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An Experimental Study on Human Supervisory Control and Its Implication to Process System and Automation System Design
Copyright © IFAC Dynamics and Control of Process Systems, Corfu, Greece, 1998
AN EXPERIMENTAL STUDY ON HUMAN SUPERVISORY CONTROL AND ITS IMPLICATION TO PROCESS SYSTEM AND AUTOMATION SYSTEM DESIGN Kang Li, Peter A. Wieringa
(Lab. for Measurement and Control, Delft University of Technology)
In this paper, a laboratory study reveals how human operators experience the complexity and difficulty in supervisory control of complex industrial processes . Four structured operation environments were introduced and examined. The implications of this research for process system and automation system design are discussed. Copyright © 1998 IFAC
information to be displayed by the interface in the control room, and which variables can be manipulated by the supervisor. Moreover, they often indicate the layout of the control room and its manmachine interface. Some times, the human factors, ergonomics, and man-machine system disciplines can contribute to the design at this phase, however, in many cases, no contribution from these experts is required. 4. Finally, man-machine system specialists define the supervisory control tasks, the recruitment, training and selecting of potential operators, and the classical human factor aspects of display and controls. Apparently, one may find that 1. The control engineer is called in too late. He has to design the automation system for the process, where the process system engineer has largely predetermined the control structure. 2. The man-machine specialist is called in too late. He has to cope with a man-machine system (or simply interfaces) that is largely determined by the control engineer. Future process industry requires the design tearn to strive for all such goals as higher requirements on system performance, operability, flexibility, profitability, energy saving, and also for safety, job satisfaction, and environmental
1. INTRODUCTION
In the last two decades, with the fast development of computer technology and tightening requirements, much progress has been made in various fields of learning relating to process industry, including process system engineering, process control engineering, and man-machine system engineering (Biegler, Grossman and VVesterberg, 1997; Ray, 1989; Skogestad, 1996; Henson and Seborg, 1997; Sheridan, 1992; Johanson, Levis and Stassen, 1994, etc). However, progress made on these areas does not mean that much progress has been made on the integration of these research activities. Current practice of new plant design generally follow the stages as: 1. Management sets the product specification. 2. On the basis of product specification, process engineers design the process. For most cases, the process engineer also determines all possible variables for manipulation, for measurement, and control. In many cases, no contribution from the control engineer is required at this stage. 3. Then the control engineers design the control and safety systems, hardware as well as software, and they also determine which
461
we designed our controller parameters, and operation and control system configuration to a certain degree of freedom . The controlled plant consists of up to five interconnected heat-exchange subsystems. The interconnections between the subsystems were optional and could be redesigned by us. The operation task for the heat-exchange system is to heat the cold water in the reservoir with warm water to a certain temperature, while the water in the reservoir is kept at a certain level. The structure of this experimental system is shown in Fig.I. The subsystems are electronically interconnected. The abbreviations in Fig. l are listed as follows :
compatibility. This calls for both human-«ntered approaches and application-oriented functions. Therefore, plant design should be based on the integration of related sciences, including process system engineering, control system engineering, and man-machine system engineering. Modem computer technology, variety of theory and methodology, as well as already existing technology, has promised the possibility for such integration. It is argued that, since the information structure is one of the most important aspects that have to be dealt with throughout the whole process of new plant design, it would be appropriate to integrate such knowledge at the very beginning. For example, the preliminary stage of a process design is to select the process flowsheet, the synthesis of a process flowsheet can be performed through a superstructure optimization in which the problem can be formulated as a Mixed Integer NonLinear Progranuning (MINLP). Apparently, such a design procedure is first of all based on the possible information structure of the new plant. In control system design for an industrial process, the first important task is to derive a suitable control structure (Foss 1973, Umeda 1978, Morari 1980, Govind and Powers 1982, Li et al 1994, Skogestad, 1996, etc), Again, structural information is required to generate a preliminary control structure (Li et ai, 1996). In man-machine system engineering, much attention has been contributed to the study of complexity, where the interaction of subsystems, components etc is regarded as the major shaping factors for complexity (Stassen et al 1993 , Wieringa et al 1993). The interaction is first of all a structural characteristic of complex systems. The above discussion reveals that, integration of such studies may possibly contribute to the development of future process industry with new quality. In this paper, four structured operation environments typically for human supervisory control are introduced. A total of 16 sessions were done to test how human operators perceive the complexity and difficulty in these four structured operation environments. The implications of this study to process system and automation system design are discussed.
