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Compu rers c/rem. E ngng, Vol. 13, No. I l/12, pp. 1291-1297, 1989 0098.13.54/89 $3.00 + 0.00
Pr inted in Gr eat B ritain. All rights reserved Copyr ight 0 1989 Pergamon Pr ess plc
INTEGRATED ADVANCED CONTROL AND ONLINE
OPTIMIZATION IN OLEFINS PLANT
R. J. L O J E K and B. D. WHITEHEAD
LI NDE AG, TVT Division, D-8023 Hoellriege lskreuth, Mun ich, F.R.G.
( R e ce i v ed f o r p u b l i c a t i o n 19 June 1989)
Abstract-The benefits of optimization cover a broad spectrum. In many cases the rewards are in the rangeof severa l million dollars per year. T he first step prior to the implementation of optimization is a detailedstudy of the current operating conditions and philosophy and market demands. Only then can anoptimizer be effectively designed an d implemented.
The system which consists of advanced control of key plant sections linked to a global online plantoptimizer will be described. Since optimum operating conditions are almost always at one or moreconstraint boundaries, a major task of the advanced control strategies is constraint riding. The advancedcontrol strategies and techniques used are described in detail.
The online optimizer is based on a detailed mod el of the whole plant. Although the major benefits arein optimal operation of the cracking section, a detailed model of the separation section is essential foraccurate prediction of constraints. Online data is used to identify changes in feed properties and a suitablestarting point for the optimization as well as to update the model correlations and improve accuracy.
The design of a simple and robust operator interface is critical to the success of such a system and willbe described in detail.
INTRODUCTION
Olefins plants are ideal candidates for application
of online optimization. These plants are extremely
integrated from the heating an d cooling requirements
and can be operationally adjusted to reflect the
market demands. The areas of global optimization
and local optimization/advanced control will be dis-
cussed in this paper (see Whitehead and Parnis, 1987
for a typical application). The typical characteristics
of olefins plants (see Fig. l), such as high throughpu t,
a complex interacting process, varying feed stocks,
wide product slate, changing market conditions for
feed stocks and products make it difficult to identify
the optimal set of operating conditions for day-to-day
operation.
Optimization is the process of finding the extreme
value of a plant objective function (either m aximum
or minimum depending on the application) under
constrained operating conditions. Typically the ob-
jective function takes the form of overall utility
consumption, profit or production.
On-line control/optimization is divided into severallevels. These are basic control (normal PID con -
troller, simple cascades), local advanced control, local
optimization and finally global optrmization. The
functional quality of each level is strongly dependent
on the functional quality of the lower levels. It is of
course impossible to have good optimizer perfor-
mance when the simplest PID control loops do not
function properly. In order to implement a global
optimization system, one must start at the lowest
level and work slowly and carefully to the top.
Some of the most valuable players in this game are
the plant maintenance personnel. It is absolutely
essential that all measurements and control points
used by the optimizer function reliably and trouble
free. A common phrase u sed in the computer industry
is “garbage in-garbage out” a nd this of course also
applies to optimization.
When more than one variable is to be optimized,
the problem becomes too difficult to be solved by
experience. A computer-based optimization system is
then required. The global optimizer enables the user
to determine the best set of operating conditions for
the given plant boundary conditions (such as feed-
stock availability, product demand, etc.) This opti-
mization has to respect plan t constraints, i.e. the
optimum is only valid when the operation does not
lead to a bottleneck situation in one or more parts of
the unit.
A plant m odel calculates for a given set of input
conditions (such as purities an d feed quantity) or set
by the boundary conditions (such as ambient tem-
peratur e, specific cost for feed/pro duct/u tilities or
product limitations) the plant profit taking into ac-count the constraint situation for all key plant items.
The results from the simulation model are fed back
to the optimizer which generates, based on present
and past results, a new set of values for the optimized
operating variables. The procedure is iterative, the
approach to the optimum depends on the number
and type of variables to be optimized and the number
of iterations.
The impact of the individual input variables on the
plant profit differs widely. Table 1 shows only those
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1292 R. J. LOIEK and B . D. WHITEHEAD
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Advanced control and online optimization 1293
Table I. Influence of major independent variables
independent variables Dependent variables
Furnace conversion Runtime of the furnaces
Runtime of the quench coolers
Cracked gas yictd pattern
Recycle flows
Steam dilution Runtime of the furnaces
Runtime of the quench coolers
Cracked gas yield pattern
Steam generation requirements
Suction pressure of thecracked gas compressor
Compressor power consumption
Compressor surge point
Cracked gas yield pattern
D&charge pressure of the
cracked gas compressor
Compressor power consumption
Compressor surge point
Product losses to fuel gas
Product purities Overall energy requirements
input variables wh ich have a considerable influence
and which are recommended to be optimized.
