Algal Oil Production Modeling and Evaluation using SuperPro Designer
INTELLIGEN, INC. Simulation, Design, and Scheduling Tools for the Process Manufacturing Industries www.intelligen.com Intelligen, Inc.
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Introduction In recent years, the scope of research on microalgae has expanded from merely improving production of traditional products (e.g., nutrients for the food supplement industry) to developing new products such as biofuels. In fact, algae are now considered one of the most promising feed stocks for biofuels. The interest in algae as a fuel source is partly due to environmental motives (e.g., reduction in nonrenewable fuel use, reduction in net CO2 production, and efficient use of farmland) and partly due to technological improvements related to cheaper and more-efficient genetic modification of algae, which has the potential to greatly improve its productivity.1 Microalgae can be used to produce a number of different biofuel products, such as ethanol, butanol, and fatty acids (lipids) which can be converted into biodiesel. Alternatively, the whole algae biomass may be processed into crude oil, although this process is relatively inefficient. As a result, production of lipids or direct production of ethanol and butanol are considered to be more promising than conversion of algal biomass into crude oil. Furthermore, although production costs of commodity products synthesized from algae in photobioreactors are currently much too high to achieve profitability, there is great potential for algae to be used for production of fuels and chemicals as the related technology (including the productivity of genetically-engineered strains) continues to develop. The SuperPro Designer model associated with this example provides a basic representation of an algae production and purification process that generates a lipid, tripalmitin, a triglyceride of palmitic acid abbreviated as “TAG” in the SuperPro model. TAG could subsequently be converted into bio-diesel or jet fuel. This example was created by modifying a related model developed by Dr. Daniel KleinMarcuschamer (DKM) at the Joint BioEnergy Institute in Emeryville, CA. DKM’s original SuperPro model can be downloaded from http://pathway.soe.uq.edu.au/mediawiki/index.php/Main_Page. DKM’s model converts TAG into aviation fuel as its main product. The “BioDiesel” example that ships with SuperPro Designer analyzes a process for converting TAG into bio-diesel.
1 Process Description This example analyzes the production and purification of TAG algal oil synthesized in “raceway” ponds. Approximately 8.3 metric tons (MT) per hour of purified algal oil is produced. The SuperPro file associated with this example, titled AlgalOil.spf, can be found in the AlgalOil subdirectory of the SuperPro EXAMPLES directory. This example has been divided into six sections: Algae Ponds, Algae Harvesting, Hexane Extraction, Degumming, Anaerobic Digestion, and Cogeneration. Each of the Intelligen, Inc.
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sections in this example has been assigned a different color (black, blue, brown, green, purple, and orange, respectively). Flowsheet sections in SuperPro are simply sets of related unit procedures (i.e., processing steps). The purpose and basic steps associated with these sections are described briefly below. Note that throughout this document, it is assumed that you have a basic understanding of how to set up and simulate processes with SuperPro Designer. Also note that this flowsheet is entirely in continuous mode of operation. Therefore, scheduling information is not specified, and all operations are assumed to run at steady state.
1.1 Algae Pond Section This section is devoted to the production of algae in raceway ponds, as well as the supporting nutrient and water supply infrastructure. The nutrients include Phosphate (DAP, 1 MT/h) and Nitrate (15 MT/h), which are fed in from raw material storage tanks through hoppers (HP-101 and HP-102). The water supply includes both Seawater (1,000 m3/h) and Groundwater (2,200 m3/h). In addition, a large quantity of aqueous feed (98,402 m3/h) is recycled from the Algae Harvesting Section back into the inlet of the raceway ponds (RP-101). In other words, the feed water (i.e., seawater and groundwater) is only about 3% of the total water flowing into the raceway pond; it is added in order to make up for evaporative losses within the ponds and to make up for water lost to the downstream processing steps. Algae production within the raceway ponds is modeled using a stoichiometric aerobic bio-oxidation unit procedure (under the menu: Unit Procedures \ Continuous Reaction \ Environmental Reaction \ Stoichiometric \ WM Aerobic Bio-oxidation). The biomass formation parameters are specified on the Reactions tab of the operation associated with this procedure (Figure 1). In this example, CO2, DAP, Nitrate, Sulfate, and Water are consumed within the raceway ponds, and Biomass, Oxygen, and Salts are produced (the stoichiometry for this reaction can be seen at the bottom of Figure 1). Note that the stoichiometry for the reaction (which can be specified by clicking on the button) must be balanced on a mass basis (i.e., total mass of reactants = total mass of products). The stoichiometric coefficients are derived from experimental data. An optimistic reaction extent of 100% was assumed. Nitrate is the limiting component. This means that 100% of the Nitrate is consumed. Substantial amounts of unconsumed Sulfate, DAP, and CO2 remain at the completion of the reaction. The excess CO2 is lost to the atmosphere as part of the Evaporation stream leaving the raceway ponds. This stream also contains Nitrogen, Oxygen, a small amount of NOx, and a large amount (1,000 MT/h) of water vapor. Note that some of the CO2 involved in the reaction (22 MT/hr) is produced within the Cogeneration section of this model and the remainder (41 MT/hr) is piped from a local electric power utility (not included in the model). Before being fed into the raceway ponds, these CO2-rich streams are combined and sent through a cooler (HX-101) in order to reduce their temperature to 32 C. Intelligen, Inc.
