Next Article in Journal
Capacity Sizing of Embedded Control Battery–Supercapacitor Hybrid Energy Storage System
Previous Article in Journal
Experimental Study and Modeling of the Effect of ESDD/NSDD on AC Flashover of SiR Outdoor Insulators
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Cascading Crypthecodinium cohnii Biorefinery: Global Warming Potential and Techno-Economic Assessment

1
Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1747-016 Lisbon, Portugal
2
Laboratório Nacional de Energia e Geologia (LNEG), I.P.-Unidade de Bioenergia e Biorrefinarias, Estrada do Paço do Lumiar 22, 1649-038 Lisbon, Portugal
3
CERI Research Centre, Sapienza University of Rome, Piazzale Aldo Moro 5, 001854 Rome, Italy
*
Author to whom correspondence should be addressed.
Submission received: 18 March 2022 / Revised: 13 May 2022 / Accepted: 16 May 2022 / Published: 20 May 2022

Abstract

:
Prior to the commissioning of a new industrial biorefinery it is deemed necessary to evaluate if the new project will be beneficial or detrimental to climate change, one of the main drivers for the sustainable development goals (SDG) of the United Nations. In particular, how SDG 7, Clean and Efficient Energy, SDG 3, Good Health and Well Being, SDG 9, Industry Innovation and Infrastructure, and SDG 12, Responsible Production and Consumption, would engage in a new biorefinery design, beneficial to climate change, i.e., fostering SDG 13, Climate Action. This study uses life cycle assessment methodology (LCA) to delve in detail into the Global Warming Impact category, project scenario GHG savings, using a conventional and a dynamic emission flux approach until 2060 (30-year lifetime). Water, heat and electricity circularity are in place by using a water recirculation process and a combined heat and power unit (CHP). A new historical approach to derive low and higher-end commodity prices (chemicals, electricity, heat, jet/maritime fuel, DHA, N-fertilizer) is used for the calculation of the economic indicators: Return of investment (ROI) and inflation-adjusted return (IAR), based upon the consumer price index (CPI). Main conclusions are: supercritical fluid extraction is the hotspot of energy consumption; C. cohnii bio-oil without DHA has higher sulfur concentration than crude oil based jet fuel requiring desulfurization, however the sulfur levels are compatible with maritime fuels; starting its operation in 2030, by 2100 an overall GHG savings of 73% (conventional LCA approach) or 85% (dynamic LCA approach) is projected; economic feasibility for oil productivity and content of 0.14 g/L/h and 27% (w/w) oil content, respectively (of which 31% is DHA), occurs for DHA-cost 100 times higher than reference fish oil based DHA; however future genetic engineering achieving 0.4 g/L/h and 70% (w/w) oil content (of which 31% is DHA), reduces the threshold to 20 times higher cost than reference fish oil based DHA; N-fertilizer, district heating and jet fuel may have similar values then their fossil counterparts.

Graphical Abstract

1. Introduction

Short-term and long-term carbon cycles are the key to exclude fossil fuels from human activities energy and chemicals production and opt for alternative sources of carbon: biomass waste or microalgae. There are four main reservoirs of carbon: atmosphere, ocean, land (plant and soil), and geological reservoirs (fossil fuels and rock carbonates) [1]. Humans, as well as other heterotrophic organisms, are carbon sinks (carbon stored in their structure) and sources (respiration). Human activities are based on a carbon economy for products and energy: products have carbon in their constitution, for example automotive parts, packaging and toys with polypropylene (C3H6)n; textiles with mainly cellulose (C6H10O5)n (cotton, lyocell, viscose, rayon, SeaCell), and alanine C3H7NO2 (silk) [2]; energy from combustion uses fuels with carbon (for example natural gas (mixture of CH4, C2H6, CO2) [3,4], wood pellets for heating or wood in furniture/houses (cellulose (C6H10O5)n). At some point in its life cycle the use of the carbon is released into the atmosphere in the form of CO2 at the end-of-life of the product/energy by combustion/incineration. Even in landfilling of products, both CO2 and CH4 are emitted due to bacterial decomposition with or without oxygen [5].
Without human interference, the carbon in fossil fuels would leak slowly into the atmosphere through volcanic activity over millions of years in the slow carbon cycle (long-term carbon cycle). Burning fossil fuels and clearing land has two coinciding effects that increase carbon in the atmosphere: the former releases carbon that was stored millions of years ago, and the latter removes forest, eliminating plants that would otherwise take carbon out of the atmosphere as they grow. Bioenergy systems are assumed to be carbon neutral on the basis that the change of carbon stock from lithosphere to atmosphere is more detrimental than from atmosphere to land. However, this assumption ignores the time lag between CO2 emissions from biomass combustion and CO2 uptake by vegetation [6]. Even if this affects climate change, temporarily, as the short-lived climate forcers ground level ozone and black carbon [7], it has a shorter effect than fossil combustion emissions [6].
Reaching net carbon removals is facilitated by bio-based systems (biorefinery) with CO2 sequestration. If the target is net zero GHG emissions, higher amounts of CO2 must be removed and it will take longer to achieve zero. For example, in 1.5 °C scenarios, net-zero CO2 occurs almost two decades before net-zero GHG (≈2050 vs. 2067). In 2 °C scenarios, net-zero CO2 occurs roughly three decades before net-zero GHG (≈2070 vs. 2100). Biorefinery systems with carbon storage in lithosphere or in long-lived materials will promote net carbon removals [8,9].
The transition from a fossil-based economy to a bio-based economy is necessary if an equilibrium is targeted avoiding displacing carbon from the long-term cycle to the short-term carbon cycle. Renewables in electricity generation (eolic, solar, hydro) rely on abiotic materials that are produced/transported via fossil fuels. Abiotic materials from biomass waste and without requiring fossil energy (bio-products) should also be preferable. The transition from a fossil-based economy to a bio-based economy should be aligned with the United Nations Sustainable Development Goals (UNSDG) [10] and underpin biorefinery projects relevance.
One of the drivers for the transition is the EU ETS (European Emission Trading Scheme), that sets benchmark stack fossil CO2eq emissions for high fossil fuel emitters [11]. The EU ETS [12] began in 2005 and covers emissions from power, industry, and flights inside the EEA, around 40% of EU emissions. It is currently the world’s largest domestic carbon market. Min value: 2.68 EUR/tonCO2eq, max value: 64.32 EUR/tonCO2eq and average value: 14.51 EUR/tonCO2eq in 2008–2020, however in 2021 the historical maximum of 88.88 EUR/tonCO2eq (see Figure 1) was reached.
The relationship between biomass, the energy transition, and the circular economy is by means of a biorefinery in a bioeconomy. Accelerating the transition to a bioeconomy in the context of sustainable development goals is deemed necessary.
The main issues regarding a biorefinery project’s sustainability assessment rely on the lack of a worldwide harmonized methodology to assess its future advantage in comparison with conventional production methods, already established at industrial level and achieving similar products. In fact, the life cycle methodology guidelines (LCA-Life Cycle Assessment, ISO 14040 [13]) are very useful but have limitations: depending on the boundaries, allocation, and hypothesis used for each input and output of the biorefinery, different electricity mixes (year and location dependent), different CO2 equivalency factors (updated by IPCC reports), and use of databases with old processes, lead to significant differences in the outcomes of the analysis and make comparisons between studies very hard. There is a need for a harmonized approach ensuring transparency, and credible and open accessibility to all the investors, engineers, scientists, and decision makers, and ensuring replicability by a third party. Besides the environmental sphere, the investment attractiveness should also rely on the same principles. The need to compare the bioenergy scenario with a reference ‘no-bioenergy’ scenario that delivers the same services to society is underpinned by Cowie et al., 2021 [14].
The marine heterotrophic microalgae Chrypthecodinium cohnii produces significant amounts of intracellular lipids that are rich in docosahexaenoic acid (DHA), a compound that has many applications in the pharmaceutical, nutraceutical, and food industries, due to its well-known benefits on human health. The C. cohnii oil fractionation, to obtain a DHA rich fraction, and another lipidic fraction, rich in saturated/unsaturated fatty acids, that can be directed towards biodiesel purposes, is a possible approach to valorize all the microalgae oil fractions, which may have environmental benefits. Because the biodiesel market is expected to slow down/decrease significantly, due to road transport electrification, in the future the needs of bio-maritime and bio-jet fuels will be the main target, and the main players in the biofuels market [15]. To ensure the biorefinery is flexible in terms of biofuel products, a catalytic HDO (hydrodeoxygenation) and HDS (hydrodesulfurization) unit are assumed to promote oxygen reduction in the form of water molecule, decarboxylation, hydrogenation, hydrogenolysis, and hydrocracking at mild conditions of temperature and pressure that in turn release oxygen in the form of CO and CO2, respectively, causing a reduced number of carbon in the hydrocarbon product [16].
Considering these issues, this research focuses on deriving a methodology to guide the GHG emissions estimation and a techno-economic (TEA) assessment of a new biorefinery based on microalgae C. cohnii, targeting the microbial production of DHA, maritime/jet fuel, N-fertilizer, and district heating, with water, heat and electricity circularity. In addition, the concept of the C. cohnii biorefinery has undergone a preliminary screening regarding social aspects, to ensure the technology develops in a satisfactory way for the consumers and society at large. An Excel spreadsheet model for GHG, for TEA and Python code is provided in the Supplementary Materials.