THE GLOBAL. PLANT OPTIMIZER
The global optimizer is a steady state simulation
which mirrors the plant performance and includes
each major piece of equipment for any constrained set
of operating conditions.
Plant models may be broken down into the follow-
ing subcategories:
t Steady state.
- Dynam ic (sometimes real-time).
l Linear models.
l Non-linear models.
l Rigorous or theoretical models.
l Empirical models.
l A mixture of some or all of the above.
A model is based on a set of modu lar blocks of
successive plant sections with yield prediction for
cracking furnaces and overall material ba lance includ-
ing the iteration of recycle streams. The modules cover
all parts ofthe olefins plant including a detailed stream
balance.
Scope and detail of the results are comparable to
the technical perform ance data of a typical ba sic
design documentation and include an economic eval-
uation. Th e complete set of results starts with the
input data set and ends with the profit for the given
type of operation.
The simulation of equipment corresponds to theactual plant performance under changing operating
conditions, based on the design data such as:
e Pressure d rops in pipes and equipm ent
+ Exchanger capacity
e Performance curves of compressors
l Furnace design
which ensures that the calculated results correspond
to the actual equipment performance under changing
operating conditions.
The yield prediction for the furnaces is based on
empirical m odels which are developed from rigorous
calculations of reaction kinetics and iterative inte-
gration of coil increments. This approach allows to
simulate the influence of all essential operating
parameters such as steam dilution, total pressure, load
or cracking severity on the furnace yields with su ffi-
cient accuracy and very short computing time. The
effect of coking in the furnace’s radiant section and
in the transfer line exchangers is predicted by models
simulating the time-dependent growth of coke. End-
of-run criteria (high pressure drop or tube wall
temperature) define the on-stream-time of the furnace.
From the yield pattern and the required plant
capacity, the overall material balance is solved by a
set of linear equations, so that all product speci-
fications are fulfilled. The calculation proceeds
through all plant sections sequentially, generating
detailed m aterial, energy and utility balances for each
section. The section results are used as a basis for the
overall summaries for material, energy and utilities.
The developm ent of fast and accurate simulationalgorithms for single- or multiple-feed separation
columns is possible based on the fact that, for a n
existing plant, the range of variations around the
design point is limited. Comm on methods for shortcut
calculations of columns a re used only to describe the
deviation from base operating points which were
calculated by rigorous m ethods for multicomponent
columns.
Equilibrium values for partial condensation steps
or for calculation of boi~ing/dew points are calculated
by calling specially developed fast subroutines. Fo r
calculation of water and steam states, a function
subroutine has been developed.
The performance of multistage compressors has astrong influence on the energy consumption. The only
way to arrive at an accurate result in the simulation
is to insert the characteristic curves for each stage
(pressure ratio over volume with speed as parameter).
This includes the simulation of the anti-surge control
(volume limit over compressor speed).
The ph ysical design data of the plant equipment are
part of the simulation. Therefore, the calculation of
the plant performance can be carried out under the
assumption that full use is made of the size of the
equipment. This principle leads to a favourable oper-
ation and has the effect that exchanger surfaces for
condensers/evaporators are covered (small tempera-
ture differences), pressure drops o ver control valvesare reduced where possibie and surge limits for
compressors are not higher than specified by the
manufacturer.
Some of the results generated by the simulation,
such as refiux quan tities, pressure levels for columns
and steam headers, turbine speeds or furnace outlet
temperature directly represent controller setpoints of
the plant and can be passed to the operating staff or
can be compared with the actual parameters of the
plant performance.
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tOCAl_ ADVANCED CONTROL calculates and implements a new coil uullet tern-
P~,fkiti0?2perature set point. The periodic f-eedhack of
tncasured srvrrity derived frutn the cracked gas
As implied by the title, local advanced control is analysis is used to calculate an outlet temperature
the control of a specific area within the plant using at. the lime of sampling. This temperature is
higher level control strategies. An area is typically compared with the actual outlet tompuraturc at
defined as a column with associated equipment. a that time. The diff‘erencc in the temperature is
reactor system, a compressor or a furnace. then used to calculate :I correction of the outlet
Optimization is typically divided into two sub- temperature set point to achic1.c II-K rcyuiredcategories. These are commonly known as local severity. This prcdicror;corwctor approach en-
optimization and global optimization. Local optimiz- sures that the long period of time between
ation is area-specific and gIoba1 optimization entails cracked gas analyst has tittle et%ct on the <tualtivthe entire unit. The following anaingy illustrates the of controi.relationship between these two types of optimization. . Computer-aided start-up and shut-down control
.4 !arge capacity vcsscl having one non-controlled assists the operator during the prace%s steam
Iiyuid inlet stream and a Aow-controlled outlet phase of start-up or shut-down by adjusting c&I
strram is equipped with a level controller. The flow slcant flow miss and outlet letnpwatur2 set poitats
controller responds very quickly- and can hold its set in accordance with the standard start-up and
point with relative ease whereas the level controller shut-down procedure. Tt also assists the operatorrecIoir<s more time in the event of a process dis- during the feed flow phase of start-up or shut-
turbance or a set point change to hold its set point. down by ad,jusiing coil feed Row rates as wc!l. It
The twu controllers work together to obtain good informs the operator when burners have to bc lit
level control. The set point of the level controller may or extinguished and continues control when rhijhe compared to an objective function. Globai opti- has been done. Following this pha~ the normal
mization and local optimization work together in a tasks take control.similar fashion, where the “set point” to the globail *The total run :imc of the furnaces and transferoptimizer is the tank level set point and the signals lint exchange?- (TLX) based on present operaringwhich are passed down to the focal optimizers arc conditions MC calcule~cd. These arc compared
comparable to the setpoint of the flow controller. with a minimum run lime SCL point. Xi‘ thcrc 1s a
problem. the strategy idcntifics the limiting factor
FWfZU(‘C coizt,m/ (e.g. pressure or temperature dil‘rerence UF iem-
The ndsanced control strategies perform the follow-perature in furnace or TLX). ff ths pressure
ing:diEerence is the Iimiting factor, the tbrnace
throughput set point is r-educed to a valur at
which the minimum run time c‘an he mci at
*The total throughput needed to achieve a spe- otherwise constant operating conditions. If thecified production rate is controlled and the hydro- temperature is the limiting factor either the
carbon feed is distributed to the individual coils throughput or the cracking severity are reduced.
to achieve equalized coil radiant zone outlet l Decoking controI assists the operator during the
retnpcratures. decoking procedure by adjusting the decoking
@ Steam-to-feed ratio control with safety features air. process steam Row rates and outlet tempera-
to avoid zero flow. ture in accordance with the standard decoking
l Combustion control where the flue gas damper is procedure. It informs the operator when burners
adjusted to achieve a given oxygen content sub- have to be lit or extinguished and continues
ject to a high limit on the fire box pressure. control when this has been done. It periodically
Feedforward compensations for variations in asks the operator if the t~lbe vcali terrqwatcrre is
both fuel gas calorific value and fuel gas pressure acceptable and the strategy continues onI> atier
are applied. confirmation.
l Outlet temperature control. The operator can t Local optimiza?~on is essentially the globaf opti-
choose to use the outlet temperature or the mization described in the previous section. sinceaverage coil radiant zone temperature in each the furnaces are the heart of the plant.
furnace. Feedforward compensations for changes
in throughput and fuel gas calorific values are CdWF&3 cmt ro
applied,
*Cracking severity control where a yield predic-The advanced control strategy performs the follow-
tion model is used to compensate for changes ining:
the set points for cracking severity. hydrocarbon l Purity control adjusts the reflux rate for the
throughput, steam-to-feed ratio and where ovcrhcad and the boil-up rate for the bottoms to
changes in the feedstock quality and cracked gas achieve the required product purities. Tray tcm-
pressure are automatically compensated for. It peraturc differences together with an analyflcal
1294 R. J. LOJEK and B. D. WHITEHEAD
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Advanced control and online optimization i 295
predictor mode1 are used as a continuous purity
indicator. The param eters of the analytical pre-
dictor model are updated when a new online
analysis becomes available. Since the measure-
ment of product quality is usually not continuous
(sometimes as long a s 30-40 min), composition
control with a normal PID-controfler is difIicult.
Purity control using an analytical predictor
model based on a rdiable correlation between atray temperature difference or a continuous tray
composition measurem ent and the product p urity
leads to far better control. In other words, the
analytical predictor model turns a discrete mea-
surement into a continuous measurem ent. Th e
advantages of this system as compared to compo-
sition dontrol based exclusively on the product
analyzer are quite ciear. This system can be
applied both to the overhead product as we11 as
the bottoms product. Con trol loop interactions
are minimized by using one-way decouplers.
* Level buffering uses available volumes in level
controlled vessels to smooth out minor fluc-
tuations in flow rates. This is a very simple
application, but in many cases the rewards are
astonishing. T he levels are controlled using a
modified error-squared plus dead-band ap-
proach. T his minimizes the disturbances to
downstream systems.
0 The reboiler duty and reflux rate are feedforward
compensated for any changes in feed flow and /or
enthatpy.
o Constraint control or anti-flooding control uses
the pressure drop over the column and the calcu-
lated column loading to approximate the liquid
and vapor flooding points. These results are used
to constrain the column operation so that flood-ing does not occur. Controller valve positions are
also monitored to detect constraint situations and
action is taken to ride the constraint.
*Local optimization of purity setpoints (using
simplified column models) an d column pressure
(where possible).
Optimization of hydrogenation reactor selectivity
is a very useful and profitable application. The over-
hydrogenation of olefins in order to prevent acetyfene
breakthrough is in many cases a typical operating
philosophy. An advanced control system equippedwith such tools as reactor mass and energy balances,
kinetic models or selectivity prediction models is able
to effectively analyze the internal reactor conditions
and mak e full use of the available catalyst activity, In
this way the overhydrogenation of olefins can be
eliminated. The following benefits are obtainabie:
-The costs of utilities will be minimized.
*The olefins which were previously overhydro-
genatcd now become products.
*The recycle back to the furnaces will be reduced
and will allow an increase in plant throughp ut.
In many cases the reactor performance is a strong
function of external non-controlled effects such as
carbon monoxide content in reactor feed. Under such
conditions the optimum operating point is con-
tinuously movin g and requires a dynamic optimiz-
ation technique to maintain optimum performance.This ca n only be realized with the use of on-line
optimization. The advanced control strategy per-
forms the following [based on an isothermal hydro-
genation process):
*Acetylene content at the reactor outlet is con-
trolled u sing correlations between a chromato-
graphic acetylene analysis at the reactor outlet
and the temperature changes in the reactor to
predict the outlet acetylene content. Anafyzer
control is stabilized by the prediction of the
anaiyzer response based on temperature changes
in the reactor. The model is updated every tim e
a new analysis becomes available using the
dynamically synchronized value for the tempera-
ture changes, whereby the synchronization be-
tween temperature change in the reactor an d the
gas chromatograph analysis is achieved by a dead
time plus a lag element. The parameters for these
dynamic elements are calculated iteratively from
historical plant data.
e The reactor intet temperature is used to adjust the
catalyst activity by comparing the ideal heat of
reaction (QID) which is to be expected when all
of the acetylene is converted to olefin, with the
actual heat of reaction (QACT), obtained by an
energy balance around the reactor system .
*The reactor pressure in the case of liquid hydro-genation is used to adjust the catalyst activity.
The activity of the cataIyst decreases during the
course of the on-stream time. At constant reactor
pressure the loss in activity is compensated by
increasing th e Ii2 flow as required to maintain
the specified acetylene content.
This also avoids the generation of dimer and trimer
components in the endothermic reaction of unsatu-
rated hydrocarbons as well as the formation of high
molecular weight compounds known as “green oil”
and results in longer catalyst life.
In the case of reduced plant load (open bypass
situation}, steam consumption savings are achieved
by operating the compressor safely closer to the
surge point with the anti-surge advanced control
strategy. B ased on plant measurem ents, the volu-
metric throughput of each compressor stage is calcu-
lated and compared with the calculated surge point.
The task adjusts the bypass flow controller set point
to ensure that no compressor stage is closer to the
surge point than a given preset value. The strategy
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1295 R. J. LOJEK and B. D. WH~TEHEAD
includes dynamic elements to handle situations where
the rate of approach to the surge line varies. i.e.
strong corrective action is taken at higher rates to
ensure safe operation.
OPTIMI%ATION TECHNIQUES AND LIMITATIONS
During the plant model calculation, the selected set
of independent variables may lead to the violation of
dependent (calculated) variables constraints. e.g. the
selected feed flow ra tc (independent variable) leads to
violation of furnace Ioad constraint (dependent vari-
able).
For all independent variables maximum and mini-
mum limits are spccificd. These limits are checked
before N plant model calculation is carried out. A
pcjint which satisfies these constraints is called a
feasible point or a feasible vector.
If. the plant optimizer detects that one or more
dependent variables or loading constraints have been
violated, the present and past sets of independentvariables and their associated plant model calculation
results are used to select a new set of independent
variables thal will not violate constraints. By
using this procedure in an iterative manner, the
plant optimizer ensures that the optimum solution
is also feasible, even if this solution lies at one or
m*le constraint boundaries. Experience has
shown that; for an olefins plant, in must cases the
optimum solution lies at one or niorc consu-aint
boundaries.
