Life cycle comparison of hydrothermal liquefaction and lipid extraction pathways to renewable diesel from algae
Algae biomass is an attractive biofuel feedstock when grown with high productivity on marginal land. Hydrothermal liquefaction (HTL) produces more oil from algae than lipid extraction (LE) does because protein and carbohydrates are converted, in part, to oil. Since nitrogen in the algae biomass is incorporated into the HTL oil, and since lipid extracted algae for generating heat and electricity are not co-produced by HTL, there are questions regarding implications for emissions and energy use. We studied the HTL and LE pathways for renewable diesel (RD) production by modeling all essential operations from nutrient manufacturing through fuel use. Our objective was to identify the key relationships affecting HTL energy consumption and emissions. LE, with identical upstream growth model and consistent hydroprocessing model, served as reference. HTL used 1.8 fold less algae than did LE but required 5.2 times more ammonia when nitrogen incorporated in the HTL oil was treated as lost. HTL RD had life cycle emissions of 31,000 gCO2 equivalent (gCO2e) compared to 21,500 gCO2e for LE based RD per million BTU of RD produced. Greenhouse gas (GHG) emissions increased when yields exceeded 0.4 g HTL oil/g algae because insufficient carbon was left for biogas generation. Key variables in the analysis were the HTL oil yield, the hydrogen demand during upgrading, and the nitrogen content of the HTL oil. Future work requires better data for upgrading renewable oils to RD and requires consideration of nitrogen recycling during upgrading.
KeywordsAlgae Life cycle analysis Hydrothermal liquefaction Greenhouse gas emissions Renewable diesel
1.1 Context and motivation
Several pathways have been studied for producing algal biofuel, but the pathway studied most often utilizes a lipid-accumulating strain from which the triacylglyceride (TAG) lipid fraction is extracted and converted to biodiesel (BD) by transesterification or in which algal lipids are extracted and converted to a renewable diesel (RD) blend stock by hydroprocessing. The remnants, or lipid extracted algae (LEA), are converted to biogas which is used to produce electricity and heat for the process. Most nutrients consumed during growth are in the LEA and a portion of them is recovered during biogas production. Previous work showed that electricity production and nutrient recycling greatly affect energy and nutrient demands in the process (Campbell et al. 2009; Clarens et al. 2010, 2011; Frank et al. 2011a, 2012; Lardon et al. 2009; Stephenson et al. 2010).
Lipid-extraction (LE) based processes suffer several disadvantages. High lipid fractions are required to improve economic viability (Davis et al. 2011) and to reduce water consumption and emissions on a fuel-basis (Wigmosta et al. 2011; Frank et al. 2011a, 2012); however, high-lipid algae have low productivity during the lipid accumulation phase (Rodolfi et al. 2009). Additionally, much of the biomass energy is not recovered as liquid fuel but remains in the LEA. Finally, wet extraction processes have not been demonstrated, yet dry processes require excessive drying energy (Vasudevan et al. 2012).
Hydrothermal liquefaction (HTL) is a thermal process that converts biomass to several products including an oil portion with high heating value (Alba et al. 2012; Biller and Ross 2011; Brown et al. 2010; Duan and Savage 2011; Jena and Das 2011; Jena et al. 2011; Minowa et al. 1995; Vardon et al. 2011; Yu et al. 2011). HTL offers several advantages compared to other approaches. HTL is a liquid-phase process that avoids the energy cost of vaporizing the process slurry. HTL can process low-lipid algae, converting some protein and carbohydrate to HTL oil (Biller and Ross 2011; Brown et al. 2010). Wet algae, around 10 wt.% to 20 wt.% solids, is an excellent substrate for HTL. Cellular disruption, required by LE, is not necessary. Thus, HTL avoids the power and capital costs of cellular disruption and avoids heat and capital costs for solvent recovery operations utilized in the LE approach.