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It was assumed that the average residence time of the liquid in the algae ponds is 4 days. This is specified on the “Volumes” tab of the dialog window shown in Figure 1. This specification results in 1084 bonds, each having a working volume of around 9,000 m3 and a total volume of around 10,000 m3. The calculated working volume can be seen on the “Volumes” tab of the operation’s dialog.
Figure 1: The Reactions tab of the aerobic bio-oxidation operation used to represent algae growth The total volume can be seen by right-clicking on RP-101 and choosing Equipment Data (see Figure 2). A depth of 0.2 m was assumed for the ponds, resulting in a surface area for each pond of approximately 50,000 m 2, or 5 hectares (ha). Consequently, the total surface area of all 1084 ponds is approximately 5,420 ha. This is a massive surface area, equivalent to 15.9 times the size of New York City’s Intelligen, Inc.
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Central Park, or 0.63 times the size of Manhattan. Thus the algae production facility would need to be located in a deserted area where there is plenty of cheap land in order for this type of project to be economically viable. The algae concentration in the ponds is 0.35 g/L. The flowrate of algal biomass exiting the ponds is 34.7 MT/h. Assuming that each pond is operational and productive for 330 days/ yr (or 7920 h/yr), this leads to annual biomass production of roughly of 275,000 MT. Based on these values, the areal productivity of the ponds is approximately 50.7 MT/ha-yr. This is consistent with estimates from the literature regarding the current upper limit of long-term commercial productivity 2, although it is expected that this areal productivity limit will increase in the future due to scientific and technological innovations. Note that the productivity is influenced by a variety of factors such as location (including temperature effects and the amount of annual solar radiation), layout of the production system, algae species, etc. Furthermore, the optimal pond design depends on factors such as the location and the algae species2. The productivity is also dependent on the time of year, since this impacts the temperature, magnitude of daily solar radiation, etc. The constant biomass formation rate of this model represents average annual performance.
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Figure 2: The Equipment Data dialog of RP-101
1.2 Algae Harvesting Section The purpose of this section is to concentrate the algae and then break it up in order to allow the oils to be recovered in the subsequent section. The first concentration step takes place in the clarification units (CL-101), where the algae settles to the bottom of the tanks with the aid of the flocculant that was added immediately upstream (MX-110). The algae-rich heavy stream which exits the clarification tanks is then sent to the centrifuge (DC-101), where it is further concentrated from 5% Biomass (dry cell mass) to 15% Biomass. The supernatant streams from the Clarification and Centrifugation units are combined and recycled back to the Raceway Ponds. The concentrated Biomass stream is sent to the sonicators (HG-101) for cell disruption, which is represented by the following mass stoichiometry: 100 Biomass 7 Ash + 42.2 Cell Debris + 0.8 NHP + 4.0 PL + 20 Proteins +26 TAG NHP stands for non-hydratable phospholipids, PL stands for hydratable phospholipids, and TAG is the triacylglycerol product. The assumed extent of cell disruption is 95%. Consequently, for every 100 kg of biomass entering the sonicators, 0.95 x 26 = 24.7 kg of TAG is released and available for further processing downstream.
1.3 Hexane Extraction Section The mixture of disrupted biomass is sent to a decanter (V-101) where the lipid-rich oil phase (99.8% TAG) is separated as the light phase and sent to the Degumming section of the process. The heavy phase leaving the decanter is combined with hexane in a mixing tank (V-102) and. Here the remaining oil components (NHP, PL, and residual TAG) are extracted from the aqueous phase into the organic phase. The contents of this tank are then centrifuged (in DC-102) in order to separate the aqueous phase and the remaining solids from the oil-rich organic phase. The separated solids and bulk aqueous phase are then sent through a protein extraction unit (GBX-101) in order to recover proteins which can be sold as animal feed. The rest of the material exiting the protein extraction unit is sent to the Anaerobic Digestion Section for additional processing. The oil-rich organic phase which exits centrifuge DC-102 is sent to an evaporator (EV-101). There most of the hexane is recovered and recycled back to a storage unit (V-103) for reuse within the hexane extraction tank. A small amount of fresh hexane is added (using MX-104) in order to make up for the hexane lost during the hexane centrifugation and evaporation steps. Meanwhile, the oil-rich phase coming off the Intelligen, Inc.
