- Email: [email protected]
Online quality control methods for steam-gas reformers
lnt. J. Hydrogen Energy, Vol. 15, No. 3, pp. 179-185, 1990.
0360-3199/90 $3.00 + 0.00 Pergamon Press plc. © 1990 International Association for Hydrogen Energy.
Printed in Great Britain.
ONLINE QUALITY CONTROL METHODS FOR STEAM-GAS REFORMERS I. M. ALATIQI Petroleum, Petrochemicals and Materials Division, Kuwait Institute for Scientific Research, P.O. Box 24885, Safat 13109, Kuwait
(Receivedfor publication 21 September 1989) Abstract--This paper presents a classification of online quality control (QC) methods from the view of process control. The QC can be applied on the control loops from each of its three sides: the input (manipulative variable) side, the output (controlled variable) side and from the disturbance side. It was found that online QC can be direct or indirect, depending on the measures taken for quality. This classification can lead to interesting and new options for the control variables that otherwise would have been obscure. Once the proper control variable is selected (in terms of adequate representation of quality) it can be used for control systems analysis and design. Process application is presented for an industrial Steam Gas Reformer. The input is the fuel gas quality for which various options were presented. A correlation was obtained to relate heat input to simple measurements. The output hydrogen quality control options were discussed. Coil outlet temperature is adequate for a crude estimate of conversion, provided that S/C ratio is controlled. S/C ratio correlation was obtained to enable its estimation and control from simple measurements. A precise quality control of hydrogen can be achieved provided that COT is also controlled to protect reformer catalyst. An improved strategy can be implemented where both COT and conversion are controlled in a multivariable sense. This strategy is economically attractive, since it allows continuous manipulation of S/C ratio to the minimum required for COT control. Savings in fuel gas can be achieved accordingly. The feasibility of multivariable control was established via interaction analysis.
NOMENCLATURE
INTRODUCTION
British thermal units Composition COT Reformer coil outlet temperature (°F) D Density of gas, relative to air Vector of disturbances d E Energy flow rate F Volumetric flow rate F~ Feed gas flow rate Moles of initial hydrogen per mole of (H2)i methane equivalent (mol/mol) K~j a process gain between input i and output j M Mass flow rate M M S C F D Million SCF per day Flow rate of methane equivalent fed (mol/h) (nCH4)i P Pressure Q Quality S Steam flow rate SCF Standard Cubic Foot (at s.t.p.) s/g Steam-to-feed gas volumetric ratio S/C Ratio of steam fed to methane equivalent in feed (mol/mol) U Vector of manipulative variables X Fraction of methane equivalent converted Vector of controlled variables Y
Many processing plants have computerized or are considering computerization of their operation. Refineries, petrochemical companies and desalination industries all are converting their analog or pneumatic instrumentation into direct digital control (DDC). Computerization investment ranges from 1 million to over 10 million US$ in the larger projects. Despite the elegance of D D C and the peripherals associated with it, it is well known that the computer cannot do much better than analogs if the control strategy remains unchanged. Hence advanced process control (APC) techniques are sought to make efficient use of this investment, especially of the huge calculation power associated with it. Process control comprises two major objectives [1, 2]: material balance stabilization and quality control (QC) of products. Most plants have little problems with material balance control, which can be handled by the traditional flow, level or pressure control loops. Previous experiences with QC on the other hand were more dependent on the quality laboratory analysis. This is due to the fact that most quality measuring instruments were not suited for online connection. Even the claimed continuous analysers are often very expensive, require excessive maintenance or have poor service factor. Thus
Btu ¢
179
I. M. ALATIQI
180
most online QC was centered around temperature and pressure control, and to a limited extent on other measurements like density or viscosity. Other analysers may also be present, but mainly for monitoring, rather than automatic correction. This is especially true for plants without DDC. With the availability of DDC, it seems appropriate to use the computing power to the maximum economic benefit. A popular and valid interpretation of the later statement is to improve quality control for less energy expenditure. This paper presents a systematic approach to the classification of methods for online quality control. It is believed that this approach will help process, operating and instrument engineers better visualizing the automatic quality control options and methods. Furthermore, a process application for a steam gas reformer will show how these methods can be used and implemented.
PROCESS C O N T R O L VARIABLES The proposed methodology depends largely on the definition of process control variables. A process control variable can be loosely defined as any operating condition in the plant, whether within the plant vessels or pertaining to an entering or leaving stream. Such variables are often described by the operators as "critical variables" or "key process parameters" and they are monitored closely for their influence on the plant operation. From process control view point, control variables can be classified into three basic types. A variable that represents an operating condition to be fixed or must be fixed for the sake of quality objective is called a state variable. Of these variables, few independent variables can fix the other state variables. The independent variables chosen to be controlled are called the controlled
variables. The variables whose quantity can be varied (or actuated) by some means will be called the manipulative variables. The variables whose magnitude varies by reasons exogenous to the plant are called disturbances or load variables. Figure 1 shows schematically the controlled, manipulative and load variables acting on a process plant. It is to be noted that the arrows in Fig. 1 have a direct algebraic significance in the differential form. If we denote the manipulative vector by u, the controlled
Disturbances
dl Man ipu Lative variables
di
dn
I["[ I T
u,
il
Ui . . . . . .
__
Un
--'-I
/"/2
d2
ControLLed variables
PLANT .
I
.
.
.
""'Yi
-"-Yn
Fig. 1. Typical plant and process control variables.
vector by y and the load vector by d, then an algebraic equation can be written as: ay ay which is valid near some operating points. The function Oy/Ou and dy/Od are matrixes which contains transfer functions relating the outputs yi's to the inputs u~'s and d~'s respectively. These functions contain dynamic information as well as steady state information. For multivariable systems, the control system design problem is complicated even by simple methods [3, 4]. These methods deal with the pairing of controlled variables y~'s with suitable inputs u~'s and then designing effective control equations. None of these methods or even the more general ones [5] give insight to the proper selection of the Yi'S or u~'s. The controlled variables may be quality related, inventory, economic or any other variable whose value is to be fixed. The present discussion is limited to the discussion of quality or quality related variables or disturbances. The discussion will be limited to the single control loop, i.e. control of a single variable, although the concepts can be extended to the multivariable case. One of the purposes of this article is to give understanding of the various options for quality control variables. Once these options are clear, proper selection can be undertaken for the control system design study. THE SCOPE OF ONLINE QUALITY CONTROL Since all the above control variables have direct influence on quality, it is intuitive to consider the quality control problem from each side of the control loop. In other words, the control of the desired quality, which is the objective of the controller can be enforced from one of three sides: (1) The controlled variable side, hereafter called the output side. In this case control is practiced on the quality variable itself and will be called Output Direct Quality Control (ODQC). If the quality variable continuous measurement is difficult for some reason, a secondary variable or combination of variables can be controlled that have a direct effect on the quality variable. This case is called Output Indirect Quality Control (OIQC); (2) The manipulated variable side, hereafter called the input side. If the quality of the manipulative variable is directly actuated, this is called Input Direct Quality Control (IDQC). Otherwise the control is by Indirect Input Quality Control (IIQC); (3) The disturbance side. The control obtained from this side is normally the feedforward type of compensation. As in the cases discussed in input and output sides, we can have Disturbance Direct Quality Control (DDQC) or Disturbance Indirect Quality Control (DIQC). In the direct method, feedforward is taken from the measured quality property of the load to the manipulated variable. In the indirect method, the quality of
STEAM-a3AS REFORMERS Table 1. Operating conditions for case study steam gas reformer Feed gas rate (MMSCFD) Fuel gas rate (MMSCFD) Fuel gas density (air = 1.0) Process steam rate (Mlb h -~)
21.7 7.169 0.825 164.362
Fuel gas composition (vol %) H2 CI C2 C3 i--C 4 n~ 4 i--Cs n~5
53.9 32.61 10.85 1.3 0.55 0.79 0.0 0.0
the load variable is difficult to measure continuously and an indirect measurement or calculation is used. PROCESS DESCRIPTION A steam-methane reformer model was developed and validated for two commercial units [6]. One of these units is used here for generating some of the sensitivity data required for control system synthesis. The unit produces refinery grade hydrogen with a capacity of 60 MMSCFD. The process is controlled by PID type controller, operating on the fuel gas valve to fix the coil outlet temperature. Operating conditions are given in Table 1.
181
OUTPUT SIDE QUALITY CONTROL
(a) Indirect QC The conversion of H2 % in the process gas can be estimated from the coil outlet temperature (COT). Sensitivity runs were done on the simulator to assess variations of COT and conversion to feed composition disturbance (density). The accuracy of COT as a conversion indicator depends on the type of feed ratio arrangement for which two cases were considered (Fig. 2). If steam and gas are on flow ratio control, which is known as steam-to-gas ratio (s/g), the H2% varies largely under feed composition variations. However, if steam-to-carbon ratio is controlled, the conversion (H2%) is less sensitive to feed composition variations. This is shown in Table 2, where feed composition variation is expressed by heavier density. This result is rather expected since steam will be increased to fix the excess amount required by s/c ratio. Methods for s/c ratio control will be presented under the next heading. The sensitivity of quality (Hs %) to the extreme density upset is within 1% error with s/c control. This may be considered acceptable enough to justify COT control for quality under moderate feed composition upsets. Transient effects must also be considered to determine 1455
(a)
1440
Steonl i~gS/g~~et
O) L
E [~
1425
~'~
501o S t e p 2 % Step
- -
----Feed gas
ii I 141C
SIC
I
I
I
15
20
Time
(rain)
25
I
30
55
Q02
(a)
S
I
I0
0
(b)
9Set 0.91
SteQm
~
.2 0.90
8
. Feed go$
//
0.88
(b) Fig. 2. Feed ratio arrangements for steam-gas reformer.
-~ ....
0.89 --.--
1
5
I
IO
I
I
]5
20
Time
(min)
2"1, Step I
25
I
30
35
Fig. 3. Response of COT and conversion to fuel inputs.
182
I. M. ALATIQI Table 2. Sensitivity analysis for the steam-reformer under COT control Extreme case (heavy feed gas) Stream condition
Base case
Process steam rate, Mlbs h -1 Fuel gas rate, MMSCFD Feed gas density (air = 1.0) s/c ratio, mol/mol s/g ratio, mol/mol COT °F H2% in reformer effluent
164.362 7.169 0.381 6.014 3.823 1430.2 78.12
Constant s/c
Constant s/g
199.008 8.545 0.450 6.014 4.629 1430.7 77.17
164.362 7.689 0.450 4.967 3.823 1430.0 75.88
Table 3. Sensitivity analysis for the steam-reformer: H2% control compared to COT control Fuel rate (MMSCFD) Base case Heavy feed case (COT control) Heavy feed case (H2% control) Heavy feed case (H2% control with COT constraint)
Set
SiC
COT (°F)
H2%
Steam rate (Mlb h -I)
s/c ratio
7.169
1430.2 78.12
164.36
6.01
8.545
1430.7 77.17
199.01
6,01
9.457
1470.0 78.12
199.01
6,01
9.454
t443.0 78.12
215.01
6.5
whether or not dynamic compensation is needed. Results of dynamic simulation [7] shows that both COT and conversion exhibit very similar response to fuel gas input variation, as seen in Fig. 3. Thus dynamic compensation is also not needed.
