018, have been reduced from previous years to 1.21 $/kg, 0.84 $/kg, 0.75 $/kg and 0.72 $/kg, respectively. The life cycle impact assessment (LCIA) focuses on three major impact categories, namely global warming potential (100 years-GWP expressed in CO2-eq per functional unit), acidification (expressed in SO2-eq per functional unit) and eutrophication (expressed in PO4-eq per functional unit). LCIA was performed using the GaBi commercial software (GaBi version: 8.7.0.18/GaBi database) available from ThinkStep [20]. Figs. 5–7 present the results for each impact category, expressed per kg of each oil. All figures show the examined cases of SCO production as well as literature data of previous studies [21] for four vegetable oils (palm oil, soybean oil, rapeseed oil and sunflower oil). Fig. 5 shows the results concerning the GWP potential of the yeast derived SCO from monosaccharide produced from two different sources, which are compared with selected vegetable oils. For the two case studies examined, the estimated quantities are 7.2 and 11.6 t CO2 -eq/t SCO for sugarcane and corn, respectively. In both cases, the major contribution is due to the production of the SCO, followed by the agricultural production stage. The processing stage of the crop has negligible effect to the total GHG emissions indicator in both cases. These results are associated with the high electricity demands during the fermentation process (agitation and aeration). It is noticeable that
To account for the co-products in both cases, system expansion was selected as a proposed method from the ISO standards [17]. Thus, the system boundaries were expanded to include the displacement of alternative products in the market. This technique will reduce the environmental burden which is related to the avoided production of displaced products. According to Renouf et al. [18] LPS from bagasse combustion substitutes LPS from natural gas, in the first case examined, while in the second case, corn gluten feed, corn gluten meal and corn oil replaces barley, soybean meal and soybean oil, respectively. It should be mentioned that the residual microbial biomass is not taken into consideration due to the amount that it is produced as well as the lack of data concerning the displacing of animal feed. 4. Results and discussion The TEA analysis is repeated for several values of the total annual production and the estimations are presented in Fig. 3 for several annual capacities in the range 2 to 40 kt/y. Three different cases are examined in which the unit price of glucose is varied: 0.1, 0.2 and 0.4 $/ kg. The minimum selling price of SCO that can be achieved is estimated to be 2.5 $/kg SCO, which corresponds to the lowest assumed cost of the glucose of 100 $/t. When the most realistic scenario is considered, in which a glucose cost of 400 $/t is assumed, the MSP of the SCO is calculated to decrease from 5.8 to 4.1 $/kg when the annual capacity increases from 2 kt/y to 40 kt/y. This range of prices remains significantly higher than the current prices of vegetable oils. According to United States Department of Agriculture (USDA) [19] the price of the vegetable oils during the last 5
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Fig. 3. Single cell oil minimum selling price as a function of production capacity and price of sugars.
Fig. 4. Vegetable oil prices in the US for the period 2009–2018.
Fig. 6. Results of the acidification potential of Single Cell Oil for two different glucose sources, compared to vegetable oils.
SCO has higher GHG emissions, in comparison with the oils produced from oleaginous plants, which vary from 2.9 to 5.1 t CO2 -eq/t oil. In Fig. 6 the acidification potential results are presented. The calculated quantities are 0.004 and 0.015 t SO2-eq/t SCO for the two different glucose sources, sugarcane and corn, respectively. The total impact is dominated by the agricultural process stage. In the first case, there is also a significant contribution from the crop processing, with the lowest impact occurring from the SCO production step, while in the second case the crop processing has slightly lower impact than the SCO production stage. The acidification potential is mostly related to the energy used and the farm equipment in the agricultural phase as well as to the field emissions associated with the use of fertilizers. In comparison with the vegetables oils that amount to between 0.005 and 0.045 t SO2 -eq/t oil, SCO seems to have the lowest value in the first case and an intermediate one in the second case. Finally, the results about the eutrophication potential are demonstrated in Fig. 7. In the first case of sugarcane derived glucose the calculated quantity is equal to 0.007 t PO4 -eq/t SCO, while in the
Fig. 5. Results of the GHG emissions of Single Cell Oil for two different glucose sources, compared to vegetable oils.
6
Fuel 264 (2020) 116839
N. Bonatsos, et al.
CRediT authorship contribution statement Nikolaos Bonatsos: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft. Constantina Marazioti: . Eleni Moutousidi: Methodology, Software, Formal analysis, Investigation, Writing - original draft. Angeliki Anagnostou: Methodology, Software, Investigation. Apostolis Koutinas: Formal analysis, Investigation, Supervision, Writing - review & editing, Funding acquisition. Ioannis K. Kookos: Methodology, Investigation, Supervision, Writing - review & editing, Project administration, Funding acquisition. Acknowledgements This work was supported by the project “INVALOR: Research Infrastructure for Waste Valorisation and Sustainable Management” (MIS 5002495) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund). Nikolaos Bonatsos is supported for this research by the General Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Research and Innovation (HFRI).
Fig. 7. Results of the eutrophication potential of Single Cell Oil for two different glucose sources, compared to vegetable oils.
References
second, is equal to 0.015 t PO4 -eq/t SCO. As in the acidification potential, the agricultural stage has the major contribution to the total impact. The crop processing and the production of microbial oil phase have significantly low effect to the total eutrophication factor. Eutrophication potential is owed to the extreme usage or loss of fertilizers, during the agricultural step. Comparing to the vegetable oil data, which appear a range from 0.005 to 0.05 t PO4 -eq/t oil, both SCO cases seem to be in a medium value. It is interesting to note before closing this discussion that, although several studies are available for the production of oils from microalgaebased processes [22], their comparison with the present work was proven impossible as all studies aim at evaluating biodiesel production. Having said that and by taking into consideration that the transformation of oil into biodiesel does not alter the results significantly, we can say that our results are comparable to the results reported in [23] (at least for the global warming potential of heterotrophic algae cultivation using sugar from sugarcane and sugar beet).