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bottom of the evaporator and the TAG oil phase from the decanter unit are combined and sent to the Degumming section for further processing.
1.4 Degumming Section In this section, the lipid rich input stream is heated to 70 C (using HX-103) and transferred into the Citric Acid Addition tank (R-101). Here citric acid is added in order to convert NHP into PL. The mixture is then cooled (in HX-104) and sent to a tank (V-104) where wash water is added. The material flowing out of the tank is then centrifuged (in DS-101) in order to separate the TAG product from the other components. This results in approximately 8.2 MT/h of TAG product, or 65 MT/yr of product assuming an annual facility uptime of 330 days/yr. The TAG product could subsequently be processed into other products such as aviation fuel, naphtha, etc. Such a process is analyzed in Dr. Klein-Marcuschamer’s original SuperPro model, which can be downloaded from http://pathway.soe.uq.edu.au/mediawiki/index.php/Main_Page). Alternatively, TAG can be converted into biodiesel using a process similar to that analyzed in the “BioDiesel” example of SuperPro.
1.5 Anaerobic Digestion Section Waste streams from the Algae Harvesting, Hexane Extraction, and Degumming sections are sent to the Anaerobic Digestion section in order to convert the remaining biomass and other organic components into methane fuel. The combined inlet streams are heated to 30 C using HX-701 prior to digestion in AD-701. The generated gaseous stream, which is rich in methane, is sent to the cogeneration unit. Most of the remaining aqueous material could be recycled back to the process to provide additional water and nutrients to the Algae Pond section.
1.6 Cogeneration Section Utilities such as cooling water, steam, and electricity play a very important role in this process due to the massive scale of the facility. The table below, which was extracted from the Excel Custom Report, displays the requirements associated with the heat transfer agents used by this process. Heat Transfer Agent Cooling Water Steam Steam (Gen)
kg/yr
kg/h
64,769,811,01 6 96,738,38 7 42,807,23 4
8,178,00 6 12,21 4 5,40 5
The role of the Cogeneration section is to meet the demands of the plant for steam and electric power. Methane produced in the anaerobic digester is combusted in a boiler (SG-701) and generates high-pressure steam. The CO2-rich flue gas from the Intelligen, Inc.
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boiler is sent to the Algae Ponds to promote algae growth, while the high-pressure steam is utilized to generate electricity in a turbogenerator (T-701). The electricity demand for this process can be viewed by selecting View \ Resource Demand Breakdown \ Power (see Figure 3). This screen shows the “Std Power” electricity usage associated with the overall process, the individual sections, and each individual procedure within the model. To see the instantaneous power demand, change the “Time Ref for Demand” option at the bottom of this dialog to “hr”. This provides the power demand in kWh per h, or kW. In this case, the instantaneous demand is 11,553 kW, or roughly 11.6 MW.
Figure 3: The “Std Power” electricity demand (on a per-hour basis) The rate of electricity produced by this process can be viewed by opening the Steam Expansion operation dialog associated with the turbogenerator and selecting the Expansion Model tab (Figure 4). On this dialog, you can see the power generated by each stage of the turbogenerator as well as the overall electrical power generation (based on the total shaft power and a user-specified efficiency factor). In this example, the 18 MW turbogenerator creates 16.2 MW of electrical power, which exceeds the 11.6 MW demand from the process. The excess electricity produced by this process is sold to the grid.
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Figure 4: Electricity production from the turbogenerator To account for the financial benefit of electricity sold to the grid, a selling price of $0.08/kW-h was specified. This was accomplished by first clicking the Process Explorer button ( ), selecting the Util tab in the Process Explorer, and doubleclicking the Std Power entry under the Power Types node. The properties dialog associated with the Std Power entry is shown in Figure 5. There you can see the purchase and selling prices for electricity within this process. SuperPro’s cost calculations account for electricity that is produced and consumed by a process as “savings” (i.e., avoidance of expenditures for electricity purchases). Electricity sold to the grid is accounted for as Revenue or Credit.
