The US Environmental Protection Agency (EPA) issued the Mobile Source Toxics (MSAT) Rule on February 26, 2007 (40 CFR Parts 59, 80, 85 and 86). This rule required refiners to reduce the average benzene concentration in the gasoline pool to 0.62 vol% or less by January 1, 2011. Refiners producing higher levels could reduce their concentration by purchasing credits, but they are capped at a maximum actual average con- centration of 1.3 vol%.
The MSAT ruling has led many refineries to upgrade pro- cessing to meet the new benzene limits. Because the majority of benzene in blended gasoline (by some estimates 70%–85%) originates from reformate, reducing the benzene levels in the reformate offers the most direct means to meet specifications for finished gasoline.1
The refinery. The Phillips 66 Wood River refinery in Roxa- na, Illinois, is a 365,000-bpd facility. In 2008, the refinery (a ConocoPhillips refinery at the time) evaluated options to meet the MSAT ruling. The plant decided to install a refor- mate splitter column to treat reformate produced by the re- finery’s catalytic reformer. This technique is a post-fraction- ation process. Benzene is concentrated in the heart cut of the splitter containing 20%–30% of benzene. The benzene can be removed from the blending pool either by saturation or by additional concentration via extraction to produce a sellable high-purity benzene product.
Selection criterion. To optimize performance of the benzene- reduction process and to ensure that the reformate complied with new blending pool specifications, the Wood River project team evaluated several analyzer technologies for real-time anal- ysis. The refiner selected two technologies: process gas chroma- tography (PGC) and Fourier transform infrared (FTIR) spec- troscopy. The technology selection phase focused on:
• Ability to make the required analytical measurements. Both the PGC and FTIR systems were capable of measuring benzene and toluene at the required levels.
• Site familiarity with technology and ability to support
systems. The Wood River refinery has numerous PGC systems
installed throughout the refinery, and it also had installed spec- troscopic near-infrared (NIR) analyzers on the gasoline blend- ers since 2001. The site recently added online NIR analysis of the diesel blending stream.
• Cycle time. Spectroscopic analyzers like the installed
FTIR system have typical cycle times of 1–2 minutes per stream.
To meet the required cycle time for the broad dynamic range of required measurements, four PGC systems would be required.
• Cost. By using one FTIR system with two measurement
flow cells (one for high-concentration analysis streams and one cell for measurement of low-concentration streams), the FTIR system cost was lower than that to install four PGCs. Addition- al cost savings for the FTIR system were calculated based on lower maintenance and utility requirements.
To measure, control and optimize the reformate splitter col- umn, Phillips 66 installed an online multistream FTIR analyzer to monitor six streams of the unit. FIG. 1 is a diagram of the split-
ter column, highlighting the analyzed streams. The light-refor- mate feed, heavy-reformate feed, side-draw feed to the benzene extraction unit, lower side-draw, tower bottoms and splitter top streams are analyzed by a two-cell FTIR system installed in a shelter near the unit.
The FTIR system was specified to monitor the benzene, toluene and butane concentrations. The analysis slate was modified to add the analysis of distillation points, aromatics
Light feed
Toluene concentrate RDC-1 feed
Heavy reformate product Splitter tops
Heavy feed
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and research octane number (RON) for the toluene concen- trate stream. Measuring butanes was not developed because of the difficulty in reliable sample handling and also the determi- nation that there was limited value in using the butanes data in the unit control scheme.
Analyzer system. A dual-stream, extended-range mid-infra- red process FTIR analyzer was installed on the splitter column. The six incoming streams were plumbed to the analyzer shel- ter. Sample conditioning systems were installed on the exterior wall of the shelter to remove particulate materials and water from the streams. The conditioning system also provided tem- perature control of the sample. A stream switching manifold is used to select the streams to be analyzed. The analyzer system is linked to the distributed control system (DCS) through a Modbus TCP connection.
The DCS system is used to activate streams, allowing the system to only measure required streams. The analyzer auto- matically cycles through selected active streams. During rou- tine operation, the two cells are alternatively analyzed. While one analysis cell is being measured, the second cell is flushed
out with the next stream to be analyzed. The system was configured with a pneumatically actuated optical switch con- trolled by the main analyzer control program, which automates system data collection and analysis, cell selection, validation, automatic background collection and data reporting. An au- tomated sample-cell wash and validation system was installed to confirm proper system performance, and it allows for auto- matic sample-cell washing in the event of window fouling. FIG. 2 is a block diagram of the analyzer system.
Calibration. A laboratory FTIR system was installed in the Wood River laboratory shortly after the decision was made to install the online analyzer. During the time that the process analyzer was being built and installed, all routine samples for the six streams were collected and taken to the lab for analy- sis by conventional analyzers to determine the property values of interest. The samples were also scanned on the lab FTIR system. When the operators ran the samples on both the lab and conventional analyzer systems, the laboratory information management system (LIMS) number was entered, allowing for easy integration of lab data with the FTIR scans. The FTIR system logged collected data and sample information into a file that was used in PC software to create a calibration database.
The laboratory system uses the same optics, interferometer and sample analysis cell as used in the online system. The ex- tremely high accuracy of FTIR-based systems allows for seam- less transfer of data and models between systems; it ensures that the models developed based on samples scanned on the lab sys- tem can be used directly online. The FTIR spectra were collected from 6,000 cm–1 to 1,000 cm–1 (1,667 nm–10,000 nm) using a
0.5-mm pathlength transmission cell. Representative calibration spectra for the six streams are shown in FIG. 3. The spectra for
the different streams are colored as indicated in the legend. The large differences in the spectra for the different streams are due to the large compositional differences for the six streams. Initial- ly, it was thought that it would be possible to combine data from several streams to minimize the modeling effort. A principal components analysis (PCA) was performed on the combined data set to investigate any relationships between the streams. FIG. 4 is a score plot of the first two factors. The six sample streams
Plant DCS system Lab FTIR analyzer Sample preconditioning enclosure Sample preconditioning enclosure
FTIR analyzer – optical and electronic enclosure
FTIR dual-cell enclosure Heavy reformate feed
Toluene concentrate
Heavy reformate
Light reformate feed
RDC feed
Splitter tops
Laboratory LIMS Analyzer shelter
Class 1, Group D, Division 2, T2c
Val Wash Wash/validation skid
Modbus
FIG. 2. FTIR installation diagram.
FIG. 3. FTIR spectra for calibration samples.
Heavy feed RDC-1 Toluene concentrate Heavy reformate Splitter tops Light feed -5 0 -5 -2 0 2 Factor 1 (80.5%) Factor 2 (10.5%)
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can be seen to be different enough in score spacing to discourage efforts to combine sample streams into any grouped or common models. Calibration models were developed for each stream, and the results were significantly better than the results obtained when data from similar streams was combined.
Calibration models were developed using chemometric modeling software.a This software uses the principal compo-
nents regression (PCR) algorithm and incorporates advanced data-filtering techniques to reduce or eliminate interferences such as those caused by ambient carbon dioxide and mois- ture, baseline effects and possible pathlength variations. The software was evaluated to investigate additional data process- ing and calibration capabilities. The spectral regions that are saturated or which contain little or no relevant information for TABLE 1. Calibration table summary
Concentration range
Stream Property APC Use Units Min Max Samples Factors SEE SECV R2
BRU heavy feed Benzene Monitor vol % 2.65 7.28 60 3 0.192 0.208 0.978
BRU heavy feed Toluene Monitor vol % 12.2 21.1 58 4 0.63 0.7 0.914
BRU rdc-1 feed Benzene Monitor vol % 25 40.5 85 4 0.77 0.81 0.944
BRU rdc-1 feed Toluene Control vol % 0.02 2.12 76 4 0.121 0.125 0.946
BRU toluene concentrate Benzene Monitor vol % 0.02 1.85 93 4 0.07 0.083 0.92
BRU toluene concentrate Toluene Monitor vol % 35 67.45 102 4 1.49 1.54 0.918
BRU toluene concentrate RON # Control ON 99.65 113.5 100 5 0.736 0.787 0.916
BRU toluene concentrate Distillation 50% Control °F 235.7 257.5 98 3 1.05 1.1 0.941
BRU heavy reformate product Toluene Control vol % 3 37.65 108 3 1.35 1.39 0.972
BRU splitter tops Benzene Control vol % 0.23 2.04 56 4 0.1 0.107 0.97
BRU light feed Benzene Monitor vol % 10.95 18.5 90 3 0.5 0.52 0.926
BRU light feed Toluene Monitor vol % 20.76 29.76 87 4 0.736 0.776 0.82
0.0 0.5 1.0 1.5 2.0 2.5 -0.5 0.0 0.5 1.0 1.5 2.0 2.5
Toluene measured by lab, %
Toluene predicted, %
RDC feed
FIG. 5. Correlation between model-estimated and lab-determined toluene for RDC feed.