• • • • • •
EOS FOS BC EC MC DDE
Enhanced Operation System Field Operation System Basic Controller Extended Controller Multi-function Controller Direct Data Exchange
'------.,.--.-----'
1
~
1
FOS
l'
Subsys 1 I~ Subsys 2 ~ .. . Subsys
Si
Fig.l System configuration Two-operation statuses are provided: • MAN --- Manual operation. • AUTO --- Plant in automation. The operator has multiple choices for key operations. The system configurations for each of the five subsystems are similar, though the model parameters for each subsystem may be different. The theoretic model for each subsystem is described as follows :
±
yes) = _k_e-uu(s)+ ~e-"ti Swj(s) Ts + 1 j~l Tjs + 1
(1)
Where, y is the temperature of the water to be heated contained in the reservoir. y(s) is the output variable. W i, i = 1,2,3 are the temperatures of
2. EXPERIMENTAL DESIGN
the cold and warm water and the flow magnitude of the wann water. The variable u is the control variable: the flow of cold water. The parameters of the various subsystems had the following ranges: Tj , i = 1,2,3 and T vary from 20 to 100 s;
In this experiment, a laboratory plant equipped with a TOC 2000 series system from Honeywell Corporation is used. This DCS system has provided multiple functions, according to which
462
't.l ' i = 1"2 3 and 't vary from 5 to 50 s;
structured operation environments are created, thus
=
ki , i 1,2 vary from 0.2 to 10 dimensional); k 3 ,andk range from 104 to Jo6 °C l m J I s .
the total experimental sessions sum up to 24 = 16 . The major operation procedure is to start up and regulate the plant to a satisfactory state, then put the system on AUTO. The operation guidance for participants is provided in appendix 1. In order to create these four structured operation environments, the interaction gains between any two subsystems are chosen among -0,5 to +0,5, considering the system' s operability (here we choose such interaction gains as to make the coupling among subsystems as strong as possible). As for how to choose the interaction gain so that the whole plant is stable or unstable for decentralized control behavior, one may consult some related books on process control. In brief, one may choose appropriate interaction gains among subsystems so that entries in the relative gain array of corresponding plant satisfy some criteria (Seborg et al1989; Skogestad, 1996). We believe that these four structured operation environments typically cover the operation environments in modern slow-response industrial processes, no matter whether the operation task is to start up the plant, to perform manual control, or for fault-management. The behavior of the operator is somewhat like the behavior of a controller in automation systems, while different operation strategies of different operators are just like different controller algorithms, therefore, human operator may be viewed as an intelligent controller. In this experiment. 6 students, all male, average age 23 years, agreed to participate The students were from the Delft University of Technology and had an engineering background. They received a fixed fee for each experimental hour. Before the experiment. we also told them that rewards would be given to those who had the best job performance for each task. In order to help the operator assess the perceived complexity and difficulty in quantity, a rating scale is designed. The rating scale ranges from 0 to 100 with respect to five different degree of complexity and difficulty, i.e. not complex, a little complex, rather complex, much complex and awfully complex. The participants received three training sessions. In the first session, they became familiarized with the system's configuration, rating scales for complexity and difficulty, and other policies and experimental goals. In the second sessions, they learned to operate the subsystems and the overall system. In the third session, they were required to operate the system for several different configurations. We studied their learning curves and made sure that their performance became stable. The experiments were time~nsuming because all experimental sessions had to be practiced several times.
(non-
Differences exist between theoretic models and experimental models. Therefor, we also examined the practical model around several working points. We have designed a decentralized controller for the whole controlled plant, e.g., each subsystem has its own controller, and there exists no coupling among the control loops of subsystems. The control algorithm takes the form of normal PlO as follows : Pc ( s)=K(l+_l_) (ST~+l}) (2) sTJ (sa. 2 + According to the theoretic and practical model of subsystems, Kin (2) ranges from 6 to 30, TJ
ranges from 0.5 to 2, T] arranges from 0.05 to 0.15,
ex.
is larger than 10. The purpose of introducing the decentralized PlO controller is to make the whole system run automatically when operator presses "AUTO" button in the operation station (either in EOS, or FOS). In this experiment, we design 16 sessions with four structured operation environments, while the number of subsystems for operation ranges from one to five. The four different structured operation environments for our participants are listed in table 1. Type No.
Properties using two subsystems Fully decoupled operation environment
1
[;~]=[~}
o
IU}]
H2 U2
Cascade operation environment 2
[Y}] [ H} Y2 - K 2J H J
o
IU}]
H2 u2
Fully coupled operation environment (Stable operation environment) 3
4
[Y}] [ H} Y2 - K 2}H J
I
K} 2H 2 U}] H2 u2
Fully coupled operation environment (Unstable operation environment for decentralized operation strategy).