2. Materials and Methods

The analysis focuses on a prospective C. cohnii biorefinery producing four products: DHA, maritime/jet fuel, N-fertilizer, and district heating. To devise the 2030–2050 C. cohnii prospective supply, a framework based on international organizations must be established. The framework in 2030–2050 will have different population growth, different nutraceuticals (omega-3) demand, and different maritime/aviation fuel needs in terms of advanced biofuels and sustainability criteria. Besides producing sustainable products, in comparison with the fossil-based counterpart, targeting climate action, the biorefinery itself should be aligned with the other UNSDG [17], i.e., promote circularity (responsible production) and foster industry, innovation, and infrastructure, such as depicted in Figure 2.
Regarding aviation and maritime needs for advanced and sustainable fuels, the ICAO (International Civil Aviation Organization, Montreal, QC, Canada)—CORSIA (Carbon Offsetting and Reduction Scheme for International Aviation) policy framework on aviation biofuels states that by 2027 all members must use up to 50% sustainable aviation fuels (SAF), which have to have environmental benefits in terms of a net CO2 reduction of at least 10% compared to the baseline life cycle emissions values for aviation fuel, equal to 89 gCO2e/MJ. Hydroprocessed esters and fatty acids (HEFA) allowed so far are tallow, global used cooking oil, global palm fatty acid distillate, global corn oil (from dry mill ethanol plant), USA soybean oil, Brazil soybean oil, EU rapeseed oil, Malaysia and Indonesia palm oil, Brazil Brassica carinata (grown as a secondary crop that avoids other crops displacement), and USA Brassica carinata (grown as a secondary crop that avoids other crops displacement), with 13.9 gCO2eq/MJ for the cooked oil and 99.1 gCO2eq/MJ for the palm oil (including indirect land use change). The standard regulating the technical certification of SAF is ASTM D7566. It can be blended up to the maximum certified blending limit. Upon release from blending, the fuel is certified to ASTM D1655 and from this point is regarded as conventional Jet A or Jet A1 kerosene. Hydroprocessed hydrocarbons from the algae (Botryococcus braunii) have a maximum allowed blend of 10% (% v/v).
The International Maritime Organization (IMO) targets a 70% reduction in the CO2 intensity by 2050, maintaining the low sulfur needs. Maersk’s 700+ container ships consume 10 million ton fuel oil/year and emit 0.1% of global CO2 emissions and is currently the largest source of anthropogenic sulfur emissions. From 2020, the IMO have restricted the maximum allowed sulfur content of marine fuels used in international waters from 3.5 wt.% to 0.5 wt.% [18]. The advantage of marine fuels is that the requirements are of lower quality and do not need intensive upgrading and refining; nevertheless, either the current fossil standard ISO 8217-2017 will need to be modified to incorporate the novel fuel and its characteristics, or, a separate new standard could be developed, which could take several years. While microalgae has been shown to be a promising source of plant-based oils with high productivities, the technology to produce commercial scale algal oil at competitive prices is not yet available [18]. Mostly expected to be diesel engines with up to 20% blend. No sustainability criteria are defined yet.
One of the initiatives being assessed under the European Green Deal is the Renewable Energy Directive 2018/2001/EU (RED II) which is the main regulatory framework for EUs renewable energy. In the RED II (criteria recognized for EU ETS) framework, renewable energy supplied to the maritime and aviation sectors are eligible for counting towards the 14% renewable energy in transport target for road transport in 2030 (by a factor of 1.2). The RED II poses a limit on food and feed crops of 2020 level of 7%. There is also an upper limit of 1.7% for biofuels produced from Annex IX-B feedstocks (used cooking oil and animal fats) and a sub-target with minimum of 3.5% in 2030 for advanced biofuels produced from Annex IX-A feedstocks (waste materials and residues). The difference between CORSIA and RED II are pointed out in [19], for example, the input used for calculations and the absence of terms for emissions for carbon capture and storage and emissions from soil carbon accumulation via improved agricultural management.
Regarding DHA nutritional needs, most recommendations are for a daily intake in the range of 0.25–0.5 g per person per day [20]. Regarding N-fertilizer, it is expected to be used in a roughly constant value of around 20 kgN/capita in 2050, similar to 2010 values [21]. By 2050, the demand for nitrogen per capita increases by 10% in the Stated Policies Scenario. In the Sustainable Development Scenario and Net Zero Emissions by 2050 Scenario, the demand per capita is similar to today, despite total nitrogen use increasing by 25%. Figure 3 shows the historical and future framework for the biorefinery. The needs of the biorefinery products will be based on these constraints (Figure 4).

2.1. Bench-Scale Inventory and Scale-Up Considerations

Wild strain C. cohnii ATCC 30772 was characterized, according to ISO 16948, and its ultimate analysis is (in wt.%), 41.2 C, 5.1 H, 4.7 N and 1.2 S. The starter vial, inoculum vial, and batch reactor have to be heated and agitated, consuming heat and electricity, carbon, and nutrients (Figure 5). The latter are used to take advantage of industrial by-products [26] and the medium is composed of industrial glycerol (supplied by IBEROL, a Portuguese biodiesel producer) as the carbon source (24.52 g/L), sea salt (25 g/L), yeast extract (2 g/L), and corn step liquor (supplied by COPAM, a national enterprise for starch production), as the nitrogen source, (4.59 g/L). The antibiotic is composed of (chloramphenicol (5 mg/L), penicillin G (62 mg/L), and streptomycin (100 mg/L)). Based on the ultimate analysis, it is expected that the biogenic CO2 release be, according to our previous findings [27], 0.412 kg C/kg dry algae × 0.9 kgCO2/kg C = 0.4 kgCO2 biogenic/kg dry algae. After the inoculum preparation, a 7 L batch reactor, with Rushton impeller, is fed by medium and antibiotic. Antibiotic is needed at this stage but not in scale-up batch reactors.
After 7 days, the culture is harvested and freeze dried (5% moisture). The energy consumption of the dryer is 2 kWh/kgremoved water [28]. The dry algae are subjected to supercritical fluid extraction, consuming electricity and heat, to separate DHA (C22H32O2) from the remaining lipids (C6.2H10.4OS0.015, 37 MJ/kg). The inventory data for electricity consumption of the supercritical fluid extraction of bio-oil was retrieved from [17,29]. This gives an overall electricity consumption of 10 kWh/kgbio-oil (cooling, pumping, extraction, separation, and CO2 heating). The biomass waste is further used to produce biogas and burned in a CHP—combined heat and power unit—thus it could provide electricity and heat to the biorefinery. The biogas composition is 70% CH4/30% CO2 by volume, the biogas heating value being 21 MJ/m3, electrical efficiency 35%, and thermal efficiency 40% [30]. Bio-oil chemical formula was characterized in bench-scale experiments. The nitrogen content of the digestate is obtained by nitrogen mass balance.
Thinking on the potential scale-up of the biorefinery (Figure 6) energy estimates for water circulation, heat, and electricity needs of the batch bioreactor and heat needs of the mesophilic anaerobic digester must be computed. Thus, the energy requirements for the batch reactor in terms of heat (Ebatch-heating) and electricity (Eelectricity) are estimated as follows. For the heat bath a simplified model is taken from [31], assuming an overall loss factor of 0.5.
Ebatch-heating = mmediumcpΔT/0.5
ΔT is assumed as the difference between 27 °C and ambient temperature of 20 °C. cp is assumed to be 4.18 kJ/kg/K and the medium density as 1000 g/L.
For electricity [32], the Rushton impeller rotational speed (N), the impeller diameter (Da = 1/3 Dreactor), the working hours (WH in seconds), the medium density, ρ (by default 1000 g/L), the power number Po (by default 5 for “High shear” impellers [33]), and the electrical motor efficiency (by default ηmotor 95%, 0.95) are used:
Eelectricity = Po × ρ × N3 × Da5 × WH × ηmotor
The energy requirements for circulating water (Epumping) are based on minimum energy requirements to undertake pipe losses and considering an efficiency, η, of 90%.
Epumping = mh2OgHL/η
HL = f L /D v 2/2 g
where mh2O is the water mass, HL are the overall losses, f is the friction factor of the pipe taken from the Moody chart (by default 0.04), L is the pipe length in m, D the pipe diameter in m, v the water flow velocity in m/s, and g the gravity constant 9.81 m/s2.
The heat requirements for the mesophilic anaerobic digester (Emesophilic digester-heating) are based on the energy needs to heat the microalgae waste (specific heat from average specific heats for proteins, ash, and assuming an overall loss factor of 0.5).
Emesophilic digester-heating = mmicroalgae wastecpmicroalgae wasteΔT/0.5
mmicroalgae waste = mdry C. cohnii − mextracted oil
The inventory and mass balances for actual bench-scale data are in Table 1 and Table 2. These values are used along with energetic needs assumptions for scale-up to estimate the inventory for 1 ton DHA. Oil without DHA (chemical formula C6.2H10.4OS0.015) has H/C similar to that of the usual crude oil-based products (~2) and has O/C of 1/6.2 = 0.16, much higher than the usual crude-oil-based products (<0.05). This means that, at least the oil upgrading step is required to improve oil quality [34]. For example, according to the Dulong formula, oxygen content (mO) decrease and higher mH/mC increases lower heating value (LHV) [35]:
LHV (MJ/kg) = 33.8 mC + 122.3 (mH − mO/8) + 9.4 mS
Jet fuel specifications require total sulfur content to be less than 0.30 wt.% (ASTM D4294 and ISO 8754) and maritime fuel 0.50 wt.% according to the International Maritime Organization (IMO) limit on ships operating outside designated emission control areas (Regulation 14 of MARPOL Annex VI) with effect from 1 January 2020. Sulfur concentration in oil without DHA (chemical formula C6.2H10.4OS0.015) is 0.015 × 32/(6.2 × 12 + 10.4 + 1 × 16 + 0.015 × 32) = 0.5%. This means that a desulfurization procedure must be undertaken for jet fuel compliance. If the electro-deoxygenation process is considered, the electricity consumption is 6600 kWh/300 t biomass and hydrogen consumption 4 g/100 g oil, for 50% oxygen removal [34]. This process removes oxygen, and, for sulfur removal an hydrotreater unit is needed. Energy for catalytic hydrotreating is required to heat the mixed feed and the gas stream to the necessary reaction temperature and to pump and compress the fluids. This unit requires electricity and hydrogen [36]. For electricity the consumption is 3.3 kWh per kg sulfur removed (19,000 btu/bbl, crude 1.26% S, 0.88 kg/L) and hydrogen 1.2 kg H2 per kg sulfur removed (223,000 btu/bbl, crude 1.26% S, 0.88 kg/L, H2 120 MJ/kg). There is heat production via exothermal reactions that could enhance district heating potential.
Knowing the volume ratio between CH4 (x mol) and CO2 (y mol), and assuming the biogas is only composed by these two gases, one can obtain a system of two unknows and two balance equations:
{ 12 x + 12 y = 14.6 x x + y = 0.7
The solution (x = 0.37, y = 0.85) allows for knowing the biogas moles (1.2 mol) and PTN (100 kPa, 25 °C) volume 1 kmol = 24.8 m3, representing a 1.2 × 10−3 × 24.8 = 0.03 m3. Therefore, we would expect an electricity production of 0.06 kWh (0.03 × 21 × 0.35/3.6) and heat production of 0.25 MJ (0.03 × 21 × 0.40). The nitrogen mass balance allows for estimating the potential of replacing of fossil N fertilizer per mass of dry algae. 0.047 kgN/kgdry algae (3.3/70.6).

