Leveraging Next Generation APC Technology to Compress Decision Cycles
APC implementation on CCR Plant 2009
1. PROCESS CONTROL AND INFORMATION SYSTEMS SPECIALREPORT
HYDROCARBON PROCESSING OCTOBER 2009
I 59
Implementing and maintaining
advanced process control on
continuous catalytic reforming
The primary benefit was an increase in reformate octane barrel yield
from operating the plant at its economic constraints
P. BANERJEE, Aspen Tech Middle East (ATME), Kuwait; and A. AL-MAJED and
S. KAUSHAL,Kuwait National Petroleum Corporation (KNPC), Kuwait
A
dvanced process controllers (APCs) were implemented
on two identical trains of continuous catalytic reforming
(CCR) plants at the Mina Al-Ahmadi (MAA) refinery
of Kuwait National Petroleum Corporation (KNPC) that paid
off the project cost within a few months. Even though the CCR
trains are identical, there were differences in the realized benefits
reflecting their unique operating constraints. A fast-track project
implementation methodology was adopted to accommodate
several components of this APC project.
The APC benefit was realized due to an increase in reformate
octane-barrel yield resulting from operating the plant at its eco-
nomic constraints. The reformate octane barrel yield increased
due to an increase in throughput, improved heavy naphtha recov-
ery, and an increase in reactor bed temperatures and reduction in
reactor pressure.
The controllers have a high online factor. To keep sustain-
ing the controller benefits after its commissioning, certain APC
parameters and key performance indicators (KPIs) are monitored
that are also briefly discussed in this article.
Introduction. Two identical trains of a catalytic naphtha
reforming plant of 18 kbpd capacity each became operational in
2004 at the KNPC MAA refinery. KNPC decided to implement
the APC project after the plant was commissioned and stabilized
at the design capacity to start getting the benefits early.
Each train is comprised of a naphtha hydrotreater (NHT)
plant followed by a stripper and splitter to separate out off-
gas, unstabilized naphtha and light naphtha (LNAP) from the
hydrotreated naphtha and supply heavy naphtha (HNAP) as
feed to the CCR Platforming unit. For simplicity, the NHT,
stripper and splitter sections are together referred to as NHT in
this article.
In a hydrogen environment, HNAP is reformed to reformate
in the presence of a moving catalytic bed in the Platformer unit.
Catalyst from the Platformer reactor is continuously regenerated
in a CCR–regenerator unit. The reformate product recovered
from the debutanizer bottom is used as a gasoline blend compo-
nent. The byproducts such as LNAP, hydrogen, LPG and fuel gas
go back to the refinery.
On several occasions prior to the APC implementation, the
plant tripped due to high temperature problems in the net gas
compressors.This problem was effectively addressed through APC
and it also stabilized the plant operation besides improving the
reformate octane-barrel yield that paid off the project cost in a few
months.
APC project implementation. An automated stepper appli-
cation was the workhorse for rapid deployment of the APC project
on both trains.1 During the pretest phase of the project a prelimi-
nary manual step test was conducted to obtain a “seed-model” for
the automated stepper that was used during the step test. It was
important to obtain reasonably good initial-level models to man-
age the NHT inventory using the stepper application.The stepper
application automatically perturbs the plant while maintaining
the process variables within acceptable operator set limits.
The initial few plant perturbations were comprised of long
steps allowing the plant response to steady out to improve esti-
mating the steady-state gains and obtain operator confidence on
the stepper application. Subsequently the stepper was switched
to a multitest1 mode whereby the plant is perturbed for sev-
eral manipulated variables (MVs) using generalized binary noise
(GBN)1 test signals at a relatively fast pace while maintaining
the plant variability within acceptable limits. The stepper makes
uncorrelated MV moves1 thereby not only reducing the step test
duration but also providing better quality reliable models. It also
aids in identifying a robust model. The combination of multitest
and “sub-space”1 identification methodology results in a good
quality reliable dynamic model that is characterized by tighter
uncertainly bounds at all the frequencies. A robust multivariable
model is controller relevant whose steady-state gain matrix is
characterized by a smaller condition number. Condition number
of the model matrix can be further improved through manual
iterations using gain-ratio analysis or using optimization tools.