[Y}] Y2
=
I
K}2H2 U}] [ H} K 21 H J H2 U2
Table 1 Four structured operation environments with two subsystems as the example For every fixed number of subsystems (ranges from 2 to 5 subsystem), the above four types of
463
3. RESULTS AND CONCLUSIONS
~ ... 20
GI tu
Figures 2, 3, 4 and 5 show how operator perceive the complexity, experience the difficulty, and how long they operate the plant, and their keystroke rate in these four structured operation environments as the function of subsystem number. In these 4 figures, the number of subsystems (range from 2 to 5) is on the x-axis, whereas the yaxis shows the perceived complexity, perceived difficulty, operation time or key-stroke rate respectively. ~
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Fig.3 Human perceived difficulty as the function of subsystem number GI
.,E
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Conclusion 1: Human perceived complexity increases with the number of subsystems. While if perceived complexity increases, human perceived difficulty also increases and operation time decreases correspondingly. Conclusion 2: Linear extrapolation revealed that the perceived complexity will exceed 100 (the full scale for human perception), if, for the first uncoupled operation environment (Cl), the number of subsystems is more than 15; if, for the second and the third operation environments (C2, C3), the number exceeds 9; and if, for the forth operation environment (C4), the number exceeds 8. Conclusion 3: Linear extrapolation revealed that the perceived difficulty will exceed 100 (the full scale), if, for the first uncoupled operation environment (01), the number of subsystems is more than 15; if, for the rest three operation environment (02, D3, D4), the number exceeds 9. Linear extrapolation also revealed that, in case 15 subsystems are controlled in the first operation environment, as well as in case of 9 subsystems are controlled in the rest three operation environments, the required operation time will exceed 30 minutes. Conclusion 4: The student Hest revealed: • There exist distinct differences between the first and the forth-structured operation environments. • The second and the third operation environments do not have distinct difference. • In these 17 sessions of experiment, there dose not exist distinct difference between perceived complexity and perceived difficulty, which reveals that complexity is the most major factor contributing to operation difficulty. Conclusion 5: According to the experimental data, key-stroke rate varied a lot among subjects in case 2 subsystems were used. For 4 and 5 subsystems, key-stroke variability was surprisingly small.
5
80
:c
~KS4
The following conclusions may be drawn.
~C4
100
~
-6-KS3
Fig.5 Key-stroke rate
Fig. 2 Human perceived complexity as the function of subsystem number
(.)
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Number of subsystems
Number of subsystems
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~
10
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~
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Fig.4 Plant operation time as the function of subsystem number
464
process (refer to the forth structured operation environment in table 1). 3. The implications to the automation system design are somewhat the same as that for process system design. When the subsystem number is not large (for example, no more than 3), cascade control strategy is encouraged, multivariable controller is also possible (according to conclusions 4 and 5). And in this case, one operator is generally enough for human supervisory control. However, in this case, a multivariable controller should be carefully designed such that if some part of this controller fails, the rest of the controller should be compatible with human operator, once the operator decide to intervene, and take part of the control function . That is to say, the real-time man-machine system with operator in loop should not cause instability. 4. When the subsystem number is large enough (such as more than 8), assigning only one operator to supervise the whole plant is not appropriate, especially in the abnormal state of the process. A team of operators in a hierarchical supervisory control structure are usually required. In this case, every low-level operator is in charge of a small number of subsystems (where the low-level controller is a cascade controller in most cases, and a few are multivariable controllers). High-level operators are in charge of a simple aggregated model of the whole plant. Thus, the whole automation system is a hierarchical multivariable controller. 5. In normal operation, the operation procedure designed for the operator (e.g. the teaching and instruction function in Sheridan's five functions of supervisory control (1992» is better designed in such way that, these procedures (or steps) are in time sequence and content independent. In an abnormal state, the operation procedure (e.g. the monitor and intervene function in Sheridans' five functions (1992» should be designed such that the procedures are in time sequence, while the procedures or steps should not form cycles. If there exist feedback interconnection, the cycle should be as small as possible. Plant design is perhaps the quintessential engineering activity, which is based on mathematics, basic science, engineering science, and flavored by humanities and social science. Engineering design involves development of specifications and criteria in light of safety, reliability, economy, aesthetic, and social consideration. Suppose a team of engineers is required to design a process plant, they should first understand the various technical standards, product specifications and technical and social constraints for the industrial processes. Then conceptualize the framework of the design problem according to their prior experience and expert knowledge. Finally, solve different technical problems according to their understanding of various principles. From the
