The selection of a process configuration for a refining and petrochemical production facility is a task that requires both the skill of an engineer and the business acumen of a finance practitioner. The selection process usually requires facility yields and feedstock requirements, relevant prices, unit capacity required, utilities consumption, capital costs for the configuration (adjusted for location, timing and location tax policies), and a financial model. The financial metrics for each of the candidate configurations are calculated and used to rank the different opportunities.
This article explores the use of linear program (LP) modeling techniques to select process technology configurations based on economic drivers (margin and capital), with the inclusion of a simplified financial model built into the LP. The financial model is used to estimate the key metrics and give the project developer an initial view of the viability of the configuration.
Configuration selection problem.
The demand for additional product in local and selected export markets are the usual drivers for new process capacity located in an area that offers economic benefits. The advantages of a potential facility location can include feedstock availability, cost, location, byproduct markets, etc. The project developers problem is how to procurewith minimum capital investmentthe lowest-cost feedstocks to produce the needed finished products, and then generate sufficient economic returns to justify the project.
Traditional problem solution.
The process configuration that will produce the desired finished products has many variations. The goal is to selecton an unbiased basisthe configuration that will meet the economic returns needed at minimum cost. The traditional methodology to perform this analysis is:
1. Construct an LP or other model of the facility that incorporates all of the potential process technologies envisioned for the project. The model should include the following:
Product specifications for transportation fuels, as well as forecast changes to these specs as a result of environmental requirements
Prices for crudes, other feedstocks and product prices in constant dollars on a consistent basis
Outside utility purchase availabilities and costs
Known or expected market limitations
Process technology capacity limitations
The latest crude assays for potential crudes processed, with sufficient detail to support product specifications of transportation fuels and process yield drivers.
2. Establish a set of case studies to evaluate each of the process technology configurations selected by the design team, including:
Configurations based on previous configuration studies
Input from subject matter experts (SMEs)
Developer companys requests.
3. Run the model for each of the cases to determine the yields, gross margin and individual unit capacity needed.
Model process yields to determine sufficient information to create an income statement for each case.
Process unit capacities are based on the economic drivers and technology licensor limitations provided for the study.
4. Use high-level capital cost estimation proceduresi.e., cost curves.
Estimate the inside battery limits (ISBL) of each process technology and other ancillary units.
Estimate the ISBL cost at the selected site(s) using a location factor.
Apply a factor to account for the offsites, owners costs and contingencies.
5. Use a financial model to determine the metrics for each case.
Use the appropriate tax, depreciation method, debt interest rates, percent equity and accepted metric calculation methods for each case.
Metrics should include internal rate of return (IRR), net present value (NPV), and after-tax payback (ATP) period.
6. Rank various configurations by capital expenditure (CAPEX) with the financial metrics to select the final configuration.
This methodology has been used in industry and has provided project developers with sufficient data to make an informed process configuration selection. However, this approach does have potential problems and requires many model runs to fully explore the configuration possibilities.
The usual start to a configuration study is an examination of what the company did in its last analysis for similar products. This can maximize the use of previous work and potentially lower the project engineering costs. An often-overlooked, but important fact is that every configuration study is based on a unique set of feedstocks, market conditions and economic drivers that can lead to different solutions.
A better approach to configuration selection.
The use of modern LP modeling systems with the inclusion of capital costs can provide a more balanced and less biased view of the process technology configuration selection. The following sections present a method of adding the impact of capital on the configuration selection problem, which is implemented in a process industry modeling system (PIMS).
This system permits the user to model key points like ISBL as a function of capacity, economy-of-scale exponents, different stream factors for each process unit, different location factors, different owners costs and contingency factors. A calculation of IRR, NPV and ATP is also included, using consistent pricing and capital costs (same dollar basis) to give the developer an initial view of the project cost and economic viability.
Methodology. LP solution techniques include the concept of recursion, which is really a version of the successive substitution method of solution to ensure that the model is optimized and converged. The concept is that the model starts with an estimated crude and feedstock composition and uses it to determine the yields and properties of all model internal streams. The LP solver then determines the optimal solution based on this data. The solution can have different crude/feedstock compositions and rates. The model internal stream yields and properties are then recalculated by the PIMS and compared to the previous values. The model is converged if all of the internal stream property differences and stream allocation dispositions are within the user-specified tolerances. This technique, depicted in Fig. 1, is used by all commercial modeling systems.
Fig. 1. Recursion in a process industry modeling system (PIMS).
The capital costs are modified during the recursion cycle via the PIMS simulator interface, and are structured to impact the economic solution (objective function) to account for the differences in investment costs based on the process unit capacities determined in the last solution cycle.
There is a major data difference in the LP that must be addressed: the capacity per calendar day in the LP vs. the capacity per stream day used in the capital cost estimation for the ISBL. The technique converts the capacities from barrels per calendar day (bpcd) to barrels per stream day (bpsd) before estimating capital cost, and then back to bpcd before insertion into the LP model. The total investment cost (TIC) for a given unit is then updated based on the new capacity calculated by the model. This is shown in algebraic form:
Unit TIC = [new capacity (bpsd)/
base capacity (bpsd)]x ×
base-capacity ISBL × factor
Where x is the economy-of-scale exponent, ISBL is the inside-battery-limit cost of the process, and factor adjusts the costs for location, offsites, owners costs and contingencies. The sum of these costs for all process units in the configuration gives an estimate of the facility TIC.