2.2. GHG Analysis

To ensure a fair comparison with biorefinery products, the reference, counterfactual, emissions should follow the same assumptions in terms of boundaries. For example, exclude, unless specified otherwise, emissions from capital goods (i.e., manufacture of machinery and equipment, and decommissioning of the power plant and machinery at the end-of-life); emissions due to the transportation and storage; and indirect land use change emissions. Allocation should also follow the same principles when comparing one fossil product with the biobased product.
The reference GHG are computed by multiplying each C. cohnii biorefinery output amount by its GHG emission factor (see Appendix A). C. cohnii biorefinery GHG is calculated by multiplying the input amounts by their GHG emission factors. Absolute (abs) and relative (rel) GHG savings are as follows:
ΔGHGabs = Reference [GHGabs] − Biorefinery[GHGabs]
ΔGHGrel = (Reference [GHGabs] − Biorefinery[GHGabs])/Reference [GHGabs]

2.2.1. Reference for DHA

Omega-3 fatty acids are proved to be beneficial in various human health disorders [37] and therapies [38]. Most recommendations are for a daily intake in the range of 0.25–0.5 g per person per day [20]. Fish-oil is a major source of long chained omega-3 fatty acids EPA and DHA (eicosapentaenoic (EPA) and docosahexaenoic acid (DHA)), having high market demand in both human nutraceutical industries and animal feed industries [39]. According to historical data, from 1950 until 2010, the top fish taxonomy that provides fish meal and fish oil is Peruvian anchovy [40].
In 2018, about 88% (or over 156 million tonnes) of the 179 million tonnes of total fish production was utilized for direct human consumption, while the remaining 12% (or about 22 million tonnes) was used for non-food purposes. Of the latter, 80% (about 18 million tonnes) was reduced to fishmeal and fish oil [24].
According to FAO data [23,41], roughly 85% of fish captures (from wild and aquaculture) is for human consumption. Fish omega-3 content is on average 387 mg/100 g [42]. Most of the 15% remaining is for fish meal (81%) and fish oil for nutraceuticals (13%). According to FAO, the level of EPA + DHA in fish oil is usually between 15 and 25%
The reduction fishery plant (Figure 7) direct CO2eq emissions are the result of natural gas burning for heat. Indirect emissions are the result of electricity and chemicals inputs. The emission factors used are listed in Table 3. A result of 8.5 gCO2eq/g DHA (mass allocation based) is achieved (see Excel calculations in Supplementary Materials).

2.2.2. Reference for Maritime or Jet Fuel

Refinery energetic consumption is 0.03 kWh/kgcrude oil and steam 650 kJ/kgcrude oil [48].
According to the latest Joint Research Centre (JRC) report on the Well-to-Tank analysis [49], the refinery emissions (allocated by product, marginal basis) and its combustion in use: refinery operations cover 7.2 gCO2eq/MJfuel and combustion in use covers 73.2 gCO2eq/MJfuel, which translates to an overall emission factor of 80.4 gCO2eq/MJfuel for diesel fuel. One-hundred-year GWP are calculated using the CO2eq values for CH4 and N2O from the Intergovernmental Panel on Climate Change (IPCC-AR5 [50]) (28 and 265, respectively). The summation of these two values excludes transportation, capital goods, and land use. Based on the same boundaries, [46] recommends the value of 78 gCO2eq/MJ for jet fuel and maritime fuel. The baseline emissions for CORSIA (Carbon Offsetting and Reduction Scheme for International Aviation) are 89 gCO2eq/MJ because it encompasses more life cycle steps, and different allocation rules, based on refinery products’ energy contents, enlarging the boundaries (see Figure 8). The difference between the two values is 14% ((89 − 78)/78).

2.2.3. Reference for N-Fertilizer

Plants obtain carbon, hydrogen, and oxygen directly from water and the atmosphere. Nutrients are also sourced from the environment, with fertilizers being used to supplement those that occur naturally in a given locality, to boost crop yields per hectare. The most used fertilizer is nitrogen (N-fertilizer) that is expected to be in use in roughly constant value of around 20 kgN/capita in 2050, similar to 2010 values [21]. Its industrial production is based on ammonia and natural gas fossil fuel by the Haber–Bosch process. Nitrogen is captured from the air and combined with hydrogen produced from natural gas steam reforming. After the gas mixture is subjected to high pressure (up to 200 bar) and medium-high temperatures (400–500 °C), the exhaust gases are condensed to produce liquid NH3. The energy requirements are 32 GJnatural gas/tNH3 [21], 20% electricity, and 80% heat [36]. This translates into an emission factor of 3.89 kgCO2eq/kgN, according to the definition of input data to assess GHG default emissions from biofuels in EU legislation and applying a factor of 85% to dismiss transportation and capital goods.
12N2(g) + 32H2↔NH3(g) △H = −93 (kJ/mol)

2.2.4. Reference for District Heating

The EU ETS benchmark for heat [12] considers only the emissions of combustion. The natural gas combustion value is 56.1 gCO2eq/MJ, then allocation between combined heat and power units and transportation losses apply. Prior to 2021 this value was set to 62.3 gCO2eq/MJ, but, from 2021 to 2025, according to the Commission Implementing Regulation (EU) 2021/447 of 12 March 2021 determining revised benchmark values for free allocation of emission allowances pursuant to Article 10a(2) of Directive 2003/87/EC of the European Parliament and of the Council, the benchmark value should be 47.3 gCO2eq/MJ (Table 1). By definition, this value excludes transportation and capital goods.

2.2.5. Net-Zero Carbon Electricity

The 26th Conference of the Parties (COP26) of the United Nations Framework Convention on Climate Change was an effort to push and strengthen global ambitions and climate action (SDG13). The International Energy Agency (IEA) produced a report [51] where it explored the way we could reach net zero carbon emissions by 2050. Regarding the electricity sector, at least 90% renewables (photovoltaics, eolic, hydro) must be in place. The remaining 10% would be nuclear, hydrogen, and carbon capture, utilization, and storage (CCUS). For life-cycle values [52,53], including capital goods materials, for renewables a range of 5–217 gCO2eq/kWh (median 45 gCO2eq/kWh) for photovoltaics, 7–56 gCO2eq/kWh (median 24 gCO2eq/kWh) for hydropower, and 3–40 gCO2eq/kWh (median 12 gCO2eq/kWh) for eolic. This means that despite stack emissions being zero, the system is not zero-emissions if the boundaries of the analysis of the system are expanded to cover materials, and, in the case of hydropower, biogenic methane emission from the water reservoirs. Figure 9 shows historical emission intensity from stack (plants operation), life cycle emissions excluding commissioning, maintenance, and end-of-life of the plant. Our hypothesis, for the sake of simplicity, is to assume the boundaries of stack emissions in 2030 (150 gCO2eq/kWh) and 2050 (0 gCO2eq/kWh).

2.2.6. Conventional versus Dynamic Inventory

Throughout the C. cohnii biorefinery operation, GHG emission will be released into the air at different time periods, thus causing a different global warming than anticipated by the conventional approach [54,55]. The Python code developed within the research (https://github.com/carlota200/GHG-dynamic-LCA, accessed on 5 January 2022) was used to more realistically cover GHG savings 100 years from the start of the operation.

2.3. TEA Analysis

Capital expenditures (CAPEX) and operational expenditures (OPEX) will be based on three main capital goods: batch reactor, freeze dryer, supercritical fluid extraction, hydrotreating unit, combined heat and power (CHP) unit and the material and energy inputs for 0.14 g/L/h C. cohni dry productivity and 27% oil content, production capacity 1 ton DHA/year (see Figure 6). The estimate of capital investment is based on the method of ‘Percentage of delivered purchased equipment cost’ [56]:
CAPEX = EquipCost 1 3 f i
fi represents a factor for equipment installation, piping, instrumentation and control, electrical systems, building-yard land, service facilities, etc. (f1 = 3), engineering and supervision, construction expenses, legal expenses, contractors fee, contingency, etc. (f2 = 1.25) and working capital (f3 = 0.75).
Yearly updates for equipment capital expenses could be corrected via the Chemical Engineering Plant Cost Index [57]. Equipment cost is based on literature review (Appendix B). For different scale equipment [56]:
Cost new/Cost reference = (Capacity new/Capacity reference)0.7
The OPEX (materials and utilities) and products revenue is based on historical commodity prices’ lower and higher ends (Figure 10). Maintenance and repair, taxes and insurance, administrative, distribution, marketing expenses, etc., are assumed to be 10% of CAPEX/year, maximum 25% according to [56]. Labor is based on 25 people at 60 k EUR/person/year including overheads [56]. Lower and higher ends of commodity prices (w/o taxes) are taken from several datasets, European Commission [58], Index Mundi [59], Organization for Economic Co-operation and Development and Food and Agriculture Organization (OECD-FAO) [41,60], LIFE public database [61], reports [62,63,64], and literature survey as presented in [26]. Table A2 summarizes this information (Appendix B). A scenario beneficial to the C. cohnii biorefinery will be explored considering the higher end of outputs revenues and lower end for inputs costs.
The economic indicators that are used are: the return of investment (ROI) and the inflation-adjusted return (IAR). Operation hours are assumed to be 7200 h/year to ensure maintenance and malfunctions downtimes.
ROI = R E V E N U E C A P E X × 100 %
IAR = 1 + R O I 1 + I n f l a t i o n   r a t e 1
The Inflation rate is estimated based on the Consumer Price Index (CPI),
I n f l a t i o n   r a t e = ( Ending   CPI   level     Beginning   CPI   level )   Beginning   CPI   level 100 %
For the sake of simplicity, a linear correlation was obtained from historical European levels (EUROSTAT), having 2015 as reference (Figure 11). Because the inflation rate is a ratio having the same reference it will hold the same results as an absolute-values ratio.
CPI = 1.628 × year − 3180.2
Between 2050 and 2030 (and assuming Equation (13)), the accumulated inflation would be 26% which means an average of 1.3% a year.

2.4. Social Analysis

Thanks to a consultancy included in the Portuguese research project OMEGAFUEL PTDC/EAM-AMB/30169/2017, a preliminary and initial exploration of socially relevant aspects of the C. cohnii biorefinery concept has been performed, in line with the European framework for Responsible Research and Innovation—RRI: “an approach that anticipates and assesses potential implications and societal expectations with regard to research and innovation, with the aim to foster the design of inclusive and sustainable research and innovation” [65]. The benefits of this new biorefinery concept have thus been considered from a social perspective and areas that need investigation have been identified, to ensure that the technology will meet the interests and demands of societal stakeholders. It is particularly important to do so in the first phases of technology development, to direct research in a way which is desirable for society and to avoid locking society in situations which later on may become difficult to manage [66,67,68].