4. GENERAL DISCUSSION AND IMPLICATION Since total automation is a fiction, human operators will always be required to remain in the supervision and control loop, it is therefore important to have a better understanding on what factors and how various factors influence human perceived complexity, perceived difficulty, thus on human operation performance, and such understandings should be incorporated at the very beginning of and throughout the plant design. In section 2, we have introduced four structured operation environments in supervisory control of complex industrial processes, 16 sessions are designed to examine human perceived complexity, perceived difficulty and their relations with operation performance (operation time and key-stroke rate). The major result in this research is that human perceived complexity thus perceived difficulty and operation performance are sensitive to the structure type of operation environments, though the number of subsystems apparently also contributes to this index. The experiment also reveals that extensive working for more than 30 minutes, or dealing with more than 8 interconnected subsystems simultaneously will be unacceptable to human operators. Since various technical system complexities (including complexity of process system and automation system) are among the most important factors contributing to human perceived complexity, some implications may be directly suggested for the process system design and automation system design. 1. In process flowsheet synthesis, sub-system are never isolated from others, interconnections of sub-systems are essential. In this case, cascade interconnection (e.g. feedforward interconnection) of sub-systems is encouraged, because, according to the results, the second structured operation environment is less complex and less difficult to operate. However, because the difference between the second and the third is not significant, the introduction of feedback interconnections (such as recycle in process) does not cause much more difference than no introduction of feedback at all. If a recycle is introduced, the structural distance between these two interconnected parts should be as small as possible, so that the structured environment of the whole process may still considered as a cascade. 2. Strong coupling of sub-systems, which may cause instability for decentralized controllers, should be avoided in general. Highly sensitive subsystems as well as strongly coupled subsystems that may cause instability for decentralized controllers will cause great increase of perceived complexity and difficulty to operators once they are directly involved in the manual control of such
465
12. Sheridan, T.B. (1992). Telerobotics, automation, and human supervisory control. MIT press, Cambridge, MA. 13. Skogestad, S (1996). Multivariable feedback control; analysis and design. WHey, Chichester. 14. Stassen, H.G., 1.H.M. Andriessen, P.A Wieringa, (1993). On the human perception of complex industrial processes. Preprints of the IFAC 1Z" World Congress. 15. Wieringa, P.A , H.G. Stassen, (1993). Assessment of complexity. In: Verification and validation of complex systems: Human factors issues, edited by J.A Wise et ai, Berling: Springer-Verlag. 173-180.
generation of various alternatives by qualitative methods, to the selection and decision of the whole system through quantitative methods, different models and different theory of learning will have to be applied. This should be done in a systematic and iterative way, and theoretic methods will be useful only they are assisted with experience. Finally, we are sure that more and more effort will be devoted to the integration of process, control and human factors in the future, for integration is the promising way for future quality of process industry.
REFERENCES
1.
2. 3. 4.
5.
6.
7.
8.
9.
10.
11.
APPENDIX 1 BRIEF VERSION OF OPERATION INSTRUCTION
Biegler, L.T., I.E. Grossmann, AW. Westemerg (1997). Systematic methods of chemical process design, Prentice Hall International series in the Physical and Chemical Engineering Sciences. Prentice Hall PTR, Upper Saddle River, New Jersey 07458. Foss, AS. (1973). Critique of chemical process control. AIChE J, Vol. 19, 209-224. Govind, G., G.J. Powers (1982). Control system synthesis strategies. AIChE J, Vol. 28, 60-73 . Henson, M. A. , Seborg, Dale E. (1997). Nonlinear process control. Prentice-Hall, Upper Saddle River. Johannsen, G., AH. Levis, H.G. Stassen, (1994). Theoretical problems in man-machine systems and their experimental validation. AUTOMATICA, 30(2), 217-231. Li, K., Y. Xi, Z. Zhang (1996). G-cactus and new results on structural controllability of composite systems. International Journal of Systems SCience , 27, 1313-1326. Li, K., Y. Xi, Z. Zhang (1994a). Synthesis of control structures for complex industrial processes. Control &DeciSion, Vol. 10, No .4, 296-303 (In chinese). Li, K., Y. Xi, Z. Zhang (1994b). A new method for selection of variables in industrial process control systems, Proceedings of the Asian Control Conference, Tokyo, 1994, Vol.3, 219222. Morari, M., et al (1980). Studies in the synthesis of control structures for chemical processes. AIChE J, Vol. 26, 220-225. Ray, W.H.(1989). Advanced Process Control. Butterworth Publishers, a devision of Reed Publishing (USA) Inc, 80 Montvale Avenue, Stoneham, MA 02180. Seborg, D.E., T.F. Edgar, D. A MeUichamp (1989). Process dynamics and Control. John Wieley & Sons.
1. Read the instruction for each session, learn the requirements of the set-point for all subsystems. Fill in the form prOVided, including your identification code, session code, and also write down the start time of this session. 2. In the EOS, press the slot key corresponding to the subsystem, then press Man in the keyboard, and bring each subsystem into a manual operation mode. 3. According to the set-points request for each subsystem, prOVided at the beginning of the experiment, give the control-input value by using the keyboard. The procedure is firstly, press Out on the keyboard, and then input a numerical value ranging from "0" to "100". This is to regulate the input value to the control valve, and make the control valve open or close incrementally. Repeat the operation until the temperature of the water in the reservoir is brought within the allowed range near the set point. 4. The allowed error between temperature and set point should be less than 1 'C. 5. Repeat step 2 to 5 for each subsystem, until each subsystem reaches stable with the e"or is in the allowed range. Finally, press Auto on the keyboard, and put each subsystem in the automation mode. 6. Write down the time at the end of your task on the form , also give the rating of the perceived complexity and difficulty. 7. Your are reqUired doing as soon as possible. Those who have best operation performance (shortest time) for each session will receive additional rewards.