The constraints check involves each major piece of
equipment. Exchangers are checked for the ratio of
heat load divided by the temperature difference.
compressors arc checked for speed and volume limit,columns are checked for limits of vapor flow. The
comprehensive check of the equipment ensures that
the optimizd values for the operating variables do
not lead to bottleneck situations.
The oprimizer is starching a multi-dimensional
surface for an extreme value (either- maximum or
minimum). Throughout the search the optimizer will
encounter valleys. peaks and ridges which it must
interpret. An effect known as surface roughness can
make the search very difficult. Sometimes the surface
roughness is only the result of noisy input signals and
results in, as the name implies. an irregular surface.
The roughness of &he objective function normally
stems from the physical limits of the plant equipment
w-hi& leads to discontinuous relationships between
the independenl variables and the dependent vari-
abies. A typicai example is the response of a cotnpres-
sor to a load reduction. When the spillback valves are
closed the relationship between power consumption
and the load is a smooth function, but as soon as the
spiflback valves start to open the function shows a
discontinuity. This is a major problem when deriva-
tive methods are used. An alternative to using a
differential method is to select a sratisticai method
which starts from a relatively broad pattern of values
for the optimizd variables and then reduces the
range of this pattern based on the results of theprevious iteration. The end-of-run criteria are either
the number of iterations or the final dimensions of the
pattern (range of variation of the independent vari-
ables). For the olefms plant optimizer the derivative-
based method is the preferred cholcc since it is faster
and usually able to achieve a solution. For problem
cases. the statistical method, whilst slower, has
proven most effective in identifying the optimum
solution. For plant modeis where the response sur-
face is flatter than for olefins models (see Fuge cl a(.
1987 for an NGL separation plant) the effects
of roughness and discontinuities have been ib~rnd to
he so strong that only the statistical method can be
used.
Op i i n i x t i o n t ec h n i q u e
The optimization technique con sists of the steps
shown in Fig. 2.
Commonly a derivative-based technique with a
quadratic convergence characteristic is used. This
method is especially suitable for solving non-linear
constrained optimization problems, howevert the
evaluation of the function [one calculation of the
plant model) is calculation intensive.
START
Fig, 2. Optimization procedure
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Advanced control a nd online optimization I297
Ob j e c r fw func t i o n
The following objective functions are typical:
* Maximum profit.
l Maximum production.
o Minimum operating cost.
o Minimum cost per ton of product.
USER INTERFACE
Interfacing man and machine is an extremely im-
portant part of any online process control system.
This is more of an art than a science. At this point
one should delineate between the operator interface,
the maintenance interface and the engineering inter-
face.
The op erator interface must be clean and simple.
Only important information should be included. The
operator should not be burdened with inputs which
he is not able to change (e.g. tuning factors, decou-
pling effects, etc.). An examp le of this would be the
advanced con trol of a fractionator. The operator may
set the following inputs:
e Overhead purity.
l Bottoms purity.
o maintenance status of all analyzers used in a
strategy.
mStarting an d stopping of the strategies.
Maintenance personnel requ ire access to controller
tuning factors, data traffic control as well as infor-
mation regarding the overall structure of the system.
Several diagnostic aids are also made available to the
maintenance personnel. One of these is the plausibil-
ity checking of the process variables used in the
advanced control system. For example, using vapor
pressure data it is possibte to comp are a temperature
and pressure in the same service.
The engineer must have easy access to all systems
information. This includes decoupling matrices, opti-
mizer tuning factors and diagnostic systems within
the optimizer.
CONCLUSIONS
The system described above combines the tw o
levels of local advanced control and global on-line
optimization in an oiefins plant. For full realization
of the potential benefits these levels must be well
integrated, for example since the optimum usually lies
on a constraint, the advanced control must be de-
signed to control at the constraints.
In addition the system must be robust and easy-to-
use to ensure operator acceptance.
Finally, the success depends on continued main-tenance of both hardware and software-
REFERENCES
Fuge C., P. Eisele and B. D. Whitehead, Optimization of theoperation of a gas terminal. Presentation to the Inf.Cr>vgenic rMateriuf CoqGrence, Cryogen i c Eng inee r i ng
Con fhv tce , Chicago (1987).Whitehead B. D. and M. Parnis, Computer control im-
proves ethylene plant operation. Hydracorbon Process.66, tos-108 (1987).