3. Results

Results are divided into four aspects: C. cohnii future supply needs (high end perspective for maximum levels requirements); biorefinery potential GHG savings in comparison with a reference fossil scenario (reference system, counterfactual); techno-economic assessment for potential investors/stakeholders foster the deploying of the technology; and, finally, each biorefinery product lifecycle value to compare against alternatives and under the sustainability criteria defined for each one if existent. For jet-fuel product, in order to allocate the emissions generated from the entire supply chain amongst all of the valuable outputs of the system, an energy allocation method for co-products is used to be consistent with CORSIA LCA guidelines for jet fuel [69]. Fuel transportation and distribution is by default the average between 0.9 and 0.3 gCO2eq/MJ, i.e., 0.6 gCO2eq/MJ according to CORSIA guidelines. Alternative DHA production from other research are also used for comparison [43]. EU ETS benchmark for heat is used.

3.1. C. cohnii Supply Needs

One critical issue, when looking to the biomass potential for replacing fossil pathways, is the necessary feedstock amounts that will meet the products’ demands. The product that was chosen as the driver for the demand is DHA. From the experimental data we found a ratio of 0.084 ton DHA/ton C. cohnii (see Section 2.1). Based on population projections and fish demand scenarios (see Figure 3 and Figure 4), higher end needs from this kind of biomass assumes that this biomass is the only feedstock available to provide DHA (and this is the reason to call it high end estimates). Figure 12 shows the C.cohnii needs supply needs results.

3.2. C. cohnii Biorefinery Global Warming Potential and Benefits

Firstly, the inventory for a prospective C. cohnii biorefinery is shown in Figure 13. Secondly, the inventory and the GHG emission factor are used in conjunction, for each productivity (g/L/h, dry cell, L of working volume) 0.06–0.4 and oil content 30–70 wt.%. See the Excel file in the Supplementary Materials.
The environmental benefits of a C. cohnii biorefinery clearly outweigh the reference fossil-based scenario for higher dry cell productivities and oil contents (marked in green Table 4). The main issue preventing achieving 100% savings is the need for hydrogen that is assumed from the current EU ETS benchmark, i.e., produced by natural gas steam reforming. The hotspots in terms of energy are shown in Figure 14, for 0.14 g/L/h C. cohnii dry productivity and 27% oil content (w/w). This value is chosen because it reveals (for the same oil content as lab-scale experiments) higher GHG savings whose values surpass the uncertainty issues related to GHG analysis [70].

3.3. C. cohnii Biorefinery Products LCA Values

The resulting GHG emission factors for 0.14 g/L/h and 27% oil content, following the same allocation rules as the reference fossil, mass-based for DHA and N-fertilizer, energy-based for heat and jet fuel in 2030 and in 2050, are shown in Table 5. Noteworthy is the comparison between N-fertilizer 0–1.6 kgCO2eq/kgN and the fossil reference of 3.89 kgCO2eq/kgN, the comparison between heat 36 gCO2eq/MJ and EU ETS benchmark for heat 47.3 gCO2eq/MJ, and the comparison between DHA 1.2–1.6 kgCO2eq/kg DHA and DHA from a reduction fishery 8.5 kgCO2eq/kg DHA.
For jet fuel and according to CORSIA guidelines, the reference value discounting transportation and distribution should be 78 CO2eq/MJ, thus the jet fuel produced from C. cohnii allows a potential reduction of, at least, 50% non-biogenic GHG emissions.

3.4. C. cohnii Biorefinery Techno-Economic Analysis

In order to study if DHA obtained from C. cohnii biomass can economically compete with fish oil, the best-case scenario is evaluated, giving insight into the best ROI and IAR possible of achieving. The HDS unit can be overlooked if maritime fuel is the targeted crude oil replacer, once the sulfur content of the bio-oil (w/o DHA) is within the IMO limits. The high productivity and oil content scenario (0.4 g/L/h; 70% oil content, Table 5) is evaluated as depicted in Figure 15. These values can be potentially achieved by means of genetic engineering [71]. A more realistic scenario of 0.06 g/L/h and 0.14 g/L/h and 50% oil content without genetic engineering [72] is also depicted in Figure 16. CAPEX and OPEX is shown in Figure 17.
The increase in oil content decreases the carbon and hydrogen content in the C. cohni waste and hence compromises the capacity to generate biogas and suppress heat coverage from the CHP unit. Nevertheless the capacity to produce DHA for the same capital goods increases almost 7 times for the same amount of inputs (a result of the ratio of the productivities and oil contents, 0.4 × 0.7/(0.14 × 0.3)). This means that the DHA cost could be more similar to the reference fish oil DHA, in this case 20 times the cost is enough to have a positive IAR after two years of operation.
The economic indicator IAR (Figure 15 and Figure 16) versus GHG savings matrix (Table 4) reveals that the sustainability indicators may be conflicting, namely, if DHA cost is below 10 times the fish oil DHA reference cost for genetic engineered 0.4 g/L/h and 70% oil content (w/w), below 80 times the fish oil DHA reference cost for 0.14 g/L/h and 27% oil content (w/w), below 50 times the fish oil DHA reference cost for 0.14 g/L/h and 50% oil content (w/w), and below 100 for 0.06 g/L/h and 50% oil content (w/w). In such case, to increase the IAR, either a government incentive is needed, to financial aid or support the C. cohnii biorefinery and promote net fossil carbon removals or other co-products’ base costs need to be increased in relation to their fossil counterparts.

3.5. C. cohnii Biorefinery Concept from a Social Point of View

The C. conhii biorefinery presents several characteristics which can potentially be of great interest for society, both at the level of the productive process and of its outcomes. Microalgae biomass is particularly suitable for the extraction and purification of omega-3 compounds, as it is cholesterol free, contaminant free (e.g., heavy metals, polychlorobiphenyls (PCBs)), and tastes good. Algal oil also has other socially desirable attributes, such as its vegetarian and vegan nature, as well as the possibility to be manufactured under kosher or halal conditions [73,74]. Regarding the plant, in the C. conhii biorefinery a variety of productive processes can meet and work in synergy (production DHA, maritime/jet fuel, N-fertilizer, and district heating), optimizing processes, and reducing waste and pollution. This could mark a significant improvement in our ability to make good use of natural resources, with a more harmonious relationship between the different phases of the productive process, where nothing is wasted and the value of each element in the process is maximized. Several components of the technology and its development have been selected by researchers to make it ethical and desirable for the consumer and society at large: (i) the production process takes advantage of all microalgal lipid fractions, maximizing the value derived from all fractions, instead of discharging the remaining lipid fraction, after the omega-3 compounds extraction; (ii) microalgae can grow on industrial wastes/residues, such as sugar beet, carob, corn, and rice residues, which is an environmental benefit and simultaneously reduces the production process costs, in terms of feedstock, adding value to biowastes of agri-food industries through biorefinery processing approaches; (iii) the process to separate the omega-3 compounds from the remaining microalgal lipid fraction for biodiesel, using urea complexes and winterization is clean, safe, and environmentally friendly. In addition, the urea complexes can be re-utilized in further separation cycles, which represents an environmental benefit; (iv) the lipid fraction that is intended to be used as biodiesel (composed of alkyl esters), is obtained in the ethyl esters form (using ethanol), instead of methyl esters (which uses methanol, the most used biodiesel composition), since ethanol is less toxic than methanol; (v) all the residues generated in the process are directed to a digestor for biogas production. It is foreseen that the biogas produced can be used as energy source for the process; (vi) the industrial by-product glycerol that is used as carbon source in the medium formulation for the microalgal biomass production, is previously distilled, which means that the toxic contaminants are removed (specially methanol). The microalgae stress response during the cultivations has been evaluated, and we concluded that the stress level is low, indicating that the environment in which the cells were growing was not toxic for them; and (vii) no harmful waste streams are envisaged to land or water. Considering air pollution, the only direct combustion source is the biogas burned at the CHP unit that will not have sulfur, and as an internal combustion engine, could have controlled air pollutant levels.
Since the technology is still in an early phase it will be possible to investigate and check beforehand that the above characteristics fit with the needs of consumers and other societal stakeholders. At the same time, it is important to note that public knowledge is still very low and communication work is required to enable productive exchange with the stakeholders. Another important area to be explored regards which characteristics become relevant at pilot and demonstration stage, especially related to upscaling and implementation at local level. This will ensure that any aspects which could be problematic for social or broad environmental reasons are dealt with, thus that the subsequent development of the technology takes them into account [66]. Based on present knowledge, some points can therefore be proposed for further investigation. Work will be necessary to: (i) understand how the biorefinery would look (and blend in the landscape), how it would use the resources of the territory, if any, and how it could interact with other activities in the area; (ii) test potential consumer satisfaction with the hypothetical products of the C. conhii biorefinery; (iii) involve societal stakeholders to scrutinize the biorefinery concept, also in its possible far reaching effects, and eventually propose improvements; (iv) produce and test end user friendly informative material for societal stakeholders, that could be used to support public awareness and facilitate interaction with local communities with regard to plant planning; (v) identify the most convenient locations for the biorefinery, for technical reasons (for instance, would locations near the sea be preferable?), and for social reasons: which communities could benefit most from hosting such facilities? If there would be any risks or disadvantages at local level with technology at scale; and (vi) verify the legislative and regulatory framework and the conformance of the technology and whether its implementation requires or could be supported by new regulations.