466
AN EXPERIMENTAL STUDY ON HUMAN SUPERVISORY CONTROL AND ITS IMPLICATION TO PROCESS SYSTEM AND AUTOMATION SYSTEM DESIGN Kang Li, Peter A. Wieringa
(Lab. for Measurement and Control, Delft University of Technology)
In this paper, a laboratory study reveals how human operators experience the complexity and difficulty in supervisory control of complex industrial processes . Four structured operation environments were introduced and examined. The implications of this research for process system and automation system design are discussed. Copyright © 1998 IFAC
information to be displayed by the interface in the control room, and which variables can be manipulated by the supervisor. Moreover, they often indicate the layout of the control room and its manmachine interface. Some times, the human factors, ergonomics, and man-machine system disciplines can contribute to the design at this phase, however, in many cases, no contribution from these experts is required. 4. Finally, man-machine system specialists define the supervisory control tasks, the recruitment, training and selecting of potential operators, and the classical human factor aspects of display and controls. Apparently, one may find that 1. The control engineer is called in too late. He has to design the automation system for the process, where the process system engineer has largely predetermined the control structure. 2. The man-machine specialist is called in too late. He has to cope with a man-machine system (or simply interfaces) that is largely determined by the control engineer. Future process industry requires the design tearn to strive for all such goals as higher requirements on system performance, operability, flexibility, profitability, energy saving, and also for safety, job satisfaction, and environmental
1. INTRODUCTION
In the last two decades, with the fast development of computer technology and tightening requirements, much progress has been made in various fields of learning relating to process industry, including process system engineering, process control engineering, and man-machine system engineering (Biegler, Grossman and VVesterberg, 1997; Ray, 1989; Skogestad, 1996; Henson and Seborg, 1997; Sheridan, 1992; Johanson, Levis and Stassen, 1994, etc). However, progress made on these areas does not mean that much progress has been made on the integration of these research activities. Current practice of new plant design generally follow the stages as: 1. Management sets the product specification. 2. On the basis of product specification, process engineers design the process. For most cases, the process engineer also determines all possible variables for manipulation, for measurement, and control. In many cases, no contribution from the control engineer is required at this stage. 3. Then the control engineers design the control and safety systems, hardware as well as software, and they also determine which
461
we designed our controller parameters, and operation and control system configuration to a certain degree of freedom . The controlled plant consists of up to five interconnected heat-exchange subsystems. The interconnections between the subsystems were optional and could be redesigned by us. The operation task for the heat-exchange system is to heat the cold water in the reservoir with warm water to a certain temperature, while the water in the reservoir is kept at a certain level. The structure of this experimental system is shown in Fig.I. The subsystems are electronically interconnected. The abbreviations in Fig. l are listed as follows :
compatibility. This calls for both human-«ntered approaches and application-oriented functions. Therefore, plant design should be based on the integration of related sciences, including process system engineering, control system engineering, and man-machine system engineering. Modem computer technology, variety of theory and methodology, as well as already existing technology, has promised the possibility for such integration. It is argued that, since the information structure is one of the most important aspects that have to be dealt with throughout the whole process of new plant design, it would be appropriate to integrate such knowledge at the very beginning. For example, the preliminary stage of a process design is to select the process flowsheet, the synthesis of a process flowsheet can be performed through a superstructure optimization in which the problem can be formulated as a Mixed Integer NonLinear Progranuning (MINLP). Apparently, such a design procedure is first of all based on the possible information structure of the new plant. In control system design for an industrial process, the first important task is to derive a suitable control structure (Foss 1973, Umeda 1978, Morari 1980, Govind and Powers 1982, Li et al 1994, Skogestad, 1996, etc), Again, structural information is required to generate a preliminary control structure (Li et ai, 1996). In man-machine system engineering, much attention has been contributed to the study of complexity, where the interaction of subsystems, components etc is regarded as the major shaping factors for complexity (Stassen et al 1993 , Wieringa et al 1993). The interaction is first of all a structural characteristic of complex systems. The above discussion reveals that, integration of such studies may possibly contribute to the development of future process industry with new quality. In this paper, four structured operation environments typically for human supervisory control are introduced. A total of 16 sessions were done to test how human operators perceive the complexity and difficulty in these four structured operation environments. The implications of this study to process system and automation system design are discussed.