[1] Kamm B, Kamm M. Principles of biorefineries. Appl Micribiol Biotechnol 2004;64:137–45. https://doi.org/10.1007/s00253-003-1537-7. [2] Cherubini F. The biorefinery concept: Using biomass instead of oil for producing energy and chemicals. Energy Convers Manage 2010;51:1412–21. https://doi.org/ 10.1016/j.enconman.2010.01.015. [3] Koutinas AA, Vlysidis A, Pleissner D, Kopsahelis N, Lopez Garcia I, Kookos IK, et al. Valorization of industrial waste and by-product streams via fermentation for the production of chemicals and biopolymers. Chem Soc Rev 2014;43:2587–627. https://doi.org/10.1039/c3cs60293a. [4] Palaiogeorgou A M, Papanikolaou S, de Castro A M, Freire D.M.G, Kookos, I K, Koutinas A A. A newly isolated Enterobacter sp. strain produces 2,3-butanediol during its cultivation on low-cost carbohydrate-based substrates. FEMS Microbiol Let 2019;366: https://doi.org/10.1093/femsle/fny280. [5] Ratledge C. Microbial oils and fats: an assessment of their commercial potential. Prog Ind Microbiol 1984;16:119–206. [6] Khot M, Raut G, Ghosh D, Alarcón-Vivero M, dContreras D, Ravikumar R. Lipid recovery from oleaginous yeasts: Perspectives and challenges for industrial applications. Fuel, 2020:259, 116292. https://doi.org/10.1016/j.fuel.2019.116292. [7] Parsons S, Chuck CJ, McManus MC. Microbial lipids: Progress in life cycle assessment (LCA) and future outlook of heterotrophic algae and yeast-derived oils. J Clean Prod 2018;172:661–72. https://doi.org/10.1016/j.jclepro.2017.10.014. [8] Koutinas AA, Chatzifragkou A, Kopsahelis N, Papanikolaou S, Kookos IK. Design and techno-economic evaluation of microbial oil production as a renewable resource for biodiesel and oleochemical production. Fuel 2014;116:566–77. https://doi.org/10. 1016/j.fuel.2013.08.045. [9] Li Y, Zhao Z, Bai F. High density cultivation of oleaginous yeast Rhodosporidium toruloides Y4 in fed-batch culture. Enz Microb Technol 2007;41:312–7. https://doi. org/10.1016/j.enzmictec.2007.02.008. [10] Douglas J. Conceptual design of chemical processes. NY: McGraw Hill; 1988. [11] Peters MS, Timmerhaus K, West RR. Plant design and economics for chemical engineers. 5th ed. Singapore: McGraw Hill; 2003. [12] Dheskali E, Michailidi K, Machado de Castro A, Koutinas AA, Kookos IK. Optimal design of upstream processes in biotransformation technologies. Bioresour Technol 2017;224:509–14. https://doi.org/10.1016/j.biortech.2016.10.084. [13] Turton R, Bailie RC, Whiting WB, Shaeiwitz JA. Analysis, synthesis and design of chemical processes. 3rd ed. Boston, MA: Prentice Hall International Series; 2009. [14] Humbird D., Davis R., Tao L., Kinchin C, Hsu D., and A. Aden. Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol, Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover. Golden, Colorado: National Renewable Energy Laboratory; 2011. [15] Kookos IK, Koutinas AA, Vlysidis A. Life cycle assessment of bioprocessing schemes for poly(3-hydroxybutyrate) production using soybean oil and sucrose as carbon sources. Resour Conserve Recy 2019;141:317–28. https://doi.org/10.1016/j. resconrec.2018.10.025. [16] Fava J.A., Denison R., Jones B., Curran M., Vigon, B., Selke, S., & Barnum, J. A Technical Framework for Life-Cycle Assessment. Society of Environmental Toxicology and Chemistry. SETAC Foundation, Vermont: Smugglers Notch; 1991. [17] Hauschild Z.M., Rosenbaum K.R., Olsen I. S. Life cycle assessment, Theory and practice. Springer International publishing; 2017. [18] Renouf MA, Wegener MK, Nielsen LK. An environmental life cycle assessment
5. Conclusions In this study, a detailed economic and environmental analysis is presented. After the detailed synthesis, optimization and cost estimation of the general process, the FCI of the overall process is calculated to be approximately 27 M$. It is easily perceived, that the FCI of the upstream section is dominated by the purchase cost of the bioreactors, including the agitator and aeration system, which is up to 70% of the total cost. In order to calculate the minimum selling price of SCO, a detailed techno-economic analysis of six different annual capacities was performed. The minimum selling price of the SCO is calculated to be between 2.4 and 5.8 $/kg (with the more realistic scenarios to correspond to the higher prices in the range 4.1 to 5.79 $/kg) which is significantly higher to current prices of vegetable oils (around 1 $/kg veg. oil). The detailed LCA analysis shows that GHG emissions are in the range of 7.2 to 11.6 kg CO2-eq/kg SCO and are higher compared to the emissions associated with common vegetable oils production (3 to 5 kg CO2-eq/kg veg. oil). The results presented in this work also demonstrate the dependence between the raw material used and the results of the impact assessment, a conclusion that has been made also for the case of other bio-chemicals [15]. 7
Fuel 264 (2020) 116839
N. Bonatsos, et al.
[22] Javed F, Aslam M, Rashid N, Shamair Z, Khan AL, Yasin M, et al. Microalgae-based biofuels, resource recovery and wastewater treatment: A pathway towards sustainable biorefinery. Fuel 2017;255:115826https://doi.org/10.1016/j.fuel.2019. 115826. [23] Orfield ND, Levine RB, Keoleian GA, Miller SA, Savage PE. Growing algae for biodiesel on direct sunlight or sugars: A comparative life cycle assessment. ACS Sust Chem Eng 2015;3(3):386–95. https://doi.org/10.1021/sc5004117.
comparing Australian sugarcane with US corn and UK sugar beet as producers of sugars for fermentation. Biomass Bioenergy 2008;32:1144–55. https://doi.org/10. 1016/j.biombioe.2008.02.012. [19] USDA. https://www.ers.usda.gov/data-products/oil-crops-yearbook/ [last accessed 25.07.19]. [20] Gabi PE. International 2018. [21] Schmidt HJ. Life cycle assessment of five vegetable oils. J Clean Prod 2014;87:130–6. https://doi.org/10.1016/j.jclepro.2014.10.011.
8
Contents lists available at ScienceDirect
Fuel journal homepage: www.elsevier.com/locate/fuel
Full Length Article
Techno-economic analysis and life cycle assessment of heterotrophic yeastderived single cell oil production process
T
Nikolaos Bonatsosa, Constantina Maraziotia,c, Eleni Moutousidia,c, Angeliki Anagnostoua, ⁎ Apostolis Koutinasb, Ioannis K. Kookosa,c, a
Department of Chemical Engineering, University of Patras, Rio, 26504 Patras, Greece Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece c INVALOR: Research Infrastructure for Waste Valorization and Sustainable Management, Caratheodory 1, University Campus, Patras GR-26504, Greece b
A R T I C LE I N FO
A B S T R A C T
Keywords: Single cell oil Optimization Techno-economic evaluation Bioconversion Life cycle assessment
The widespread application and utilization of vegetable oils and fats has led to a significant increase of their annual production. Fats and oils are mainly consumed as food and as animal feed, as raw material in the chemical industry and more recently as raw material for biofuel production. In this work the economics of the biochemical production of microbial oil is investigated and the life cycle assessment is performed for first time in the open literature. The production process is simulated using commercial simulators to perform accurate material and energy balances and all necessary data are collected from the open literature. Following the process simulation step a detailed technoeconomic analysis is performed and the fixed capital investment is estimated for a production that varies from 2 to 40 kt of microbial oil per year. Having completed the technoeconomic analysis the Life Cycle Inventory Analysis is performed, and the environmental impacts are estimated, using the comprehensive Life Cycle Assessment methodology.
1. Introduction The environmental impact of human activity combined with the intensive extraction and use of nonrenewable fossil resources have resulted in a significant increase in greenhouse gases with direct consequence of increased radiation absorption, global warming, air pollution and acid rain. Thus, efforts are being made to introduce “green” or sustainable, environmentally friendly processes that are designed to minimize these effects by using renewable resources and sustainable production technologies. One of these efforts is the transition toward a biobased economy, where innovative use of primary or residual biomass for the production of biobased products and bioenergy will be driven by well-developed biorefining systems. Biorefining is the sustainable processing of biomass into a spectrum of marketable products and energy. The biorefinery concept embraces a wide range of technologies able to separate biomass resources (wood, grasses, corn) into their building blocks (carbohydrates, proteins, triglycerides) which can be converted to value added products, biofuels and chemicals. A biorefinery is a facility (or network of facilities) that integrates biomass conversion processes and equipment to produce transportation biofuels, power and chemicals from biomass. This concept is analogous to today’s petroleum, wheat or corn refineries which ⁎
produce multiple products from a common raw material and achieve significant economic and environmental benefits by the complete material utilization and process integration [1,2]. In recent years the production of chemicals and fuels through microbial bioconversion technologies using renewable resources has attracted significant attention from both industry and academia. Waste and by-product streams from existing industries can be used as renewable energy sources for the development of bio-refineries or as raw materials to produce chemicals [3]. Any co-product stream of a food or agricultural industrial activity can potentially be used, with or without prior treatment, as a carbon or nutrients source for a great variety of biotechnological processes. One of the most important groups of raw materials used in these ‘green’ technologies is the oils and fats, although the biggest amount of their quantities produced annually is used to satisfy nutrition needs. Scientists have turned their interest on Microbial Oils (MO) or Single Cell Oils (SCO), which can also be produced through fermentation by many different oleaginous microorganisms, using a variety of renewable sources [4]. In The last 60 years many studies have been conducted for Single Cell Oil production. These investigations include the use of a wide variety of raw materials used as a carbon source and different types of microorganisms and bacteria [5]. Microorganisms that can
Corresponding author at: Department of Chemical Engineering, University of Patras, Rio, 26504 Patras, Greece. E-mail address: [email protected] (I.K. Kookos).
https://doi.org/10.1016/j.fuel.2019.116839 Received 8 August 2019; Received in revised form 24 November 2019; Accepted 5 December 2019 Available online 14 December 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Fig. 1. Process flow diagram of the Single Cell Oil production process.
literature). The aim of this study is to revise and update a previous technoeconomic analysis (TEA) by the same authors on the SCO production using oleaginous yeasts [8] in order to incorporate several developments in the bioprocess design such as the optimal design of the bioreactors and the availability of recently developed equations for estimating the production cost of bioprocesses. More importantly, our aim is to undertake a comprehensive “cradle to gate” Life Cycle Inventory (LCI) and LCA of the SCO using oleaginous yeasts cultivated in conventional stirred tank bioreactors. Therefore, an integrated study is described that evaluates the economic and environmental performance of the underlying technology so as to evaluate the potential for its industrial implementation, identify the elements that may be the sources of risk and concern and finally contribute towards identifying the issues that are critical to a successful commercialization of this important and promising technology.