The TIC recovery for each unit is then the TIC calculated as above, divided by all of the following: the capacity in bpsd, multiplied by the stream factor, multiplied by 365 calendar days, multiplied by five (years). These values are captured using a PIMS utility row, which debits the economics (objective function) of the model.
The methodology presented in this article uses a utility to capture the amount of cash needed on a daily basis to pay for the total installed process unit cost over a five-year period. This technique also uses PIMS utilities to report the IRR, NPV and ATP for the adjusted capital costs and process yields during the recursion step of the solution. This is the simplest method of transmitting solution information to the existing PIMS reporting structure.
This technique also permits economics to drive the selection of the process technologies used in the configuration. The inclusion of the capital costs inside the LP greatly reduces the number of LP runs and provides a reasonable estimate of the IRR and other financial metrics for the selected configuration.
PIMS model with cost estimates.
The general configuration is designed to produce transportation fuels, monomers, aromatics and polymers, and has many processing paths open; few unit capacities are limited or at a minimum. The assumptions presented in Table 1 were used in the creation of this model. The units have to pay for the capital cost and operating costs when the capacity limitations are free. The units are also limited by the maximum product demand and licensor constraints.
Other financial model assumptions used in this example include the following:
Max. investment: $13 billion (B)
Min. IRR: 13%
Discount factor for NPV: 10%
Depreciation: 14 years with 10% salvage value
Construction period: four years (yearly spending pattern of total installed cost: 24.4%, 43.2%, 25.2%, 7.2%)
Tax rate: 25%
Offsites: 60% of ISBL
Owners cost: 10% of ISBL
Contingency factor: 20% of ISBL
Selected process configuration.
The results of the optimal process technology arrangement determined by the PIMS model using the economic drivers specified, the financial assumptions noted, and the technologies available will be presented in three sections:
Finished product yields, feedstock consumption and gross margin
Process technologies selected and their capacities
Financial metrics from the LP and from a detailed stand-alone financial model.
The facility production of salable products and the feedstocks required to produce them are shown in Table 2.
No jet fuel or petroleum coke was produced. The pricing supplied made it more profitable to produce only ULSD while still meeting the flash point requirement. The only aromatics produced were paraxylene and benzene. All of the intermediate aromatics were converted or blended into gasoline. The selected configuration for the refinery and petrochemical facilities is shown in Fig. 2.
The capacity-limiting process unit in the facility is the RDS, as it limits the crude rate and sets the RFCC capacity. The RFCC gasoline treater is not needed because the 77-ppm gasoline it produces can be blended with the other gasoline components to meet the sulfur specification of 45 ppm max. The RDS is more valuable in this case than the delayed coker because the RDS configuration produces more net liquid that can be upgraded to salable product, and the availability of relatively inexpensive natural gas and coal mitigate the value of petroleum coke as fuel. The process unit capacities required to produce the finished petrochemicals and transportation fuels are shown in Table 3.
The financial metrics for the LP model are compared below to those calculated by a stand-alone financial model with the same capital, yields, capital spending pattern, depreciation and other financial assumptions:
1. PIMS approximate method
NPV at 10%: $6.5 B
After-tax payback: 5.1 years
2. Detailed financial model
NPV at 10%: $6.3 B
After-tax payback: 5.2 years
3. Accuracy delta
NPV at 10%: 3.2%
After-tax payback: 1.9%
The financial metrics are not the same because of the following factors:
PIMS results contain non-sold items (nitrogen, spent lime, combustion gases) to material-balance the model
The detailed financial model excludes the non-sold items and only uses what is sold
These extra tons sold at a low price still impact the PIMS view of the configuration profitability and make the PIMS results higher for the IRR and NPV, but lower for the payback time.
The inclusion of capital costs and financial metrics in an LP model of a potential configuration provides the user with an unbiased view of the facility, based on economics. The identified configuration is a viable starting point for the selection analysis and should be part of every feasibility study where the LP is a key analysis tool.
This technique does not eliminate the need for sound engineering judgment nor the role of the SMEs. These are needed when the optimal configuration is converted to a practical solution with all of the elements not considered in the LP model. These elements include sour water stripping; practical unit capacities; multiple pressure steam levels; ancillary utilities; flares; and health, safety and environment concerns. HP
|The author |
||Timothy E. Swaty has more than 37 years of professional experience in most aspects of petroleum refining, and broad exposure to the petrochemical industry. He is currently a principal technical specialist with Fluor. His work at Fluor includes the application of management science techniques to refining and petrochemical problems, process optimization and financial analysis. Mr. Swaty has a BSChe degree from the University of Houston and an MBA from Texas A&M University (Corpus Christi). He is a registered professional engineer in Texas, Kansas and California. |