4. Discussion

The transition from a fossil-based economy to a bio-based economy is necessary if an equilibrium is targeted avoiding displacing carbon from the long-term cycle to the short-term carbon cycle. Electrification and electricity generation by 100% renewables is necessary but insufficient in the sense that we still need materials and chemicals, “building blocks”, to be produced without fossil contribution.
A prospective C. cohnii biorefinery regarding the sustainability related aspects was analyzed, in-line with United Nations Sustainable Development Goals: GHG emission savings from a reference fossil-based system, and the return of investment corrected by the inflation rate (IAR). The physical biorefinery was assumed to have a capacity of 4 ton DHA/year, working hours 7200 h/year, for a batch production of 0.14 g/L/h and oil content 30 wt.%, of which 31 wt.% is DHA. Energy and water circularity were assumed, and extra-heat used as a potential product providing extra income. In this case it was possible to achieve up to 71% GHG emission savings and a positive IAR for DHA cost 100 times the assumed cost of the fish-oil base (or 800 EUR/kg DHA). If a scenario with increased productivity and oil accumulation is considered (0.4 g/L/h and oil content 70 wt. %, 31 wt.% DHA) a cost 20 times the fish oil reference of 8 EUR/kg DHA, 160 EUR/kg DHA ensures an IAR of 20% after 5 years of operation.
Other research projects, PUFACHAIN and ProAlgae, respectively, European and Norwegian Seafood Research Fund—FHF funded projects [75,76,77]—also reach the conclusion that microalgae based omega-3 needs to have a higher cost than the fish-oil-based. The PUFACHAIN project focused on phototrophic algae. The cost price for DHA was found to be in a range of 700–4000 EUR/kgDHA. The ProAlgae project for Spanish and Netherlands case studies found a production cost of 39–135 USD/kg DHA, higher than the fish oil reference of 9.2 USD/kgDHA. These values need to be compared with caution due to the different assumptions in the calculations, nevertheless the common insight is that DHA from microalgae is more costly than the fish-oil based.
There is no a unique criterion to assess sustainability of a fuel or material/chemical, which makes it difficult to evaluate alternative products/fuels based on biorefinery in a comparable, fair, and transparent manner. This applies to both GHG analysis and TEA analysis. Focusing on GHG analysis, glycerol is a good example. Some advocate that it should have a zero GHG emission factor because it is a “waste” from biodiesel production. However, in this study it is considered a co-product, a commodity with a market value, and hence with an associated GHG emission factor. Considering 0 kgCO2eq/kg instead of 0.2358 kgCO2eq/kg, this results in a change from 71 to 86% of GHG savings.
Other aspect can be illustrated with the yeast extract input GHG emission factor. Different datasets can be used for retrieving different values (see Table A1, 0.15 kgCO2eq/kgYeast extract or 0.617 kgCO2eq/kgYeast extract) and reflecting different assumptions behind the number. The consideration of 0.617 kgCO2eq/kgYeast extract instead of 0.15 kgCO2eq/kgYeast extract, results in a change from 71 to 68% of GHG savings.
Another aspect usually disregarded is the dynamic nature of the GHG emissions in the atmosphere, for every flux, decaying after a certain time and having different impacts throughout the biorefinery operational years. To gain insight into this aspect, a Phyton model by the grant holder of the Portuguese research project OMEGAFUEL PTDC/EAM-AMB/30169/2017 was utilized. An insight is given considering a constant emission for each year of operation of the biorefinery, for operation lifetimes 30 years and 50 years. Relative GHG savings would be 69% instead of 71% in 2050, and 85% instead of 73% in 2100 (see Supplementary Info). This means that in the long run the C. cohnii biorefinery is more beneficial than anticipated by conventional LCA. This analysis introduces an additional level of complexity to the GHG savings but should be regarded as a complementary insight.
Future research directions are highlighted hereafter.
It is important to ensure that the freezing point of the product biojet is lower than −47 °C according to the specification of aviation fuel. Different process evaluations, for example, the replacement of supercritical fluid extraction by a less expensive Soxhlet followed by the separation of both fractions (DHA and bio-oil) using the urea complexation method [78], would be helpful to observe the impact in the GHG and IAR indicators.
The sea salt used in culture media formulation can be replaced by seawater, with some dilution with tap water. Moreover, seawater can provide some trace elements, decreasing the overall cost of the culture medium. This could contribute towards decreasing OPEX, saving chemicals, labor, energy, and freshwater, especially if the future production facility is located near the seaside.
The CAPEX was assumed to be the total capital investment (TCI) in the first year of operation. Some authors consider CAPEX as a linear depreciation of the equipment part of the TCI, direct fixed capital costs, over 15 years lifetime with 8% interest rate and additional taxes of 3.1% [79]. It will be interesting to observe the differences in the cost price for DHA and jet fuel by pursuing this method; nevertheless, it will be quite challenging to consider a specific value and different depreciation rates for different equipment, for the first of a kind biorefinery.
From a social point of view, the concept of the C. conhii biorefinery and its productive processes, which appear to be very promising thanks to the characteristics selected up to now by the researchers, still need to be verified in terms of consumers and societal stakeholders’ satisfaction. Such a verification, together with investigation of social implications of upscaling and of local implementation of the technology, could form a solid base for further development. However, it must be noted that, given the very innovative nature of the technology, appropriate conditions for the consultation with consumers and societal stakeholders still need to be created, in terms of knowledge, awareness, and relationship with implementers.

5. Conclusions

The aviation, shipping, and chemical industries are doing their own evaluations of biomass availability and sustainability. Sustainability criteria for sustainable fuels and blends in aviation are far more developed than for maritime transport and concentration of the GHG emissions. This means that there is no unique criterion to assess sustainability, which make it difficult to evaluate alternative fuels/products based on a biorefinery in a comparable, fair, and transparent manner. The same applies to the techno-economic assessment where there is no unique set of economic indicators, reference monetary units and year, and percentages of CAPEX as a function of main equipment unit cost.
This research proposes a method to:
  • Estimate future biomass feedstock needs (in support of the energy transition);
  • Evaluate GHG savings and product GHG emission factors according to both conventional and dynamic LCA, and covering the same boundaries and allocation rules;
  • Evaluate TEA based on main units (batch bioreactors, freeze drying, supercritical fluid extraction, CHP, and historically lower and higher commodity cost (prices w/o vat), reference EUR 2018;
Limitations of the analysis are firstly related to the TRL of the experiments, bench-scale 7 L batch reactor, a pilot scale experimental set-up would be preferable (~70 L); heat and electricity balances are estimated, in many occasions, by minimum scale-up requirements which means there is a potential overestimation of district heating offer and electricity demand; the reference fossil based processes do not take into account energetic improvements, for example, regarding electricity net zero by 2050. This later assumption is a way to penalize the reference fossil system because it uses carbon material from geological reservoirs. Likewise, the exclusion of the biogenic CO2 from C. cohnii growth, HDO, and from the combustion of the digestate gas, in the CHP unit, 13.9 kgCO2/kgDHA, favors non-geological carbon sources.
Social, techno-economic, and environmental issues are interconnected, and the resulting features of the technology will make it more or less appealing to society. The concept of the C. conhii biorefinery offers solutions that address societal needs both in terms of products and processes. It is therefore well positioned for gaining public support, especially having the possibility to verify at an early stage the interest of societal stakeholders for its development and of being further improved following their input.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/en15103784/s1, Excel files 1 and 2.

Author Contributions

Conceptualization and methodology, C.S. for LCA and TEA, S.V. for social analysis; experimental data, P.M., A.C.O. and T.L.d.S.; validation, P.M., A.C.O., T.L.d.S. and A.R.; writing—original draft preparation, C.S.; writing—review and editing, all authors; project administration, T.L.d.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work is financed by national funds through FCT—Fundação para a Ciência e a Tecnologia, I.P., within the scope of the project PTDC/EAM-AMB/30169/2017, titled “OMEGAFUEL—New platform for biofuels and omega-3 compounds production, from the marine microalga Crypthecodinium cohnii sustainable biorefinery”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data can be found in two Excel files and one Python code.

Acknowledgments

Financiado pela Fundação para a Ciência e a Tecnologia, I.P./MCTES através de fundos nacionais (PIDDAC)—UIDB/50019/2020.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. GHG emission factors.
Table A1. GHG emission factors.
ItemEmission FactorReference
Aviation kerosene78.3 gCO2eq/MJ[46]
Fish oil1.72 tCO2eq/tOwn study based on [43,80]
Fish oil DHA8.62 tCO2eq/tOwn study based on [43,80]
N-fertilizer3886.12 gCO2eq/kg[46]
District heating47.3 tCO2eq/TJ[12]
Antibiotics6 kgCO2eq/kg[81]
Yeast extract0.15 kgCO2eq/kgGabi database *
CSL0.5 kgCO2eq/kg[82]
Glycerol0.2358 kgCO2eq/kg[83]
Sea salt0.0133 kgCO2eq/kg[84]
Hydrogen6.848 tCO2eq/t[12]
Electricity0 or 150 gCO2eq/kWhOwn study based on [46]
* 0.617 kgCO2eq/kg Ecoinvent 2.2 database [85].

Appendix B

Capital costs and commodity wholesale.
Table A2. Commodity wholesale.
Table A2. Commodity wholesale.
ItemHigher End Cost (EUR)Lower End Cost (EUR)Reference
Electricity (EUR/kWh)0.170.01[58]
Diesel (EUR/L)0.950.33[58]
Jet fuel (EUR/L)0.560.12[59]
Demineralized water (EUR/m3)0.900.45[61]
Yeast extract (EUR/kg)5.830.80[26]
CSL (corn step liquor) (EUR/kg)0.160.00[26]
Glycerol (EUR/kg)0.400.08[26]
Glucose (EUR/kg)0.960.08[26]
Fish oil (EUR/kg)1.630.20[41]
DHA/EPA (EUR/kg)8.171.00Own estimation based on fish oil and 20% Omega-3 content
N-fertilizer (EUR/kg)0.520.08[60]
Rock salt (EUR/kg)0.060.02[86]
Hydrogen (EUR/kg)3.200.80[63]
District heating (EUR/MJ)0.020.01[62]
Table A3. Capital cost of main C. cohnii biorefinery equipment for 4 Ton DHA/year production capacity.
Table A3. Capital cost of main C. cohnii biorefinery equipment for 4 Ton DHA/year production capacity.
EquipmentCost (EUR)Reference
4 × Batch reactor (2000 L) *4 × 28,077[87]
Freeze dryer (250 L, 4 shelves, 800 10R vials per cycle)100,000[88]
Supercritical fluid extraction (50 L extraction vessel)567,000[89]
HDS (small scale)73,000[90]
CHP 330,000[91,92,93]
* based on 50% working volume.