produce lipids at a minimum percentage of 20% of their biomass are recognized as oleaginous species [5,6]. A new, promising, way to produce microbial lipids with the lowest cost is the use of waste materials of agro-industrial processes [3]. These fats, can be converted to a great variety of high added value products, such as lubricants, surfactants, pharmaceuticals and cosmetics, and a polymer additive, but also as a raw material for the emerging biofuels industry. The utilization of oleaginous microorganisms for the industrial, large-scale production of oils and fats presents a great challenge and has attracted industrial interest. The production costs of SCOs can be high (in comparison with the traditional or common oils and fats) due to the need of maintaining aseptic conditions. However, alternative options for large-scale production of SCOs can be considered and the production cost must be compared with that of various naturally occurring lipids and fats of the plant and animal kingdom as the price of the latter vary tremendously (from 0.3 to up to 100 $/kg) [4,6]. Identification of microorganisms capable of overproducing lipids with structure, composition and applications similar to that of high-value fats can be of enormous financial interest, especially when use is made of wastes or industrial by-products. Parsons et al. [7] have recently presented a thorough review of the life cycle assessment (LCA) studies available in the open literature concerning the environmental performance of heterotrophic algae and yeast-derived SCOs. They conclude that the impact assessment presented in these studies is restricted to (non-renewable) energy use and climate change (global warming potential). The authors stress the need for further LCA studies to answer wider implications questions as they consider them vital in achieving public acceptance of the technology so as to ensure sustainability and commercial viability. From this comprehensive study it also follows that there is lack of any published study on the LCA of heterotrophic yeast-derived oils which have, according to many authors, significant advantages over the algae oils (while a significant number of LCA studies for the latter are available in the open
2. Techno-economic analysis 2.1. Description of the heterotrophic yeast-derived SCO process The conditions and parameters on which the design of the process for the yeast-derived SCO is based are obtained from the open literature and are mainly based on experimental work reported by Li et al. [9] as discussed in detail in Koutinas et al. [8]. The microorganism used is Rhodosporidium toruloides Y4 grown on glucose as a carbon source to produce microbial oil. For the fermentation process a fermentation time of 134 h is reported with a final concentration (titer) of 71.9 g SCO/L and 106.5 g/L of microbial cells (67.5% wt in SCO) corresponding to a SCO volumetric productivity of 0.54 g/(L⋅h). The designed plant is assumed to operate for approximately 95% of the year (8,300 h). Six different SCO annual production capacities are considered: 2, 5, 10, 20, 30 and 40 kt/y. 2
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Fig. 2. Process flow diagram of the Single Cell Oil extraction unit.
The typical process flow diagram for the biotechnological process for yeast-derived SCO is presented in Fig. 1 and Fig. 2. The process can be divided into two areas: Area 100 (Fig. 1, the bioreaction area) and Area 200 (Fig. 2, the SCO recovery and purification area). The carbon source and the nutrients are mixed in vessel MT-101 and then sterilized in heat exchangers E-101, E-102 and holding tube (HT-101). The sterilized mixture is fed to the bioreactor (R-101) together with the inoculum. The broth is then constantly aerated (C-101), stirred (A-101) and cooled while operated in a fed-batch mode until the completion of the SCO production and then the broth is heated (heat exchanger E103) to deactivate the enzymes. Area 100 is completed by a rotary vacuum filter (RF-101) where the microbial mass is recovered and then dried in a fluidized bed drier (FBD-101). The dried microbial mass is then mixed with hexane in the mixing tank V-201 and fed to a homogenizer (HG-201) that breaks down the cells and makes oil amenable to the solvent. A centrifugal separator (DS-201) is used to remove the residual microbial mass and the process is finally completed by performing phase separation in a tank (V-202) where the volatile solvent is (thermally) recovered and recycled while the SCO is obtained in high purity. Details about the specific process conditions selected and process performance can be found in our previous publication [8].
An enlightening and comprehensive discussion of these issues is presented in Douglas [10] and Peters et al. [11]. Following the completion of the material and energy balances rules of thumb and prior experience are used in order to estimate the characteristic size(s) of major process equipment. A preliminary estimation of the equipment purchase cost is performed using historical data available in the literature [11,12]. The purchase cost of the major process units is then used to determine the fixed capital investment. The cost of utilities and the direct labor cost are also estimated using the energy balances and historical data. Finally, based on the material balances and process information the raw materials consumed are estimated and their cost is determined. All these cost elements are combined using appropriate coefficients so as to determine the cost of manufacture and the cash flows for the time horizon of the project examined. This offers the opportunity to estimate several indices of economic performance such as the net present value (NPV) of the project or the internal rate of return (IRR) achieved. The analysis is, in some cases, accompanied by a classical sensitivity analysis or evaluation of the financial risks associated with the project under investigation.
2.2. Techno-economic analysis
The calculations of the equipment purchase cost and installed equipment cost for an annual production capacity of 10 kt/y are summarized in Table 1. In the same table the utilities consumption is also summarized. The installed equipment cost is estimated to be M$22.74 and the Fixed Capital Investment (FCI) is then estimated to be FCI = M $27.29. The utilities cost (CUT) can be obtained by assuming a value for the electricity (0.06 $/kWh) and low-pressure steam (30 $/t) and the data of Table 1 (CUT = M$3.97). The direct operating labor cost (COL) is then determined based on the estimated number of workers (26 workers) COL = M$1.17. To determine the cost of raw material, it is noted that the glucose consumed is 4.35 kg per kg of SCO produced and the consumption of yeast extract 0.42 kg per kg of SCO produced. The raw materials cost is calculated for a unit price of glucose of 0.4 $/kg and a unit price of extract of 1.5 $/kg. Peptone and hexane have also been considered with a unit price of 1.5 $/kg and 0.4 $/kg, respectively. So, it follows that the raw materials cost is CRM = M$23.76. The total annual cost of manufacture (without depreciation COMwoD) is finally determined using the following equation proposed by Turton et al. [13] :
2.3. SCO production cost estimation and minimum selling price estimation
The term technoeconomic analysis (TEA) is broadly used in process systems engineering to denote the assessment and evaluation of the economic performance of a project that is relevant to the chemical/ petrochemical industry. This is normally performed at several levels of detail depending on whether the project is just an interesting and promising investment idea or close to potential commercialization. The present work belongs to the former case where a preliminary examination is normally performed, and the results are accurate within ± 30%. A preliminary TEA is based on the synthesis of a process flow diagram that incorporates the most important processing units for which material and energy balances are performed. Needless to say, that irrespectively of whether a commercial simulator is used or not, the number of assumptions that are normally needed to complete the balances is extensive as in most cases vital qualitative and quantitative information is missing. This classifies the TEA as an art rather than as a science. Experience is a key element in the completion of the material and energy balances and the subsequent equipment sizing and costing. 3
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Standards Organization ISO 14,040 series) standards LCA is a technique for assessing the potential impacts associated with a product or a process, by compiling an inventory of inputs and outputs and then evaluate the potential impacts associated with those inputs and outputs; and finally interpreting the results of the Inventory Analysis in relation to the study’s objective(s). The present work follows the recommendations of the ISO standards. Application of the LCA methodology generally follows four steps that are defined in several standards according to Hauschild [17]: goal and scope definition, life cycle inventory, life cycle impact assessment and interpretation. In the Goal and Scope the reason(s) for conducting the study and its intended audience are specified and the system boundaries are chosen. Based on the above elements the data requirements and the limitations of the study (including the time and geographic reference) are identified and acknowledged. In the Inventory Analysis the aim is to collect and aggregate all input and output data so as to quantify material use, energy use, and environmental emissions/ discharges associated with each stage of the process. The Impact Assessment step that follows includes the selection of impact categories and several multifaceted methodologies with the aim to translate the raw data into potential impact on human health and/or the environment. In this study, a “cradle to gate” LCA study for the production of 1 t of microbial oil, which is the functional unit considered, is presented. The system boundaries include the production of agricultural crops, their pretreatment to produce the monosaccharide (glucose monomers on which the yeast is grown), the sterilization section and fermentation process that includes the cells recovery and drying process (process shown in Fig. 1) and the oil extraction and purification of the final product (process shown in Fig. 2). Emissions that take place during the construction of the production facilities and the machinery and emissions due to administration, maintenance and supervision of their operation are excluded. In the present LCA studies for the heterotrophic yeast derived SCOs production either corn derived glucose or sugar cane derived glucose are considered as fermentation feedstock (carbon source). Several impact categories are considered and the environmental performance of SCOs is compared with that of representative vegetable oils. The inventory of the “cradle-to-gate” process, using glucose from sugarcane as a raw material, is summarized in Table 2. The functional unit selected is 1 t of heterotrophic yeast-derived SCO. The inventories for the agricultural production stage and the crop fractionation are obtained from Renouf et al. [18] and refer to the cases of raw material production from sugar cane and corn. Note that for the biotechnological process for SCO production the main incoming materials flows are the monosaccharide used as carbon source (4.35 t per t of SCO), yeast extract (0.42 t per t of SCO) and water (estimated to 10 t of water per t of SCO). The incoming energy flows are in the form of electrical energy demand (5,193 kWhe per t of SCO) and low-pressure steam (2.84 t of steam or per t of SCO). There are also direct emissions to the atmosphere that are mainly CO2 (4.08 t biogenic CO2 emissions per t of SCO) generated from the cells metabolism and hexane (2.8 kg of hexane per t of SCO) due to fugitive emission in the solvent recovery and storage system. It is important to note at this point that the CO2-uptake in the cultivation stage that ends up as stored carbon in the SCO is approximately 2.82 t biogenic CO2/t SCO. Residual microbial biomass (0.48 t biomass per t of SCO) is the main co-product of the production process (an additional 0.88 t of CO2 captured at the agricultural stage is stored in the residual biomass produced per t of SCO). In both cases examined, the production of the monosaccharide results in different co-products. In the first case, low pressure steam (LPS at 120 °C) is the co-product that is produced at the crop processing step at the significant amount of 18.2 t LPS/t SCO. Correspondingly, in the case of glucose production from corn, the co-products are corn gluten feed, corn gluten meal and corn oil at amounts of 1.166 t, 0.35 t and 0.12 t per t of SCO, respectively.