• • • • • •
EOS FOS BC EC MC DDE
Enhanced Operation System Field Operation System Basic Controller Extended Controller Multi-function Controller Direct Data Exchange
'------.,.--.-----'
1
~
1
FOS
l'
Subsys 1 I~ Subsys 2 ~ .. . Subsys
Si
Fig.l System configuration Two-operation statuses are provided: • MAN --- Manual operation. • AUTO --- Plant in automation. The operator has multiple choices for key operations. The system configurations for each of the five subsystems are similar, though the model parameters for each subsystem may be different. The theoretic model for each subsystem is described as follows :
±
yes) = _k_e-uu(s)+ ~e-"ti Swj(s) Ts + 1 j~l Tjs + 1
(1)
Where, y is the temperature of the water to be heated contained in the reservoir. y(s) is the output variable. W i, i = 1,2,3 are the temperatures of
2. EXPERIMENTAL DESIGN
the cold and warm water and the flow magnitude of the wann water. The variable u is the control variable: the flow of cold water. The parameters of the various subsystems had the following ranges: Tj , i = 1,2,3 and T vary from 20 to 100 s;
In this experiment, a laboratory plant equipped with a TOC 2000 series system from Honeywell Corporation is used. This DCS system has provided multiple functions, according to which
462
't.l ' i = 1"2 3 and 't vary from 5 to 50 s;
structured operation environments are created, thus
=
ki , i 1,2 vary from 0.2 to 10 dimensional); k 3 ,andk range from 104 to Jo6 °C l m J I s .
the total experimental sessions sum up to 24 = 16 . The major operation procedure is to start up and regulate the plant to a satisfactory state, then put the system on AUTO. The operation guidance for participants is provided in appendix 1. In order to create these four structured operation environments, the interaction gains between any two subsystems are chosen among -0,5 to +0,5, considering the system' s operability (here we choose such interaction gains as to make the coupling among subsystems as strong as possible). As for how to choose the interaction gain so that the whole plant is stable or unstable for decentralized control behavior, one may consult some related books on process control. In brief, one may choose appropriate interaction gains among subsystems so that entries in the relative gain array of corresponding plant satisfy some criteria (Seborg et al1989; Skogestad, 1996). We believe that these four structured operation environments typically cover the operation environments in modern slow-response industrial processes, no matter whether the operation task is to start up the plant, to perform manual control, or for fault-management. The behavior of the operator is somewhat like the behavior of a controller in automation systems, while different operation strategies of different operators are just like different controller algorithms, therefore, human operator may be viewed as an intelligent controller. In this experiment. 6 students, all male, average age 23 years, agreed to participate The students were from the Delft University of Technology and had an engineering background. They received a fixed fee for each experimental hour. Before the experiment. we also told them that rewards would be given to those who had the best job performance for each task. In order to help the operator assess the perceived complexity and difficulty in quantity, a rating scale is designed. The rating scale ranges from 0 to 100 with respect to five different degree of complexity and difficulty, i.e. not complex, a little complex, rather complex, much complex and awfully complex. The participants received three training sessions. In the first session, they became familiarized with the system's configuration, rating scales for complexity and difficulty, and other policies and experimental goals. In the second sessions, they learned to operate the subsystems and the overall system. In the third session, they were required to operate the system for several different configurations. We studied their learning curves and made sure that their performance became stable. The experiments were time~nsuming because all experimental sessions had to be practiced several times.
(non-
Differences exist between theoretic models and experimental models. Therefor, we also examined the practical model around several working points. We have designed a decentralized controller for the whole controlled plant, e.g., each subsystem has its own controller, and there exists no coupling among the control loops of subsystems. The control algorithm takes the form of normal PlO as follows : Pc ( s)=K(l+_l_) (ST~+l}) (2) sTJ (sa. 2 + According to the theoretic and practical model of subsystems, Kin (2) ranges from 6 to 30, TJ
ranges from 0.5 to 2, T] arranges from 0.05 to 0.15,
ex.
is larger than 10. The purpose of introducing the decentralized PlO controller is to make the whole system run automatically when operator presses "AUTO" button in the operation station (either in EOS, or FOS). In this experiment, we design 16 sessions with four structured operation environments, while the number of subsystems for operation ranges from one to five. The four different structured operation environments for our participants are listed in table 1. Type No.
Properties using two subsystems Fully decoupled operation environment
1
[;~]=[~}
o
IU}]
H2 U2
Cascade operation environment 2
[Y}] [ H} Y2 - K 2J H J
o
IU}]
H2 u2
Fully coupled operation environment (Stable operation environment) 3
4
[Y}] [ H} Y2 - K 2}H J
I
K} 2H 2 U}] H2 u2
Fully coupled operation environment (Unstable operation environment for decentralized operation strategy).