References

  1. Intergovernmental Panel on Climate Change. The Carbon Cycle and Atmospheric Carbon Dioxide. Chapter 3. IPCC Third Assessment Report; Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2001. [Google Scholar]
  2. Rana, S.; Pichandi, S.; Parveen, S.; Fangueiro, R. Regenerated Cellulosic Fibers and Their Implications on Sustainability. In Textile Science and Clothing Technology; Springer: Berlin/Heidelberg, Germany, 2014. [Google Scholar]
  3. Baylis, S.A.; Hall, K.; Jumeau, E.J. The analysis of the C1–C5 components of natural gas samples using gas chromatography-combustion-isotope ratio mass spectrometry. Org. Geochem. 1994, 21, 777–785. [Google Scholar] [CrossRef]
  4. Nasir, Q.; Sabil, K.M.; Nasrifar, K. Measurement and Phase Behavior Modeling (Dew Point + Bubble Point) of CO2 Rich Gas Mixture. J. Appl. Sci. 2014, 14, 1061–1066. [Google Scholar] [CrossRef]
  5. European Commission. A Study on the Economic Valuation of Environmental Externalities from Landfill Disposal and Incineration of Waste. Final Main Report; European Commission: Brussels, Belgium, 2000. [Google Scholar]
  6. Cherubini, F.; Gasser, T.; Bright, R.M.; Ciais, P.; Strømman, A.H. Linearity between temperature peak and bioenergy CO2 emission rates. Nat. Clim. Chang. 2014, 4, 983–987. [Google Scholar] [CrossRef]
  7. UNEP/WMO. Integrated Assessment of Black Carbon and Tropospheric Ozone; United Nations Environment Programme (UNEP): Nairobi, Kenya, 2011. [Google Scholar]
  8. Rogelj, J.; Shindell, D.; Jiang, K.; Fifita, S.; Forster, P.; Ginzburg, V.; Handa, C.; Kheshgi, H.; Kobayashi, S.; Kriegler, E.; et al. Mitigation Pathways Compatible with 1.5 °C in the Context of Sustainable Development. In Global Warming of 1.5 °C, (IPCC Special Report); Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2018. [Google Scholar]
  9. Huppmann, D.; Kriegler, E.; Krey, V.; Riahi, K.; Rogelj, J.; Calvin, K.; Humpenoeder, F.; Popp, A.; Rose, S.K.; Weyant, J.; et al. IAMC 1.5 °C Scenario Explorer and Data Hosted by IIASA; (Version release 2.0) [Data set]; Integrated Assessment Modeling Consortium & International Institute for Applied Systems Analysis: Laxenburg, Austria, 2019. [Google Scholar]
  10. Dahiya, S.; Katakojwala, R.; Ramakrishna, S.; Mohan, S.V. Biobased Products and Life Cycle Assessment in the Context of Circular Economy and Sustainability. Mater. Circ. Econ. 2020, 2, 7. [Google Scholar] [CrossRef]
  11. European Commission. Commission Regulation (EU) No 601/2012 on the Monitoring and Reporting of Greenhouse Gas Emissions Pursuant to Directive 2003/87/EC of the European Parliament and of the Council; European Commission: Brussels, Belgium, 2012. [Google Scholar]
  12. European Union. Directive (EU) 2018/410 of the European Parliament and the Council of 14 March 2018 Amending Directive 2003/87/EC to Enhance Cost-Effective Emission Reductions and Low-Carbon Investments, and Decision (EU) 2015/1814; European Commission: Brussels, Belgium, 2018; Volume 1933. [Google Scholar]
  13. International Organization for Standardization. ISO 14040; Environmental Management—Life Cycle Assessment—Principles and Framework. International Organization for Standardization: Geneva, Switzerland, 2006.
  14. Cowie, A.L.; Berndes, G.; Bentsen, N.S.; Brandão, M.; Cherubini, F.; Egnell, G.; George, B.; Gustavsson, L.; Hanewinkel, M.; Harris, Z.M.; et al. Applying a science-based systems perspective to dispel misconceptions about climate effects of forest bioenergy. GCB Bioenergy 2021, 13, 1210–1231. [Google Scholar] [CrossRef]
  15. Prussi, M.; Panoutsou, C.; Chiaramonti, D. Assessment of the Feedstock Availability for Covering EU Alternative Fuels Demand. Appl. Sci. 2022, 12, 740. [Google Scholar] [CrossRef]
  16. Ding, S.; Parlett, C.M.A.; Fan, X. Recent developments in multifunctional catalysts for fatty acid hydrodeoxygenation as a route towards biofuels. Mol. Catal. 2021, 523, 111492. [Google Scholar] [CrossRef]
  17. Blair, M.J.; Gagnon, B.; Klain, A.; Kulišić, B. Contribution of Biomass Supply Chains for Bioenergy to Sustainable Development Goals. Land 2021, 10, 181. [Google Scholar] [CrossRef]
  18. Simonsen, T.; Weiss, N.D.; Dick, S.V.; Thuijl, V.T.; Thomsen, S.T. Progress Towards Biofuels for Marine Shipping. Status and Identification of Barriers for Utilization of Advanced Biofuels in the Marine Sector. 2021. Available online: https://www.ieabioenergy.com/blog/publications/progress-towards-biofuels-for-marine-shipping/ (accessed on 1 March 2022).
  19. Prussi, M.; Weindorf, W.; Buffi, M.; Sánchez López, J.; Scarlat, N. Are algae ready to take off? GHG emission savings of algae-to-kerosene production. Appl. Energy 2021, 304, 117817. [Google Scholar] [CrossRef]
  20. Pike, I.H.; Jackson, A. Fish oil: Production and use now and in the future. Lipid Technol. 2010, 22, 59–61. [Google Scholar] [CrossRef]
  21. International Energy Agency. Ammonia Technology Roadmap. Towards More Sustainable Nitrogen Fertiliser Production; International Energy Agency: Paris, France, 2021. [Google Scholar]
  22. United Nations. World Population Prospects: The 2015 Revision; United Nations: New York, NY, USA, 2015; Volume XXXIII. [Google Scholar]
  23. Oliver, L.; Dietrich, T.; Marañón, I.; Villarán, M.C.; Barrio, R.J. Producing Omega-3 Polyunsaturated Fatty Acids: A Review of Sustainable Sources and Future Trends for the EPA and DHA Market. Resources 2020, 9, 148. [Google Scholar] [CrossRef]
  24. FAO. The State of World Fisheries and Aquaculture 2020. Sustainability in Action; FAO: Rome, Italy, 2020. [Google Scholar]
  25. International Energy Agency. Database Documentation Oil Information 2021 Edition; International Energy Agency: Paris, France, 2021. [Google Scholar]
  26. Moniz, P.; Silva, C.; Oliveira, A.C.; Reis, A.; Lopes da Silva, T. Raw Glycerol Based Medium for DHA and Lipids Production, Using the Marine Heterotrophic Microalga Crypthecodinium cohnii. Processes 2021, 9, 2005. [Google Scholar] [CrossRef]
  27. Lopes da Silva, T.; Moniz, P.; Silva, C.; Reis, A. The dark side of microalgae biotechnology: A heterotrophic biorefinery platform directed to ω-3 rich lipid production. Microorganisms 2019, 7, 670. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Khanum, F.; Giwa, A.; Nour, M.; Al-Zuhair, S.; Taher, H. Improving the economic feasibility of biodiesel production from microalgal biomass via high-value products coproduction. Int. J. Energy Res. 2020, 44, 11453–11472. [Google Scholar] [CrossRef]
  29. Martin, L.; Skinner, C.; Marriott, R.J. Supercritical extraction of oil seed rape: Energetic evaluation of process scale. J. Supercrit. Fluids 2015, 105, 55–59. [Google Scholar] [CrossRef]
  30. Murphy, J.; McKeogh, E.; Kiely, G. Technical/economic/environmental analysis of biogas utilisation. Appl. Energy 2004, 77, 407–427. [Google Scholar] [CrossRef]
  31. Johnson, M.; Heggs, P.J.; Mahmud, T. Assessment of Overall Heat Transfer Coefficient Models to Predict the Performance of Laboratory-Scale Jacketed Batch Reactors. Org. Process Res. Dev. 2016, 20, 204–214. [Google Scholar] [CrossRef] [Green Version]
  32. Paixão, S.M.; Alves, L.; Pacheco, R.; Silva, C.M. Evaluation of Jerusalem artichoke as a sustainable energy crop to bioethanol: Energy and CO2eq emissions modeling for an industrial scenario. Energy 2018, 150, 468–481. [Google Scholar] [CrossRef] [Green Version]
  33. Ducoste, J.J.; Clark, M.M.; Weetman, R.J. Turbulence in flocculators: Effects of tank size and impeller type. AIChE J. 1997, 43, 328–338. [Google Scholar] [CrossRef]
  34. Elangovan, S.; Elliott, D.C.; Santosa, D.; Spatari, S.; Karanjikar, M. Novel Electro-Deoxygenation Process for Bio-Oil Upgrading; U.S. Department of Energy: Golden, CO, USA, 2018.
  35. Hosokai, S.; Matsuoka, K.; Kuramoto, K.; Suzuki, Y. Modification of Dulong’s formula to estimate heating value of gas, liquid and solid fuels. Fuel Process. Technol. 2016, 152, 399–405. [Google Scholar] [CrossRef]
  36. Energetics Incorporated. Energy and Environmental Profile of the U.S. Chemical Industry; Energetics Incorporated: Columbia, MD, USA, 2000. [Google Scholar]
  37. Swanson, D.; Block, R.; Mousa, S.A. Omega-3 Fatty Acids EPA and DHA: Health Benefits Throughout Life. Adv. Nutr. 2012, 3, 1–7. [Google Scholar] [CrossRef] [PubMed]
  38. D’Eliseo, D.; Velotti, F. Omega-3 Fatty Acids and Cancer Cell Cytotoxicity: Implications for Multi-Targeted Cancer Therapy. J. Clin. Med. 2016, 5, 15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Toppe, J. Farmed fish: A major provider or a major consumer of omega-3 oils? Int. News Fats Oils Relat. Mater. 2013, 24, 477–479. [Google Scholar]
  40. Cashion, T.; Le Manach, F.; Zeller, D.; Pauly, D. Most fish destined for fishmeal production are food-grade fish. Fish Fish. 2017, 18, 837–844. [Google Scholar] [CrossRef]
  41. OECD; FAO. OECD-FAO Agricultural Outlook 2019–2028; OECD: Paris, France; FAO: Rome, Italy, 2020. [Google Scholar]
  42. Rincón-Cervera, M.Á.; González-Barriga, V.; Romero, J.; Rojas, R.; López-Arana, S. Quantification and Distribution of Omega-3 Fatty Acids in South Pacific Fish and Shellfish Species. Foods 2020, 9, 233. [Google Scholar] [CrossRef] [Green Version]
  43. Bartek, L.; Strid, I.; Henryson, K.; Junne, S.; Rasi, S.; Eriksson, M. Life cycle assessment of fish oil substitute produced by microalgae using food waste. Sustain. Prod. Consum. 2021, 27, 2002–2021. [Google Scholar] [CrossRef]
  44. European Environment Agency. Greenhouse Gas Emission Intensity of Electricity Generation in Europe; European Environment Agency: Copenhagen, Denmark.