HTL may suffer several disadvantages. Many aspects of HTL have been studied including reaction temperature, pressure, retention time, feedstock species, influence of solvents, effects of catalysts, elemental distribution in products, and energy recovery. That work indicates that high temperatures (250°C to 350°C) and high pressures (approximately 10 to 20 MPa) are required and that the HTL oil can contain substantial amounts of nitrogen incorporated from the algal biomass (Alba et al. 2012; Biller and Ross 2011; Brown et al. 2010; Duan and Savage 2011; Jena and Das 2011; Jena et al. 2011; Minowa et al. 1995; Valdez et al. 2011; Vardon et al. 2011; Yu et al. 2011). From a life cycle perspective considering total energy demand and total emissions, these aspects suggest net energy demand may be higher than for lipid-extracted pathways because heat will be needed to establish the process conditions. This energy demand must be compared with that for solvent recovery in the LE approach. As mentioned, previous work showed that electricity produced from LEA strongly affects life cycle analysis (LCA) results. Thus, the potential for co-generated electricity must be assessed for HTL processes. If nitrogen is incorporated into the oil, then nitrogen fertilizer demand may be increased compared to the LE pathway which has no nitrogen in its TAG product.
In this study, we explore the tradeoffs between the benefits and challenges in the HTL pathway as compared to a LE pathway. Despite rising interest in HTL, many operational parameters are uncertain and are rarely all measured in a single experiment. Therefore, we analyze HTL based upon a model that includes several assumptions for the unknown or uncertain portions of the system and consider the effects of these uncertainties via sensitivity analysis based upon ranges of values in the research literature.
The objective of this work is to identify the key relationships affecting life cycle energy consumption and emissions for HTL-based RD blend stock production when the whole pathway is considered. Our goal is not to make an absolute prediction of energy demand and GHG emissions so much as to compare HTL with LE on an equal basis and explore ranges of the key unknown parameters. Therefore, we consider HTL and LE in the context of a particular algal growth model and keep that portion of the model fixed over the course of the study while HTL and LE parameters are varied.
Throughout this report, all weights are ash free dry weights (afdw) unless noted otherwise.
1.2 Hydrothermal liquefaction process
Feedstock and yields from several representative HTL studies. Weights and yields include ash
Yield (wt% of dry algae)
HTL oil composition
(wt% of oil)
Minowa et al. 1995b
Brown et al. 2010c
Duan and Savage 2011
Biller and Ross 2011
Jena and Das 2011
Jena et al. 2011
Valdez et al. 2011e
Yu et al. 2011
Alba et al. 2012f
Vardon et al. 2011
Although detailed yield distributions differ, several general behaviors are observed uniformly. First, the HTL oil has a high heating value, approximately 35 MJ/kg. The gas fraction contains mostly CO2 and carries little energy compared to the oil and aqueous phases. The aqueous phase contains many dissolved organic molecules and nitrogen, the later largely as ammonia. The HTL oil properties shown in Table 1 indicate that the HTL oil is unsuitable for engine use directly, e.g., oxygen and nitrogen levels are high. Thus, upgrading is required and stabilization may be required prior to transportation to upgrading facilities.
The aqueous phase contains dissolved soluble organics plus ammonia. Yu et al. (2011) examined carbon and nitrogen distributions in HTL products formed from low-lipid, high-protein Chlorella. At 300°C, 30 % of the feedstock nitrogen went to the oil and solid phases and 70 % went to the aqueous phase. The aqueous phase contained 45 wt.% of the algae feedstock, much of it organic carbon, but had 19,400 mg/L total nitrogen (TN) and a C/N ratio of 2. Since C/N should be 20 to 30 for successful AD (Fricke et al. 2007) and since methanogenic activity ceased at 6,000 mg NH4–N/L (Sawayama et al. 2004), the nitrogen level is too high for treatment with anaerobic digestion (AD) unless special configurations are used that reduce the nitrogen content, e.g., by precipitation (Uludag-Demirer and Othman 2009). Nevertheless, much of the biomass energy remains in the dissolved organics. Therefore, the pathway models in our work replace AD with an alternative technology, catalytic hydrothermal gasification (CHG). CHG is a thermal aqueous process similar to HTL that reduces carbon and nitrogen in a wet organic feed stream to biogas and ammonia via catalysis with over 99 % efficiency for organic carbon (Elliott et al. 1993; Elliott and Sealock 1996).
