NATIONAL PETROCHEMICAL & REFINERS ASSOCIATION 1899 L STREET, N.W., SUITE 1000 WASHINGTON, D.C. 20036 CC-00-145 USE COLUMN DATA TO INFER AND CONTROL CRUDE FRACTIONATION PRODUCT PROPERTIES By Mark Schuler Control Systems Engineer United Refining Company Warren, Pennsylvania Y. Zak Friedman Principal Petrocontrol New York, New York And Michael G. Kesler President Paul Belanger Kesler Engineering, Inc. Brunswick, New Jersey Presented at the NPRA 2000 Computer Conference November 13-15, 2000 Palmer House Hilton Hotel Chicago, Illinois
Transcript
NATIONAL PETROCHEMICAL & REFINERS ASSOCIATION1899 L STREET, N.W., SUITE 1000
WASHINGTON, D.C. 20036
CC-00-145
USE COLUMN DATA TO INFER AND CONTROL CRUDEFRACTIONATION PRODUCT PROPERTIES
By
Mark SchulerControl Systems EngineerUnited Refining Company
Warren, Pennsylvania
Y. Zak FriedmanPrincipal
PetrocontrolNew York, New York
And
Michael G. KeslerPresident
Paul Belanger
Kesler Engineering, Inc.Brunswick, New Jersey
Presented at the
NPRA2000 Computer Conference
November 13-15, 2000Palmer House Hilton Hotel
Chicago, Illinois
This paper has been reproduced for the author or authors as a courtesy by the National Petrochemical &Refiners Association. Publication of this paper does not signify that the contents necessarily reflect theopinions of the NPRA, its officers, directors, members, or staff. NPRA claims no copyright in this work.Requests for authorization to quote or use the contents should be addressed directly to the author(s)
CC-00-145Page 1
Use Column Data To Infer and Control CrudeFractionation Product Properties
Mark Schuler – United Refining CorporationY. Zak Friedman – Petrocontrol
Michael G. Kesler & Paul Belanger - Kesler Engineering, Inc.
Abstract
Qualities of products of crude fractionation tend to vary because of changing crudes andoperating conditions. On-line measurement of these properties is costly and, often, unreliableoff-line, lab measurements are not timely enough to help operators keep the qualities on target.
GCC (Generalized Cutpoint Calculation) is an inferential model for estimating crude and productproperties. It uses heat balances, standard conversions of EFV to TBP values, and semi-rigorousengineering techniques to construct the crude TBP from column data. It, then, predicts, andcalculates set-points for product flows to maintain on-target product qualities. It also calculatesset-points of pumparound flows to maintain satisfactory internal-reflux flows. It focuses on heatbalances for fast detection of heat disturbances due to changes in crude composition. As a result,GCC is able to predict and control product properties through major crude switches, as has beendemonstrated in the several dozen crude unit installations of GCC. To increase speed andsimplicity, GCC takes into account a finite set of instrument measurements. However, with noredundancy GCC could lose accuracy, should one of these measurements be erroneous.
CPPM (Crude Plant Performance Monitor) is a rigorous distillation program, used to analyze alldata available around the crude column: measurements of temperatures, pressures, and productflows. CPPM works by reconciling the crude distillation profile vs. vol% (TBP) against processmeasurements. CPPM uses redundant measurements to construct the crude TBP, and should ameasurement be erroneous CPPM can detect and disregard the measurement. Once reconciliationis achieved, CPPM calculates product ASTM’s, V/L profiles, and other column parameters.
The United Refining Corporation (URC) system incorporates GCC and CPPM working side byside. GCC works every minute, at all times and particularly through crude switches. CPPMworks every several minutes and whenever it detects steady state. It issues a set of biases tocorrect the GCC inferences. Combining the two models retained the speed and robustness ofGCC and the rigor of CPPM.
The paper presents results that show dramatic improvements in maintaining on-target productqualities compared with data preceding system installation. The paper also discusses also thesystem architecture and its integration with PI and ProcessBook, products of OSI Software, Inc.
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BACKGROUND
The paper describes recent installation of an on-line closed-loop system to control product
qualities of the Preflash and Atmospheric Columns in a 65,000 BPD refinery of United Refining
Corporation (URC) in Warren, PA. Figure 1 is a schematic of the two columns. The crude, after
preheat, enters the Preflash Column at about 320°F. An additional small feed, Wild Naphtha of
varying composition, enters the top tray of the Preflash Column with the reflux. The reflux
controls the top-tray temperature, and hence the heavy-tail composition of the Light Naphtha
product. A small off-gas stream leaves the Overhead Receiver.