98 100 102 104 106 108 110 112 114 98 100 102 104 106 108 110 112 114
RON measured by lab, %
RON predicted
Toluene concentrate
FIG. 6. Correlation between model-estimated and lab-determined RON for the toluene concentrate stream.
235
Distillation point measured by lab, % Toluene concentrate
Distillation point predicted, 50% 235
240 245 250 255 260 235 240 245 250 255 260
FIG. 7. Correlation between model-estimated and lab-determined 50% distillation point for toluene concentrate.
0 0
Toluene measured by lab, %
Toluene predicted, % 5 10 15 20 25 30 35 40 5 10 15 20 25 30 35 40 Heavy reformate
FIG. 8. Correlation between model-estimated and lab-determined toluene for heavy reformate.
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the property of interest, were excluded from the calibration. All calibrations were developed using random subset cross valida- tion. Models were developed generally following the recom- mendations of ASTM E-1655, Standard Practices for Infrared
Multivariate Quantitative Analysis. A minimum number of “out-
lier” samples were identified and removed from the calibration sets. Several of the removed outliers were based on incorrect sampling; others were identified as misidentified samples.
TABLE 1 summarizes the results of modeling. For all mea-
surements examined, a strong correlation (R2) was attained
between the spectral response and the primary values, and the
standard error of cross validation (SECV) essentially matched the standard error of estimate (SEE). All models used a sig- nificant number of calibration samples, ranging from 56 to 108 samples. The number of factors used for all models varied from 3 to 5. The optimum number of factors was selected by evaluating the predictive residual error sum of squares plots, loadings and regression vectors calculated to avoid “over-fit- ting” of the data.
Streams listed as “control” in TABLE 1 are used for advanced
process control (APC) of the splitter column. FIGS. 5–9 show
the strong correlation (fit) obtained for several of the key con- trol properties used.
Control. The FTIR analyzer provides product stream analysis that is used in an APC application to maximize recovery, and to reduce energy at the benzene-removal unit (BRU) column. The controlled variables of this application are:
• Percent benzene in the overhead liquid product • Percent toluene in the feed to the extraction unit • RON of the toluene concentrate stream
• 50 % Distillation point of the toluene concentrate stream • 5% Distillation point of the toluene concentrate stream. The other calibrated properties are used for monitoring the system and are not actively used in the APC logic. However, they are available to operators and control engineers through the DCS system. All data are archived in the plant historian.
A single FTIR system has been installed to analyze six streams on a reformate splitter column. Calibration models were developed and installed to monitor the concentration of benzene and toluene, as well as additional properties, includ- ing the RON and several distillation points. The accuracies at- tained using the FTIR system are comparable to those attained using the conventional analytical techniques. The system was installed to meet the MSAT deadline, and it has enabled the Wood River refinery to meet the regulated MSAT specifica- tion for blended gasoline and to optimize operation of the splitter column.
LITERATURE CITED
1 Palmer, R. E., “Options for reducing benzene in the refinery gasoline pool,” 2008
NPRA Annual Meeting, San Diego.
2 McDermott, L., “Cetane benefits,” Hydrocarbon Engineering, July 2003, Vol. 8 No.
7, pp. 69–73.
3 “Cutting through the haze: How will refiners meet EPA’s new benzene standards?,
”Benchmark Newsletter, 2007.
4 Uvland, K. A., Oil and Gas Process International, 1997
5 Vickers, G. H., “On-line determination of naphtha properties to control a refinery
process using near infrared spectroscopy,” IFPAC Conference, 2001.
NOTES
a Calibration models were developed using chemometric modeling software avail-
able from Applied Instrument Technologies.
DR. ATIQUE MALIK holds a BS degree and PhD in chemical engineering from the University of Leeds. He has worked in the area of model predictive control for various companies. Dr. Malik has reviewed publications for the Journal of
Process Control and has been associate editor for Control Engineering Practice. He is the team Lead for APC at the Wood River Phillips 66 refinery.
LARRY MCDERMOTT holds a BA degree in chemistry, an MS degree in marketing and technological innovation and an MS degree in management engineering. He has worked in the field of process analyzers based on spectroscopic systems for over 20 years. He is the technical applications manager for Applied Instrument Technologies, focusing on chemometric modeling of refinery process streams.
Benzene measured by lab, %
Benzene predicted, 50% 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 0.0 0.5 1.0 1.5 2.0 2.5 Splitter tops
FIG. 9. Correlation between model-estimated and lab-determined benzene for splitter tops.