2.1 Life cycle analysis system boundary
This study employed the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model developed at Argonne National Laboratory with support from the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy. GREET is a publicly available LCA tool that investigates numerous fuel and vehicle cycles (Wang 1999a, b; GREET 2011). GREET computes fossil, petroleum, and total energy use (including renewable energy in biomass), emissions of greenhouse gases (CO2, CH4, and N2O), and emissions of six criteria pollutants: carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NOx), sulfur oxides (SOx), particulate matter with a diameter below 10 μm (PM10), and particulate matter with a diameter below 2.5 μm (PM2.5). GREET includes gasoline, diesel, biofuels, hydrogen, natural-gas-based fuels, and electricity. Vehicle technologies include gasoline engines, diesel engines, hybrid electric vehicles with gasoline and diesel engines, plug-in hybrid electric vehicles with gasoline and diesel engines, battery-powered electric vehicles, and fuel cell vehicles. We used the Algae Process Description (APD) tool in GREET to implement the models described here after suitable extension and modification (Frank et al. 2011a; b).
2.2 Algal growth and lipid extraction model
Algae biomass model. All weights are ash free dry weights
Table 1 average
Williams and Laurens 2010
Williams and Laurens 2010
mol : mol
Williams and Laurens 2010
Williams and Laurens 2010
Williams and Laurens 2010
Stoichiometric CO2 demand a
g CO2 / g algae
CO2 utilization efficiency
Lundquist et al. 2010b
Adopted Williams and Laurens (2010)
Recycle pump, Wh/L
6 m head assumed
Water supply, Wh/L
30 m head assumed
Dissolved air flotation output, wt%
Dewatering centrifuge power, Wh/g algae out
CHP flue gas
See Section 4
Hexane extraction heat, kWh/kg oil
Hexane extraction power, kWh/kg oi
The algae macromolecular composition and productivity depend upon species, specific growth conditions, and extrapolation from diverse experiments. The biomass model is summarized in Table 2. The model assumes 25 wt.% lipids and 25 g/m2/day productivity similar to our earlier work. The lipid fraction is similar to several of the HTL studies in Table 1. This lipid fraction corresponds to the medium-lipid algae case in Williams and Laurens (2010) which has 55 wt.% carbon and C:N:P molar ratios of 175:25:1.
2.3 HTL model
The CHG heat and electricity demands are based upon conversations with the developer. In their experiments, they only need to make a 30°C temperature rise (Q2) entering CHG and require 3.1 × 10-4 kWh electricity per g of CH4 produced when integrated with HTL. The CHG biogas is 60 % CH4 and 40 % CO2 volumetrically. The fate of HTL solids is unclear in the literature, sometimes being reported in the oil phase, and sometimes in the aqueous phase. We neglect separation of the solids in the HTL model.
2.4 HTL product distribution
The gas, liquid, and solid yields in Table 1 are difficult to interpret because of different experimental methods. Minowa, Jena, and Yu found the gas mass by difference from the mass of the reactor and its contents before and after the reaction (having released the gas products). The oil was then separated into dry oil and dry solids fractions, e.g., by solvent extraction. Experimenters subtracted these dry masses and the gas mass from the dry feed to compute the dry solids in the aqueous phase. Brown et al. (2010) and Valdez et al. (2011) assayed the gas fraction by GC-MS relative to a standard loaded at the start of the experiment without opening the reactor. Their gas values do not include gases still dissolved in the product liquids and thus likely underestimate the gas yield. Vardon et al. (2011) measured dissolved solids in the aqueous phase by filtration and evaporation and subtracted this from the feed to estimate gas yields. If the aqueous phase had light volatile compounds, they would have been lost from the aqueous phase when heated causing overestimation of gas yields, which may be why the Vardon yields are so much higher than the others in Table 1. Biller and Ross (2011) took yet another approach by measuring gas pressures and using the ideal gas law via an assumed gas composition to compute gas mass. For these reasons, we base our gas, solids, and aqueous yields on the data in Minowa, Jena and Yu.
Distribution of carbon in HTL products. The recovery rates are the percentages of feedstock carbon in each of the four products
HTL carbon mass balance, g/g on ash free dry basis computed from Table 4
C in algae a
C in oil b
C in solids
C in gas
C in aq.
The nitrogen flow is determined by averaging the oil composition data in Table 1. Thus, the HTL oil contains 5.7 wt.% nitrogen. This nitrogen is considered to be lost in the process (See Section 4). The balance of the feedstock nitrogen is recovered as ammonia by the CHG process (Section 2.6) and is returned to the pond with 5 % losses from volatilization.