[Y}] Y2
=
I
K}2H2 U}] [ H} K 21 H J H2 U2
Table 1 Four structured operation environments with two subsystems as the example For every fixed number of subsystems (ranges from 2 to 5 subsystem), the above four types of
463
3. RESULTS AND CONCLUSIONS
~ ... 20
GI tu
Figures 2, 3, 4 and 5 show how operator perceive the complexity, experience the difficulty, and how long they operate the plant, and their keystroke rate in these four structured operation environments as the function of subsystem number. In these 4 figures, the number of subsystems (range from 2 to 5) is on the x-axis, whereas the yaxis shows the perceived complexity, perceived difficulty, operation time or key-stroke rate respectively. ~
100
·xCl)
E
"C Cl)
40
(.)
20
~
Cl)
Cl.
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Conclusion 1: Human perceived complexity increases with the number of subsystems. While if perceived complexity increases, human perceived difficulty also increases and operation time decreases correspondingly. Conclusion 2: Linear extrapolation revealed that the perceived complexity will exceed 100 (the full scale for human perception), if, for the first uncoupled operation environment (Cl), the number of subsystems is more than 15; if, for the second and the third operation environments (C2, C3), the number exceeds 9; and if, for the forth operation environment (C4), the number exceeds 8. Conclusion 3: Linear extrapolation revealed that the perceived difficulty will exceed 100 (the full scale), if, for the first uncoupled operation environment (01), the number of subsystems is more than 15; if, for the rest three operation environment (02, D3, D4), the number exceeds 9. Linear extrapolation also revealed that, in case 15 subsystems are controlled in the first operation environment, as well as in case of 9 subsystems are controlled in the rest three operation environments, the required operation time will exceed 30 minutes. Conclusion 4: The student Hest revealed: • There exist distinct differences between the first and the forth-structured operation environments. • The second and the third operation environments do not have distinct difference. • In these 17 sessions of experiment, there dose not exist distinct difference between perceived complexity and perceived difficulty, which reveals that complexity is the most major factor contributing to operation difficulty. Conclusion 5: According to the experimental data, key-stroke rate varied a lot among subjects in case 2 subsystems were used. For 4 and 5 subsystems, key-stroke variability was surprisingly small.
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process (refer to the forth structured operation environment in table 1). 3. The implications to the automation system design are somewhat the same as that for process system design. When the subsystem number is not large (for example, no more than 3), cascade control strategy is encouraged, multivariable controller is also possible (according to conclusions 4 and 5). And in this case, one operator is generally enough for human supervisory control. However, in this case, a multivariable controller should be carefully designed such that if some part of this controller fails, the rest of the controller should be compatible with human operator, once the operator decide to intervene, and take part of the control function . That is to say, the real-time man-machine system with operator in loop should not cause instability. 4. When the subsystem number is large enough (such as more than 8), assigning only one operator to supervise the whole plant is not appropriate, especially in the abnormal state of the process. A team of operators in a hierarchical supervisory control structure are usually required. In this case, every low-level operator is in charge of a small number of subsystems (where the low-level controller is a cascade controller in most cases, and a few are multivariable controllers). High-level operators are in charge of a simple aggregated model of the whole plant. Thus, the whole automation system is a hierarchical multivariable controller. 5. In normal operation, the operation procedure designed for the operator (e.g. the teaching and instruction function in Sheridan's five functions of supervisory control (1992» is better designed in such way that, these procedures (or steps) are in time sequence and content independent. In an abnormal state, the operation procedure (e.g. the monitor and intervene function in Sheridans' five functions (1992» should be designed such that the procedures are in time sequence, while the procedures or steps should not form cycles. If there exist feedback interconnection, the cycle should be as small as possible. Plant design is perhaps the quintessential engineering activity, which is based on mathematics, basic science, engineering science, and flavored by humanities and social science. Engineering design involves development of specifications and criteria in light of safety, reliability, economy, aesthetic, and social consideration. Suppose a team of engineers is required to design a process plant, they should first understand the various technical standards, product specifications and technical and social constraints for the industrial processes. Then conceptualize the framework of the design problem according to their prior experience and expert knowledge. Finally, solve different technical problems according to their understanding of various principles. From the
4. GENERAL DISCUSSION AND IMPLICATION Since total automation is a fiction, human operators will always be required to remain in the supervision and control loop, it is therefore important to have a better understanding on what factors and how various factors influence human perceived complexity, perceived difficulty, thus on human operation performance, and such understandings should be incorporated at the very beginning of and throughout the plant design. In section 2, we have introduced four structured operation environments in supervisory control of complex industrial processes, 16 sessions are designed to examine human perceived complexity, perceived difficulty and their relations with operation performance (operation time and key-stroke rate). The major result in this research is that human perceived complexity thus perceived difficulty and operation performance are sensitive to the structure type of operation environments, though the number of subsystems apparently also contributes to this index. The experiment also reveals that extensive working for more than 30 minutes, or dealing with more than 8 interconnected subsystems simultaneously will be unacceptable to human operators. Since various technical system complexities (including complexity of process system and automation system) are among the most important factors contributing to human perceived complexity, some implications may be directly suggested for the process system design and automation system design. 1. In process flowsheet synthesis, sub-system are never isolated from others, interconnections of sub-systems are essential. In this case, cascade interconnection (e.g. feedforward interconnection) of sub-systems is encouraged, because, according to the results, the second structured operation environment is less complex and less difficult to operate. However, because the difference between the second and the third is not significant, the introduction of feedback interconnections (such as recycle in process) does not cause much more difference than no introduction of feedback at all. If a recycle is introduced, the structural distance between these two interconnected parts should be as small as possible, so that the structured environment of the whole process may still considered as a cascade. 2. Strong coupling of sub-systems, which may cause instability for decentralized controllers, should be avoided in general. Highly sensitive subsystems as well as strongly coupled subsystems that may cause instability for decentralized controllers will cause great increase of perceived complexity and difficulty to operators once they are directly involved in the manual control of such
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12. Sheridan, T.B. (1992). Telerobotics, automation, and human supervisory control. MIT press, Cambridge, MA. 13. Skogestad, S (1996). Multivariable feedback control; analysis and design. WHey, Chichester. 14. Stassen, H.G., 1.H.M. Andriessen, P.A Wieringa, (1993). On the human perception of complex industrial processes. Preprints of the IFAC 1Z" World Congress. 15. Wieringa, P.A , H.G. Stassen, (1993). Assessment of complexity. In: Verification and validation of complex systems: Human factors issues, edited by J.A Wise et ai, Berling: Springer-Verlag. 173-180.
generation of various alternatives by qualitative methods, to the selection and decision of the whole system through quantitative methods, different models and different theory of learning will have to be applied. This should be done in a systematic and iterative way, and theoretic methods will be useful only they are assisted with experience. Finally, we are sure that more and more effort will be devoted to the integration of process, control and human factors in the future, for integration is the promising way for future quality of process industry.
REFERENCES
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APPENDIX 1 BRIEF VERSION OF OPERATION INSTRUCTION
Biegler, L.T., I.E. Grossmann, AW. Westemerg (1997). Systematic methods of chemical process design, Prentice Hall International series in the Physical and Chemical Engineering Sciences. Prentice Hall PTR, Upper Saddle River, New Jersey 07458. Foss, AS. (1973). Critique of chemical process control. AIChE J, Vol. 19, 209-224. Govind, G., G.J. Powers (1982). Control system synthesis strategies. AIChE J, Vol. 28, 60-73 . Henson, M. A. , Seborg, Dale E. (1997). Nonlinear process control. Prentice-Hall, Upper Saddle River. Johannsen, G., AH. Levis, H.G. Stassen, (1994). Theoretical problems in man-machine systems and their experimental validation. AUTOMATICA, 30(2), 217-231. Li, K., Y. Xi, Z. Zhang (1996). G-cactus and new results on structural controllability of composite systems. International Journal of Systems SCience , 27, 1313-1326. Li, K., Y. Xi, Z. Zhang (1994a). Synthesis of control structures for complex industrial processes. Control &DeciSion, Vol. 10, No .4, 296-303 (In chinese). Li, K., Y. Xi, Z. Zhang (1994b). A new method for selection of variables in industrial process control systems, Proceedings of the Asian Control Conference, Tokyo, 1994, Vol.3, 219222. Morari, M., et al (1980). Studies in the synthesis of control structures for chemical processes. AIChE J, Vol. 26, 220-225. Ray, W.H.(1989). Advanced Process Control. Butterworth Publishers, a devision of Reed Publishing (USA) Inc, 80 Montvale Avenue, Stoneham, MA 02180. Seborg, D.E., T.F. Edgar, D. A MeUichamp (1989). Process dynamics and Control. John Wieley & Sons.
1. Read the instruction for each session, learn the requirements of the set-point for all subsystems. Fill in the form prOVided, including your identification code, session code, and also write down the start time of this session. 2. In the EOS, press the slot key corresponding to the subsystem, then press Man in the keyboard, and bring each subsystem into a manual operation mode. 3. According to the set-points request for each subsystem, prOVided at the beginning of the experiment, give the control-input value by using the keyboard. The procedure is firstly, press Out on the keyboard, and then input a numerical value ranging from "0" to "100". This is to regulate the input value to the control valve, and make the control valve open or close incrementally. Repeat the operation until the temperature of the water in the reservoir is brought within the allowed range near the set point. 4. The allowed error between temperature and set point should be less than 1 'C. 5. Repeat step 2 to 5 for each subsystem, until each subsystem reaches stable with the e"or is in the allowed range. Finally, press Auto on the keyboard, and put each subsystem in the automation mode. 6. Write down the time at the end of your task on the form , also give the rating of the perceived complexity and difficulty. 7. Your are reqUired doing as soon as possible. Those who have best operation performance (shortest time) for each session will receive additional rewards.
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