Table 1 Summary of fixed capital investment (FCI) estimation and utilities consumption for the SCO production (10 kt SCO/y). Unit
Short description
Cp (M $@ 2017)
#
AREA 100: R-101–6 A-101–6 C-101–6 MT-101 A-107 E-101 E-102 HT-101
bioreaction and cell drying 594.4 m3, SS304 0.566 6 530 kW, SS316 0.557 6 456 kW, CS 0.221 6 3 600 m , SS304 0.382 1 530 kW, CS 0.557 1 550 m2, SS316 0.203 1 32 m2, SS316 0.032 1 0.18 m × 120 m, 0.103 1 SS316 Subtotal (increased by 10% to account for seed fermentors) 0.005 1 E-103 6.1 m2, SS316 0.031 1 E-104 30 m2, SS316 RF-101 57 m2, CS 0.382 3 C-107 795 kW, CS 0.289 1 3 FBD-101 20 m , CS 0.281 1 Total area 100 AREA 200: SCO recovery and purification V-201 13.75 m3 0.070 1 HG-201 10.96 m3/h 0.188 1 DS-201 10.96 m3/h 0.124 1 V-202 5.5 m2 0.126 1 Total area 200 Total
Finst
Cinst (M$@ 2017)
106 kWh/ y
LPS (t/y)
2 1.5 1.6 1.8 1.5 2.2 2.2 2.2
6.792 5.013 2.122 0.687 0.835 0.447 0.070 0.227
– 20.71 17.80 – 0.93 – – –
– – – – – 2,788 –
17.506
43.38
3,067
2.2 2.2 2.32 1.6 2.2
0.011 0.068 2.658 0.463 0.618 21.630
– – 6.60 – 49.98
9,908 8,332 – – – 21,306
3.5 2.06 1.3 2.5
0.245 0.387 0.161 0.315 1.108 22.738
0.13 0.66 1.16 – 1.95 51.93
– – – 7,158 7,158 28,464
COMwoD = 0.18FCI + 2.73COL + 1.23(CRM + CUT + CWT )
(1)
The estimated COM is M$42.45 or 4.245 $/kg SCO. The minimum selling price (MSP) of the SCO is finally determined by setting the net present value of the project equal to zero. The assumption for performing the discounted cash flow (DCF) analysis are based on the 2011 NREL bioethanol production report [14] and are summarized as follows: 10% discount rate, 30 y plant lifetime, 100% equity financing, 7 years MACRS depreciation, 35% corporate tax rate, 3 years of construction time and working capital that is 5% of the FCI. The resulting MSP, for an annual capacity of 10 kt/y and a raw materials price of 400 $/t, is estimated to be 4.613 $/kg SCO. 3. Life cycle assessment The main goal of the present work is to present, for first time in the open literature, the “cradle-to-gate” life cycle inventory and life cycle assessment of the heterotrophic yeast-derived SCO. The evaluation of an innovative process for the sustainable production of a chemical, energy or fuel cannot be considered comprehensive without the careful assessment of its environmental performance (impacts). The widespread belief that all biotechnological processes are “by definition” environmentally friendly or “green” is proven to be wrong when specific cases are examined in detail (see for instance Kookos et al.) [15]. Life cycle assessment is a systematic method to evaluate the environmental impact related to a product, a process, or an activity by determining all energy and materials flows that take place across the boundaries of a well-defined system. According to Fava et al. [16], “the assessment includes the entire life cycle of the product, process, or activity, encompassing extraction and processing of raw materials, manufacturing and distribution, use/reuse/maintenance, recycling, and final disposal”. Typical types of systems boundaries include (among others): cradle-to-gate (i.e., from raw materials extraction to plant “gate”) and cradle-to-grave (i.e., from raw materials extraction through product use and final disposal). LCA can also be a useful technique when comparing two or more different processes according to their environmental impact is needed. According to the ISO (International 4
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Table 2 Summary of “Cradle to gate” inventory for the production of 1 t SCO from sugarcane.
Inputs Energy
Fertilizers
Chemicals and nutrients
Water Transportation
Emissions Emissions to air
Emissions to water
Material or energy
Unit
Agricultural production
Diesel Electricity from grid Electricity on-site Coal Low Pressure steam Nitrogen fertilizer as N Phosphorous fertilizer as P Potassium fertilizer as K Agrilime Pesticide (active ingredient) Lime (CaO) Phosphoric acid Flocculants Yeast Extract Hexane Irrigation water Process Water Transport/Shipping Transport/Agricultural truck Transport/Rigid truck Transport/Rail Transport/Road
L kWh kWh MJ kg kg kg kg kg kg kg kg kg kg kg m3 kg tkm tkm tkm tkm tkm
79.38 657.89
Direct emissions to air: N2O Direct emissions to air: CH4 Direct emissions to air: NOx Direct emissions to air: SOx Direct emissions to air: NH3 Direct emissions to air: NMVOC Direct emissions to air: CO2 biogenic Emissions to water: NO3 Emissions to water: P Emissions to water: pesticide
kg kg kg kg kg kg kg kg kg kg
Outputs (product and co-products) Product SCO Co-Products Residual microbial mass Low pressure steam
Processing of crop
SCO production unit
5,193 –
304.50 2,848
59.68 6.83 23.36 240.51 1.08 15.23 1.22 0.0035 420 2.80 1,869.42 10,500 1,898.90 84.12 8.99 516.97 136.25 6.18 1.04 14.02 0.47 1.62 2.34
0.335 0.756 6.240 2.608 0.007 4,080
16.82 0.86 0.0136
kg kg kg
1,000 480 18,270
Total
79.38 657.89 5,193.00 304.50 2,848 59.68 6.83 23.36 240.51 1.08 15.23 1.22 0.0035 420 2.8 1,869.42 10,500 1,898.90 84.12 8.99 516.97 136.25 6.52 1.80 20.26 3.08 1.62 2.35 4,080.00 16.82 0.86 0.0136 1,000 480 18,270
three years presents a big reduction. As mentioned in the latest Oil Crop Yearbook report, vegetable oils prices stay at low levels, and mark the lowest level since 2007. The trend of the prices over the last years are shown on Fig. 4. In accordance with this report, the prices of the main vegetable oils sunflower, rapeseed, palm and soybean, in 2018, have been reduced from previous years to 1.21 $/kg, 0.84 $/kg, 0.75 $/kg and 0.72 $/kg, respectively. The life cycle impact assessment (LCIA) focuses on three major impact categories, namely global warming potential (100 years-GWP expressed in CO2-eq per functional unit), acidification (expressed in SO2-eq per functional unit) and eutrophication (expressed in PO4-eq per functional unit). LCIA was performed using the GaBi commercial software (GaBi version: 8.7.0.18/GaBi database) available from ThinkStep [20]. Figs. 5–7 present the results for each impact category, expressed per kg of each oil. All figures show the examined cases of SCO production as well as literature data of previous studies [21] for four vegetable oils (palm oil, soybean oil, rapeseed oil and sunflower oil). Fig. 5 shows the results concerning the GWP potential of the yeast derived SCO from monosaccharide produced from two different sources, which are compared with selected vegetable oils. For the two case studies examined, the estimated quantities are 7.2 and 11.6 t CO2 -eq/t SCO for sugarcane and corn, respectively. In both cases, the major contribution is due to the production of the SCO, followed by the agricultural production stage. The processing stage of the crop has negligible effect to the total GHG emissions indicator in both cases. These results are associated with the high electricity demands during the fermentation process (agitation and aeration). It is noticeable that