(b ) Direct QC: conversion control with COT constraint
Steam
Feed gas
,,
;_
[a)
In this case, an analyser is used to measure H2 % in the effluent stream, and manipulate fuel rate accordingly. As can be shown in Table 3, this method will result in excessively high COT for heavy feeds which may exceed metallurgical limits for tube and/or catalyst material. A constraint control (override) is needed to limit the COT variation. The constraint control system would cause more steam to pass with the feed than what is allowed by the fixed s/c (Fig. 4a). As shown in Table 3 an additional fuel gas is to be supplied to account for the added steam, almost one million SCFD over the amount required by COT control. Besides, the additional flows of steam and fuel puts more restriction to the unit capacity when production rate is to be maximized.
(c) Direct QC: conversion and COT control
S/C
Set
Steam
(b) Fig. 4. Multiloop control system.
A viable alternative to the scheme presented above is to manipulate s/c ratio continuously in order to fix COT. This can be done via a feedback controller resetting the s/c ratio controller. This leads to the scheme shown in Fig. 4(b) which we denote by "Reverse Feedforward Control". The name comes from the fact that, contrary to common practice, the feedback controller (TC) resets the feedforward controller (RC). This arrangement effectively adds another control loop to the system which increases the dimension of the control system to a 2 x 2
STEAM~3AS REFORMERS
183
Table 4. Effect of increasing COT set point under multivariable control scheme on manipulative variables Cot, °F Effluent H 2 % Steam-flow rate (Mlb h -~) s/c ratio Fuel gas rate M MSCFD
1430 78.1 164.4 6.01 7.169
multivariable problem. The other control loop is of course, the conversion-energy input loop. A major advantage of the reverse feedforward control is that the s/c ratio is continuously kept at the minimum required to control COT at all times. In this case, saving in both steam and fuel gas is simultaneously achieved. Further, tighter COT control is allowed and its set point can be brought closer to the mechanical constraint thus further energy reduction is achieved. This concept is illustrated in Table 4 where various COT set points were considered under fixed H2 effluent quality. A simple calculation shows that savings in the 450 psia steam is around 11.17 Mlb per day per unit increase in COT. Since effluent H2 % would slightly drop with the reduced s/c ratio, a slight increase in fuel gas rate is needed to fix the H2 %, as shown in Table 4. A final caution must be taken when considering the multivariable control system to ensure that interaction between the two loops is not severe enough to deteriorate the stability and performance of the control system. This point is clarified using the relative gain array 2 concept [8]. This concept is now a standard performance test for multivariable systems. For a 2 x 2 system a non-negative diagonal 2 indicates that the multivariable system is stabilizable [9]. Further 2 values closer to 1 indicates moderate interaction. From the steam reformer model, sensitivity runs enabled calculation of the process gain matrix as: 0H2% 0H2% 63Fg 0S/C K =
0.4
(2)
21.79
1440 78.1 159.72 5.84 7.176
1450 78.1 155.06 5.67 7.181
input as long as the fuel gas density is relatively constant. If the density of fuel gas varies, then a number of possibilities can be applicable. Let's take the case where the major constituents of the fuel gas are paraffinic hydrocarbons and possibly hydrogen. It is well known that the calorific value (C.V.) of each gas mixture depends on its composition. If the composition can be represented by the average gas density then an estimate can be obtained of the calorific value by means of density measurement. The net calorific value for paraffinic hydrocarbons does not vary widely on mass basis. Methane has a C.V. of 21520 Btu lb-1 gas; n-hexane has C.V. = 19,403 and the other gas members are in between [10]. Hence we can conclude that if H2 content is negligible, then the weight based calorific value is relatively constant. This means that energy input can be fixed by control of mass of the fuel gas. This can be implemented as shown in Fig. 5(b), i.e. multiplying density and flow measurements. If H2 content is considerable, then the density can fall below 0.6 and in this case
Process fluid
0.056
=
COT 0 COT OVg Os/c
1435 78. I 162.04 5.93 7.173
-69.13
Fuel gos
Fuel gos ( a ) Cascade t o flow controller
( b ) Moss cokcutotion and control
It should be noted that equation (2) describes the steady state part of the matrix Oy/Ou of equation (1). Then
l
211 =
= 1.05 KI2K21/Kn K22 which indicates satisfactory interaction properties. 1 -
INPUT SIDE QUALITY CONTROL In this case the desired quality is the required energy input supplied by gas firing.
(a) Indirect QC The simplest common scheme is shown in Fig. 5(a). The COT is controlled via cascade to a fuel flow controller, which compensates for small variations in fuel pressure. This scheme will deliver the required heat
Fuel gos ( c ) ControL by correLoted heat input : f(D) = 1 9 8 + 12BSD + 4 2 . 5 D2 ( B T U / S C F )
Fig. 5. Input indirect quality control.
184
I. M. ALATIQI
.j/
~3700
3357! o~ 3014 267 I 23ze
FueL gQs
~1985 .E 1299 "~ 956 "1- 613 0
Q25
0.5
i
0.75
I
I
I
I
1.25 1.5 Specific g r a v i t y
I
1.75
I
2
I
2.25
E/s 2.5
Fig. 6. Relation of heating value of fuel gases to their specific gravity.
i ....." - - i
EISCF
(G)
mass control will not be efficient since H 2 heating value is over 50,000 Btu lb -~ . However, an estimation can be obtained by curve fitting the line corresponding to the volume based calorific value shown in Fig. 6. The curve was best fitted with a second order equation:
E/F = 198 + 1285D + 42.5 D2(BTU/SCF).
I FueL 'gas
(3)
This leads to the scheme shown in Fig. 5(c).
