This document discusses using a three model approach to accurately predict the performance of geothermal power plants. Model 1 is created during the design phase to predict performance and size equipment. Model 2 reflects actual plant operating conditions after commissioning. Model 3 combines the real equipment sizes from Model 1 with the actual operating conditions from Model 2 to predict off-design performance with high accuracy, allowing performance guarantees to be met with confidence. The three model approach is an effective solution for confidently delivering geothermal plant performance over varying operating conditions.

1. Designing and Achieving Geothermal Power Plant Performance with Confidence
Guofu Chen, TAS Energy Inc. 6110 Cullen Blvd, Houston, TX 77021
Abstract
The design and the actual performance of a geothermal air-cooled power plant utilizing a
supercritical refrigerant of R134a as the working fluid are discussed. A supercritical Organic
Rankine Cycle (“ORC”) in many cases outperforms a sub-critical cycle, from the net kilowatt
(kW) generated point of view. Additionally, the plant configuration is simpler to design and
easier to operate. An additional advantage of using non-flammable working fluid in the cycle,
such as R134a, eliminates the risk of fires. During the design stage, a preliminary process flow
diagram is established based on the standard process engineering practices in HYSYS, a
simulation software from ASPENTECH. Based on the preliminary process requirement, the
components of the cycle, including the shell and tube heat exchanger(s), expansion turbine, air-cooled
condenser, and working fluid feed pump are sized and selected. A true simulation model
is built to analyze the off design performance of a “virtual plant”. Given the geothermal heat
source information and the ambient conditions, the power output is maximized and committed to
the customer (Model 1 with geometries). After the plant is successfully commissioned, by
measuring the flow rate, temperature and pressure, a plant reality model is built to reflect the
actual plant operating conditions (Model 2 without geometries). Normally the process conditions
of Model 2 are different from Model 1. To validate Model 1, developed in the design stage, the
process conditions of Model 2 are extracted and input into Model 1, thus Model 3 with actual
process conditions and actual geometries is established. Model 2 is the reality, while Model 3 is
used to predict the reality with actual process conditions and actual equipment selection. By
comparing these two models, Model 3 accurately predicts the gross power and net power
generated at various operating conditions.
The Challenge
When it comes to the bottom line of a geothermal project, a high confidence calculation of the
plants performance over the projected economic life of the project with varying plant conditions
is a fundamental input in the project proforma and a key driver on whether the project moves
forward or not. There are two (2) important inputs to the performance calculation: the first one is
the output kilowatt (kW) at various conditions, and the second one is the capital cost of the plant
equipment to achieve that performance. In most cases, manufacturers are able to get accurate
plant capital costs at the plant design point. However, the challenge is to accurately predict the
performance output during the annual ambient temperature conditions and varying resource input
conditions. These “off design” assumptions can be too aggressive on the output which results in
the guaranteed performance at other conditions not being met, or too conservative so that the
projected performance doesn’t support further development. TAS Energy has delivered several
geothermal ORC plants based on their refrigerant based cycle designs, and after successfully
passing the performance tests of all the commissioned plants provided, a consistent approach to
the design and modeling has been established to deliver the performance with confidence.

2. The Solution
For illustrative purposes, an example of a project which utilized a supercritical R134a Organic
Rankine Cycle (ORC) system was selected to describe the modeling and performance prediction
process. In comparison with a subcritical pentane ORC system, there are three (3) principal
benefits: 1) a supercritical cycle produces more net output than a subcritical system for many
geothermal resource conditions; 2) R134a is non-flammable so it improves the operational risk
of the project; and 3) the supercritical cycle has only one (1) conceptual vaporizer, while
subcritical has potentially as many as four (4), thus the supercritical cycle is easier to design and
simpler to operate.
After the conceptual budgetary phase of the project development cycle was complete and
resource conditions were validated, a firm proposal inquiry was requested. At this point a
thermal calculation (Model 1) was created to provide a general idea on how many kilowatts
could be generated, based on sound and proven engineering practices and assumptions, such as
the heat exchanger pinches, turbo expander isentropic efficiencies, and other commercial
component performance expectations. Process engineers utilized the theoretical thermal
performance data of Model 1 to size the shell and tube heat exchangers, the air cooled condenser
array, the turbo expanders, and working fluid pumps. In an effort to leverage the best practices of
past efforts and the economies of repeatability, if the equipment duty was similar to comparable
equipment utilized on prior projects, then an economic evaluation of the component from the
past project was performed balancing the performance versus cost benefit. After all of the
equipment selections were finalized, Model 1 integrated all geometries from the actual selections
and then off design performance runs were commenced to predict the performance at various
conditions, including the net output of the plant at various ambient dry bulb temperatures. The
end result was performance correction curves that became the basis for the purchase contract of
the plant.
After completion of the commissioning and startup of the plant, TAS Energy analyzed the
performance output at various conditions to verify how accurate our predictions were. Based on
the measured resource flow rate, operating cycle pressures and temperatures, a thermal model
(Model 2) in HYSYS was established. The goal of Model 2 was to get the actual process
conditions at the various component operating boundaries, since measurement devices were not
installed at every boundary. In addition, Model 2 was able to identify areas of inconsistent data
based on the analysis of data measured and calculation.
The third modeling exercise was to combine the real geometries in Model 1 and the actual
process conditions identified through plant measurement and used in Model 2 to create an
operationalized Model 3. Additional minor refinements of Model 3 were required to match
Model 2 exactly, due to some factors such as fouling and other operational factors. Once Model
3 was calibrated to match Model 2, it could then be used to accurately predict the performance at
other conditions.
The actual operation data was extracted from the plant and plotted in the following chart, all blue
diamonds are actual operation points. The dark red straight line is the predictions calculated from
Model 3. On average, the prediction matches the actual plant operation well. However, we do see

3. a wide range variance of output at the same dry bulb temperature. The thermal inertia of the large
heat exchanger surfaces is believed to be the principal reason for the variation. Further analysis
of the dynamic response to this thermal inertia is being investigated.
10000
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0
Model 3 Prediction Vs. Operation Data
Operation Data
Prediction
0 20 40 60 80 100 120
Output (kW)
Dry Bulb (F)
The Conclusion
By using the “3 Model” approach mentioned above, TAS Energy is able to confidently deliver a
plant that met its design performance guarantees but just as importantly accurately predict the off
design performance. Model 1 is created during the proposal phase to predict the off design
performance and provide the process data to accurately provide a capital cost for the project
economics. Model 2 is established after the plant is commissioned to reflect the actual plant
operation. Finally Model 3 combines the real geometries and the actual plant process conditions
to predict the plant performance at other conditions. Through this illustrative project, the “3
Model” approach proves to be the solution to deliver geothermal power plant performance with
confidence.