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Figure 5: The Std Power properties dialog In addition to producing electricity, the 6.99 MT/h of 5 bar steam exiting the turbogenerator (top stream) is used to meet the heating needs associated with the evaporator (EV-101), the degumming heater (HX-103), and the degumming wash tank (V-104) since these three units use a total of only 5.4 MT/h of 152 °C steam. To accomplish this heat recovery, a new heating agent called “Steam (Gen)” was created (see the Util tab of the Process Explorer). The cost of this heat transfer agent was set as $0.00. Then “Steam (Gen)” was selected as the heat transfer agent for the operations within units EV-101, HX-103, and V-104. The result of these specifications is a reduction in the overall utility usage and associated utility costs since there is no longer any cost associated with heating EV-101, HX-103, and V104. The cost of utilities was reduced further through “virtual” heat exchange between the HX-701 Preheater (P-42) of the anaerobic digester and the HX-101 Flue Gas Cooler (P-8). In this case, all the heat required by HX-701 is supplied by exchanging heat between HX-101 and HX-701. In addition, some of the cooling required for HXIntelligen, Inc.
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101 is met through this match. Virtual heat integration between two units is accomplished using SuperPro’s Energy Recovery interface, which allows you to establish relationships between operations that require cooling at high temperatures (heat sources) and operations that need heating at low temperatures (heat sinks). To view the Energy Recovery interface, right-click on a blank area of the flowsheet and select Energy Recovery. This will display the dialog window shown in Figure 6. Here you can view the operations of the flowsheet that require cooling (i.e., potential heat sources). Notice that the first operation in the list (P-8: Cool-1) has an extraordinarily high cooling requirement (40 million kcal/h) at a high temperature. This operation requires roughly 8,000 MT/h of cooling water. Also notice that the “Recovered” checkbox is checked for this operation, and there is a “Matching %” of 14.89%. This means approximately 15% of the cooling load for the P-8: Cool-1 operation can be met by exchanging heat between P-8: Cool-1 and P-42: Heat-1. The choice of P-42: Heat-1 as the recipient for this excess heat was made by clicking on the View/Edit button for that operation (see Figure 7). Notice that 100% of the heat required by P-42: Heat-1 can be supplied by P-8: Cool1 (see Figure 7). In other words, the heat integration between P-8 and P-42 fully eliminates the heating load of P-42 and reduces by approximately 15% the cooling load of P-8 .
Figure 6: The Energy Recovery dialog For more information on SuperPro Designer’s virtual Energy Recovery capabilities, please refer to the SuperPro manual and the ReadMe file of the BioDiesel example that ships with SuperPro.
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Figure 7: View/Edit the Energy Recovery matches
2 Simulation Results 2.1 Material Balances This plant produces approximately 65,500 MT of algal oil per year. The quantities of each raw material needed to produce this amount of oil are displayed in the table below, which shows the material requirements in MT/yr, MT/h and MT/MT MP (MP = main product, which is algal oil in this case). This table was extracted from the Excel version of the Materials & Streams report of this example. Reports in SuperPro are generated through the Reports menu of the main menu bar. The format and contents of the reports can be customized by selecting Reports \ Options from the main menu bar.
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BULK MATERIALS (Entire Process) Material Air
MT/yr
MT/h
MT/MT MP
1,316,174
166.18
20.10
Citric Acid
119
0.02
0.00
CO2
322,073
40.67
4.92
DAP
7,920
1.00
0.12
Flocculant
1,026
0.13
0.02
Hexane
2,372
0.30
0.04
Nitrate
118,800
15.00
1.81
Nitrogen
919,846
116.14
14.05
7,631
0.96
0.12
NOx Oxygen Salts
42,808
5.41
0.65
258,392
32.63
3.95
SOx
601
0.08
0.01
21,579
2.73
0.33
Water
26,285,961
3,318.94
401.39
TOTAL
29,305,303
3,700.16
447.50
Sulfate
SuperPro’s Streams & Mat. Balance report also contains tables which summarize the consumption of each raw material within each specific section of the process (e.g., Algae Ponds, Hexane Extraction, Degumming, etc.). In addition, this report provides tables with detailed stream information (e.g., flowrate, composition, temperature, pressure, etc.) A small portion of the Stream Details table is shown below. Stream Name
Nitrate
S-105
Phosphate (DAP)
S-107
Source
INPUT
P-4
INPUT
P-5
P-4
P-3
P-5
P-3
0.00
0.00
0.00
0.00
25.00
25.00
25.00
25.00
1.01
1.01
1.01
1.01
2105.66
2105.66
1556.86
1556.86
Destination Stream Properties Activity (U/ml) Temperature (°C) Pressure (bar) Density (g/L) Total Enthalpy (kW-h)
190.70
190.70
8.18
8.18
Specific Enthalpy (kcal/kg)
10.94
10.94
7.04
7.04
Heat Capacity (kcal/kg-°C)
0.44
0.44
0.28
0.28
0.00
0.00
1.00
1.00
Nitrate
15.00
15.00
0.00
0.00
TOTAL (MT/h)
15.00
15.00
1.00
1.00
TOTAL (m3/h)
7.12
7.12
0.64
0.64
Component Flowrates (MT/h) DAP
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2.2 Cost Analysis SuperPro Designer performs thorough cost analysis and generates three pertinent reports (through the Reports menu). The table below displays the key economic evaluation figures for this example. This table was extracted from the Economic Evaluation Report (EER), generated in Excel format. For a facility of this size (5,420 ha of pond surface area), the total capital investment is roughly $305 million. The estimated annual operating cost is $112 million, which results in a unit production cost of $1.71/kg of algal oil. The results calculated for the Return-OnInvestment, Payback Time, etc. are based on selling prices of $1.80/kg of Algal Oil and $180/MT of the Protein for Animal Feed byproduct.
