integrated advanced control and online optmization in olefins plant

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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

CACE 13-11;12-H 1291



1292 R. J. LOIEK and B . D. WHITEHEAD



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.



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



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



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



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).

integrated advanced control and online optmization in olefins plant

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