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Process Control and Information Systems
SUPPORTING CALCULATIONS IPPP DEVELOPMENT
To calculate the sulfur content of the FCC feed inlet, several predicted values were considered. By using the flowrate and sul- fur quantity of all streams listed in TABLE 3, the total sulfur value
can be calculated at the FCCU feed inlet. The calculation used to estimate the sulfur content is:
=(79FC803.PV S1 density) + (79FC802.PV S2 density) + (79FC801.PV S3 density) + (7FC6701.PV S4 density) + (12FIC100.PV S6 (if crude_select.op=1) density)
or (MRA.12FIC100.PV S7 (if crude_select.op = 2) density)
or (MRA.12FIC100.PV S8 (if crude_select.op = 3) density) + ((2FC0708.PV S5)/1000) / (79FC803.PV + 79FC802.PV + 79FC801.PV + 7FC6701.PV + 12FIC100.PV + 2FC0708.PV)
where S1–S8 are sulfur values that are entered by the operator.
Sulfur content of FCC gasoline splitter. Feed to FCCGSU is compensated by two streams—hot feed from the debutaniz- er (306FI0105.PV) and cold feed from recycle (306FIC0101. PV). Calculations to estimate sulfur at FCCGSU feed are:
= ((DSU_SULFUR.PV 306FI0105) + (STABBTM_ SULFUR 306FIC0101.PV-5.5*)) / {(306FI0105) + (306FIC0101-5.5*)}
where DSU_SULFUR.PV and STABBTM_SULFUR are the IPPP sulfur estimations.
*5.5 is the flow correction since the control valve has a zero error.
IPPP applications. Several IPPP models were developed for the FCC gasoline desulfurization unit and include:
• FCCDSU hot feed sulfur estimation • HDS feed sulfur estimation
• Stabilizer bottom sulfur estimation.
FCCDSU feed sulfur. This model used several inputs:
Tag name Tag description
FCCUFD_SULFUR.PV Sulfur at FCCU (calculation)
19TRC153.PV FCCU main fractionator
top temperature
20TI99.PV FCCU debutanizer bottom
temperature.
To estimate the sulfur content of DSU feed, the following linear equation is used:
P = Ax1 + Bx2 + Cx3 + Bias where: P = DSU_SULFUR.PV
(FCCDSU feed sulfur in hot feed)
A = Coefficient 0.041417 x1 = FCCUFD_SULFUR.PV B = Coefficient 1.6497 x2 = 19TRC153.PV C = Coefficient 5.736500 x3 = 20TI99.PV Bias = –1067.4
FIG. 3. First-order process model response to reactor inlet temperature
control. FIG. 4. Quality and process improvement achieved through APC IPPP.
TABLE 1. APC variables for the sub-controller for the selective hydrogenation unit—SHUCON
Description Interface point
Manipulated variables:
Flow of SHU gasoline recycle 306FIC0101.SP SHU feed/effl uent excahnger bypass 306FIC0202.SP Steam fl ow to SHU pre heater 306FIC0203.SP
Disturbance variables:
FCCU debutanizer fl ow 306FI0105.PV Flow to SHU from surge drum 306FIC0104.PV
Controlled variables:
Feed surge drum (306-V-01) level 306LIC0103.PV SHU reactor (306-R-01 B) inlet
temperature
306TIC0270.PV
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HDS feed sulfur. This model used several inputs: Process inputs used
Tag name Tag description
GSUFD_SULFUR.PV Feed to FCCGSU (calculation)
20PI0802.PV FCCGSU top pressure
20FC0306.PV FCCGSU light cut draw flow
20FC0404.PV FCCGSU heart cut draw flow
The following linear equation is used:
P = Ax1 + Bx2 + Cx3 + Dx4 + Bias where: P = HDSFD_SULFUR.PV A = Coefficient 1.097890 x1 = GSUFD_SULFUR.PV B = Coefficient –272.28299 x2 = 20PI0802.PV C = Coefficient 7.0273 x3 = 20FC0306.PV D = Coefficient 3.291770 x4 = 20FC0404.PV Bias = 598.81
Stabilizer-bottom sulfur. This model used several inputs: Process inputs used
Tag name Tag description
HDSFD_SULFUR.PV HCN sulfur (HDS feed sulfur IPPP estimation)
TABLE 3. Process monitoring points used to estimate sulfur level for the FCC feed inlet
Description Tag name
OHCU bottom from tank 79FC803.PV LS VGO from tank 79FC802.PV BH VGO from tank 79FC801.PV OHCU bottom hot feed 7FC6701.PV HOT feed from AVU 12FIC100.PV DHDS VGO fl ow 2FC0708.PV AVU crude select tag* crude_select.op
*The sulfur quantity for each of the fl ow was operator entry.
AVU crude select tag is a digital tag pulled from the AVU having three values.