EXECUTIVE SUMMARY (2015 prices) Total Capital Investment
305,344,000
$
Capital Investment Charged to This Project
305,344,000
$
Operating Cost
111,793,000
$/yr
11,436,880
$/yr
Savings (due to Power Recycled) Savings (due to Heat Recovery) Net Operating Cost Main Revenue Other Revenues Total Revenues Cost Basis Annual Rate
1,632,497
$/yr
98,723,849
$/yr
117,876,000
$/yr
8,727,032
$/yr
126,603,000
$/yr
65,486,691
kg MP/yr
Unit Production Cost
1.71
$/kg MP
Net Unit Production Cost
1.51
$/kg MP
1.93
$/kg MP
Unit Production Revenue Gross Margin
22.02
%
Return On Investment
14.41
%
Payback Time IRR (After Taxes) NPV (at 10.0% Interest)
6.94
years
8.20
%
4,119,000
$
MP = Total Flow of Stream 'Algal Oil'
The EER also provides a summary of the magnitude of each component of the annual operating cost, as shown in Figure 8. To automatically include charts such as this one in SuperPro’s reports, select Reports \ Options and check the Include Charts checkbox (lower right corner of the dialog). In this example, the facilitydependent cost is the greatest contributor to the annual operating cost, followed by raw materials and utilities. The facility-dependent cost is calculated based on estimates of depreciation, maintenance, and miscellaneous factory expenses.
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Figure 8: Annual operating costs breakdown The economic results in the table and figure above were calculated based upon a set of default values and user specifications entered into the model. The parameters which impact the economic results can be classified into the following categories: 1) Information associated with individual operations and streams. This includes the unit costs and quantities of each raw material, the disposal costs and amounts of relevant waste products, the unit costs and hourly requirements for various labor types, the unit costs and amounts of various utilities, etc. The selling prices and quantities of the products are also taken into account for certain calculations. 2) Equipment-related information. This includes the capital and operating costs for each equipment unit (e.g., purchase cost, installation, maintenance, consumables, etc). 3) Section-Related information. This includes cost factors that are used to determine the capital and operating costs for each section of the process. 4) Process-related information. This includes economic evaluation parameters that are specified at the process level, such as time valuation, financing, production level and additional operating cost information for the entire project. Each of these cost categories are described in greater detail below:
2.2.1 Operation and stream costs and revenues Resource requirements associated with materials are determined based on the mass and composition specifications for each of the flowsheet’s input streams. To Intelligen, Inc.
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determine material costs, the annual amounts of each raw material are calculated by SuperPro and multiplied by the unit costs specified by the user on the Economics tab of each material. The material costs for this example are shown in the table below.
MATERIALS COST - PROCESS SUMMARY Bulk Material Citric Acid DAP
Unit Cost ($) 1.07
Annual Amount 118,800
Annual Cost ($)
%
127,116
0.43
kg
70.00
7,920
554,400
1.85
1.00
1,025,571
kg
1,025,571
3.43
Hexane
2.00
2,372,040
kg
4,744,080
15.86
Nitrate
50.00
118,800
MT
5,940,000
19.86
Sulfate
0.35
139,392
kg
Water
1.00
17,469,286
Flocculant
TOTAL
MT
m3(STP)
48,787
0.16
17,469,286
58.41
29,909,272
100.00
Other costs and revenues associated with streams are determined based upon specifications in the Stream Classification dialog (Figure 9). This dialog can be accessed by selecting Stream Classification on the Tasks menu. From this dialog you may specify the following:
Selling prices for output streams classified as revenue or credit Treatment/disposal costs for output streams classified as one of the waste types (solid waste, aqueous waste, organic waste or emission)
Furthermore, for an input stream classified as a raw material, a purchasing price is automatically calculated by SuperPro based on the stream’s composition and the purchasing prices of its ingredients.