To account for the co-products in both cases, system expansion was selected as a proposed method from the ISO standards [17]. Thus, the system boundaries were expanded to include the displacement of alternative products in the market. This technique will reduce the environmental burden which is related to the avoided production of displaced products. According to Renouf et al. [18] LPS from bagasse combustion substitutes LPS from natural gas, in the first case examined, while in the second case, corn gluten feed, corn gluten meal and corn oil replaces barley, soybean meal and soybean oil, respectively. It should be mentioned that the residual microbial biomass is not taken into consideration due to the amount that it is produced as well as the lack of data concerning the displacing of animal feed. 4. Results and discussion The TEA analysis is repeated for several values of the total annual production and the estimations are presented in Fig. 3 for several annual capacities in the range 2 to 40 kt/y. Three different cases are examined in which the unit price of glucose is varied: 0.1, 0.2 and 0.4 $/ kg. The minimum selling price of SCO that can be achieved is estimated to be 2.5 $/kg SCO, which corresponds to the lowest assumed cost of the glucose of 100 $/t. When the most realistic scenario is considered, in which a glucose cost of 400 $/t is assumed, the MSP of the SCO is calculated to decrease from 5.8 to 4.1 $/kg when the annual capacity increases from 2 kt/y to 40 kt/y. This range of prices remains significantly higher than the current prices of vegetable oils. According to United States Department of Agriculture (USDA) [19] the price of the vegetable oils during the last 5
Fuel 264 (2020) 116839
N. Bonatsos, et al.
Fig. 3. Single cell oil minimum selling price as a function of production capacity and price of sugars.
Fig. 4. Vegetable oil prices in the US for the period 2009–2018.
Fig. 6. Results of the acidification potential of Single Cell Oil for two different glucose sources, compared to vegetable oils.
SCO has higher GHG emissions, in comparison with the oils produced from oleaginous plants, which vary from 2.9 to 5.1 t CO2 -eq/t oil. In Fig. 6 the acidification potential results are presented. The calculated quantities are 0.004 and 0.015 t SO2-eq/t SCO for the two different glucose sources, sugarcane and corn, respectively. The total impact is dominated by the agricultural process stage. In the first case, there is also a significant contribution from the crop processing, with the lowest impact occurring from the SCO production step, while in the second case the crop processing has slightly lower impact than the SCO production stage. The acidification potential is mostly related to the energy used and the farm equipment in the agricultural phase as well as to the field emissions associated with the use of fertilizers. In comparison with the vegetables oils that amount to between 0.005 and 0.045 t SO2 -eq/t oil, SCO seems to have the lowest value in the first case and an intermediate one in the second case. Finally, the results about the eutrophication potential are demonstrated in Fig. 7. In the first case of sugarcane derived glucose the calculated quantity is equal to 0.007 t PO4 -eq/t SCO, while in the
Fig. 5. Results of the GHG emissions of Single Cell Oil for two different glucose sources, compared to vegetable oils.
6
Fuel 264 (2020) 116839
N. Bonatsos, et al.
CRediT authorship contribution statement Nikolaos Bonatsos: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft. Constantina Marazioti: . Eleni Moutousidi: Methodology, Software, Formal analysis, Investigation, Writing - original draft. Angeliki Anagnostou: Methodology, Software, Investigation. Apostolis Koutinas: Formal analysis, Investigation, Supervision, Writing - review & editing, Funding acquisition. Ioannis K. Kookos: Methodology, Investigation, Supervision, Writing - review & editing, Project administration, Funding acquisition. Acknowledgements This work was supported by the project “INVALOR: Research Infrastructure for Waste Valorisation and Sustainable Management” (MIS 5002495) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund). Nikolaos Bonatsos is supported for this research by the General Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Research and Innovation (HFRI).
Fig. 7. Results of the eutrophication potential of Single Cell Oil for two different glucose sources, compared to vegetable oils.
References
second, is equal to 0.015 t PO4 -eq/t SCO. As in the acidification potential, the agricultural stage has the major contribution to the total impact. The crop processing and the production of microbial oil phase have significantly low effect to the total eutrophication factor. Eutrophication potential is owed to the extreme usage or loss of fertilizers, during the agricultural step. Comparing to the vegetable oil data, which appear a range from 0.005 to 0.05 t PO4 -eq/t oil, both SCO cases seem to be in a medium value. It is interesting to note before closing this discussion that, although several studies are available for the production of oils from microalgaebased processes [22], their comparison with the present work was proven impossible as all studies aim at evaluating biodiesel production. Having said that and by taking into consideration that the transformation of oil into biodiesel does not alter the results significantly, we can say that our results are comparable to the results reported in [23] (at least for the global warming potential of heterotrophic algae cultivation using sugar from sugarcane and sugar beet).