Both CCR trains were sequentially step tested to develop
separate models to reflect their unique operating characteristics.
The use of an automated stepper helped to reduce step testing
duration by about 50% compared to manual stepping. To meet
the APC requirements all level loops in the NHT sections were
2. PROCESS CONTROL AND INFORMATION SYSTEMSSPECIALREPORT
60
I OCTOBER 2009 HYDROCARBON PROCESSING
broken and were managed using the stepper application. This
relieved the operators and the project team from managing them
manually during the step test. The final model curves get pretty
much ready toward the end of the step test. Hence, the step
test can be concluded by running the stepper in control mode
to assess the quality of model predictions and initial control
actions. It took even less time to step test the second parallel
train since it started directly with the multitest using the final
models of the first train as seed models. The controllers were
commissioned after reviewing the models and simulating the
controller performances.
Partial least squares (PLS)-based algebraic steady-state infer-
ential models such as LNAP 95%, HNAP 5% and reformate
Rvp were deployed for predicting the product properties that
are required to be controlled as controlled variables (CVs) by
the APC. Controlling the inferential CVs to their desired limits
greatly contributes to the economic benefits.
The plant was manually tested for different steady-state condi-
tions for developing inferential models.
Rigorous kinetics-based proprietary steady-state online models
for predicting the RON, heater duties, TMTs and catalyst coking
were also deployed and integrated with the APC for controlling
them as CVs. Such models can also be developed using commer-
cially available offline kinetics modeling tools tuned to the plant
data. These models can then be deployed on line either through
their online application if available or by further developing non-
linear regression-based inferential models.
Custom screens were developed for the proprietary online
and inferential models on the distributed control system (DCS)
for operator interface. The screens display the model predictions
and allow the operator to enter laboratory data for bias correc-
tion. Two examples of custom-developed DCS screenshots for
the proprietary models and lab update for the inferential models
are illustrated in Fig. 1. Sometimes developing the APC–DCS
interface for the operators can be involved and time consuming.
In this project, a commercial APC–DCS interfacing package was
available that automatically generated the necessary interfaces and
the APC operator screens on the DCS thereby saving a consider-
able amount of system engineering time. There are two servers,
one supporting the APC and inferential applications and the other
hosting the proprietary online models. Data communication
between these servers with the DCS is via a dedicated gateway
that is always a recommended practice to maintain robustness of
the data communication.
Each train has its own operating console and there is hardly
any interaction between them. Hence, there is a dedicated APC
for each train. Fig. 2 shows there are three controllers per train:
NHT/Platformer, regenerator and debutanizer.The APC control-
ler is divided into several subcontrollers for operational ease where
each subcontroller typically represents a section of the plant. The
NHT/Platformer controller is divided into four subcontrollers
representing the NHT, stripper, splitter and platformer sections
shown in Fig. 2.
Overall controller objectives. The controllers are designed
with the overall objective of maximizing the reformate octane
barrel yield by operating the plant at its economic constraints.
The benefits are realized by maximizing the recovery of HNAP,
operating Platformer temperatures against a minimum RON,
minimizing Platformer pressure and maximizing the reformate
Rvp subject to the process constraints. The controller maximizes
HNAP flow to the Platformer while balancing the inventories in
the NHT, stripper and splitter. A single controller is designed for
the NHT and Platformer sections since the NHT section man-
ages the supply of HNAP feed of desired spec. to the Platformer.
A separate controller strives to maintain a flat burn profile in the
Examples of custom-developed DCS screenshots.FIG. 1
NHT
reactor
system
Naphtha
feed
NHT-plat APC HNAP Reformate
product
Naphtha
stripper
Unstab. naphtha + offgas Light naphtha H2
N2
Air
LPG + fuel gas
Naphtha
splitter
Platformer
reactor
system
Regenerator
APC
Continuous
catalytic
regenerator
system
De-
butanizer
APC
Overview of APC boundaries.FIG. 2
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4. PROCESS CONTROL AND INFORMATION SYSTEMSSPECIALREPORT
62
regenerator to enhance the catalyst life. A detailed description of
the controller strategies is described next.
Subcontrollers in the NHT section. The NHT reactor2
heats the sour naphtha feed coming from the upstream crude
units and hydrotreats it in a fixed catalytic bed in the presence of
hydrogen sourced from the Platformer section. The hydrotreating
reaction decomposes organic sulfur and nitrogen components
and removes organic metallic components2 that are detrimental
for the Platformer catalyst. The hydrotreating reaction saturates
the olefinic components thereby preventing certain operating
problems in the Platformer reactors.
In the NHT section APC takes feed as much as required to
maintain the Platformer throughput. The NHT feed runs against
the constraints of furnace firing, minimum hydrogen-to-feed
ratio and a minimum reactor bed temperature while ensuring
adequate hydrotreating. The operator adjusts the lower limit
of reactor bed temperature depending on sulfur analysis of the
HNAP feed.
Hydrotreated naphtha from the NHT goes to the stripper to
strip off the H2S and unstabilized naphtha and the bottom flows
to the naphtha splitter. The controller in the stripper section
manipulates the reboiler steam to maintain a minimum reflux
ratio to achieve adequate stripping as per the operating guidelines.
The controller balances the input and output flow to maintain
the stripper level. The stripper pressure is moved the least only to
address the constraints.
The naphtha splitter separates out LNAP from the stripper
stabilized naphtha to obtain HNAP from the bottom to feed the
Platformer reactor. It is important to maintain C6 components
(benzene precursor) in HNAP below a specified limit to limit
benzene below 1% in the reformate. C6 in HNAP is indirectly
monitored by analyzing the 5% ASTM point.
The controller maximizes the yield of HNAP by maintaining
its 5% point just above a minimum limit to meet the Platformer
feed spec. The controller maintains the LNAP 95% point above a
minimum product spec. to indirectly control C7 in the LNAP. An
inferential model is used for predicting the HNAP 5% and LNAP
95% points. The splitter column runs against the constraints of
column pressure drop and valve openings while maintaining
HNAP 5% and LNAP 95% points above their lower limits.
The Platformer subcontroller dictates the HNAP flow; con-
sequently the other subcontrollers manage the NHT intake and
balance the inventories in the NHT, stripper and splitter sections.
Fig. 3 shows that the splitter bottom level control improved by
80% after implementing the APC. Prior to APC, the naphtha
splitter level used to swing to the alarm limits for which the opera-
tor was required to take large corrective actions for the NHT
inventory and the HNAP feed. With APC only smaller correc-
tions are required while maximizing HNAP feed to the Platformer
subject to its constraints.
Platformer subcontroller. The HNAP feed is preheated
and mixed with hydrogen in a combined feed exchanger before
entering the Platforming reactors. The Platforming reactions3,4,5
take place in a hydrogen-rich environment in the presence of
a moving bed of catalyst passing through a series of four reac-
tors. The heat of reaction is provided by separate natural-draft
furnaces associated with each reactor. High temperature and low
pressure favors the Platforming reactions3,4,5 such as dehydroge-
nation and isomerization of naphthenes and dehydrocyclization
and isomerization of paraffins that increase the reformate RON.
pre-APC APC
Naphtha splitter bottom level control
UL
LL
Inventory control in naphtha splitter.FIG. 3
■ A benefit of about 12 cents/barrel
at 2004 KNPC price levels was realized
after implementing the APC that
mainly manifested from an increase in
the reformate octane-barrel yield and
improvement in the recovery of the
by-products.
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5. PROCESS CONTROL AND INFORMATION SYSTEMS SPECIALREPORT
63
Higher pressure favors hydrocracking, demethylation and aro-
matic dealkylation that end up in consuming more hydrogen.
Depending on the Platformer conditions and feed composi-
tion, the proprietary online model makes the predictions that are
used by the APC to make necessary MV moves. For example, Fig.
4 shows that APC maintains the H2/feed ratio almost toward its
lower limit while maintaining coke on spent catalyst between its
limits based on the coke predicted on the catalyst exiting the last
reactor by the proprietary online model.
The controller accords maximum priority to push the HNAP feed
to the Platformer subject to the constraints such as the lower limit of
H2/feed ratio and RON, heater temperatures and firing constraints.
The reformate effluent from the last reactor is cooled, then
compressed in a series of compressors. The compression section
separates out hydrogen from the reformate. The recovered hydro-
gen is consumed by the NHT and Platformer reactors and the
remaining hydrogen goes to the refinery header. Liquid reformate
flows to the debutanizer column.
The controller minimizes the reactor pressure on a priority basis
to maximize the reforming conversion. Pressure minimization is
done against the constraints of the upper limits of the net gas com-
pressor maximum temperature and current and upper limit of pre-
dicted coke. The controller increases the recycle gas valve opening
and the recycle gas compressor speed preferentially over reducing
the feed for controlling the coke and H2/feed ratio. The controller
balances the net gas compressor stages by maintaining compressor
maximum temperature and current consumption below an upper
limit and avoids flaring.The controller optimizes the H2/feed ratio
to trade off greater heat sink in the reactors, decreased coking,
increased compressor load, reduced recycle H2 purity and increased
reactor yield. Fig. 5 shows that prior to APC the net gas tempera-
ture often used to hit the maximum limit that sometimes led to a
plant trip even though the inlet pressure was set high. With APC,
the Platformer pressure was reduced yet consistently maintained
the net gas maximum temperature below an upper limit. Based on
the prevailing compressor constraints in train 1, the pressure was
reduced by 0.1 kg/cm2 and 0.17 kg/cm2 in train 2.
The controller manipulates the Platformer heater temperatures
to maintain the RON prediction around its lower limit. The con-
troller strives to maintain a flat inlet temperature profile across all
four reactor beds by holding the temperature differences between
the adjacent heaters close to zero with an allowance of ±1°C to
allow the controller to attend to the heater constraints.The reactor
inlet temperatures get reduced if any of the heater constraints such
as the maximum TMT, convection temperatures, heater duties
or the fuel gas pressure hit their upper limits. RON reduces with
the reduction in the reactor inlet temperatures and the HNAP
throughput can reduce to prevent RON from falling below its
lower limit. To maintain the target RON, Fig. 6 shows that the
APC increases the weighted average inlet temperature (WAIT):
WAIT = xiTi
i=1
4
where xi and Ti are the percent of catalyst in the ith reactor and the
inlet temperature respectively.
Platformer pressure
Net gas compressor
maximum temperature
Pressurereduction
duetoAPC
Platformer pressure
APCpre-APC
Pressure reduction due to APC in the Platformer.FIG. 5
LL
LL
Steady-state coke H2-to-feed ratio
Control of coke and H2/feed ratio in Platformer.FIG. 4
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6. PROCESS CONTROL AND INFORMATION SYSTEMSSPECIALREPORT
64
For train 1, APC could increase the WAIT by around 9.5°C
and for train 2 it increased by around 6.5°C.
Regenerator controller. Coke builds up on the catalyst as
it slowly cycles through the reactor thereby deactivating its sur-
face. The spent catalyst is regenerated in a CCR-regenerator3,5
in a number of steps where in one of the steps the coke in the
catalyst gets burned leading to peaks in the catalyst temperature.
It is desired to maintain a relatively flat temperature profile in the
regenerator to prevent catalyst degeneration.
The APC minimizes O2 content in the burn air while ensuring
O2 controller output in the operable range. This helps in flatten-
ing the temperature profile and helps to reduce the temperature
peaks in the burning zone to enhance the catalyst life. The con-
troller ensures maximum coke burn rate without shifting the burn
profile toward the bottom and maintains the air heater outlet
bundle element temperature below the maximum limit.
Fig. 7 shows that the APC could reduce peaks in the second
temperature element (that indicates highest temperature) in the
burn zone and it also shows that the heater temperature element
is better controlled.
Fig. 8 shows a flattening of the temperature profile in the peak
burning region of the regenerator.
Debutanizer controller. The reformate from the Platformer
compressor section goes to a debutanizer where the lighter LPG
and off-gas is stripped off to obtain the final reformate product
from the bottom. While the reformate RON gets determined in
the Platformer reactor, its Rvp is controlled in the debutanizer.
The controller maximizes the reformate Rvp while maintaining
the overhead accumulator level to maximize the reformate yield.
APC benefits. A benefit of about 12 cents/barrel at 2004
KNPC price levels was realized after implementing the APC that
mainly manifested from an increase in the reformate octane-
barrel yield and improvement in the recovery of the by-products.
The increase in throughput and operating the NHT/Platformer
units at their economic severity constraints helped to increase the
reformate octane-barrel yield. The contributors to the benefit for
both trains are summarized in Fig. 9.
After implementing APC, the reformate yield increased by
approximately 3% for both trains. However, the change in specific
utility consumption was significantly different for both trains (Fig.
10) reflecting their unique operating constraints even though the
pre-APC
20°C
Catalyst flow along the regenerator
1 2 3 4 5 6 7 8 9
Temperature
APC
Temperature profile control due to APC in the regenerator.FIG. 8
pre-APC APC
Heater temperature
2nd temperature element
(highest temperature)
Temperature control due to APC in regenerator.FIG. 7
Convection temperatureConvection temperature
APC
WAIT
WAITpre-APC
IncreaseinWAIT
duetoAPC
Temperature increase due to APC in Platformer.FIG. 6
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8. PROCESS CONTROL AND INFORMATION SYSTEMSSPECIALREPORT
66
I OCTOBER 2009 HYDROCARBON PROCESSING
trains are identical. The increase in specific utility consumption
for train 2 did not reduce profitability much since the utility cost
is less than 1% of the feed cost at the KNPC site.
Controller maintenance. KNPC is maintaining the control-
lers using an APC performance monitoring application. A high
operator acceptance of the APC can be gauged by nearly 100%
uptime for the CCR controllers since their commissioning in
November 2005. Fig. 11 shows uptime for the main CCR APC
for the past 22 months for one of the trains. Even the uptimes of
the regenerator controllers have a very good track record given the
fact that their operation is affected relatively more frequently due
to reasons such as catalyst entrainment and choking.
However, high uptime does not guarantee optimum control-
ler performance hence, key performance indicators (KPIs) have
been developed by KNPC MAA to help monitor the controller
effectiveness.
After evaluating various APC KPIs available in some commer-
cial APC monitoring packages and in the literature, KNPC MAA
defined the KPIs namely the effective index (EI) and Kuwaiti dinar
index (KDI) based on the APC utilization calculation proposed by
A. G. Kern.6 KNPC MAA EI is then defined as APC utilization
normalized for unit shutdown and upsets so as to reflect the true
controller effectiveness. KDI is an online indication of the mon-
etary benefits realized from APC using post-audit benefit analysis
carried out after controller commissioning as a base case. It is
assumed that post audit benefits will be realized if EI is 100%.
EI = APC utilization / plant availability
KDI = APC utilization x post audit KD value/100
44% Train 1
increase in reformate
octane barrel
7% Increase in
train 2 byproducts
9% Increase in
train 1 byproducts
40% Train 2 increase
in reformate
octane barrel
Contributors to the overall benefit.FIG. 9
Reformate
yield
3% 2.90%
7.50%
6.90%
-2.80%
0.40%
Train 1
Train 2
Percent
-4
-2
0
2
4
6
8
10
Specific FG
consumption
Changes in specific production/consumption
Specific steam
consumption
Changes in yield and specific consumption.FIG. 10
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9. PROCESS CONTROL AND INFORMATION SYSTEMS SPECIALREPORT
HYDROCARBON PROCESSING OCTOBER 2009
I 67
These KPIs are helping to manage about 25 APCs operational
at KNPCs MAA Refinery. HP
ACKNOWLEDGMENTS
The authors thank their respective management for its support and thank
Lamia Al-Khandari, Yousuf Al-Sairafi, Subhash Chander Singhal, operations,
lab and process staff from KNPC for supporting the project during its different
implementation phases; Anand Shah and Altaf Khan from ATME for building
the APC–DCS interface and providing the maintenance support respectively and
other previous implementation team members.
LITERATURE CITED
1 Kalafatis, A., K. Patel, M. Harmse, Q. Zheng and M. Craik, “Multivariable
step testing for MPC projects reduces crude unit testing time,” Hydrocarbon
Processing, February 2006.
2 Cabrera, C. N., “UOP Hydrotreating Technology,” Section 6.3, Handbook
of Petroleum Refining Processes, editor, Robert A. Meyers, McGraw Hill Book
Co., 1986.
3 Weiszmann, J. A., “UOP Platforming Process,” Section 3.1, Handbook of
Petroleum Refining Processes, editor, Robert A. Meyers, McGraw Hill Book
Co., 1986.
4 Conser, R. E., T. Wheeler and F.G. McWilliams, “Isomerization,” pp 723–
747, Chemical Processing Handbook, editor, John J. McKetta, Marcel Dekker
Inc., 1993.
5 UOP Website (http://www.uop.com)
6 Kern, A. G., “Online monitoring of multivariable control utilization and
benefits,” Hydrocarbon Processing, October 2005.
Pranob Banerjee is services manager with ATME Kuwait and
heads the APC group. He is a chemical engineer with 20 years of
industrial experience and holds a PhD degree in APC from the Univer-
sity of Alberta, Canada. Dr. Banerjee has APC implementation expe-
rience in refinery, LNG/NGL, fertilizer and petrochemical processes.
Previously he worked with Engineers India Ltd and Reliance Industries Ltd in India.
Suresh Kaushal is lead process control engineer at Kuwait
National Petroleum Company’s Mina Al-Ahmadi Refinery. He holds a
BTech degree in chemical engineering from IIT Kanpur and has over
24 years’ experience in refinery DCS systems and APC implementation
in CCR, HCR, ARD, NGOD, CDU, VR, PRU, FCC and gas plants.
Ahmad Al-Majed is a senior process control engineer at
Kuwait National Petroleum Company’s Mina Al-Ahmadi Refinery.
He has a BS degree in chemical engineering from Kuwait University
and an MBA from Leeds University. Mr. Al-Majed has over 17 years’
experience in process engineering and APC in different refinery
processes such as ARD, HCR, HP, VR, FCC and gas plants.
Oct-06
Dec-06
Feb-07
Apr-07
June-07
Aug-07
Oct-07
Dec-07
Feb-08
Apr-08
Jun-08
0
20
40
60
80
100
120
CCR APC controller online factor for a train
Controller on-time for the past 22 months.FIG. 11
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Now Available On-Demand
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10. HOW WOULD YOU RATHER ACCESS REMOTE
GAS LINES TO MEASURE MOISTURE CONTENT?
ON FOOT. ONLINE.
AMETEK’s dependable 5100 NCM noncontact moisture analyzer for natural
gas applications has all the convenience, performance and features you
demand. Moisture reading verification, combined with its Ethernet/Web
browser-based interface, eliminates your need to be on-site at all! The 5100
NCM features all-digital signal processing and an accuracy to ±4 ppm over
a 5-2500 ppm range, with a 0.25 lb./MMscf limit of detection. It meets
CL1 DIV 2 Groups A-D approvals*.
With simple analyzer setup and system checks, the 5100 NCM provides
readout information anywhere it’s needed, reliably and online—no complex
software required. Remote readouts and diagnostics lower maintenance
costs and reduce downtime. With no exposed components and an
IP-65/NEMA 4 weatherproof enclosure designed to endure -20°C to
+50°C, it’s rugged as all outdoors.
So rest your feet, and leave the rest to AMETEK. Learn more at:
412-828-9040 or www.ametekpi.com
*Other approvals pending.
New AMETEK 5100 NCM™ laser analyzer
combines integrated moisture verification
with Ethernet-based user interface.
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