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Figure 9: Stream Classification dialog In the lower right corner of Figure 9, a Main Product/Revenue stream and its flow basis can be specified (note that the drop-down list for the Main Product will only contain input and output streams that are classified as Revenue streams). Annual revenues for the process are then calculated based on the unit prices and quantities of all revenue streams associated with the process. The table below shows the various revenue sources (and savings) associated with this example: Revenues/Savings Algal Oil (Main Revenue) Protein for Animal Feed (Revenue) Std Power(Revenue) Std Power(Savings) Steam(Savings) Cooling Water(Savings) Total Revenues Total Savings
117,876,044 7,611,730 1,115,302 11,436,880 1,160,827 471,670 126,603,051 13,069,378
$/yr $/yr $/yr $/yr $/yr $/yr $/yr $/yr
Note: “Revenues” in the table above are related to products (including power) that are sold. “Savings” in this table are related to avoidance of expenses due to energy integration and in-process electric power generation and use. Intelligen, Inc.
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Labor requirements are specified on the Labor, etc. tab of an operation’s dialog window. The total requirement for each resource in each operation in the process is then multiplied by its respective unit cost to compute the associated operating costs. The annual labor requirements and costs associated with this process are shown in the table below.
LABOR COST - PROCESS SUMMARY Unit Cost ($/h)
Annual Amount (h)
Annual Cost ($)
%
Operator
25.00
196,703
4,917,586
89.0
Supervisor
35.00
Labor Type
TOTAL
17,356
607,467
11.0
214,060
5,525,053
100.0
2.2.2 Equipment Costs The purchase cost of equipment is an important parameter that affects the direct fixed capital investment of a project and indirectly affects the annual operating cost. SuperPro is equipped with correlations for estimating the purchase cost of equipment based on its type and size. However, the built-in correlations for most types of equipment are more suitable for fine chemical and pharmaceutical types of facilities than for large-scale algae production plants. For these types of processes, we advise users to enter their own equipment cost data for better accuracy. Userdefined costs may either be specified for equipment of a certain size (e.g., $164,000 per raceway pond) or as a User-Defined Cost Model (UDCM). Figure 10 displays the Purchase Cost tab of the equipment data dialog of the Raceway Pond unit procedure. This tab can be accessed by right clicking on the unit’s icon, selecting Equipment Data, and then switching to the Purchase Cost tab. In this case, a UDCM has been chosen for the equipment cost estimate. The UDCM allows a user to specify a cost vs. size correlation that is used by the tool to estimate the cost of a piece of equipment. Data are entered in the form of the power-law equation: C = Co x (Q/Qo)a where Co is a reference cost, Qo is a reference size, and a is an exponent (usually less than 1). The user must also provide a range of size values where a given cost correlation is valid. In this example, the specifications ensure that for all ponds up to a maximum volume of 10,000 m 3, the capital cost will be $25,000 x (PondVolume/1000)0.8. Note that this value is also adjusted for inflation based on the Year of Analysis and the Inflation rate specified at the project level in the Economic Evaluation Parameters dialog (you can right-click on the flowsheet to bring up its context menu and select Economic Evaluation Parameters to modify these values.) Also note that it is possible to store the UDCM information associated with each of your equipment types in the “User” database for future reuse. Detailed information on how to do this is provided in the ReadMe file of the Lysine example that ships with SuperPro Designer.
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Figure 10: Equipment Purchase Cost tab and User Defined Cost Model parameters After the base purchase cost for the equipment is estimated, there are several adjustment parameters that are applied to it. These parameters (such as material of construction, installation factor, annual maintenance cost, depreciation, number of standby units, etc.) affect the total purchase cost of the equipment, the direct fixed capital, the facility operating cost, and other economic results. The equipment adjustment parameters may be accessed on the Adjustments tab of the equipment data dialog. Furthermore, costs associated with consumables (i.e., items that are used by the equipment unit for one or more batches before being disposed) can be specified on the Consumables tab. Using the cost models for each equipment unit and their respective cost adjustments, the total equipment cost is calculated based upon the quantity of each equipment item that is required (see table below). Notice that roughly 80% of the total equipment cost is associated with the algae ponds (RP-101) due to the very large number of ponds required.