Tag value Crude type Sulfur quantity, ppm
1 Bombay High 4,000 2 High Sulfur 30,000
3 Nigerian 6,000
Description Densities
OHCU bottom from tank 0.875 LS VGO from tank 0.9 BH VGO from tank 0.9 OHCU bottom hot feed 0.875 HOT feed from AVU 0.9 DHDS VGO fl ow
AVU crude select tag
TABLE 2. APC variables for the sub-controller for the HDS unit— HDSCON
Description Interface point
Manipulated variables:
Fuel gas fl ow 307FIC0684.SP HDS reactor 2nd bed quench 307FIC0605.SP Stabilizer bottom steam pressure 307PIC1003.SP
Disturbance variables:
HDS feed from GSU 307FI0606.PV Stabilizer light end feed from GSU 306FIC0502.PV HDS reactor 2nd bed bottom
temperatue
307TI0630.PV
HDS feed temperature at GSU 20TI0804.PV HDS feed sulfur HDSFD_SULFUR.PV
Controlled variables:
HDS reactor 1st bed inlet (307R01) temp 307TI0642.PV HDS reactor 2nd bed inlet (307R01) temp 307TIC0635.PV Feed effl uent exchanger inlet temp 307TI0607.PV Stabilizer (307-C-02) bottom temp 307TI1014.PV Refl ux fl ow to the stabilizer 307FIC1003.PV Online stabilizer bottom sulfur 307AI1001.PV Stabilizer bottom sulfur (inferred) STABBTM_SULFUR.PV
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Process Control and Information Systems
307TI0642.PV HDS reactor 1st bed inlet temperature
307TI0630.PV HDS reactor 2nd bed bottom
temperature
307TI1014.PV Stabilizer bottom temperature. To estimate the sulfur content of HDS feed, the following linear equation is used:
P = Ax1 + Bx2 + Cx3 + Dx4 + Bias where: P = STABBTM_SULFUR.PV A = Coefficient 0.115679 x1 = HDSFD_SULFUR.PV B = Coefficient –3.90 x2 = 307TI0642.PV C = Coefficient –3.59673 x3 = 307TI0630.PV D = Coefficient –0.341067 x4 = 307TI1014.PV Bias = 1067.5
From FIG. 4, the quality estimation using the IPPP has good
agreement with the actual sulfur content as measured from unit and lab analyzers. TABLE 4 summarizes the economic func-
tions and RON improvement possible with APC. PROJECT MILESTONES
Implementing APC on the HDS unit has yielded substantial tangible and intangible benefits. While the annual monetary gain is of the order of Rs. 39 lakhs, significant improvement via process control and optimization was achieved as measured through tighter control of the SHU and HDS reactor inlet tem- peratures. More accurate estimation of the stabilizer-bottom sulfur inferential was possible, which facilitated proper control action via the APC. With tighter control and action via APC, adjusting and preferentially lowering the reactor-inlet tempera- tures were possible. The effect of crude changes in the atmo- spheric and vacuum distillation unit is also incorporated into the model. The resultant sulfur changes in the FCC feed are transmitted via means of intermediate calculations and inferen- tial estimations to the final stabilizer-bottom sulfur prediction. Operators now have more confidence when implementing control and optimization strategies. This has resulted in better operations of the refinery. Accordingly, APC was successfully implemented and is yielding expected benefits.
LITERATURE CITED
1 Perry, R. H., Chemical Engineers Handbook, Sixth Ed., New York, McGraw Hill,
1984.
2 Levenspiel, O., Chemical Reaction Engineering, Third Ed., Singapore, John Wiley
and Sons, 1999.
3 Stephanopoulos, G., Chemical Process Control, Dorling Kindersley (India) Pvt.
Ltd., 2007.
SHYAMAL DEBNATH is the chief technical services manager at Indian Oil Corp. (IOC) Ltd.’s Mathura refinery. His primarily responsibilities include providing technical services for strategic initiatives and advanced process control (APC). Mr. Debnath has more than 25 years of experience in unit operations, strategic initiatives (process and projects), research, troubleshooting and APC for all the major process units at various IOC refineries. He holds an MS degree in chemical engineering from Indian Institute of Technology, Kharagpur, India.
HITESH SHAH is a senior technical services manager with Indian Oil Corp. (IOC) Ltd.’s Mathura Refinery. His primary
responsibilities include providing technical services for strategic initiatives and APC. Mr. Shah has more than 14 years of experience in strategic initiatives, planning and coordination, and APC. At present, he is working as a senior technical services manager at IOC’s Gujarat refinery. Mr. Shah holds an MS degree in chemical engineering from Indian Institute of Technology, Bombay, India.
PRASHAT DUBE is a senior process engineer at Indian Oil Corp. (IOC) Ltd.’s Mathura Refinery. He is primarily responsible for providing technical services for APC implementation and maintenance. Mr. Dube has five years of experience in APC for all major process units at the Mathura Refinery and holds a BS degree in chemical engineering from Indian Institute of Technology, New Delhi, India.
MS. VARSHA YADAV is a senior process engineer at Indian Oil Corp. (IOC) Ltd.’s Mathura refinery. She is primarily responsible for providing technical services for APC implementation and maintenance. Ms. Yadav has three years of experience in APC for all major process units at the Mathura Refinery and holds a BS degree in chemical engineering from Regional Institute of Technology, Raipur, India.
TABLE 4. Economic benefi t and octane conservation possible through APC
Economic function name Maximization of sulfur
Speed factor 0.10
Economic coeffi cients
MAX_AI 10 STEAMMIN 10 MAX_SULFUR 10 MINFG 100 MINRIT1 0 MINRIT2 0 RON improvement
RON improvement after MVPC implementation from the rundown stream (MS) of HDS unit
0.114
1 unit of RON improvement corresponds to (1 metric ton of MS processed)
Rs. 91.30
Annual processing of feed (MS) in the HDS unit (not considering the heart cut drawn from FCCU-GS)
376,487 metric ton
Estimated annual benefi t due to MVPC application in HDS unit
Rs. 39,32,517.86
≈ Rs.39. 32 Lakhs (Rupees thirty nine lakhs thirty
two thousand fi ve hundred and seventeen only) Sulfur in the stabilizer bottom
MS stream improved
15 ppmw
Sulfur in the rundown MS improved 11 ppmw
Targeted benefi ts due to RON improvement
Targeted annual benefi t due to MVPC application in HDS unit
Rs. 24.46 Lakhs
Targeted sulfur improvement in the rundown MS