[1] Kamm B, Kamm M. Principles of biorefineries. Appl Micribiol Biotechnol 2004;64:137–45. https://doi.org/10.1007/s00253-003-1537-7. [2] Cherubini F. The biorefinery concept: Using biomass instead of oil for producing energy and chemicals. Energy Convers Manage 2010;51:1412–21. https://doi.org/ 10.1016/j.enconman.2010.01.015. [3] Koutinas AA, Vlysidis A, Pleissner D, Kopsahelis N, Lopez Garcia I, Kookos IK, et al. Valorization of industrial waste and by-product streams via fermentation for the production of chemicals and biopolymers. Chem Soc Rev 2014;43:2587–627. https://doi.org/10.1039/c3cs60293a. [4] Palaiogeorgou A M, Papanikolaou S, de Castro A M, Freire D.M.G, Kookos, I K, Koutinas A A. A newly isolated Enterobacter sp. strain produces 2,3-butanediol during its cultivation on low-cost carbohydrate-based substrates. FEMS Microbiol Let 2019;366: https://doi.org/10.1093/femsle/fny280. [5] Ratledge C. Microbial oils and fats: an assessment of their commercial potential. Prog Ind Microbiol 1984;16:119–206. [6] Khot M, Raut G, Ghosh D, Alarcón-Vivero M, dContreras D, Ravikumar R. Lipid recovery from oleaginous yeasts: Perspectives and challenges for industrial applications. Fuel, 2020:259, 116292. https://doi.org/10.1016/j.fuel.2019.116292. [7] Parsons S, Chuck CJ, McManus MC. Microbial lipids: Progress in life cycle assessment (LCA) and future outlook of heterotrophic algae and yeast-derived oils. J Clean Prod 2018;172:661–72. https://doi.org/10.1016/j.jclepro.2017.10.014. [8] Koutinas AA, Chatzifragkou A, Kopsahelis N, Papanikolaou S, Kookos IK. Design and techno-economic evaluation of microbial oil production as a renewable resource for biodiesel and oleochemical production. Fuel 2014;116:566–77. https://doi.org/10. 1016/j.fuel.2013.08.045. [9] Li Y, Zhao Z, Bai F. High density cultivation of oleaginous yeast Rhodosporidium toruloides Y4 in fed-batch culture. Enz Microb Technol 2007;41:312–7. https://doi. org/10.1016/j.enzmictec.2007.02.008. [10] Douglas J. Conceptual design of chemical processes. NY: McGraw Hill; 1988. [11] Peters MS, Timmerhaus K, West RR. Plant design and economics for chemical engineers. 5th ed. Singapore: McGraw Hill; 2003. [12] Dheskali E, Michailidi K, Machado de Castro A, Koutinas AA, Kookos IK. Optimal design of upstream processes in biotransformation technologies. Bioresour Technol 2017;224:509–14. https://doi.org/10.1016/j.biortech.2016.10.084. [13] Turton R, Bailie RC, Whiting WB, Shaeiwitz JA. Analysis, synthesis and design of chemical processes. 3rd ed. Boston, MA: Prentice Hall International Series; 2009. [14] Humbird D., Davis R., Tao L., Kinchin C, Hsu D., and A. Aden. Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol, Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover. Golden, Colorado: National Renewable Energy Laboratory; 2011. [15] Kookos IK, Koutinas AA, Vlysidis A. Life cycle assessment of bioprocessing schemes for poly(3-hydroxybutyrate) production using soybean oil and sucrose as carbon sources. Resour Conserve Recy 2019;141:317–28. https://doi.org/10.1016/j. resconrec.2018.10.025. [16] Fava J.A., Denison R., Jones B., Curran M., Vigon, B., Selke, S., & Barnum, J. A Technical Framework for Life-Cycle Assessment. Society of Environmental Toxicology and Chemistry. SETAC Foundation, Vermont: Smugglers Notch; 1991. [17] Hauschild Z.M., Rosenbaum K.R., Olsen I. S. Life cycle assessment, Theory and practice. Springer International publishing; 2017. [18] Renouf MA, Wegener MK, Nielsen LK. An environmental life cycle assessment
5. Conclusions In this study, a detailed economic and environmental analysis is presented. After the detailed synthesis, optimization and cost estimation of the general process, the FCI of the overall process is calculated to be approximately 27 M$. It is easily perceived, that the FCI of the upstream section is dominated by the purchase cost of the bioreactors, including the agitator and aeration system, which is up to 70% of the total cost. In order to calculate the minimum selling price of SCO, a detailed techno-economic analysis of six different annual capacities was performed. The minimum selling price of the SCO is calculated to be between 2.4 and 5.8 $/kg (with the more realistic scenarios to correspond to the higher prices in the range 4.1 to 5.79 $/kg) which is significantly higher to current prices of vegetable oils (around 1 $/kg veg. oil). The detailed LCA analysis shows that GHG emissions are in the range of 7.2 to 11.6 kg CO2-eq/kg SCO and are higher compared to the emissions associated with common vegetable oils production (3 to 5 kg CO2-eq/kg veg. oil). The results presented in this work also demonstrate the dependence between the raw material used and the results of the impact assessment, a conclusion that has been made also for the case of other bio-chemicals [15]. 7
Fuel 264 (2020) 116839
N. Bonatsos, et al.
[22] Javed F, Aslam M, Rashid N, Shamair Z, Khan AL, Yasin M, et al. Microalgae-based biofuels, resource recovery and wastewater treatment: A pathway towards sustainable biorefinery. Fuel 2017;255:115826https://doi.org/10.1016/j.fuel.2019. 115826. [23] Orfield ND, Levine RB, Keoleian GA, Miller SA, Savage PE. Growing algae for biodiesel on direct sunlight or sugars: A comparative life cycle assessment. ACS Sust Chem Eng 2015;3(3):386–95. https://doi.org/10.1021/sc5004117.
comparing Australian sugarcane with US corn and UK sugar beet as producers of sugars for fermentation. Biomass Bioenergy 2008;32:1144–55. https://doi.org/10. 1016/j.biombioe.2008.02.012. [19] USDA. https://www.ers.usda.gov/data-products/oil-crops-yearbook/ [last accessed 25.07.19]. [20] Gabi PE. International 2018. [21] Schmidt HJ. Life cycle assessment of five vegetable oils. J Clean Prod 2014;87:130–6. https://doi.org/10.1016/j.jclepro.2014.10.011.
8