MAJOR EQUIPMENT SPECIFICATION AND FOB COST (2015 prices) Quantity/ Standby/ Staggered
Name
Description
1/0/0
HP-101
Hopper
Unit Cost ($)
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Cost ($)
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16,000
16,000
5,000
5,000
51,000
51,000
107,000
107,000
151,000
755,000
164,000
177, 776,000
548,000
548,000
272,000
272,000
42,000
42,000
20,000
20,000
28,000
28,000
14,000
14,000
42,000
42,000
32,000
32,000
115,000
115,000
44,000
44,000
Vessel Volume = 1633.99 L 1/0/0
HP-102
Hopper Vessel Volume = 108.93 L
1/0/0
P-101
Centrifugal Pump Pump Power = 81.04 kW
1/0/0
P-102
Centrifugal Pump Pump Power = 181.52 kW
5/0/0
HX-101
Heat Exchanger Area = 689.24 m2
1084 / 0 / 0
RP-101
Aeration Basin Volume = 9997.33 m3
1/0/0
V-102
Blending Tank Volume = 1767.3 m3
1/0/0
DC-102
Decanter Centrifuge Throughput = 265.1 m3/h
1/0/0
EV-101
Evaporator Transfer Area = 17.36 m2
1/0/0
V-103
Flat Bottom Tank Volume = 13.7 m3
1/0/0
HX-103
Heat Exchanger Area = 7.14 m2
1/0/0
R-101
Stirred Reactor Volume = 4804.49 L
1/0/0
HX-104
Heat Exchanger Area = 56.72 m2
1/0/0
V-104
Blending Tank Volume = 16.7 m3
1/0/0
DS-101
Disk-Stack Centrifuge Throughput = 15 m3/h
1/0/0
HX-701
Heat Exchanger Area = 14.02 m2
1/0/0
AD-701
Anaerobic Digester
8,002,000
8, 002,000
Volume = 76893.19 m3
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1/0/0
SG-701
Steam Generator
1,320,000
1, 320,000
3,212,000
3, 212,000
Throughput = 140 MT/h 1/0/0
T-701
Multi-Stage Steam Turbine Shaft Power = 18 MW
1/0/0
DC-101
Decanter Centrifuge
272,000
272,000
167,000
835,000
Throughput = 691.1 m3/h 5/0/0
HG-101
Homogenizer Throughput = 45.6 m3/h
31 / 0 / 0
CL-101
Clarifier
1,090,000
33, 790,000
Surface Area = 2434 m2 1/0/0
V-101
Decanter Tank
823,000
823,000
Volume = 204 m3 TOTAL
228, 121,000
2.2.3 Section-related costs: In SuperPro Designer, the direct fixed capital (DFC) contributions are estimated for each individual section of a process. Each section’s DFC is calculated as the sum of direct, indirect, and miscellaneous costs associated with that section’s capital investment. The direct costs include elements that are directly related to an investment, such as the cost of equipment, process piping, instrumentation, buildings, facilities, etc. The indirect costs include elements that are indirectly related to an investment, such as the costs of engineering and construction. Additional costs such as the contractor’s fee and contingencies are included in miscellaneous costs. By default, the DFC is estimated based on the purchase costs of all major process equipment multiplied by cost factors that are applied to the purchase costs. The cost factors include installation factors which are equipmentspecific, as well as other factors specified at the section level. The default sectionlevel factors can be edited through the DFC tab of a section’s Capital Investment Dialog. This may be accessed by first selecting the desired section in the ‘Section Name’ drop-down list on the ‘Section’ toolbar and then clicking the Section Capital Cost Adjustments button ( ) on the same toolbar. The capital cost adjustments dialog for the Algae Ponds section is shown in Figure 11. Note that the default process-section-specific cost factors for piping, instrumentation, buildings, etc., are more appropriate for high-value chemical and biochemical plants and they will greatly overestimate the total capital cost associated with an algae production facility. In order to more-accurately estimate the total DFC of the facility in this example, we increased the equipment-specific installation factors and zeroed all the Intelligen, Inc.
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section specific multipliers. The installation cost factors are assigned to each individual equipment unit through the Adjustments tab of each of their Equipment Data dialogs. For instance, the installation cost factor for centrifuge DC-101 is shown in Figure 12. In this case, the installation cost associated with the centrifuge is set to be equal to two times its purchase cost (the default is 0.5). Therefore the total DFC associated with the centrifuge is three times its purchase cost. The increased installation cost factor accounts for the costs of foundation, piping, instrumentation, insulation, buildings, engineering costs, etc. associated with the centrifuge. For units that are constructed on-site, the installation factor is much smaller. For instance, the installation factor for the Raceway Ponds is only 0.2.