The bottoms of the Preflash Column flow to the Atmospheric Heater. Here it is heated to about
700°F before entering the Flash Zone of the Atmospheric Column. The top product is Heavy
Naphtha, whose heavy- tail composition is controlled by reflux and top-pumparound. Three side
products (Kero/Jet, Furnace Oil (F.O.), and Wax Oil (W.O.)) are stripped with steam in the side
strippers. The Reduced Crude goes directly to the Vacuum Heater. Two pumparounds control
heat removal and internal reflux.
Operations of the column present several noteworthy problems:
• The crude flow and composition vary significantly, as shown in Figure 2. Major switches
of crude occur almost daily. Crude is often not uniformly blended.
• Lab analyses of products are typically done every four hours. Varying crude composition
makes it difficult for the operator to interpret past lab results for current conditions,
particularly if a sample was pulled just prior to, or during a crude switch.
• The composition and flow of the Wild Naphtha is variable and not well-known,
compromising control of the Light Naphtha tail end.
• Draw-off trays in the Kero and Furnace Oil sections of the Atmospheric Column often
run dry, due to tray leakage/weeping.
• Measuring instruments of the Atmospheric Column Reflux and the Upper and Middle
Circulating Refluxes often bump up against process constraints.
As a result of these difficulties, the quality of products varies widely from desired specifications.
Figures 3, 4 & 5 show typical 90% ASTM’s of Heavy Naphtha, Kero, and Fuel Oil, during one
month’s operation this year, prior to installing the subject system. The temperature spread in
these figures often exceeds 50°F, well outside the desired range of product qualities.
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PROJECT OVERVIEW
The main objective of this project has been to improve product-quality control, and in particular,
to control the columns and product qualities during crude switch-overs. Another objective has
been to improve control of pumparound heat removal, hence internal reflux of the Atmospheric
Column.
To achieve these objectives, the project utilized inferential technology, which included:
i. Use of temperature profiles, pressures, product flows and other process measurements
of the two columns, to construct the TBP of the crude being fed to the unit.
ii. Prediction of product qualities and V/L profiles, based on the constructed TBP of the
crude.
iii. Calculation of set-points of side-stream and pumparound flows, to achieve desired
product qualities and reasonable internal reflux.
Two main programs, working side-by-side, have been installed in the plant, to implement the
inferential technology.
• GCC – the Generalized Cut Point Controller, a product of PetroControl, and
• CPPM – the Crude Plant Performance Monitor, a product of Kesler
Engineering, Inc. (KEI).
GCC is a compact, integrated package of constraints, dynamics, manipulated variables and
inferential models. It uses assumptions that enhance its speed and robustness (Reference 1).
CPPM uses, at its core, a rigorous, proprietary tray-to-tray model (Reference 2) and redundant
measurements that enhance its accuracy. It periodically updates selected parameters to correct
the GCC biases (See Figure 6). Combining CPPM’s rigorous solution with the speed-of-
response and robustness of GCC – particularly during crude switchovers – has resulted in
superior prediction and control of inferred product ASTM’s, e.g., of Furnace Oil.
The two programs, GCC and CPPM, are integrated with PI, a product of OSI Software, Inc. to
retrieve on-line data and store results in real time. Set-point values that GCC stores in PI are
relayed, via an interface, to DCS for closed-loop control of selected manipulated variables. This
PI is a dedicated, local system, not connected to the refinery information system for reasons of
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security. In addition to the control functions, PI is used for performance monitoring of the
application.
Further details of GCC, CPPM and the overall system architecture follow.
GCC
GCC performs the following functions:
• Constructs a crude TBP
• Predicts product properties and V/L profiles
• Calculates set-points for product flows required to meet quality specs and for pump-
around flows to provide reasonable internal reflux
i. Constructing the Crude TBP. GCC calculates the portion of the crude vaporized, at two
anchor points: the column flash-zone and top-tray overhead. It assumes a linear slope for the
crude TBP between the two anchor points. To calculate the TBP of the crude at the flash-
zone, GCC considers the temperature of the flash-zone, corrected for partial pressure, to be
EFV (Equilibrium Flash Vaporization) end-point (dew point) of the vaporized portion of the
crude. It uses API data book procedures (Reference 3) to convert the EFV to TBP values.
Further, GCC uses a heat balance to calculate the portion of the crude vaporized, rather than
obtaining it from the sum of the products. This approach is significantly more accurate,
particularly during unsteady operations following crude switchovers. One reason is that a
shock resulting from changes in mass flow – e.g., due to a lighter crude – can be masked by
tray and vessel hold-ups. A second reason is that changes in heat- flux can not be hidden and
are immediately detected by a heat balance.