2.5 Oil upgrading to RD
Elemental composition of LE, HTL, and soybean oils
Unfortunately, few data are available for upgrading hydrothermal liquefaction oils. Baker and Elliott (1988) describe treatment of HTL oils from woody biomass, but woody biomass contains negligible nitrogen compared to algal biomass. We therefore utilize a stoichiometric hydroprocessing model to explore the effect of N, O, and H levels on hydrogen demand for fully deoxygenated and denitrogenated RD. This calculation cannot account for hydrogen losses in an actual process, but can guide us in selecting plausible ranges of values for yields and hydrogen demands.
Values for a and f are estimated as follows. Four experiments in Baker and Elliott (1988) upgraded HTL oil from woody biomass to an oil with H/C ranging from 1.32 to 1.65. These experiments achieved average carbon efficiency (f) of 92 % but did not include hydrocracking, which might have increased the H/C ratio further. Marker et al. (2005) reports upgrading vegetable oil to renewable diesel by hydroprocessing. Small autoclave experiments achieved deoxygenation between 85 % and 99 %. The latter had an RD yield of 84 wt.% and was achieved with conditions favoring hydrodeoxygenation over decarboxylation. In those experiments, the light hydrocarbons were propane ((H/C)light = 2.67). By computation from Marker, the scenario had 95 % carbon efficiency (f = 0.95), 86 % of non-RD product carbon went to light hydrocarbons (a = 0.86), and (H/C)RD = 1.81. Our model, Eqn. 1, predicts 0.012 g H2 per g oil, near the range of 0.015 to 0.038 g H2 in Marker. The experimenters commented that their gas measurements were overestimated so a is likely higher than 0.86. Also, (H/C)RD of 1.81 is less than that of the soybean oil feed (1.86). If we use (H/C)RD = 2.0, then our model estimates 0.024 g H2 and 85 wt.% yield, consistent with 84 wt.% reported by Marker. If, also, a were 0.95, then the demand would be 0.026 g H2 per g oil.
Estimated yields and hydrogen demands to upgrade LE and HTL oils
g RD/g oil
g H/g oil
g H/g RD
It is clear that experimental data for algal LE and HTL oil upgrading are needed, but the analysis just presented suggests that hydrogen demand will be higher for HTL oil than for LE oil for a broad range of parameters, suggests that both will be higher than for soybean oil, and estimates the relative amounts on a consistent basis. This treatment allows us to use LE as a reference scenario for studying HTL.
2.6 Biogas production
The CHG process for LEA feeds was described in Frank et al. 2011a. That model has been revised based upon further discussions with the developers (Genifuel 2011) and now is based upon the organic carbon content of the feed which is converted to biogas (60 % methane, 40 % CO2 by volume) with 99 % efficiency. Minowa et al. (1995) reported that all C in the HTL aqueous phase was organic. The methane yield from CHG processing of the HTL aqueous phase can thus be computed from the organic C in the aqueous phase, Table 5. The LEA is estimated to have 48 wt.% C based upon its protein and carbohydrate content in Table 2. Developers report almost complete recovery of N and P, but in the current model, 95 % and 90 % recoveries were assumed, respectively.
Summary of HTL process parameter values. The low and high values were used in sensitivity analyses
Hydrogen demand, g H / g RD a
RD yield, g RD/g HTL oil a
HTL oil yield, g/g afdw algae
Reaction temperature, °C
Slurry solids (afdw), wt%
Nitrogen, wt% in oil
Phosphorus loss, %
In this section, results are first presented for the nominal parameter values followed by results from sensitivity analysis. The driving parameters so identified are then explored with Monte Carlo simulation. Results are sometimes broken into three stages of the life cycle. The well to pump (WTP) stage includes all activities up through fuel delivery to the filling station. The pump to wheels (PTW) stage includes all aspects of vehicle operation (combustion) but not vehicle manufacturing. The sum of WTP and PTW is the whole fuel-cycle result, also called the well to wheels (WTW) result. GHG emissions are reported as grams CO2 equivalent (gCO2e) by combining CO2, CH4, and N2O scaled by their global warming potentials (IPCC 2007). The CO2 emissions include CO2 from degradation of CO and degradation of volatile organic compounds, both of which have short lifetimes in air.