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Figure 11: Capital Cost Adjustments: DFC tab
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Figure 12: Capital Cost Adjustments: DFC tab In addition to the Section-level capital cost specifications, there are also specifications at the Section level related to operating costs. These can be accessed by first selecting the desired section in the ‘Section Name’ drop-down list box that is available on the ‘Section’ toolbar and then clicking the Section Operating Cost Adjustments button ( ) on the same toolbar. Here you can specify how various operating costs should be calculated for each section. For instance, the specifications that are used to calculate the facility-related annual operating costs are shown in Figure 13. Like the Capital Cost Adjustments dialog, the Operating Cost Adjustments dialog provides a variety of ways to have costs calculated for various aspects of the process. In this case, the facility-related operating costs are calculated based on depreciation, maintenance, and miscellaneous costs. Other tabs of this dialog provide options for calculating the annual costs related to labor, lab/QC/QA expenses, utilities, and miscellaneous expenses (e.g., ongoing R&D, process validation, etc.)
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Figure 13: Operating Cost Adjustments dialog for the Algae Ponds section
2.2.4 Process-level cost parameters To view or modify the project level cost parameters, click on an empty area of the flowsheet and then select Economic Evaluation Parameters. This will bring up the dialog shown in Figure 14. Here you can specify parameters which impact the economic calculations such as the year of analysis, construction period, inflation rate, etc. On the other tabs of this dialog, you can specify information related to
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financing of the project and depreciation, production rate and product failure rate, and expenses related to taxes, sales/marketing, and royalties.
Figure 14: Economic evaluation parameters for the project Based on the capital and operating cost specifications, SuperPro estimates the total fixed capital, annual operating cost, profitability (including margin, return on investment, and payback time), cash flow calculation results, etc. Various economic results may then be viewed from the Executive Summary (View \ Executive Summary), the Economic Evaluation Report (Reports \ Economic Evaluation), the Itemized Cost Report (Reports \ Itemized Cost), and the Cash Flow Analysis Report (Reports \ Cash Flow Analysis). The tables shown previously in this section were all extracted from the Economic Evaluation Report. NOTE: Most of the multipliers used in cost analysis can be stored in the User Database of SuperPro and retrieved for future work on similar projects. This facilitates standardization and improves the accuracy of cost estimation. For information on how to take advantage of the database capabilities of SuperPro, please consult the SynPharmDB document in the …EXAMPLES \ SYNPHARM subdirectory of the SuperPro installation. Please refer to the SuperPro Designer Intelligen, Inc.
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Help facility or manual for more detailed information on economic analysis using SuperPro.
3 Final Thoughts on Algae as a Fuel Source According to the United States Energy Information Administration, 134.5 billion gallons of gasoline were consumed in the U.S. in 2013 3. In addition, the annual usage of jet fuel in the U.S. was 20.2 billion gallons in 2009 4, and U.S. diesel usage was approximately 50 billion gallons per year in 2006. 5 Based on these numbers, the densities of each fuel, assumed conversion ratios from TAG to various products, and the areal productivity calculated from this model, the total landmass required to replace all US fossil fuel consumption with algae-derived fuel may be estimated. The results are shown in the table below. Annual Annual TAG Total Land % of US Demand Demand Equivalent Required Land (Gal) (MT) (TAG : Fuel) (ha) Mass 134,500,000, 371,630,22 Gasoline 000 5 1 : 0.5 61,965,052 7.7% Aviation 20,200,000,0 Fuel 00 61,165,600 1 : 0.5 10,198,658 1.3% 50,000,000,0 165,593,75 Diesel 00 0 1:1 13,805,424 1.7% As this table demonstrates, current US transportation fuel demands could be met through conversion of algae products into fuel. However, doing so would require extremely large facilities. Furthermore, based on the assumptions within this model, replacing current production of these three fuel types would require roughly 10.7% of the entire continental US land mass (i.e., roughly 85 million ha of the 808 million ha land mass of the continental US – excluding Alaska and Hawaii). However, research into methods of maximizing algae productivity is ongoing, and the estimates for future algae productivity vary tremendously from one source to another. For instance, the US Department of Energy estimates that it may be possible to produce enough fuel from algae to replace all petroleum fuel in the United States by using only 0.42% of the US land mass, assuming substantial gains in algae productivity in the future. This is less than 15% of the area of corn grown in the US.6
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1References
R.H. Wijffels et al., Potential of industrial biotechnology with cyanobacteria and eukaryotic microalgae, Current Opinion in Biotechnology (2013), vol. 24. http://dx.doi.org/10.1016/j.copbio.2013.04.004.
2 G2 – ALGAL paper: P.M. Slegers, et al., Scenario evaluation of open pond microalgae production, Algal Research (2013), http://dx.doi.org/10.1016/j.algal.2013.05.001. 3 http://www.eia.gov/tools/faqs/faq.cfm?id=23&t=10 4 http://en.wikipedia.org/wiki/Jet_fuel 5 http://en.wikipedia.org/wiki/Diesel_fuel 6 http://en.wikipedia.org/wiki/Algae_fuel
