Jordan Kern Research Assistant Professor. University of North Carolina at Chapel Hill

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1 Jordan Kern Research Assistant Professor University of North Carolina at Chapel Hill

2 Adam Hise Greg Characklis (UNC Chapel Hill) Robin Gerlach Sridhar Viamajala (University of Toledo) Robert Gardner (University of Minnesota) (Harbor Research, Boulder, CO) (Montana State University) US Dept. of Energy Grant # DE-EE /000 Sustainable Energy Pathways Award # SEP

3 Algal Biofuel Pathways Plant Design Cultivation Water Nutrients Energy & Industrial Ecology HVP Harvesting Conversion Energy Energy Minimum Fuel Price ($/gge) Fuel (gge) Global Warming Potential (% of diesel)

4 Life Cycle Assessment (LCA) & Techno-economic Analysis (TEA) Plant Design & Construction Plant Operations Life Cycle Impacts Project Financing Annual Costs & Revenues Net Present Value / MFSP Year -2 to 0 Year 1 to 30 Plant Lifetime

5 Life Cycle Assessment (LCA) & Techno-economic Analysis (TEA) Parameters are uncertain but not time- varying Plant Operations Probability Annual Costs & Revenues Probability Fertilizer Price Year 1 to 30 NPV

6 An uncertain & dynamic system Fertilizer Price Time

7 LCA/TEA underestimates risks and may miss opportunities for improved plant design. It does not value operational flexibility as a design feature. Ability to adjust plant output in response to price fluctuations

8 Nutrients Water CO 2 Algae Growth Evaporation Auto- Flocculation Flocculant DAF Transesterification Pathway Centrifuge Thermal Drying Makeup Solvent Lipid Extraction Recycle Solvent Recycle Water Solvent Recovery Recycle Water & Nutrients Anaerobic Digestion 1 2 Methanol Transesterification Combined Heat & Power Algal Meal Biodiesel Glycerol

9 An up-front investment in anaerobic digestion/chp adds operational flexibility. But does it make financial sense? LCA/TEA falls short here.

10 Real Options Analysis (ROA) A way to quantify the value of different infrastructure choices under uncertainty Used extensively in the energy industry; has been proposed for other biofuels (1 st and 2 nd generation ethanol)

11 1. How often would a plant take advantage of added flexibility (switch between selling algal meal and recovering nutrients/energy)? 2. Do the financial benefits outweigh up-front cost of anaerobic digestion/chp?

12 1: Algal Meal Only 2: Higher Cost, Flexible Pathway (anaerobic digestion/chp) LCA/TEA Cultivation Harvesting Conversion

13 Algal Meal vs. Nutrient & Energy Recovery Algal Meal Closed Loop Inputs Production Anaerobic Digestion CHP Net Inputs Electricity (kwh) Natural Gas (MMBtu) Nitrogen (kg) Phosphorus (kg) Algal Meal (tons) E E E E E E E E E E E E E E E+04 Functional unit = 10 million gals/yr biodiesel

14 1: Algal Meal Only 2: Higher Cost, Flexible Pathway (anaerobic digestion/chp) LCA/TEA Cultivation Harvesting Conversion MILP Optimization max Profits: f(a, B) A = decision variables B = {prices}

15 Mixed Integer Linear Program Maximize Z: VW UXY MealON t (Meal PriceMeal U ) + RecoverON t (Elec PriceElec U + NatGas PriceGas U Where + Nitrogen PriceNitr U + Phosphorus PricePhos U ) MealON U = binary 0,1 variable; triggers algal meal production RecoverON U = binary 0,1 variable; triggers nutrient & energy recovery

16 1: Algal Meal Only 2: Higher Cost, Flexible Pathway (nutrient & energy recovery) 1000-run Ensemble of 30-year Price Scenarios LCA/TEA MILP Optimization Cultivation Harvesting Conversion max Profits: f(a, B) A = decision variables B = {prices} 2016 time 2045

17 Pearson Correlation Matrix Crude Oil (WTI) B99 Electricity Natural Gas Soybean Meal Anhydrous Ammonia B Electricity Natural Gas Soybean Meal Anhydrous Ammonia Diammonium Phosphate

18 Vector Autoregressive (VAR) Model Describes multivariate time series in cases where significant linear dependency exists among (k) variables. y t = C + A 1 y t 1 + A 2 y t A p y t p + ε t C = k x 1 vector of constants A Š = k x k matrix of coefficients ε U = k x 1 vector of error terms t = time period p = model "lag"

19 Historical Mean Simulated

20 Historical Simulated

21 1: Algal Meal Only 2: Higher Cost, Flexible Pathway (nutrient & energy recovery) 1000-run Ensemble of 30-year Price Scenarios LCA/TEA MILP Optimization Cultivation Harvesting Conversion max Profits: f(a, B) A = decision variables B = {prices} 2016 time 2045 Plant Configuration f(x) g(x) GWP x x f(y) g(y) NPV y y

22 Results Under current price dynamics, the plant with anaerobic digestion/chp switches between selling algal meal and recovering nutrients and energy in about 0.4% of all simulation years. Frequency Anaerobic Digestion/CHP Algal Meal Only Net Present Value ($M)

23 Algal meal prices are too high. Production Prices ($/unit) Annual Value ($ millions) Algal Meal Nutrient & Energy Recovery Low Mean High Low Mean High Electricity (kwh) 7.48E Natural Gas (MMBtu) Anhydrous Ammonia (kg) Diammonium Phosphate (kg) Algal Meal (tons) 2.41E E E E

24 And they are positively correlated with fertilizer prices. Crude Oil (WTI) B99 Electricity Natural Gas Soybean Meal Anhydrous Ammonia B Electricity Natural Gas Soybean Meal Anhydrous Ammonia Diammonium Phosphate

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26 Frequency NPV ($ millions) Price Reduction (75%) Recovery Utilization: 100% Frequency Price Reduction (90%) Recovery Utilization: 100% NPV ($ millions)

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28 Algal Meal Price Reduction: Recovery Utilization: 0% 1%

29 Algal Meal Price Reduction: Recovery Utilization: 15% 2.5%

30 Algal Meal Price Reduction: Recovery Utilization: 35% 22%

31 Algal Meal Price Reduction: Recovery Utilization: 45% 50%

32 Algal Meal Price Reduction: Recovery Utilization: 55% 85%

33 Algal Meal Price Reduction: Recovery Utilization: 65% 99%

34 Algal Meal Price Reduction: 70% Recovery Utilization: 100%

35 Conclusions 1. Anaerobic digestion/chp vs. algal meal 2. ROA adds useful insights about plant design that LCA/TEA cannot provide Dynamic view of the system Value of operational flexibility Flexible operation as a source of uncertainty (financial and environmental outcomes)

36 Future work Non-stationary price dynamics Changes in mean, variance, autocorrelation, cross correlation Evolving market, technological and regulatory conditions Other applications for algal biofuels Hydrothermal liquefaction Industrial ecology (wastewater)

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