GCC calculates the TBP of the lower-boiling anchor of the crude, in a similar fashion, by
considering the top-tray temperature, corrected for partial pressure, to be the EFV of the end-
point of that vaporized portion of the crude, namely, the Heavy Naphtha.
GCC then constructs the TBP curve of the crude. Generally, GCC uses the sidestream draw
temperatures to determine the degree of nonlinearity in the curve. However, for this project,
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given the better steady state results of CPPM, GCC assumes the TBP profile of the crude
between the two anchor points to be linear.
ii. Prediction of product ASTM’s. Having established a TBP profile, GCC calculates product
flows from heat-balances around each side-stream section, and product cut- points from the
crude TBP vs. vol%. It uses API procedures to convert the TBP’s to ASTM’s, and short-cut
methods to correct tail-end ASTM’s for internal reflux.
iii. Calculating Set-Points. GCC provides on-time set-point values of key manipulated variables
to DCS via PI. These values insure on-spec product yields and reasonable internal V/L
profiles.
GCC performs the above functions approximately once every minute. Its speed - as well as
robustness – is due, in part, to the short-cut methods that it uses. GCC calibrates the correlations
and the model that it generates with several sets of historic data. CPPM, described in the next
paragraphs, provides a more rigorous basis for tuning key parameters of GCC, approximately
once every 10 minutes.
An important aspect of the on-line implementation is to insure validity and reasonableness of the
plant data obtained from PI. GCC includes data conditioning procedures to check the data, and to
identify outliers, faulty instrumentation, etc.
Figure 6a shows GCC controls in the plant environment.
CPPM
The core of CPPM is a rigorous tray-to-tray model of the two columns. The T-t-T calculations
use “open-equation” formulation with proprietary procedures of lumping components/trays to
enhance solution speed and robustness (Reference 2). The model has been calibrated with several
historic data sets, to establish tray efficiencies in the various sections of the columns. The
calibrated model has been incorporated in a solver/optimizer, which retrieves from PI – a product
of OSI Software, Inc. – temperature profiles and product flows, and constructs interactively a
crude TBP, by minimizing errors between calculated and measured values.
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Having established the composition of the crude, CPPM predicts, with the T-t-T model, 90% -
ASTM’s of the key products, as well as V/L profiles for the given set of pumparound duties. It
stores the calculated values in PI. CPPM also calculates and stores in PI updated parameters for
GCC, approximately every 10 minutes.
Figure 7 presents an overview of the system. The figure shows two blocks essential for on-line,
closed loop operations; namely, Data Conditioning and, Checking Results. CPPM includes
statistical tests and heuristics to eliminate inconsistent or suspect data. It also checks to insure the
column has reached a reasonable steady state of operations. Similarly, CPPM checks
reasonableness and consistency of results, limiting changes to a certain percentage of proceeding
values. These “filters” have improved performance and conformity of the predicted qualities
with lab measurements.
RESULTS AND CONCLUSIONS
Figure 8 shows an example of dramatically improved control of Heavy Naphtha 90% ASTM at
closed-loop, during a 5-hr period, compared with open-loop operation. Figure 9 shows 90%
ASTM closed-loop control of Kero (lower graph) and Furnace Oil vs. lab data. As can be seen,
the system predicts values that match well with the lab measurements. Furthermore, it is obvious
that the current product 90%-ASTM’s are significantly closer to desired specs, and have a much
narrower scatter, than those recorded before installing the system, shown in Figures 3-5. These
results show significant benefits beyond original expectations. Other benefits of the project have
been:
• Shortening crude switchover time from about four hours to 90 minutes, and maintaining
product qualities during crude switchovers.
• Better control of internal reflux, reducing the frequency of side-stripper level loss events
from daily occurrences to once every two months.
• Higher unit efficiency and increased throughput due to better internal-reflux control.
• Reduction of operator loads.
In terms of operator acceptance, following training and a period of open loop operations, closing
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the quality-control loop was accomplished with relatively little trouble. Then the application
quickly gained popularity, due to its ability to handle crude switches effectively. Several months
later the internal reflux control part was tuned, reducing the frequency of sidestream level losses.
Since then (8/15/00), the application has run practically all the time. Recently the operators
noticed that the PI displays provide more and better information than DCS. They requested –
and will shortly receive – a PI screen in the control room, showing trends of key variable and
product property predictions.
Literature Cited
1. Friedman, Y.Z. “Model-Based Control of Crude Product Qualities” HydrocarbonProcessing, February 1994, pp. 96-106
2. Kesler, M.G. et al: “New Method Dynamically Models Hydrocarbon Fractionation”Hydrocarbon Processing, October 1995, pp. 93-100
3. Chapter 3 in the API Technical Data Book – Petroleum Refining, March 1988
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