Direct energy and material usage for the nominal parameter values in Table 8
Operation on site
Direct energy use (BTU/BTU RD)
Growth & 1st dewatering
Material consumption (kg/MMBTU RDb)
Diammonium phosphate c
RD production (BTU/BTU RD)
Total direct energy d
Life cycle energy use (BTU) and GHG emissions (gCO2e) per MMBTU of RD by stage for HTL and LE
We now consider direct energy use in the oil production steps of the HTL and LE pathways, i.e., for the HTL and LE unit operations themselves. For HTL, this includes all the steps shown in Fig. 3 while for LE this includes high-pressure homogenization, lipid recovery, and solvent recovery. Table 9 indicates that the direct energy use for oil production was less for HTL than for LE despite the high temperature and pressure required for the operation. The higher electrical demand for LE arose from the high-pressure homogenizer (not required for HTL). The higher LE heat demand is from solvent recovery but there is considerable uncertainty in this value: The low and high LE heat demand in Frank et al. (2011a) gave 0.55 and 0.09 BTU/BTU RD when applied to the current LE model. Those results correspond, roughly, to the heat demands in Lardon et al. (2009) and Stephenson et al. (2010). The heat demand is higher for LE than for HTL in two of three models, but this conclusion does not consider uncertainties in the HTL model, which we now examine by sensitivity analysis.
Figure 7a indicates that the key variables affecting fossil energy use for the HTL pathway are the hydrogen demand during RD production, nitrogen loss through nitrogen incorporation into the HTL oil, and the HTL yield. Our objective is to compare HTL and LE for the specific putative growth pathway presented and for algae as described in Table 2. In that context, the key parameters for the LE pathway are the homogenizer electricity consumption and the combined homogenizer (90 %), hexane extraction (95 %) efficiencies. A Monte Carlo simulation was run in which the HTL yield, the nitrogen loss into the HTL oil, and the HTL upgrade hydrogen demand, were randomly selected from triangular distributions defined by Table 8. LE was varied by taking all the uncertainty in the homogenizer step for sake of simplicity; the intention, though, is to explore the net homogenizer, lipid recovery system. The homogenizer electrical demand was sampled from a triangular distribution with minimum of 0.01 kWh/kg-algae homogenized and maximum of 0.03 kWh/kg algae which was also adopted as the mode. The latter is the nominal value in the LE pathway and the sampling explores a bias that less energy intensive cellular disruption methods may be possible. The homogenization efficiency was sampled between 80 % and 100 % with mean of 90 %.
The analysis proceeded from nominal values to sensitivity analysis that changed one parameter at a time and then, finally, to changing the three most important HTL variables and two LE variables simultaneously (uncorrelated) in a Monte Carlo study. The Monte Carlo study therefore offered the chance of discovering places in parameter space where parameter perturbations conspire to give different performance than at the central, nominal point.
The mean algae consumption, Fig. 8b, was significantly less for HTL than for LE, although the extremes in the distributions come close to being equal. The likely conclusion is that HTL will reduce biomass requirements, but it is possible that actual performance may give comparable requirements if the LE cellular disruption and extraction processes efficiencies were higher or if HTL yields were lower.
Regardless of reduced algae demand, HTL required substantially more nitrogen than did LE because of its incorporation into the HTL oil and because our study treated nitrogen in the HTL oil as lost. We assumed that nitrogen incorporated into the HTL oil was lost because of uncertainties in scale and uncertainties in the upgrading process: Nitrogen removal by hydrodenitrogenation converts nitrogen in the HTL oil to ammonia dissolved in the process water. Many water treatment methods are employed at refineries to treat process waters in order to reduce water consumption and to meet discharge requirements. The method employed for any specific refinery depends upon the refinery size, refinery age, and depends upon the types and concentrations of species in the water. These species may include ammonia, phenols, cyanide, hydrogen sulfide, and selenium, as examples (Armstrong et al. 1996). The concentrations will depend upon the particular crude oil being processed, the proportions of petroleum crude and HTL oil, and upon how HTL oil processing is integrated into the refinery.