(b ) Direct QC This can be obtained using a calorific value analyser, which allows for accurate energy input control. Due to the problems discussed above with online analysers, these methods should be limited to the cases where the above indirect methods cannot give accurate account of C.V. A direct QC implementation is shown in Fig. 7(a). A complication may arise here because of the cascade configuration. If the analyser dead time is appreciable, stability problems may be encountered. A compromise can be obtained by using the analyser for monitoring and/or letting it reset the estimator by updating its coefficients, as shown in Fig. 7(b). D I S T U R B A N C E SIDE QUALITY CONTROL This is possibly the most difficult QC types to handle, especially if the disturbance is unpredictable and unmeasurable. Disturbances may come from varying demand for the product or products. This is a material balance disturbance and can be easily handled by synchronizing supply with demand, i.e. by feedforward compensation. Product quality disturbances are not very frequent and can be handled by set point adjustments, with possible variations in some operating conditions. Feed quality disturbances are usually the most troublesome, since rarely are feed conditions continuously measured. For the steam-gas reformer, the feed gas composition is the main disturbance, where the desired quality is steam-tocarbon ratio.
(a) Indirect QC
c3Z I AnaLyser I
,.,
_ I Energy q estimator
(b) Fig. 7. Input direct quality control.
The equivalent methane flow rate needs to be estimated in order to calculate the s/c. The following equation was found satisfactory for light hydrocarbons (up to C6). (n C H 4 ) i --- Fg(-0.14404 + 2.0660).
(5)
Hence the s/c can be fixed by measuring steam and gas flows and gas density.
(b ) Direct QC This can be done by directly analysing the feed gas, e.g. via gas chromatograph or FTIR. The direct measurement, although giving accurate s/c is not convenient for advanced control implementation. For example, if s/c is to be used in a feedforward control algorithm to control COT or conversion, then the control loop will suffer from analyser delay. Further, the cost of on-line analyser is still rather high to justify its use in applications where estimation is easy and reliable. CONCLUSIONS
Let s/c be the steam-to-carbon ratio defined by steam (mol h - 1 ) s/c = (n CH 4)i (mol h - 1)"
•L
(4)
A systematic approach to control system design is presented. The scope of process control is classified into input, output and disturbance sides. For all sides,
STEAM ~GAS REFORMERS alternative direct and indirect quality control methods may be considered. Online Quality Control of a steam-gas reformer heater was discussed in terms of the above approach. Various options were entertained for each side with view of direct analysis availability or alternatively correlation with simple measurements. It was found that effective indirect control methods can be achieved by correlation of s/c and fuel gas energy input with simple density and flow measurements. These correlations offer valuable alternative to analyser based control. In this case the analyser may be used to check and update the correlation coefficients. For output side quality control it was found that under the conditions of s/c and energy input control, COT control provides a fair inference of conversion. This method is especially accurate if COT control was based on an average composition (density) feed. A direct composition control will require an override on s/c set point in order to narrow variations in COT below the catalyst coking limit. Alternatively, a multivariable control scheme can be used to control COT by manipulating s/c ratio; and conversion by manipulating fuel gas flow. In all cases, the feedback loop will have to cope with unmeasured or unaccounted disturbances. With the increasing adaptation of distributed control systems, numerous alternatives can be easily implemented which involve various indirect quality control methods. These calculation based methods should be considered before deciding to invest in the more expensive analyser based control systems. An important observation is that the choice of quality variable and control method in one side of the loop is dependent on the choices adopted elsewhere in the loop. It is to be noted that the issue of better quality control is closely related to better energy management. With thc feed quality fluctuating largely with time, the operator is forced to increase the s/c ratio set point and hence larger energy input is consumed. It should be also noted that the present methodology does not address process control loop design. In fact, process control studies usually starts with assumed control variables (y's, u's and d's), upon which extensive
185
dynamic and control analysis is performed. The proposed methodology would limit the arbitrariness of the selection of these variables by tying them to some quality objective. It should be clear from the discussion that this is a key controllers design step, which can have a great impact on unit performance. Unfortunately, this step is often overlooked, and hence not enough thought is given to explore alternatives that could be extremely valuable. Acknowledgements--The author is grateful to Kuwait National
Petroleum Company (KNPC) and Kuwait Institute for Scientific Research (KISR) for their support (KISR project ASD-18) and permission to publish this article. REFERENCES 1. G. Stephanopoulos. Chemical Process Control, Prentice Hall, Englewood Cliffs (1984). 2. F. G. Shinskey. Controlling Multivariable Processes, Instrument Society of America, Research Triangle Park, NC (1981). 3. C. C. Yu and W. L. Luyben. Design of multiloop SISO controllers in multivariable processes, Ind. Engng Chem. Process Des. Dev. 25, 498-503 (1986). 4. I. M. Alatiqi. A practical procedure for multivariable control systems design, in the Proc. 1FAC Workshop on Automatic Control In Petroleum, Petrochemical and Desalination Industries, Kuwait (January 1986).
5. M. Kummel and H. W. Andersen. Controller adjustment for improved nominal performance and robustness, Chem. Engng Science 42, 2005-2010 (1987). 6. I. M. Alatiqi, A. M. Meziou and (3. A. Gasmelseed. Modelling simulation and sensitivity analysis of steam methane reformers hit. J. Itydrogen Energy 14, 241-256 (1989). 7. I. M. Alatiqi, A. M. Meziou and G. A. Gasmelseed. Static and dynamic simulation of steam methane reformers. Presented at Catalysts in Petroleum Refining Conference, Kuwait (6-10 March, 1989). 8. G. F. Shinskey, Process Control Systems 2nd edn, McGraw Hill, New York (1979). 9. P. Grosdidier and M. Morari. A computer aided methodology for the design of decentralized controllers, Comput. Chem. Engng ll, 423~33 (1987). i0. R. H. Perry and C. H. Chilton. Chemical Engineering Handbook, 5th edn. pp. 9 16 (1973).
0360-3199/90 $3.00 + 0.00 Pergamon Press plc. © 1990 International Association for Hydrogen Energy.
Printed in Great Britain.
ONLINE QUALITY CONTROL METHODS FOR STEAM-GAS REFORMERS I. M. ALATIQI Petroleum, Petrochemicals and Materials Division, Kuwait Institute for Scientific Research, P.O. Box 24885, Safat 13109, Kuwait
(Receivedfor publication 21 September 1989) Abstract--This paper presents a classification of online quality control (QC) methods from the view of process control. The QC can be applied on the control loops from each of its three sides: the input (manipulative variable) side, the output (controlled variable) side and from the disturbance side. It was found that online QC can be direct or indirect, depending on the measures taken for quality. This classification can lead to interesting and new options for the control variables that otherwise would have been obscure. Once the proper control variable is selected (in terms of adequate representation of quality) it can be used for control systems analysis and design. Process application is presented for an industrial Steam Gas Reformer. The input is the fuel gas quality for which various options were presented. A correlation was obtained to relate heat input to simple measurements. The output hydrogen quality control options were discussed. Coil outlet temperature is adequate for a crude estimate of conversion, provided that S/C ratio is controlled. S/C ratio correlation was obtained to enable its estimation and control from simple measurements. A precise quality control of hydrogen can be achieved provided that COT is also controlled to protect reformer catalyst. An improved strategy can be implemented where both COT and conversion are controlled in a multivariable sense. This strategy is economically attractive, since it allows continuous manipulation of S/C ratio to the minimum required for COT control. Savings in fuel gas can be achieved accordingly. The feasibility of multivariable control was established via interaction analysis.
NOMENCLATURE
INTRODUCTION
British thermal units Composition COT Reformer coil outlet temperature (°F) D Density of gas, relative to air Vector of disturbances d E Energy flow rate F Volumetric flow rate F~ Feed gas flow rate Moles of initial hydrogen per mole of (H2)i methane equivalent (mol/mol) K~j a process gain between input i and output j M Mass flow rate M M S C F D Million SCF per day Flow rate of methane equivalent fed (mol/h) (nCH4)i P Pressure Q Quality S Steam flow rate SCF Standard Cubic Foot (at s.t.p.) s/g Steam-to-feed gas volumetric ratio S/C Ratio of steam fed to methane equivalent in feed (mol/mol) U Vector of manipulative variables X Fraction of methane equivalent converted Vector of controlled variables Y
Many processing plants have computerized or are considering computerization of their operation. Refineries, petrochemical companies and desalination industries all are converting their analog or pneumatic instrumentation into direct digital control (DDC). Computerization investment ranges from 1 million to over 10 million US$ in the larger projects. Despite the elegance of D D C and the peripherals associated with it, it is well known that the computer cannot do much better than analogs if the control strategy remains unchanged. Hence advanced process control (APC) techniques are sought to make efficient use of this investment, especially of the huge calculation power associated with it. Process control comprises two major objectives [1, 2]: material balance stabilization and quality control (QC) of products. Most plants have little problems with material balance control, which can be handled by the traditional flow, level or pressure control loops. Previous experiences with QC on the other hand were more dependent on the quality laboratory analysis. This is due to the fact that most quality measuring instruments were not suited for online connection. Even the claimed continuous analysers are often very expensive, require excessive maintenance or have poor service factor. Thus
Btu ¢
179
I. M. ALATIQI
180
most online QC was centered around temperature and pressure control, and to a limited extent on other measurements like density or viscosity. Other analysers may also be present, but mainly for monitoring, rather than automatic correction. This is especially true for plants without DDC. With the availability of DDC, it seems appropriate to use the computing power to the maximum economic benefit. A popular and valid interpretation of the later statement is to improve quality control for less energy expenditure. This paper presents a systematic approach to the classification of methods for online quality control. It is believed that this approach will help process, operating and instrument engineers better visualizing the automatic quality control options and methods. Furthermore, a process application for a steam gas reformer will show how these methods can be used and implemented.
PROCESS C O N T R O L VARIABLES The proposed methodology depends largely on the definition of process control variables. A process control variable can be loosely defined as any operating condition in the plant, whether within the plant vessels or pertaining to an entering or leaving stream. Such variables are often described by the operators as "critical variables" or "key process parameters" and they are monitored closely for their influence on the plant operation. From process control view point, control variables can be classified into three basic types. A variable that represents an operating condition to be fixed or must be fixed for the sake of quality objective is called a state variable. Of these variables, few independent variables can fix the other state variables. The independent variables chosen to be controlled are called the controlled
variables. The variables whose quantity can be varied (or actuated) by some means will be called the manipulative variables. The variables whose magnitude varies by reasons exogenous to the plant are called disturbances or load variables. Figure 1 shows schematically the controlled, manipulative and load variables acting on a process plant. It is to be noted that the arrows in Fig. 1 have a direct algebraic significance in the differential form. If we denote the manipulative vector by u, the controlled
Disturbances
dl Man ipu Lative variables
di
dn
I["[ I T
u,
il
Ui . . . . . .
__
Un
--'-I
/"/2
d2
ControLLed variables
PLANT .
I
.
.
.
""'Yi
-"-Yn
Fig. 1. Typical plant and process control variables.
vector by y and the load vector by d, then an algebraic equation can be written as: ay ay which is valid near some operating points. The function Oy/Ou and dy/Od are matrixes which contains transfer functions relating the outputs yi's to the inputs u~'s and d~'s respectively. These functions contain dynamic information as well as steady state information. For multivariable systems, the control system design problem is complicated even by simple methods [3, 4]. These methods deal with the pairing of controlled variables y~'s with suitable inputs u~'s and then designing effective control equations. None of these methods or even the more general ones [5] give insight to the proper selection of the Yi'S or u~'s. The controlled variables may be quality related, inventory, economic or any other variable whose value is to be fixed. The present discussion is limited to the discussion of quality or quality related variables or disturbances. The discussion will be limited to the single control loop, i.e. control of a single variable, although the concepts can be extended to the multivariable case. One of the purposes of this article is to give understanding of the various options for quality control variables. Once these options are clear, proper selection can be undertaken for the control system design study. THE SCOPE OF ONLINE QUALITY CONTROL Since all the above control variables have direct influence on quality, it is intuitive to consider the quality control problem from each side of the control loop. In other words, the control of the desired quality, which is the objective of the controller can be enforced from one of three sides: (1) The controlled variable side, hereafter called the output side. In this case control is practiced on the quality variable itself and will be called Output Direct Quality Control (ODQC). If the quality variable continuous measurement is difficult for some reason, a secondary variable or combination of variables can be controlled that have a direct effect on the quality variable. This case is called Output Indirect Quality Control (OIQC); (2) The manipulated variable side, hereafter called the input side. If the quality of the manipulative variable is directly actuated, this is called Input Direct Quality Control (IDQC). Otherwise the control is by Indirect Input Quality Control (IIQC); (3) The disturbance side. The control obtained from this side is normally the feedforward type of compensation. As in the cases discussed in input and output sides, we can have Disturbance Direct Quality Control (DDQC) or Disturbance Indirect Quality Control (DIQC). In the direct method, feedforward is taken from the measured quality property of the load to the manipulated variable. In the indirect method, the quality of
STEAM-a3AS REFORMERS Table 1. Operating conditions for case study steam gas reformer Feed gas rate (MMSCFD) Fuel gas rate (MMSCFD) Fuel gas density (air = 1.0) Process steam rate (Mlb h -~)
21.7 7.169 0.825 164.362
Fuel gas composition (vol %) H2 CI C2 C3 i--C 4 n~ 4 i--Cs n~5
53.9 32.61 10.85 1.3 0.55 0.79 0.0 0.0
the load variable is difficult to measure continuously and an indirect measurement or calculation is used. PROCESS DESCRIPTION A steam-methane reformer model was developed and validated for two commercial units [6]. One of these units is used here for generating some of the sensitivity data required for control system synthesis. The unit produces refinery grade hydrogen with a capacity of 60 MMSCFD. The process is controlled by PID type controller, operating on the fuel gas valve to fix the coil outlet temperature. Operating conditions are given in Table 1.
181
OUTPUT SIDE QUALITY CONTROL
(a) Indirect QC The conversion of H2 % in the process gas can be estimated from the coil outlet temperature (COT). Sensitivity runs were done on the simulator to assess variations of COT and conversion to feed composition disturbance (density). The accuracy of COT as a conversion indicator depends on the type of feed ratio arrangement for which two cases were considered (Fig. 2). If steam and gas are on flow ratio control, which is known as steam-to-gas ratio (s/g), the H2% varies largely under feed composition variations. However, if steam-to-carbon ratio is controlled, the conversion (H2%) is less sensitive to feed composition variations. This is shown in Table 2, where feed composition variation is expressed by heavier density. This result is rather expected since steam will be increased to fix the excess amount required by s/c ratio. Methods for s/c ratio control will be presented under the next heading. The sensitivity of quality (Hs %) to the extreme density upset is within 1% error with s/c control. This may be considered acceptable enough to justify COT control for quality under moderate feed composition upsets. Transient effects must also be considered to determine 1455
(a)
1440
Steonl i~gS/g~~et
O) L
E [~
1425
~'~
501o S t e p 2 % Step
- -
----Feed gas
ii I 141C
SIC
I
I
I
15
20
Time
(rain)
25
I
30
55
Q02
(a)
S
I
I0
0
(b)
9Set 0.91
SteQm
~
.2 0.90
8
. Feed go$
//
0.88
(b) Fig. 2. Feed ratio arrangements for steam-gas reformer.
-~ ....
0.89 --.--
1
5
I
IO
I
I
]5
20
Time
(min)
2"1, Step I
25
I
30
35
Fig. 3. Response of COT and conversion to fuel inputs.
182
I. M. ALATIQI Table 2. Sensitivity analysis for the steam-reformer under COT control Extreme case (heavy feed gas) Stream condition
Base case
Process steam rate, Mlbs h -1 Fuel gas rate, MMSCFD Feed gas density (air = 1.0) s/c ratio, mol/mol s/g ratio, mol/mol COT °F H2% in reformer effluent
164.362 7.169 0.381 6.014 3.823 1430.2 78.12
Constant s/c
Constant s/g
199.008 8.545 0.450 6.014 4.629 1430.7 77.17
164.362 7.689 0.450 4.967 3.823 1430.0 75.88
Table 3. Sensitivity analysis for the steam-reformer: H2% control compared to COT control Fuel rate (MMSCFD) Base case Heavy feed case (COT control) Heavy feed case (H2% control) Heavy feed case (H2% control with COT constraint)
Set
SiC
COT (°F)
H2%
Steam rate (Mlb h -I)
s/c ratio
7.169
1430.2 78.12
164.36
6.01
8.545
1430.7 77.17
199.01
6,01
9.457
1470.0 78.12
199.01
6,01
9.454
t443.0 78.12
215.01
6.5
whether or not dynamic compensation is needed. Results of dynamic simulation [7] shows that both COT and conversion exhibit very similar response to fuel gas input variation, as seen in Fig. 3. Thus dynamic compensation is also not needed.