  45. UK DEFRA. 2021 Government Greenhouse Gas Conversion Factors for Company Reporting: Methodology Paper for Conversion Factors Final Report; UK DEFRA: Birmingham, UK, 2021.
  46. Annex C: Methodology for Calculation of GHG Emission Avoidance. European Comission. 2021. Available online: https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/innovfund/wp-call/call-annex_c_innovfund-lsc-2020-two-stage_en.pdf (accessed on 1 January 2022).
  47. Kalogerakis, N.; Politi, M.; Foteinis, S.; Chatzisymeon, E.; Mantzavinos, D. Recovery of antioxidants from olive mill wastewaters: A viable solution that promotes their overall sustainable management. J. Environ. Manag. 2013, 128, 749–758. [Google Scholar] [CrossRef] [Green Version]
  48. Wang, M.; Lee, H.; Molburg, J. Allocation of Energy Use in Petroleum Refineries to Petroleum Products: Implications for Life-Cycle Energy Use and Emission Inventory of Petroleum Transportation Fuels. Int. J. Life Cycle Assess. 2004, 9, 34–44. [Google Scholar] [CrossRef]
  49. Prussi, M.; Yugo, M.; De Prada, L.; Padella, M.; Edwards, R. JEC Well-To-Wheels Report v5; Publications Office of the European Union: Luxembourg, 2020. [Google Scholar]
  50. IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Core Writing Team, Pachauri, R.K., Meyer, L.A., Eds.; IPCC: Geneva, Switzerland, 2014. [Google Scholar]
  51. International Energy Agency. Net Zero by 2050: A Roadmap for the Global Energy Sector; International Energy Agency: Paris, France, 2021. [Google Scholar]
  52. Bruckner, T.; Bashmakov, I.A.; Mulugetta, Y.; Chum, H.; de la Navarro, A.V.; Edmonds, J.; Faaij, A.; Fungtammasan, B.; Garg, A.; Hertwich, E.; et al. Energy Systems. In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Cambridge, UK; New York, NY, USA, 2014. [Google Scholar]
  53. Clauß, J.; Stinner, S.; Solli, C.; Lindberg, K.B.; Madsen, H.; Georges, L. Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating. Energies 2019, 12, 1345. [Google Scholar] [CrossRef] [Green Version]
  54. Silva, C. Greenhouse Gas Emission Assessment of Simulated Wastewater Biorefinery. Resources 2021, 10, 78. [Google Scholar] [CrossRef]
  55. Vance, C.; Sweeney, J.; Murphy, F. Space, time, and sustainability: The status and future of life cycle assessment frameworks for novel biorefinery systems. Renew. Sust. Energ. Rev. 2022, 159, 11225. [Google Scholar] [CrossRef]
  56. Henrich, E.; Dahmen, N.; Dinjus, E. Cost estimate for biosynfuel production via biosyncrude gasification. Biofuels Bioprod. Biorefining 2009, 3, 28–41. [Google Scholar] [CrossRef]
  57. Jenkins, S. Chemical Engineering Plant Cost Index. Available online: https://www.chemengonline.com/2019-cepci-updates-january-prelim-and-december-2018-final/?printmode=1 (accessed on 1 January 2022).
  58. Heald, S.; Debrosses, N.; Rademaekers, K. Study on Energy Prices, Costs and Subsidies and Their Impact on Industry and Households: Final Report; European Commission: Brussels, Belgium, 2019. [Google Scholar]
  59. IndexMundi. Available online: https://www.indexmundi.com/commodities/?commodity=jet-fuel&months=240&currency=eur (accessed on 1 January 2022).
  60. OECD; FAO. OECD-FAO Agricultural Outlook 2018–2027; OECD: Paris, France; FAO: Rome, Italy, 2018; Volume 181. [Google Scholar]
  61. European Commission. Demineralised Water Production from River Water Using Membrane Separation Technology; European Commission: Brussels, Belgium, 2000. [Google Scholar]
  62. Werner, S. European District Heating Price Series; Report 2016:316; Energiforsk: Stockholm, Sweden, 2016. [Google Scholar]
  63. Platts. S&P Global Platts Press Release, S&P Global Platts Launches World’s First Hydrogen Price Assessments, 18 December 2019. Available online: https://www.spglobal.com/commodityinsights/en/about-commodityinsights/media-center/press-releases/2019/18-12-2019-hydrogen-launch, (accessed on 1 January 2022).
  64. Hydrogen Council. Path to Hydrogen Competitiveness: A Cost Perspective; Hydrogen Council: Brussels, Belgium, 2020. [Google Scholar]
  65. Owen, R.; Macnaghten, P.; Stilgoe, J. Responsible research and innovation: From science in society to science for society, with society. Sci. Public Policy 2012, 39, 751–760. [Google Scholar] [CrossRef] [Green Version]
  66. Tempels, T.H.; Van den Belt, H. Once the rockets are up, who should care where they come down? The problem of responsibility ascription for the negative consequences of biofuel innovations. Springerplus 2016, 5, 135. [Google Scholar] [CrossRef] [Green Version]
  67. Parada, M.P.; Asveld, L.; Osseweijer, P.; Posada, J.A. Setting the design space of biorefineries through sustainability values, a practical approach. Biofuels Bioprod. Biorefining 2018, 12, 29–44. [Google Scholar] [CrossRef] [Green Version]
  68. Asveld, L. Towards including social sustainability in green and sustainable chemistry. Curr. Opin. Green Sustain. Chem. 2019, 19, 61–65. [Google Scholar] [CrossRef]
  69. ICAO. CORSIA Eligible Fuels—Life Cycle Assessment Methodology; ICAO: Montreal, QC, Canada, 2019. [Google Scholar]
  70. Pacheco, R.; Silva, C. Global Warming Potential of Biomass-to-Ethanol: Review and Sensitivity Analysis through a Case Study. Energies 2019, 12, 2535. [Google Scholar] [CrossRef] [Green Version]
  71. Diao, J.; Song, X.; Zhang, X.; Chen, L.; Zhang, W. Genetic Engineering of Crypthecodinium cohnii to Increase Growth and Lipid Accumulation. Front. Microbiol. 2018, 9, 492. [Google Scholar] [CrossRef] [Green Version]
  72. Didrihsone, E.; Dubencovs, K.; Grube, M.; Shvirksts, K.; Suleiko, A.; Suleiko, A.; Vanags, J. Crypthecodinium cohnii Growth and Omega Fatty Acid Production in Mediums Supplemented with Extract from Recycled Biomass. Mar. Drugs 2022, 20, 68. [Google Scholar] [CrossRef]
  73. Cannon, D. From Fish Oil to Microalgae Oil…A Win-Win Shift for Humans and Our Habitat. Explore 2009, 5, 299–303. [Google Scholar] [CrossRef]
  74. Lane, K.; Derbyshire, E.; Li, W.; Brennan, C. Bioavailability and Potential Uses of Vegetarian Sources of Omega-3 Fatty Acids: A Review of the Literature. Crit. Rev. Food Sci. Nutr. 2014, 54, 572–579. [Google Scholar] [CrossRef] [PubMed]
  75. Van der Voort, M.P.J.; Spruijt, J.; Potters, J.; de Wolf, P.L.; Elissen, H.J.H. Socio-Economic Assessment of Algae-Based PUFA Production. Public Output Report of the PUFAChain Project; PUFAChain: Göttingen, Germany, 2017. [Google Scholar]
  76. Chauton, M.S.; Reitan, K.I.; Norsker, N.H.; Tveterås, R.; Kleivdal, H.T. A techno-economic analysis of industrial production of marine microalgae as a source of EPA and DHA-rich raw material for aquafeed: Research challenges and possibilities. Aquaculture 2015, 436, 95–103. [Google Scholar] [CrossRef]
  77. Kleivdal, H.; Chauton, M.S.; Reitan, K.I. ProAlgae. Industrial Production of Marine Microalgae as a Source of EPA and DHA Rich Raw Material in Fish Feed—Basis, Knowledge Status and Possibilities; SINTEF: Trondheim, Norway, 2013. [Google Scholar]
  78. Mendes, A.; Da Silva, T.L.; Reis, A. DHA concentration and purification from the marine heterotrophic microalga Crypthecodinium cohnii CCMP 316 by winterization and urea complexation. Food Technol. Biotechnol. 2007, 45, 38–44. [Google Scholar]
  79. Slegers, P.M.; Olivieri, G.; Breitmayer, E.; Sijtsma, L.; Eppink, M.H.M.; Wijffels, R.H.; Reith, J.H. Design of Value Chains for Microalgal Biorefinery at Industrial Scale: Process Integration and Techno-Economic Analysis. Front. Bioeng. Biotechnol. 2020, 8, 550758. [Google Scholar] [CrossRef]
  80. Fréon, P.; Durand, H.; Avadí, A.; Huaranca, S.; Orozco Moreyra, R. Life cycle assessment of three Peruvian fishmeal plants: Toward a cleaner production. J. Clean. Prod. 2017, 145, 50–63. [Google Scholar] [CrossRef]
  81. Brunet, R.; Guillén-Gosálbez, G.; Jiménez, L. Combined simulation–optimization methodology to reduce the environmental impact of pharmaceutical processes: Application to the production of Penicillin V. J. Clean. Prod. 2014, 76, 55–63. [Google Scholar] [CrossRef]
  82. Mu, D.; Seager, T.; Rao, P.S.; Zhao, F. Comparative Life Cycle Assessment of Lignocellulosic Ethanol Production: Biochemical Versus Thermochemical Conversion. Environ. Manag. 2010, 46, 565–578. [Google Scholar] [CrossRef]
  83. European Comission. Environmental Factsheet: Glycerol; European Comission: Brussels, Belgium, 2014. [Google Scholar]
  84. Edwards, R.; O’Connell, A.; Padella, M.; Giuntoli, J.; Koeble, R.; Bulgheroni, C.; Marelli, L.; Lonza, L. Definition of Input Data to Assess GHG Default Emissions from Biofuels in EU Legislation; Publications Office of the European Union: Luxembourg, 2019. [Google Scholar]
  85. Numjuncharoen, T.; Papong, S.; Malakul, P.; Mungcharoen, T. Life-Cycle GHG Emissions of Cassava-Based Bioethanol Production. Energy Procedia 2015, 79, 265–271. [Google Scholar] [CrossRef] [Green Version]
  86. US Statistcis. Producer Price Index by Commodity: Chemicals and Allied Products: Rock Salt [WPU06130271]; Federal Reserve Bank of St. Louis: St. Louis, MO, USA, 2022. [Google Scholar]
  87. Appendix D: Capital Cost Guidelines. In Rules of Thumb in Engineering Practice; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2007; pp. 376–436.
  88. Stratta, L.; Capozzi, L.C.; Franzino, S.; Pisano, R. Economic Analysis of a Freeze-Drying Cycle. Processes 2020, 8, 1399. [Google Scholar] [CrossRef]
  89. Attard, T.; McElroy, C.; Hunt, A. Economic Assessment of Supercritical CO2 Extraction of Waxes as Part of a Maize Stover Biorefinery. Int. J. Mol. Sci. 2015, 16, 17546–17564. [Google Scholar] [CrossRef] [Green Version]
  90. NREL Equipment Design and Cost Estimation for Small Modular Biomass Systems, Synthesis Gas Cleanup, and Oxygen Separation Equipment; NREL: Golden, CO, USA, 2006.