If the HTL oil is co-processed with the petroleum crude or if water streams from the various refinery operations are comingled, then the process water will contain mixtures of species originating from both HTL and crude oils. Refineries handling relatively small HTL oil feeds and low-nitrogen crudes may have low ammonia concentrations despite the large nitrogen content in HTL oil and may use wastewater treatment (WWT) methods that stabilize ammonia to N2 and release it. In other scenarios, plants may use steam stripping methods and either incinerate the ammonia or recover it, but recovery is only economical at higher concentrations (Armstrong et al. 1996; Chevron 1998).
Stripping processes can be complex and can involve degassing, acid gas (H2S) stripping, ammonia stripping, scrubbing, and cooling operations (Chevron 1998) that require steam and electricity. A credible model should consider heat integration at the refinery and should consider process integration between HTL and petroleum crude oil processing at least with regard to process water flows. These considerations were beyond the scope of the present study and require technoeconomic analysis to guide model definition. For these reasons, we treated the nitrogen in the HTL oil as lost, effectively analyzing a scenario in which the HTL oil volumes are small relative to crude oil volumes and in which the ammonia concentrations are too low for economical recovery. If ammonia were recovered at the refinery, the GHG emissions for ammonia production (2,600 gCO2e per kg of fossil NH3) can be subtracted as a rough estimate; however, this estimate will not consider energy consumption during ammonia recovery and may overestimate the benefits of recycling at the refinery.
The consequences of nitrogen loss go beyond energy consumption and emissions. The 2.7 kg NH3 per MMBTU RD ammonia demand implies 3.3 million metric tons (MMT) of ammonia are required to produce 10 billion gallons per year (BGY) of RD by HTL. In the US, 14 MMT of NH3 are utilized directly or indirectly as fertilizer (Glauser and Kumamoto 2010). Clearly future work must consider alternative scenarios that recycle nitrogen or that keep it from incorporating into the HTL oil.
Previous algae life cycle analyses demonstrated that balancing power on site by recovering energy from process residuals is key to reducing life cycle emissions and fossil energy use. LE can achieve this by either AD or CHG, but the nitrogen levels in the HTL aqueous phase were too high for AD. The organic carbon in the HTL aqueous phase, though, was sufficient to meet process energy demands on site for HTL when converted to biogas by CHG. The algae lost to the dewatering centrifuge supernatant were too dilute for processing by CHG in the HTL pathway, but the centrifuge supernatant provided reasonable carbon concentrations for CHG processing in the LE pathway because it could be combined with the concentrated LEA stream.
Since less of the biomass energy is recovered as oil in the LE pathway, the LE process produced and exported substantially more electricity per MMBTU of RD than did HTL Therefore, the energy based allocation of emissions for oil production was different between the two: HTL exported 1 % of the produced energy as electricity while LE exported 14 % corresponding to allocation factors of 99 % for HTL and 86 % for LE. If no market were available for the electricity co-product, the unused biomethane could be sold and the allocation factors would be mostly unchanged.
The WTW GHG emissions for the LE pathway, Table 10, are substantially less than our previous study of algal biodiesel production by transesterification. That work computed 55,400 gCO2e per MMBTU of biodiesel (Frank et al. 2011a; 2012) compared to 21,500 gCO2e/MMBTU RD reported here. The difference was examined as follows. The developments in the model presented in Table 3 and the change from producing biodiesel to producing RD lead to a result of 62,300 gCO2e/MMBTU RD. The work reported here replaced AD with CHG because of the high ammonia levels in the HTL aqueous product. This avoided 18,000 gCO2e mostly because CHG has lower fugitive CH4 emissions than AD and avoids N2O emissions from AD digestate when used as crop fertilizer (Frank et al. 2012). Updates to the CHG model (Section 2.6) increased methane yields and avoided another 8,300 gCO2e. These CHG updates were needed because the old CHG biogas estimation method could not be applied to the HTL aqueous phase because of its composition and concentration. The updated CHG model was used in the LE model for sake of consistency when comparing the two pathways. Finally, venting the biogenic CO2 from biogas combustion rather than returning it to the pond avoided 13,000 gCO2e because substantial blower power is required to move large volumes of hot dilute flue gas across a diffuser (1 psi) under 1.5 m of water. Future work will consider other methods of CO2 recycling but the recycle was dropped here for fear it would confound the HTL and LE comparison since LE produces more biogas.
The HTL GHG emissions, Fig. 8a, were higher than those for LE but both were less than those of low sulfur petroleum diesel (100,000 gCO2e/MMBTU). The long tail at higher emissions for HTL arises from cases when the HTL oil yield is too high to leave sufficient organic carbon for heat and power generation by CHG. The tail may be removed by reducing power demand in other operations such as pond mixing. The largest contributions to the higher GHG emissions for HTL in Fig. 7a are associated with higher hydrogen consumption during upgrading (+8,140 gCO2e), higher nutrient demand in HTL (+5,918 gCO2e), but less power for transferring CO2 into the culture because of the reduced algae demand (-3,129 gCO2e) and fewer fugitive CH4 emissions because less biogas is handled (-2,055 gCO2e). Reflecting upon the earlier discussion of nitrogen recycling during upgrading, the largest single factor affecting the GHG result was hydrogen consumption although nutrient demand ranks second. Hydrogen demand derives, in part, from nitrogen levels in the HTL oil. Reducing nitrogen levels in the oil, then, improves the pathway both by reducing challenges in recycling and by reducing hydrogen demand.
Several questions have not been addressed. Separation of solids was not considered. If solids are in the oil phase, either they must be separated before transporting the HTL oil to a refinery or the refinery must manage them. The HTL oil may not be stable enough for transporting without at least partial upgrading. We have tacitly assumed stabilization, if required, will be achieved by hydroprocessing and that the total hydrogen computed in our model will simply split between use on site and at the refinery. Since energy for hydrogen production dominates the upgrade energy consumption, the current model should be approximately correct even if stabilization before transportation is required. The proportion of RD to other products from hydroprocessing will vary with processing conditions. We neglected this effect and used an energy-based allocation of 94.5 % RD, 5.5 % other products to match current values in GREET for soybean oil upgrading (to facilitate comparison). To study temperature dependence, we assumed the pressure would be 20 % above the conditions for saturated steam. This under predicts pressures at low temperatures but increasing the pressure in those cases had little effect on the results. The study held the algae growth parameters constant in the sensitivity and Monte Carlo analyses and it is possible that varying those parameters may affect the results. This will be explored in the future.
Our objective was to identify and explore the important variables affecting HTL life cycle analysis. Since many questions remain regarding algae biomass production, e.g., algae productivity and composition, the analysis used the LE production pathway as a reference and explored the effect of key variables on the relative performance, HTL vs. LE, when both pathways used identical growth models and used consistent assumptions for upgrading to RD. In that context, HTL offered advantages over LE, especially with regard to efficient utilization of biomass, which was 1.8 fold less for HTL than for LE. On the other hand, if nitrogen is incorporated into the HTL oil at the rates reported in current literature, and if that nitrogen is not recycled during upgrading, then emissions and scalability are adversely affected. The mass balance for phosphorus must be clarified to assess further with life cycle analysis. HTL can produce adequate heat and electricity from process residuals via CHG in many cases, but when HTL yields exceeded 0.40 g HTL oil per g algae (ash free basis), insufficient heat and electricity were produced on site to meet growth and harvesting needs. Power reduction, e.g., during algae growth, would make room for taking advantage of higher HTL yields, but using the HTL aqueous phase for culture nutrients would preclude heat and electricity production altogether, jeopardizing energy and emissions reductions. Key variables affecting the analysis centered on upgrading, especially hydrogen demand, ammonia recycling rate, and ammonia recycling associated energy consumption in a refinery context. Future analysis would benefit from data obtained by studying both HTL and LE applied to aliquots of a common feedstock including data for LE and HTL oil elemental composition; upgrading performance for LE and HTL oils; characterization of the HTL aqueous phase (especially organic carbon content); carbon, nitrogen and phosphorus balance amongst the HTL products; and clarification of HTL solids handling.
E.D. Frank, A. Elgowainy, J. Han, and Z. Wang performed this work for UChicago Argonne LLC, the author in fact, as a Works Made for Hire (17 USC 201(b)). This work was sponsored by the Office of Biomass Program in the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy. Argonne National Laboratory is a DOE laboratory managed by UChicago Argonne, LLC, under Contract No. DE-AC02-06CH11357. The authors thank K.C. Das (University of Georgia, Athens), Ryan Davis (National Renewable Energy Laboratory), Jeff Miller and Meltem Urgun-Demirtas (Argonne National Laboratory), George Oyler (Genifuel Corp.), Y. Zhang, and G. Yu (University of Illinois) for helpful discussions.
This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
ash free dry weight
Algae Process Description tool in GREET
Billion gallons per year
Catalytic hydrothermal gasification
Combined heat and power
Carbon-to-nitrogen molar ratio
Department of Energy
global warming potential in grams CO2 equivalent
Greenhouse Gases Regulated Emissions, and Energy Use in Transportation
Molar hydrogen-to-carbon ratio of renewable diesel
Molar hydrogen-to-carbon ratio of light hydrocarbons
Life cycle analysis
Lipid extracted algae
Million British thermal unit
Million metric tons
National Renewable Energy Laboratory
Pump to wheel (fuel usage stage)
Well to pump (fuel production stage)
Well to wheels (full life cycle)
- Armstrong T, Scott B, Taylor K, Gardner A (1996) Refining details notebook/Sour water stripping. Today’s Refinery June:1-5Google Scholar
- Campbell PK, Beer T, Batten D (2009) Greenhouse gas sequestration by algae - energy and greenhouse gas life cycle studies. CSIRO, ClaytonGoogle Scholar
- Chevron (1998) WWT Two-state sour water stripping. Available via http://www.chevron.com/products/sitelets/refiningtechnology/documents/wwt.pdf. Cited 6 May 2012
- Elliott D, Sealock L Jr (1996) Chemical processing in high-pressure aqueous environments: low-temperature catalytic gasification. Chem Eng Res Des 74:563–566Google Scholar
- Frank E, Han J, Palou-Rivera I, Elgowainy A, Wang M (2011a) Life-cycle analysis of algal lipid fuels with the GREET model. ANL/ESD/11-5. Argonne National Laboratory. (available at http://greet.es.anl.gov/publications)
- Frank E, Han J, Palou-Rivera I, Elgowainy A, Wang M (2011b) User manual for algae LCA with GREET: Version 0.0. ANL/ESD/11-7. Argonne National Laboratory. (available at http://greet.es.anl.gov/publications)
- Genifuel (2011) Genifuel Corporation. (available at http://www.genifuel.com/, accessed 15 Jul 2011)
- Glauser J, Kumamoto T (2010) Ammonia. In: Chemical Economics Handbook. SRI Consulting, Menlo ParkGoogle Scholar
- GREET 2011, Argonne GREET Model. (available at http://greet.es.anl.gov/main, accessed 11 Nov 2011)
- IPCC (2007) Climate Change 2007: The Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge: Cambridge University Press)Google Scholar
- Lundquist T, Woertz I, Quinn N, Benemann J (2010) Realistic Technology and Engineering Assessment of Algae Biofuel Production (Berkeley, CA: Energy Biosciences Institute) (available at http://works.bepress.com/tlundqui/5)
- Lupton S, Kassa N, Galloway D, Lynch S (2011) Conversion of algal oils to hydrocarbon fuels. Presented at the 1st international conference on algal biomass, biofuels & bioproducts, St. Louis MO, 17-20 July 2011Google Scholar
- Marker TL, Petri J, Kalnes T, McCall M, Mackowiak D, Jerosky B, et al. (2005) Opportunities for biorenewables in oil refineries. Final Technical Report No. DOEGO15085Final, U.S. Department of EnergyGoogle Scholar
- Sawayama S, Tada C, Tsukahara K, Yagishita T (2004) Effect of ammonium addition on methanogenic community in a fluidized bed anaerobic digestion. J Biosci Bioeng 97(1):65–70Google Scholar
- Shah RK, Sekulic DP (2002) Fundamentals of heat exchanger design. Wiley, HobokenGoogle Scholar
- Wang M (1999a) The Greenhouse gases, regulated emissions, and energy use in transportation (GREET) model version 1.5. Argonne National LaboratoryGoogle Scholar
- Wang M (1999b) GREET 1.5-transportation fuel-cycle model-Vol. 1: methodology, development, use, and results. ANL/ESD-39. Argonne National LaboratoryGoogle Scholar
- Wang Z (2011) Reaction mechanisms of hydrothermal liquefaction of model compounds and biowaste feedstocks. Dissertaion. University of Illinois at Urbana-ChampaignGoogle Scholar