(b ) Direct QC: conversion control with COT constraint
Steam
Feed gas
,,
;_
[a)
In this case, an analyser is used to measure H2 % in the effluent stream, and manipulate fuel rate accordingly. As can be shown in Table 3, this method will result in excessively high COT for heavy feeds which may exceed metallurgical limits for tube and/or catalyst material. A constraint control (override) is needed to limit the COT variation. The constraint control system would cause more steam to pass with the feed than what is allowed by the fixed s/c (Fig. 4a). As shown in Table 3 an additional fuel gas is to be supplied to account for the added steam, almost one million SCFD over the amount required by COT control. Besides, the additional flows of steam and fuel puts more restriction to the unit capacity when production rate is to be maximized.
(c) Direct QC: conversion and COT control
S/C
Set
Steam
(b) Fig. 4. Multiloop control system.
A viable alternative to the scheme presented above is to manipulate s/c ratio continuously in order to fix COT. This can be done via a feedback controller resetting the s/c ratio controller. This leads to the scheme shown in Fig. 4(b) which we denote by "Reverse Feedforward Control". The name comes from the fact that, contrary to common practice, the feedback controller (TC) resets the feedforward controller (RC). This arrangement effectively adds another control loop to the system which increases the dimension of the control system to a 2 x 2
STEAM~3AS REFORMERS
183
Table 4. Effect of increasing COT set point under multivariable control scheme on manipulative variables Cot, °F Effluent H 2 % Steam-flow rate (Mlb h -~) s/c ratio Fuel gas rate M MSCFD
1430 78.1 164.4 6.01 7.169
multivariable problem. The other control loop is of course, the conversion-energy input loop. A major advantage of the reverse feedforward control is that the s/c ratio is continuously kept at the minimum required to control COT at all times. In this case, saving in both steam and fuel gas is simultaneously achieved. Further, tighter COT control is allowed and its set point can be brought closer to the mechanical constraint thus further energy reduction is achieved. This concept is illustrated in Table 4 where various COT set points were considered under fixed H2 effluent quality. A simple calculation shows that savings in the 450 psia steam is around 11.17 Mlb per day per unit increase in COT. Since effluent H2 % would slightly drop with the reduced s/c ratio, a slight increase in fuel gas rate is needed to fix the H2 %, as shown in Table 4. A final caution must be taken when considering the multivariable control system to ensure that interaction between the two loops is not severe enough to deteriorate the stability and performance of the control system. This point is clarified using the relative gain array 2 concept [8]. This concept is now a standard performance test for multivariable systems. For a 2 x 2 system a non-negative diagonal 2 indicates that the multivariable system is stabilizable [9]. Further 2 values closer to 1 indicates moderate interaction. From the steam reformer model, sensitivity runs enabled calculation of the process gain matrix as: 0H2% 0H2% 63Fg 0S/C K =
0.4
(2)
21.79
1440 78.1 159.72 5.84 7.176
1450 78.1 155.06 5.67 7.181
input as long as the fuel gas density is relatively constant. If the density of fuel gas varies, then a number of possibilities can be applicable. Let's take the case where the major constituents of the fuel gas are paraffinic hydrocarbons and possibly hydrogen. It is well known that the calorific value (C.V.) of each gas mixture depends on its composition. If the composition can be represented by the average gas density then an estimate can be obtained of the calorific value by means of density measurement. The net calorific value for paraffinic hydrocarbons does not vary widely on mass basis. Methane has a C.V. of 21520 Btu lb-1 gas; n-hexane has C.V. = 19,403 and the other gas members are in between [10]. Hence we can conclude that if H2 content is negligible, then the weight based calorific value is relatively constant. This means that energy input can be fixed by control of mass of the fuel gas. This can be implemented as shown in Fig. 5(b), i.e. multiplying density and flow measurements. If H2 content is considerable, then the density can fall below 0.6 and in this case
Process fluid
0.056
=
COT 0 COT OVg Os/c
1435 78. I 162.04 5.93 7.173
-69.13
Fuel gos
Fuel gos ( a ) Cascade t o flow controller
( b ) Moss cokcutotion and control
It should be noted that equation (2) describes the steady state part of the matrix Oy/Ou of equation (1). Then
l
211 =
= 1.05 KI2K21/Kn K22 which indicates satisfactory interaction properties. 1 -
INPUT SIDE QUALITY CONTROL In this case the desired quality is the required energy input supplied by gas firing.
(a) Indirect QC The simplest common scheme is shown in Fig. 5(a). The COT is controlled via cascade to a fuel flow controller, which compensates for small variations in fuel pressure. This scheme will deliver the required heat
Fuel gos ( c ) ControL by correLoted heat input : f(D) = 1 9 8 + 12BSD + 4 2 . 5 D2 ( B T U / S C F )
Fig. 5. Input indirect quality control.
184
I. M. ALATIQI
.j/
~3700
3357! o~ 3014 267 I 23ze
FueL gQs
~1985 .E 1299 "~ 956 "1- 613 0
Q25
0.5
i
0.75
I
I
I
I
1.25 1.5 Specific g r a v i t y
I
1.75
I
2
I
2.25
E/s 2.5
Fig. 6. Relation of heating value of fuel gases to their specific gravity.
i ....." - - i
EISCF
(G)
mass control will not be efficient since H 2 heating value is over 50,000 Btu lb -~ . However, an estimation can be obtained by curve fitting the line corresponding to the volume based calorific value shown in Fig. 6. The curve was best fitted with a second order equation:
E/F = 198 + 1285D + 42.5 D2(BTU/SCF).
I FueL 'gas
(3)
This leads to the scheme shown in Fig. 5(c).
(b ) Direct QC This can be obtained using a calorific value analyser, which allows for accurate energy input control. Due to the problems discussed above with online analysers, these methods should be limited to the cases where the above indirect methods cannot give accurate account of C.V. A direct QC implementation is shown in Fig. 7(a). A complication may arise here because of the cascade configuration. If the analyser dead time is appreciable, stability problems may be encountered. A compromise can be obtained by using the analyser for monitoring and/or letting it reset the estimator by updating its coefficients, as shown in Fig. 7(b). D I S T U R B A N C E SIDE QUALITY CONTROL This is possibly the most difficult QC types to handle, especially if the disturbance is unpredictable and unmeasurable. Disturbances may come from varying demand for the product or products. This is a material balance disturbance and can be easily handled by synchronizing supply with demand, i.e. by feedforward compensation. Product quality disturbances are not very frequent and can be handled by set point adjustments, with possible variations in some operating conditions. Feed quality disturbances are usually the most troublesome, since rarely are feed conditions continuously measured. For the steam-gas reformer, the feed gas composition is the main disturbance, where the desired quality is steam-tocarbon ratio.
(a) Indirect QC
c3Z I AnaLyser I
,.,
_ I Energy q estimator
(b) Fig. 7. Input direct quality control.
The equivalent methane flow rate needs to be estimated in order to calculate the s/c. The following equation was found satisfactory for light hydrocarbons (up to C6). (n C H 4 ) i --- Fg(-0.14404 + 2.0660).
(5)
Hence the s/c can be fixed by measuring steam and gas flows and gas density.
(b ) Direct QC This can be done by directly analysing the feed gas, e.g. via gas chromatograph or FTIR. The direct measurement, although giving accurate s/c is not convenient for advanced control implementation. For example, if s/c is to be used in a feedforward control algorithm to control COT or conversion, then the control loop will suffer from analyser delay. Further, the cost of on-line analyser is still rather high to justify its use in applications where estimation is easy and reliable. CONCLUSIONS
Let s/c be the steam-to-carbon ratio defined by steam (mol h - 1 ) s/c = (n CH 4)i (mol h - 1)"
•L
(4)
A systematic approach to control system design is presented. The scope of process control is classified into input, output and disturbance sides. For all sides,
STEAM ~GAS REFORMERS alternative direct and indirect quality control methods may be considered. Online Quality Control of a steam-gas reformer heater was discussed in terms of the above approach. Various options were entertained for each side with view of direct analysis availability or alternatively correlation with simple measurements. It was found that effective indirect control methods can be achieved by correlation of s/c and fuel gas energy input with simple density and flow measurements. These correlations offer valuable alternative to analyser based control. In this case the analyser may be used to check and update the correlation coefficients. For output side quality control it was found that under the conditions of s/c and energy input control, COT control provides a fair inference of conversion. This method is especially accurate if COT control was based on an average composition (density) feed. A direct composition control will require an override on s/c set point in order to narrow variations in COT below the catalyst coking limit. Alternatively, a multivariable control scheme can be used to control COT by manipulating s/c ratio; and conversion by manipulating fuel gas flow. In all cases, the feedback loop will have to cope with unmeasured or unaccounted disturbances. With the increasing adaptation of distributed control systems, numerous alternatives can be easily implemented which involve various indirect quality control methods. These calculation based methods should be considered before deciding to invest in the more expensive analyser based control systems. An important observation is that the choice of quality variable and control method in one side of the loop is dependent on the choices adopted elsewhere in the loop. It is to be noted that the issue of better quality control is closely related to better energy management. With thc feed quality fluctuating largely with time, the operator is forced to increase the s/c ratio set point and hence larger energy input is consumed. It should be also noted that the present methodology does not address process control loop design. In fact, process control studies usually starts with assumed control variables (y's, u's and d's), upon which extensive
185
dynamic and control analysis is performed. The proposed methodology would limit the arbitrariness of the selection of these variables by tying them to some quality objective. It should be clear from the discussion that this is a key controllers design step, which can have a great impact on unit performance. Unfortunately, this step is often overlooked, and hence not enough thought is given to explore alternatives that could be extremely valuable. Acknowledgements--The author is grateful to Kuwait National
Petroleum Company (KNPC) and Kuwait Institute for Scientific Research (KISR) for their support (KISR project ASD-18) and permission to publish this article. REFERENCES 1. G. Stephanopoulos. Chemical Process Control, Prentice Hall, Englewood Cliffs (1984). 2. F. G. Shinskey. Controlling Multivariable Processes, Instrument Society of America, Research Triangle Park, NC (1981). 3. C. C. Yu and W. L. Luyben. Design of multiloop SISO controllers in multivariable processes, Ind. Engng Chem. Process Des. Dev. 25, 498-503 (1986). 4. I. M. Alatiqi. A practical procedure for multivariable control systems design, in the Proc. 1FAC Workshop on Automatic Control In Petroleum, Petrochemical and Desalination Industries, Kuwait (January 1986).
5. M. Kummel and H. W. Andersen. Controller adjustment for improved nominal performance and robustness, Chem. Engng Science 42, 2005-2010 (1987). 6. I. M. Alatiqi, A. M. Meziou and (3. A. Gasmelseed. Modelling simulation and sensitivity analysis of steam methane reformers hit. J. Itydrogen Energy 14, 241-256 (1989). 7. I. M. Alatiqi, A. M. Meziou and G. A. Gasmelseed. Static and dynamic simulation of steam methane reformers. Presented at Catalysts in Petroleum Refining Conference, Kuwait (6-10 March, 1989). 8. G. F. Shinskey, Process Control Systems 2nd edn, McGraw Hill, New York (1979). 9. P. Grosdidier and M. Morari. A computer aided methodology for the design of decentralized controllers, Comput. Chem. Engng ll, 423~33 (1987). i0. R. H. Perry and C. H. Chilton. Chemical Engineering Handbook, 5th edn. pp. 9 16 (1973).