  91. ESC. CHP Engineering and Instalation Costs. Available online: https://understandingchp.com/chp-applications-guide/6-8-rules-of-thumb-for-chp-engineering-and-installation-costs/ (accessed on 1 January 2022).
  92. EPA. Financing CHP Projects at Wastewater Treatment -Facilities with Clean Water State Revolving Funds; EPA: Frankfurt Germany, 2014. [Google Scholar]
  93. DBEIS. Combined Heat and Power—Finance; DBEIS: London, UK, 2021.
Figure 1. One of the drivers for the energy transition the CO2eq tax (EUR CO2eq/ton) volatility in 2021 (https://ember-climate.org/data/carbon-price-viewer/, accessed on 1 January 2022).
Figure 1. One of the drivers for the energy transition the CO2eq tax (EUR CO2eq/ton) volatility in 2021 (https://ember-climate.org/data/carbon-price-viewer/, accessed on 1 January 2022).
Energies 15 03784 g001
Figure 2. C. cohnii biorefinery and UNSDG relationship (own reasoning).
Figure 2. C. cohnii biorefinery and UNSDG relationship (own reasoning).
Energies 15 03784 g002
Figure 3. Constraints for future needs, own elaboration: (a) history and scenarios for population evolution [22], (b) history and scenario for fish oil consumption [23,24], (c) history and scenarios for fossil fuels in transportation [25], (d) history and scenarios for N-fertilizer [21].
Figure 3. Constraints for future needs, own elaboration: (a) history and scenarios for population evolution [22], (b) history and scenario for fish oil consumption [23,24], (c) history and scenarios for fossil fuels in transportation [25], (d) history and scenarios for N-fertilizer [21].
Energies 15 03784 g003
Figure 4. Analyzed frameworks for C. cohnii supply (higher-end needs).
Figure 4. Analyzed frameworks for C. cohnii supply (higher-end needs).
Energies 15 03784 g004
Figure 5. Cultivation step. Starter, inoculum, bench-scale batch reactor.
Figure 5. Cultivation step. Starter, inoculum, bench-scale batch reactor.
Energies 15 03784 g005
Figure 6. Cascading products scheme.
Figure 6. Cascading products scheme.
Energies 15 03784 g006
Figure 7. Reduction fishery generic data input and output [43].
Figure 7. Reduction fishery generic data input and output [43].
Energies 15 03784 g007
Figure 8. Boundaries in the life-cycle of jet fuel: CORSIA (all) and this study (processes in grey) for the reference scenario.
Figure 8. Boundaries in the life-cycle of jet fuel: CORSIA (all) and this study (processes in grey) for the reference scenario.
Energies 15 03784 g008
Figure 9. Electricity CO2eq emissions’ intensity (historical data from EU27 [44], combustion only); JRC WTT reports data [49] (includes both combustion and upstream emissions occurring before the gates of the power plant and resulting from the production and transport of the fuel, infrastructure materials not included); hypothesis assumed for calculations in 2030 and 2050.
Figure 9. Electricity CO2eq emissions’ intensity (historical data from EU27 [44], combustion only); JRC WTT reports data [49] (includes both combustion and upstream emissions occurring before the gates of the power plant and resulting from the production and transport of the fuel, infrastructure materials not included); hypothesis assumed for calculations in 2030 and 2050.
Energies 15 03784 g009
Figure 10. Historical commodity wholesale (EUR 2018) (Appendix B and Supplementary Excel file).
Figure 10. Historical commodity wholesale (EUR 2018) (Appendix B and Supplementary Excel file).
Energies 15 03784 g010
Figure 11. European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020) harmonized CPI (reference 2015) from EUROSTAT.
Figure 11. European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020) harmonized CPI (reference 2015) from EUROSTAT.
Energies 15 03784 g011
Figure 12. (a) DHA needs from alternative sources other than fish, (b) C. cohnii supply needs (higher-end estimates for 100% DHA coverage) and potential coverage of N-fertilizer, jet-fuel, and district heating (0.14 g/L/h C. cohnii dry productivity and 27% oil content).
Figure 12. (a) DHA needs from alternative sources other than fish, (b) C. cohnii supply needs (higher-end estimates for 100% DHA coverage) and potential coverage of N-fertilizer, jet-fuel, and district heating (0.14 g/L/h C. cohnii dry productivity and 27% oil content).
Energies 15 03784 g012
Figure 13. Inventory overview for 1 ton DHA, 0.14 g/L/h C. cohnii dry productivity and 30% oil content (used for TEA analysis, see Section 3.4).
Figure 13. Inventory overview for 1 ton DHA, 0.14 g/L/h C. cohnii dry productivity and 30% oil content (used for TEA analysis, see Section 3.4).
Energies 15 03784 g013
Figure 14. Hotspots of the C. cohnii biorefinery, (a) electricity distribution, (b) heat distribution, (c) GHG distribution in 2030 and 2050.
Figure 14. Hotspots of the C. cohnii biorefinery, (a) electricity distribution, (b) heat distribution, (c) GHG distribution in 2030 and 2050.
Energies 15 03784 g014
Figure 15. IAR economic indicator as a function of fish oil DHA reference cost, (a) 0.14 g/L/h and 27% oil content, (b) optimized C. cohnii 0.4 g/L/h and 70% oil content.
Figure 15. IAR economic indicator as a function of fish oil DHA reference cost, (a) 0.14 g/L/h and 27% oil content, (b) optimized C. cohnii 0.4 g/L/h and 70% oil content.
Energies 15 03784 g015
Figure 16. IAR economic indicator as a function of fish oil DHA reference cost, (a) 0.06 g/L/h and 50% oil content, (b) 0.14 g/L/h and 50% oil content.
Figure 16. IAR economic indicator as a function of fish oil DHA reference cost, (a) 0.06 g/L/h and 50% oil content, (b) 0.14 g/L/h and 50% oil content.
Energies 15 03784 g016
Figure 17. CAPEX (a), OPEX (b), and materials and utilities (c) for 0.14 g/L/h and 27 % oil content.
Figure 17. CAPEX (a), OPEX (b), and materials and utilities (c) for 0.14 g/L/h and 27 % oil content.
Energies 15 03784 g017
Table 1. Inventory for bench-scale.
Table 1. Inventory for bench-scale.
Unit ProcessInputsOutputs
StarterWild C. cohnii
Medium
C. cohnii starter
InoculumC. cohnii starter
Medium
C. cohnii inoculum
Batch (1)C. cohnii inoculum 0.1 (% v/v)
Medium 0.9 (% v/v)
Incubator (168 h, 50% capacity, 33.6 kWh)
Centrifugation (0.5 h, 100% capacity, 0.4 kWh)
C. cohnii
(77% moisture)
Freeze dryingVacuum + freezing + heating + condenser
2 kWh/kgH2O [28]
Removed water 0.72 kgH2O/kgdry algae
70.6 g C. cohnii (5% moisture)
0.06 g/L/h (e.g., 3 L 168 h, 70.6 g dry)
27% fatty acids (31% DHA)
Supercritical fluid extractionCooling + pumping + heating + extraction + separation
10 kWh/kgbio-oil [29]
13 g Oil w/o DHA
6 g DHA
(1) Bench-scale data electricity needs based on maximum power equipment and working hours is overestimated up to 10,000 times [32].
Table 2. Mass balances for carbon and nitrogen.
Table 2. Mass balances for carbon and nitrogen.
InOut
70.6 × 0.412 = 29.1 g CBio-oil 9.7 g C
DHA 4.7 g C
Biomass waste by difference 14.6 g C
70.6 × 0.047 = 3.3 g NBio-oil 0 g N
DHA 0 g N
Biomass waste by difference 3.3 g N
Table 3. Selected emission factors.
Table 3. Selected emission factors.
ItemUnitgCO2eq/UnitDatabase
HeatMJ47.3EU ETS benchmark [12]
ElectricitykWh230.7EU27 [44]
Tap waterL0.149UK DEFRA [45]
Sodium hydroxide (NaOH)kg450 (1) (529.7 × 0.85)Definition of input data to assess GHG default emissions from biofuels in EU legislation [46]
Sodium chlorine (NaCl)kg11.3 (1) (13.3 × 0.85)
Antioxidantskg10,000[47]
Wastewaterm3272UK DEFRA [45]
(1) Original values have been reduced by 15% to deduct emissions from the extraction and transport of crude oil, NG, etc., as well as transport and distribution of the final fuel that are comprised in REDII but are not accounted for in EU ETS.
Table 4. Matrix of relative GHG emissions savings as a function of average productivity 0.06–0.4 (g/L/h, dry cell, L of working volume) and oil content of 30–70 wt.%. Actual bench-scale data is marked in red [26]. Green values show GHG emission savings higher than 70%.
Table 4. Matrix of relative GHG emissions savings as a function of average productivity 0.06–0.4 (g/L/h, dry cell, L of working volume) and oil content of 30–70 wt.%. Actual bench-scale data is marked in red [26]. Green values show GHG emission savings higher than 70%.
Dry C. cohnii g/L/h
0.060.140.280.4
Oil %
3032718487
5060818991
7070859192
Table 5. Selected emission factors for N-fertilizer, jet fuel, DHA, and district heating from a C. cohnii biorefinery.
Table 5. Selected emission factors for N-fertilizer, jet fuel, DHA, and district heating from a C. cohnii biorefinery.
ItemUnit2030 gCO2eq/Unit 2050 gCO2eq/Unit2030 gCO2eq/Unit 2050 gCO2eq/Unit
Mass Allocation Energy Allocation
HeatMJ5.95.235.531.0
Jet fuelMJ35.931.435.531.0
N-Fertilizerkg1585.21385.60.00.0
DHAkg1585.21385.61404.01227.2
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Silva, C.; Moniz, P.; Oliveira, A.C.; Vercelli, S.; Reis, A.; da Silva, T.L. Cascading Crypthecodinium cohnii Biorefinery: Global Warming Potential and Techno-Economic Assessment. Energies 2022, 15, 3784. https://0-doi-org.brum.beds.ac.uk/10.3390/en15103784

AMA Style

Silva C, Moniz P, Oliveira AC, Vercelli S, Reis A, da Silva TL. Cascading Crypthecodinium cohnii Biorefinery: Global Warming Potential and Techno-Economic Assessment. Energies. 2022; 15(10):3784. https://0-doi-org.brum.beds.ac.uk/10.3390/en15103784

Chicago/Turabian Style

Silva, Carla, Patricia Moniz, Ana Cristina Oliveira, Samuela Vercelli, Alberto Reis, and Teresa Lopes da Silva. 2022. "Cascading Crypthecodinium cohnii Biorefinery: Global Warming Potential and Techno-Economic Assessment" Energies 15, no. 10: 3784. https://0-doi-org.brum.beds.ac.uk/10.3390/en15103784

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop