- Email: [email protected]
Applications of a Dynamic Simulation of Distillation Columns for a New Manufacturing Plant
Co pn-iglu © (FAC Control of ()istillation Columns and Chemical Reactors. Bourne mouth, L' t;. 1986
APPLICATIONS OF A DYNAMIC SIMULATION OF DISTILLATION COLUMNS FOR A NEW MANUFACTURING PLANT Chiu-Nan Wei Exxoll Chemical Americas, PO Box 4004, Bay/o wn , TX 77522, USA
Abstract. This paper presents a case study of applications of a dynarric sirrulation of distillation columns for a new, corrplex manufacturing plant. The applications included process feasibility studies, dynamic analyses during the design phase, control syste~ development and imple~entation, and operator traininF prior to start-up. The sirrulation was developed in a plant environment with a minimal expenditure of money, but yielded significant savings in capital and operating costs for the new plant. The plant was started up successfully with advanced controls and produced on-specification products within ten days. Keywords. Computer applications; dynamic simulation; conputer control; control system analysis; dynamic response; multivariable systeffi; distillation colurr.n.
What control scheme would best maintain product specifications even under plant upsets.
INTRODUCTION In the chemical industry, dynamic simulations have found widespread use in dyna~ic analysis (McCune and Gallier, (973), the evaluation of alternative control systems (CheunF and M.arlin, (982), the determination of safety shutdown system (Ochiai, (980), operator training (Vervalin, (984), and so on. However, it is seldom that a single simulation model is developed to aid the process engineer in plant design, to help the control engineer in control development, and to assist the operations engineer in new plant start-up. In this paper we will discuss an example of a dynamic sirrulation that provided the functions described above. It was developed with a minirral expenditure of money, but yielded significant savings in capital and operating costs for a new manufacturing plant.
How the operators could be trained to st~rt up, operate, and shut down such a complex process. To answer these questions, a dynamic simulation of the distillation columns was developed and employed. The mathematical model developed for the distillation columns was presented at the 1984 Summer Computer Simulation Conference in Boston, Massachusetts (Wei, 1984). The presentation initiated enthusiastic discussions with the audience. Questions were raised such as how well the model identified and resolved the potential problems and what the results were. Therefore, this paper will emphasize these points and depict plant data with those predicted from the model. Recent benefits to our operation as a result of the simulator will also be included.
For a process plant expansion, a set of stacked distillation columns was proposed to convert the process from its original single feed with two product streams to two feeds and three product streams (FijZ. 1) . The six components for
FEASIBILITY STUDY - DYNAMIC ANI
separation in the columns are A, B, C, 0, E, 8fid
F (listed from light to heavy) . Four specifications are required in these three high purity products: top product (150 ppm of conponents B, C, D, E), middle sidestream (60 ppm of component F maximum; 1.0 wt% of component A maximum), bottom product (150 ppm of components B, C, D, E). Off-specification production fr~ the top or the bottom stream could cause overall plant product to go off-specification, and any component F, which is very expensive, in the middle sidestream is considered a loss. Operability of the columns played an important role in the project success. A major concern of operations was the column dynarric transients during plant upsets.
To minimize column interaction,
The main purpose of this study was to deterrrine whether an additional intercolumn heat exchanger was needed to improve operability. The overall tower performance was exarrined by runninjZ a number of tests which were considered to be possible upsets in the actual plant operation. These upsets, as defined in Tabl~ 1, were prepared by operations personnel. Since the mathematical models describing the tower dynamics and its related equipment, instrumentation and control system were implemented on a minicomputer to perform interactive on-line simulation, a wide variety of upset tests could be simulated by simply changing the value of the particular upset variable from the CRT keyboard. For analysis, the transient responses of key process variables were displayed on the CRT screen or plotted on a hard copy frorr a line printer. The upset tests were conducted with step changes from the normal operation conditions .
one
intercolumn heat exchanger and accumulator system were initially proposed for column decoupling. Operations personnel desired to know: If the heat exchanger system was required.
117
118
Chiu-Na n Wei
Test 10 is given as an ex~, ple to illustrate the study procedures. Figure 2 shows the process transient when the concentration of F, the heaviest cOlI'ponent in the heavy feed, was decreased from 62 Wt% to 40 Wt%. As depicted in the figure, the concentration of F component in the bottom product stream decreased from 99.99 percent to 91 percent, and the impurity of component F in the middle sidestream increased from 80 ppm to 550 ppm. Both of the stream products are off-specification. Fractionation is inadequate. Two corrections are required to effectively operate the tower under this upset: 1.
Increase the firing rate to provide enough heat for fractionation.
2.
Regulate properly the liquid flow from C-2 to C-l to provide enough reflux.
Corrections were tried one at a time. First, the firing rate was kept constant. The temperature controller, T-14, was placed in ",anual and the intercolumn flow was regulated manually. As shown in Fig. 3, the concentration of component F from the middle sidestream decreased significantly, but the impurity of C-l bottom increased (i.e . , F concentration decreased). On the other hand, if the firing rate was increased by 20 percent of the design rate and T-14 was put on automatic, the bottom product was onspecification, but the loss of component F from the middle sidestream increased during the transient period of four hours by approximately 1.2 barrel more than that under normal operating conditions. This is illustrated in Fig. 4. As shown in Fig. 5, all products were or.specification and the loss of component F reduced significantly when the firing rate was increased by 20 percent of the design rate and the intercolumn liquid flow was manually regulated. This implies that the present process design configuration can handle this upset if it is provided with a better control scheme. The firing control scheme should respond to feed rate and composition changes, and an analyzer controller should regulate the temperature setpoint of T-14 due to the shift of tray temperature profile . Also a more sensitive temperature control point is required. The other tests were carried out in the same way as the example described above. Since the objective of the study at this stage is a process evaluation rather than control system design, an overall supervisory control systelI' has not been pursued. However, for some upsets, the columns could not maintain smooth operation or could not produce on-specification products; these problem areas were investigated, identified, and corrected during the design phase. Based on the results of this study, the following conclusion was made : The process design configuration as shown in Fig. 1 is capable of producing onspecification products. No additional intercolumn heat exchanger is required . However, a better control system is needed to improve the tower performance . In addition, several shortcomings were revealed in the study and hence, the following modificat ions were made to improve the process operability and system controllability.
Modification of the flooded condenser : The condenser would be operated at approximately 80 percent of capacity rather than 50 percent as described in the vendor's design specification because the operational tower pressure would be lewer than that designed. Relocation of tray temperature control point (T-14) from tray No . 36 to tray No. 33. Changes of pipe size, control valve, and flow transmitter range for the middle sidestream. Installation of one analyzer in each feed stream: Component A analyzer in the light feed stream and component F in the heavy feed st ream. CONTROL SYSTEM DESIGN Process responses from the upset tests were used to guide the design of the control scheme. This is probably the easiest and the most effective way to test alternative control schemes prior to plant start-up . Many experiments can be performed in a short time and the results ar e not distorted by noise or other disturbances. Not only the basic regulatory control system (i.e., properly pairing of the controlled variables and the manipulated variables), but also advanced control systems such as feed forward-feedback control system, inferential model-based control system, etc., can be desil'ned and tested. Further~ore, any desip.n changes can easily be implemented, less expensively, during the design stage. As discussed in the previous section, we have changed the location of tray temperature control point (T-14). Figure 6 shows the actual plant data; it indicates that the sensitivity of the two temperatures is quite different even though only three trays apart . Temperature which had a shorter deadtime corresponded to feed changes, also ilI'proved process controllability. The overall control scheme as illustrated in Fig. 7 was designed and implemented as follows : 1.
2.
Regulatory controls Material control (Cheung and Marlin, 1982):
balance
Control Variables
Regulated Variables
Pressure Top sidestream spec. T-l bottom level Middle sidestream spec . T-2 bottom level Bottom produce spec.
Reflux flow Top sidestream flow Middle sidestream flow Intercolumn transfer flow Bottom product flow Firing rate (furnace duty)
Advanced controls - Model-based (dynamic reconciliation) control (Bartman, 1981): A cascaded scheme (analyzer to temperature to feed fcrward ratio to flow) ultimately sets the sidestream flow to control the sidestream specification as shown in Fig. 8 . The analyzer feedback controller uses a modelbased control. The technique uses the following relation between the heavier ilI'purity and C-2 tray No. 77 temperature.
119
Distillation Columns for a :--Jew Manufacturing Plant C = a + b where
C
T a b
*
T
(1)
impurity analysis tray temperature
= constant steady state process gain
Feedback control is achieved by updatin" the constant in equation (I). Dynamics are taken into account by delaying and laggin" the temperature by the deadtime and time constant between analysis and temperature. Delaying and layying the temperature synchronizes it with the current analysis. Using the updated constant in equation (I), a new temperature setpoint is calculated. The models required were obtained from the dynamic tests on the simulator. Those models were also used for the plant start-up. Using the simulator, all the control schemes were developed and tested in both the same process control computer (Honeywell 4500 system), and Foxboro Spec 200/Videospec as would be used for the actual plant. The software developed was used for the plant start-up. It is preferred that a test or evaluat ion uses th e actual hardware of the system, rather than a mathematical representation of the hardware. Thus, it avoided some of the problems that are related to the specific implementation or to characteristics of the process control computer system. We did not put all the supervisory controls in service when the plant started up even though all the control software was ready; all the instrument validity and analyzer reliability needed to be checked first. Therefore, the flow rate was initially on DDC (direct digital control) and its setpoint was changed manually . As shown in Fig. 9, when the heavy feed rate increased, the heavy component (F) increased in the middle sidestream due to the increase of heat input and the failure to increase the intercolumn transfer flow. As the operator noticed the problem, he increased the flow, but increased it a little too much, causing the lighter components to 1'0 down to the bottom product. This oscillation continued as operators had difficulty regulating the flow correctly. After two days we added temperature control on top of the flow control to regulate the flow setpoint. The temperature controller detected the heavy comFonent going up before it showed up in the middle product stream . The operation would have been fine if the operator kept alert and correctly chanyed the setpoint of the temperature controller when the feed rate changed. When the analyzer reliability increased two days later, we added the analyzer controller to regulate the set point for the temperature controller. As depicted in Fig. 10, the tower ran smoothly even when the heavy feed rate changed. The control loop discussed above is given as an example of commissioning advanced control loops in the plant start-up. The same procedures applied to the other critical control loops. The plant was started up successfully with advanced controls and produced on-specification products within ten days.
OPERATOR TRAINING Since the model was also developed with
DCCR-I
the
intent to provide operator tralnlng, the program was designed and executed in a reasonable amount of computer time, 0.25 sec/cycle . The model was then integrated with the system executive software, the plant process control cOlI'puter system (Honeywell 4500 system), and the Foxboro Spec 200/Videospec instrumentation system to perform an interactive simulation. As indicated in the simulator configuration in Fig . 11, operators were trained on the same
operation
consoles as those they would use to operate the actual plant. Te provide thorough training, two separate simulators were constructed. They are the tower simulator and
which
integrated
the
the IIpl ant " simulator,
tower model with other
process models such as reactors, adsorbers, heat exchangers, steam system, etc.
The simulator allows dynamic transients from a cold start-up condition to a normal (desiFn) operatiny condition and vice versa. A number of possible plant upsets and malfunctions were included and could be entered from the instructor station. Thus, operators were given training in troubleshootiny. The simulator provided more than ten initial conditions for different operating conditions and was capable of making "snapshot" recordings of the simulated plant conditions at any time . The snapshot contained sufficient information to allow restart of the simulator from that point. The simulator also provided selection of fast-mode operation (up to ten till'es of real-time) for certain slow processes.
CONCLUSION Dynamic simulation can be used as a powerful tool for operational analyses, process and control design assessment, and operator training in the manufacturing environment. This paper presented a case study of a dynamic simulation of distillation columns for a new manufacturing plant to illustrate such applications. As discussed in the paper, the model has been used for wide ranging "what if" type of studies to find the process operability. An expensive intercolumn heat exchanger system which costs millions of dollars was eliminated. In addition, several design flaws were found and corrected prior to start-up. The simulation was also used to develop and test the control systems. To implement model-based controls, several dynamic tests were conducted on the simulator to obtain simplified process models. It has not only saved enormous time for plant testing, but also determined the best controls for initial startup. Finally, the simulator was used to train operators in the same environment as they would operate the actual plant. The start-up team acquired many hours of realistic hands-on training, including cold starts and upsets. This created a feeling of confidence and eliminated many surprises for the plant start-up. All the activities described above contributed greatly to the successful plant start-up. It is as though the simulator "ives us the ability to look into the future to run the unit before we have even finalized its desi"n. This helps ensure that we are doing the right things and we are doin" them in the most cost-effective way.
Chiu-:'>Ian Wei
120 REFERENCES
Bartrr.an, R. V. (1981). Dual composition control in a C3/C4 splitter. Chemical Engineerin~ Progress, 9, pp. 58-62 . Cheung, T. F.~ and T. E. Marlin (1982). Regulatory control of distillation towers: material balance vs. energy balance control. Paper presented in the Lehigh University Distillation Control Short Course. McCune, L. C., and P. W. Gallier (1973). Digital simulation: a tool for the analysis and design of distillation control. ISA Transactions, Vol. 12, No. 3, pp. 193-207. Ochi~ (1980). Simulate safety shutdown systems for des ign confidence. InTech, 8, pp. 33-38. Vervalin, C. R. (1984). Training by simulation . Hydrocarbon Processing, 12, pp. 42-50. Wei, C. N. (1984). Dynamic simulation of multicoD!ponent distillation columns: a tool for process analysis, control design and operator training . 1984 Sumrr.er Computer Simulation Conference, Boston, Massachusetts,
Vol. 1, pp. 616-623.
TABLE 1 A.
Upset Tests
Light Feed Rate 1. 2. 3.
Terminates completely. Flows at half desi~n rate. Flows at 1.2 ti~es design rate. Fig. 1
B.
Heavy Feed Rate 4. 5. 6.
C.
Terminates corr.pletely. Flows at half design rate. Flows at 1 . 2 times design rate.
Concentration of Component A in Light Feed 7. 8.
D.
Simulation diagram o f multicomponent distillation columns
Increases from 54 Wt% to 80 Wt%. Decreases from 54 Wt% to 40 Wt%.
Concentration of Component F in Heavy Feed 9. 10.
Increases from 62 Wt% to 80 Wt%. Decreases from 62 Wt% to 40 Wt% . z
o
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Heatin~
Value
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Fig. 2
Response of process to a step change in concentration of component F in heavy feed Firing rate: unchanged T-14 on control (cascaded to F-15)
121
Distillation Columns for a Ne,," Manufacturing Plant
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Response of process to a step change in concentration of component F in heav y feed
Fig. 3
.:
:.
0
Fig. "
Firing rate: unchanged Inter-column flow (F-15) on manual operation
Response of column temperatures to changes of feed rat e and composition of middle sidestream product
,---------------------------------- -------1 1 1
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Fig. 4
Response of process to a step change in concentration of component F in heavy feed
M!DDLE SIDESTREAH
Firing rate: increased by 20% T-14 on control (cascaded to F-15)
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:
BOTTOM I tI ______________ ___ ___ _______ .:. . ______ YBQP.Y£T __ _ _ J
Response of process to a step change in concentration of component F in heavy feed Firing rate : increased by 20% Inter-column flow (F-15) on manual operation
Column supervisory controls
Chiu-~an
122
Wei
~-- -- -- - -- - - - ----------- --- --- --- ------- - - - - ~
I I
f
Ao FEED RA TE
r.;·
~ ~G~6~~o~O~~~~
/
,rc,
LIGHT FEEO
-
Fig . 10
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- - - -- T I H[
Plant start-up data after commissioning sup e rvis o ry controls
Fig. 8
Control strategy for top product draw- off
FIELD
PLANT
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CO~PUTER
ROOM
UN!SC ,,"
HON[r IoiELL
CO"'DUTER 4500
OPEPIITOR
CQ"ISOLE
CONTROL ROO~
QPERIHOR CO"l50LE
P~C'CESS
~O~. BOqO
Co u o:.JTER
v:OEOSPEC
eRT
Fig. 11
Fig. 9
Plant start-up data prior to commissioning supervisory controls
Configuration of simu l ator
APPLICATIONS OF A DYNAMIC SIMULATION OF DISTILLATION COLUMNS FOR A NEW MANUFACTURING PLANT Chiu-Nan Wei Exxoll Chemical Americas, PO Box 4004, Bay/o wn , TX 77522, USA
Abstract. This paper presents a case study of applications of a dynarric sirrulation of distillation columns for a new, corrplex manufacturing plant. The applications included process feasibility studies, dynamic analyses during the design phase, control syste~ development and imple~entation, and operator traininF prior to start-up. The sirrulation was developed in a plant environment with a minimal expenditure of money, but yielded significant savings in capital and operating costs for the new plant. The plant was started up successfully with advanced controls and produced on-specification products within ten days. Keywords. Computer applications; dynamic simulation; conputer control; control system analysis; dynamic response; multivariable systeffi; distillation colurr.n.
What control scheme would best maintain product specifications even under plant upsets.
INTRODUCTION In the chemical industry, dynamic simulations have found widespread use in dyna~ic analysis (McCune and Gallier, (973), the evaluation of alternative control systems (CheunF and M.arlin, (982), the determination of safety shutdown system (Ochiai, (980), operator training (Vervalin, (984), and so on. However, it is seldom that a single simulation model is developed to aid the process engineer in plant design, to help the control engineer in control development, and to assist the operations engineer in new plant start-up. In this paper we will discuss an example of a dynamic sirrulation that provided the functions described above. It was developed with a minirral expenditure of money, but yielded significant savings in capital and operating costs for a new manufacturing plant.
How the operators could be trained to st~rt up, operate, and shut down such a complex process. To answer these questions, a dynamic simulation of the distillation columns was developed and employed. The mathematical model developed for the distillation columns was presented at the 1984 Summer Computer Simulation Conference in Boston, Massachusetts (Wei, 1984). The presentation initiated enthusiastic discussions with the audience. Questions were raised such as how well the model identified and resolved the potential problems and what the results were. Therefore, this paper will emphasize these points and depict plant data with those predicted from the model. Recent benefits to our operation as a result of the simulator will also be included.
For a process plant expansion, a set of stacked distillation columns was proposed to convert the process from its original single feed with two product streams to two feeds and three product streams (FijZ. 1) . The six components for
FEASIBILITY STUDY - DYNAMIC ANI
separation in the columns are A, B, C, 0, E, 8fid
F (listed from light to heavy) . Four specifications are required in these three high purity products: top product (150 ppm of conponents B, C, D, E), middle sidestream (60 ppm of component F maximum; 1.0 wt% of component A maximum), bottom product (150 ppm of components B, C, D, E). Off-specification production fr~ the top or the bottom stream could cause overall plant product to go off-specification, and any component F, which is very expensive, in the middle sidestream is considered a loss. Operability of the columns played an important role in the project success. A major concern of operations was the column dynarric transients during plant upsets.
To minimize column interaction,
The main purpose of this study was to deterrrine whether an additional intercolumn heat exchanger was needed to improve operability. The overall tower performance was exarrined by runninjZ a number of tests which were considered to be possible upsets in the actual plant operation. These upsets, as defined in Tabl~ 1, were prepared by operations personnel. Since the mathematical models describing the tower dynamics and its related equipment, instrumentation and control system were implemented on a minicomputer to perform interactive on-line simulation, a wide variety of upset tests could be simulated by simply changing the value of the particular upset variable from the CRT keyboard. For analysis, the transient responses of key process variables were displayed on the CRT screen or plotted on a hard copy frorr a line printer. The upset tests were conducted with step changes from the normal operation conditions .
one
intercolumn heat exchanger and accumulator system were initially proposed for column decoupling. Operations personnel desired to know: If the heat exchanger system was required.
117
118
Chiu-Na n Wei
Test 10 is given as an ex~, ple to illustrate the study procedures. Figure 2 shows the process transient when the concentration of F, the heaviest cOlI'ponent in the heavy feed, was decreased from 62 Wt% to 40 Wt%. As depicted in the figure, the concentration of F component in the bottom product stream decreased from 99.99 percent to 91 percent, and the impurity of component F in the middle sidestream increased from 80 ppm to 550 ppm. Both of the stream products are off-specification. Fractionation is inadequate. Two corrections are required to effectively operate the tower under this upset: 1.
Increase the firing rate to provide enough heat for fractionation.
2.
Regulate properly the liquid flow from C-2 to C-l to provide enough reflux.
Corrections were tried one at a time. First, the firing rate was kept constant. The temperature controller, T-14, was placed in ",anual and the intercolumn flow was regulated manually. As shown in Fig. 3, the concentration of component F from the middle sidestream decreased significantly, but the impurity of C-l bottom increased (i.e . , F concentration decreased). On the other hand, if the firing rate was increased by 20 percent of the design rate and T-14 was put on automatic, the bottom product was onspecification, but the loss of component F from the middle sidestream increased during the transient period of four hours by approximately 1.2 barrel more than that under normal operating conditions. This is illustrated in Fig. 4. As shown in Fig. 5, all products were or.specification and the loss of component F reduced significantly when the firing rate was increased by 20 percent of the design rate and the intercolumn liquid flow was manually regulated. This implies that the present process design configuration can handle this upset if it is provided with a better control scheme. The firing control scheme should respond to feed rate and composition changes, and an analyzer controller should regulate the temperature setpoint of T-14 due to the shift of tray temperature profile . Also a more sensitive temperature control point is required. The other tests were carried out in the same way as the example described above. Since the objective of the study at this stage is a process evaluation rather than control system design, an overall supervisory control systelI' has not been pursued. However, for some upsets, the columns could not maintain smooth operation or could not produce on-specification products; these problem areas were investigated, identified, and corrected during the design phase. Based on the results of this study, the following conclusion was made : The process design configuration as shown in Fig. 1 is capable of producing onspecification products. No additional intercolumn heat exchanger is required . However, a better control system is needed to improve the tower performance . In addition, several shortcomings were revealed in the study and hence, the following modificat ions were made to improve the process operability and system controllability.
Modification of the flooded condenser : The condenser would be operated at approximately 80 percent of capacity rather than 50 percent as described in the vendor's design specification because the operational tower pressure would be lewer than that designed. Relocation of tray temperature control point (T-14) from tray No . 36 to tray No. 33. Changes of pipe size, control valve, and flow transmitter range for the middle sidestream. Installation of one analyzer in each feed stream: Component A analyzer in the light feed stream and component F in the heavy feed st ream. CONTROL SYSTEM DESIGN Process responses from the upset tests were used to guide the design of the control scheme. This is probably the easiest and the most effective way to test alternative control schemes prior to plant start-up . Many experiments can be performed in a short time and the results ar e not distorted by noise or other disturbances. Not only the basic regulatory control system (i.e., properly pairing of the controlled variables and the manipulated variables), but also advanced control systems such as feed forward-feedback control system, inferential model-based control system, etc., can be desil'ned and tested. Further~ore, any desip.n changes can easily be implemented, less expensively, during the design stage. As discussed in the previous section, we have changed the location of tray temperature control point (T-14). Figure 6 shows the actual plant data; it indicates that the sensitivity of the two temperatures is quite different even though only three trays apart . Temperature which had a shorter deadtime corresponded to feed changes, also ilI'proved process controllability. The overall control scheme as illustrated in Fig. 7 was designed and implemented as follows : 1.
2.
Regulatory controls Material control (Cheung and Marlin, 1982):
balance
Control Variables
Regulated Variables
Pressure Top sidestream spec. T-l bottom level Middle sidestream spec . T-2 bottom level Bottom produce spec.
Reflux flow Top sidestream flow Middle sidestream flow Intercolumn transfer flow Bottom product flow Firing rate (furnace duty)
Advanced controls - Model-based (dynamic reconciliation) control (Bartman, 1981): A cascaded scheme (analyzer to temperature to feed fcrward ratio to flow) ultimately sets the sidestream flow to control the sidestream specification as shown in Fig. 8 . The analyzer feedback controller uses a modelbased control. The technique uses the following relation between the heavier ilI'purity and C-2 tray No. 77 temperature.
119
Distillation Columns for a :--Jew Manufacturing Plant C = a + b where
C
T a b
*
T
(1)
impurity analysis tray temperature
= constant steady state process gain
Feedback control is achieved by updatin" the constant in equation (I). Dynamics are taken into account by delaying and laggin" the temperature by the deadtime and time constant between analysis and temperature. Delaying and layying the temperature synchronizes it with the current analysis. Using the updated constant in equation (I), a new temperature setpoint is calculated. The models required were obtained from the dynamic tests on the simulator. Those models were also used for the plant start-up. Using the simulator, all the control schemes were developed and tested in both the same process control computer (Honeywell 4500 system), and Foxboro Spec 200/Videospec as would be used for the actual plant. The software developed was used for the plant start-up. It is preferred that a test or evaluat ion uses th e actual hardware of the system, rather than a mathematical representation of the hardware. Thus, it avoided some of the problems that are related to the specific implementation or to characteristics of the process control computer system. We did not put all the supervisory controls in service when the plant started up even though all the control software was ready; all the instrument validity and analyzer reliability needed to be checked first. Therefore, the flow rate was initially on DDC (direct digital control) and its setpoint was changed manually . As shown in Fig. 9, when the heavy feed rate increased, the heavy component (F) increased in the middle sidestream due to the increase of heat input and the failure to increase the intercolumn transfer flow. As the operator noticed the problem, he increased the flow, but increased it a little too much, causing the lighter components to 1'0 down to the bottom product. This oscillation continued as operators had difficulty regulating the flow correctly. After two days we added temperature control on top of the flow control to regulate the flow setpoint. The temperature controller detected the heavy comFonent going up before it showed up in the middle product stream . The operation would have been fine if the operator kept alert and correctly chanyed the setpoint of the temperature controller when the feed rate changed. When the analyzer reliability increased two days later, we added the analyzer controller to regulate the set point for the temperature controller. As depicted in Fig. 10, the tower ran smoothly even when the heavy feed rate changed. The control loop discussed above is given as an example of commissioning advanced control loops in the plant start-up. The same procedures applied to the other critical control loops. The plant was started up successfully with advanced controls and produced on-specification products within ten days.
OPERATOR TRAINING Since the model was also developed with
DCCR-I
the
intent to provide operator tralnlng, the program was designed and executed in a reasonable amount of computer time, 0.25 sec/cycle . The model was then integrated with the system executive software, the plant process control cOlI'puter system (Honeywell 4500 system), and the Foxboro Spec 200/Videospec instrumentation system to perform an interactive simulation. As indicated in the simulator configuration in Fig . 11, operators were trained on the same
operation
consoles as those they would use to operate the actual plant. Te provide thorough training, two separate simulators were constructed. They are the tower simulator and
which
integrated
the
the IIpl ant " simulator,
tower model with other
process models such as reactors, adsorbers, heat exchangers, steam system, etc.
The simulator allows dynamic transients from a cold start-up condition to a normal (desiFn) operatiny condition and vice versa. A number of possible plant upsets and malfunctions were included and could be entered from the instructor station. Thus, operators were given training in troubleshootiny. The simulator provided more than ten initial conditions for different operating conditions and was capable of making "snapshot" recordings of the simulated plant conditions at any time . The snapshot contained sufficient information to allow restart of the simulator from that point. The simulator also provided selection of fast-mode operation (up to ten till'es of real-time) for certain slow processes.
CONCLUSION Dynamic simulation can be used as a powerful tool for operational analyses, process and control design assessment, and operator training in the manufacturing environment. This paper presented a case study of a dynamic simulation of distillation columns for a new manufacturing plant to illustrate such applications. As discussed in the paper, the model has been used for wide ranging "what if" type of studies to find the process operability. An expensive intercolumn heat exchanger system which costs millions of dollars was eliminated. In addition, several design flaws were found and corrected prior to start-up. The simulation was also used to develop and test the control systems. To implement model-based controls, several dynamic tests were conducted on the simulator to obtain simplified process models. It has not only saved enormous time for plant testing, but also determined the best controls for initial startup. Finally, the simulator was used to train operators in the same environment as they would operate the actual plant. The start-up team acquired many hours of realistic hands-on training, including cold starts and upsets. This created a feeling of confidence and eliminated many surprises for the plant start-up. All the activities described above contributed greatly to the successful plant start-up. It is as though the simulator "ives us the ability to look into the future to run the unit before we have even finalized its desi"n. This helps ensure that we are doing the right things and we are doin" them in the most cost-effective way.
Chiu-:'>Ian Wei
120 REFERENCES
Bartrr.an, R. V. (1981). Dual composition control in a C3/C4 splitter. Chemical Engineerin~ Progress, 9, pp. 58-62 . Cheung, T. F.~ and T. E. Marlin (1982). Regulatory control of distillation towers: material balance vs. energy balance control. Paper presented in the Lehigh University Distillation Control Short Course. McCune, L. C., and P. W. Gallier (1973). Digital simulation: a tool for the analysis and design of distillation control. ISA Transactions, Vol. 12, No. 3, pp. 193-207. Ochi~ (1980). Simulate safety shutdown systems for des ign confidence. InTech, 8, pp. 33-38. Vervalin, C. R. (1984). Training by simulation . Hydrocarbon Processing, 12, pp. 42-50. Wei, C. N. (1984). Dynamic simulation of multicoD!ponent distillation columns: a tool for process analysis, control design and operator training . 1984 Sumrr.er Computer Simulation Conference, Boston, Massachusetts,
Vol. 1, pp. 616-623.
TABLE 1 A.
Upset Tests
Light Feed Rate 1. 2. 3.
Terminates completely. Flows at half desi~n rate. Flows at 1.2 ti~es design rate. Fig. 1
B.
Heavy Feed Rate 4. 5. 6.
C.
Terminates corr.pletely. Flows at half design rate. Flows at 1 . 2 times design rate.
Concentration of Component A in Light Feed 7. 8.
D.
Simulation diagram o f multicomponent distillation columns
Increases from 54 Wt% to 80 Wt%. Decreases from 54 Wt% to 40 Wt%.
Concentration of Component F in Heavy Feed 9. 10.
Increases from 62 Wt% to 80 Wt%. Decreases from 62 Wt% to 40 Wt% . z
o
E.
Fuel Gas
Heatin~
Value
u
<
0::_
11. 12.
Increases from 1,000 BTU/SCF to 1,200 BTU/SCF. Decreases from 1,000 BTU/SCF to 800 BTU/SCF.
/"
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::I
~~~~:n o
0
40
80
120
J60
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200
2 40
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~
0000 ' 0 N 0
Fig. 2
Response of process to a step change in concentration of component F in heavy feed Firing rate: unchanged T-14 on control (cascaded to F-15)
121
Distillation Columns for a Ne,," Manufacturing Plant
«Cl:
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_
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40
90
120
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o
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, M !N . I
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Response of process to a step change in concentration of component F in heav y feed
Fig. 3
.:
:.
0
Fig. "
Firing rate: unchanged Inter-column flow (F-15) on manual operation
Response of column temperatures to changes of feed rat e and composition of middle sidestream product
,---------------------------------- -------1 1 1
I I
1 1
1 1
:
1
p
:
I I I
1 1 I
~
1 1
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Fig. 4
Response of process to a step change in concentration of component F in heavy feed
M!DDLE SIDESTREAH
Firing rate: increased by 20% T-14 on control (cascaded to F-15)
C-1
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:
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Fig . 5
:
BOTTOM I tI ______________ ___ ___ _______ .:. . ______ YBQP.Y£T __ _ _ J
Response of process to a step change in concentration of component F in heavy feed Firing rate : increased by 20% Inter-column flow (F-15) on manual operation
Column supervisory controls
Chiu-~an
122
Wei
~-- -- -- - -- - - - ----------- --- --- --- ------- - - - - ~
I I
f
Ao FEED RA TE
r.;·
~ ~G~6~~o~O~~~~
/
,rc,
LIGHT FEEO
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Fig . 10
CONPON£ NT F
IN MI DDLE SlOE S TRE AM
- - - -- T I H[
Plant start-up data after commissioning sup e rvis o ry controls
Fig. 8
Control strategy for top product draw- off
FIELD
PLANT
IOOY-BORD SPI:.C 200
CO~PUTER
ROOM
UN!SC ,,"
HON[r IoiELL
CO"'DUTER 4500
OPEPIITOR
CQ"ISOLE
CONTROL ROO~
QPERIHOR CO"l50LE
P~C'CESS
~O~. BOqO
Co u o:.JTER
v:OEOSPEC
eRT
Fig. 11
Fig. 9
Plant start-up data prior to commissioning supervisory controls
Configuration of simu l ator