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Techno-Economic Modeling:
An Invaluable Tool for Invention and Investment

Editor’s note: The potential for the algae production market is nearly infinite, and will accelerate as more people are able to benefit from this industry’s collective wisdom, discoveries and efficiencies. We invite readers who have financial tools and techniques for scaling up algae production to share their insights with others in this space. A.I.M. does not endorse any specific approaches presented here, nor does it verify the information presented by any of the authors. The following opinions are from Brad Bartilson, President and CEO of Photon8, Inc. who describes their approach to techno-financial modeling.

October 14, 2010, by Brad W. Bartilson

Techno-economic modeling is that well-established process, which when developed in concert with technology, ensures that market-driven prices can be achieved. Typically, this is part of the “stage-gate” process in the corporate management of product development and related research. As the foreground, and with its components embedded in a selected design’s system model, techno-economic modeling becomes an invaluable, direction-steering tool.

In the case of algae-biofuels, dramatically rising fuel prices in 2006, and the re-discovered opportunity of algae as a fuel feedstock, gave rise to invention and investment ahead of the typical rigors of techno-economic modeling, leading typically to unmet expectations.

Elementary market analysis would yield that abruptly increased fuel prices, as experienced in 2006, would give rise to decreased demand and socio-economic changes which would bring about a lower balance point in fuel prices. Operating under the false assumption of continued high prices and of simplistic cost reduction-with-scale, many algae ventures would later find their ‘solutions’ non-competitive for fuels and hence, point their “solutions” toward higher-priced product markets (perhaps again without proper market assessment).

Applying techno-economic modeling at the outset of a venture can greatly assist in averting misspent efforts and investment. Such modeling provides cost and performance boundaries that actually assist in the creative process, i.e., forcing scientific teams to work within these confines leads to new thought processes and solutions.

A complete techno-economic model, and its integration into a system design model, is an extensive undertaking. In the case of Photon8’s effort, this occurred over the pre-construction phase, 6 months in length. Significant effort was spent in gathering and evaluation existing market and technical reports, customer surveys, and expert interviews. The general process and a few of the highlights are summarized in the following sequences of steps:

Financial Limit

1. Define Financial Constraints, Goals. Market & Product Definition:

  • Biodiesel, potential market size: 1.5Bbbl/year.
  • Requirements: ~$1.00/gallon (converted biodiesel) cost for 70% gross profit margin if sold directly into blenders (no subsidy assumed, 5% price increase/year, year 5 of production, 8% interest rate for 1st facility capital loan, 20% down).
  • Minimum order size 100M gal/year.
  • Low temperature behavior and long-term storage issues suggest an improved product (over existing FAME specifications for blending), is required for full market penetration.

2. Define Physics Constraints, Goals: Sunlight irradiation limits the photosynthetic reactions involved in algae growth and lipids accumulation. Photosynthetic Active Region (400-700nm) yields 105W/m2 in SW United States. Calculated losses from reflection of light, transmission losses, light losses within the media, other cell use of energy, and a minimum of 8 photons to fixate one hydrocarbon molecule translates to ~10W/m2 remaining for energy to make lipids.

The limits defined by physics and financial analysis provide the boundaries for a working concept. Expressed in $/gallon, the capex values in the above figure indicate the degree to which some existing solutions are from the tolerable working space. The significance of capex is realized herein – that not even the most developed, “super” algae can provide a working solution when capex is above $0.20/gallon or $10/m2.  Alternatively the model information may be expressed in $/m2, with gross margin varying as a function of capex.

3. Select & Model an Existing, Verified, Process Which Most Closely Approximates the Requirement: Selection was made for closed (photobioreactor) type system, as it averts limiting issues of open ponds (40% less efficiency, evaporation (/salinity) issues, inefficient use of CO2, and direct exposure to environmental events), assuming a way can be found to address higher capex cost: open ponds cost <$50/m2, acrylic tube photobiorector: $140/m2.

Operational costs (labor, materials, utilities, etc.) amount to $62/gallon. (reference work by Chisti, Molina, & Aquatic Species Program team). Analysis of dewatering to 10% moisture level finds cost at $26/gallon of lipids produced.

Gross Margin

4. Identify Process Cost Targets:

  • Maximum expected yield set at 45% lipids (converted to fuels) sets area production rate at 2.5gal/m2/year with a corresponding revenue of $7/m2/year.
  • Clearly capex and operational costs must be reduced by over an order-of-magnitude to begin making a profit.
  • For 70% gross margin, new targets set at <$10/m2 capex ($0.20/gallon) and $0.60/gallon for opex (both through the extraction step).

5. Invent Solutions to Targets: The dramatic capex and opex reduction requirements evoked a ‘slash and burn’ mindset, wherein every capex item was challenged. Identifying that only the top layer of a pond is photo-active, and that no more photons are secured with vertical structures, a concept was proposed to capture the top layer of a pond in plastic, and use elegant mixing techniques to avert photo-inhibition.

Preliminary bills-of-materials developed show cost <$3/m2. In the effort to stay within the financial confines, the growth and extraction related costs are seen as plausible, however, the dewatering is seen as highly unlikely, forcing the process down alternative paths, in the case of Photon8, choosing to eliminate the dewatering process altogether as a base assumption.

6. Develop Technical Process Model:

  • Growth process dynamics ultimately drive capex and opex rates.
  • Broth density, lipid content and harvest rate directly determine the lipid production rate.
  • Circulation rate hydraulics determine pumping rates, pump sizes.
  • Harvest rates determine raw material consumption rates.
  • Linear systems of equations constructed, fully-constrained by the relationship of these variables with system design parameters yield a process model for optimization.


7. Couple Techno-Financial Model: Operational, capex, G & A, marketing/sales costs which drive the Income Statement, Cash Flow and Balance Sheets, are linked.

8. Parametric Optimization: With the technical process model coupled to the financial sheets, technical process model parameters can be varied, and the corresponding capex, opex and larger financial performances observed. Caution: Capex (equipment) does not behave as a continuum, e.g. unitized pump pricing ($/gpm) changes across pump size, requiring iterative solving.

In the case of Photon8’s proposed PFR solution, hydraulic parameters are a strong driver of the technical model. In the above illustration one optimization effort indicates that increasing the broth density reduces flow rate (hence pump cost), however this increases the pressure and the power consumption. Increased reactor length reduces flow rate and power, however, length can be limited by other alga growth concerns such as oxygen release. A complete series of multi-parameter variances and analysis were used to arrive at the targeted solution.

9. Build, Test, Validate: Validation of the model and optimization is achieved through physical construction and testing. However, in that algae is a complex organism, physical variables are likely to be encountered which have not been accounted for in the techno-economic model (e.g. material compatibilities and/or “period of adjustment”, varying nutrient conditions/trends, culture conditions before test, etc.). Actual lab/field data, including addition of new observed system variables, can then be used to update the model.

Summary: The value of techno-economic modeling is seen throughout the project, providing a guide to invention, and assurance to investors that the solution path is financially viable. It is a fundamental tool in valuation basis, influencing design decisions throughout the development process, providing the framework for test and analysis, and being the basis for continual process improvement. As in any modeling effort, its viability is dependent on the underlying logic and data used in its construction. When used throughout the process, however, the risk or uncertainty can be seen decreasing as the level of detail and testing accumulates and is fed back into the model throughout the process.

Performed in a diligent manner, the resultant system model should yield highly accurate predictions of costs before pilot plant construction, lessening investor risk. In Photon8’s case, understanding the market forces for a $1/gallon biodiesel, the 10,000 gallon/acre/year physics limit, and the early-on determination of the requirement for less than $10/m2 capex were fundamental elements in its arriving at its combined technical and business model.

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