Miguel Angel Ramirez Canelon, WSEAS TRANSACTIONS on … · 2009-09-02 · Physical Agents - Agent...

INTELLIGENT SUPERVISION OF PETROLEUM PROCESSESBASED ON MULTI-AGENT SYSTEMS

Miguel Angel Ramírez CanelónJacinto Dávila

Eliezer Colina MorlesUniversidad de los Andes: Facultad de Ingeniería

Complejo La Hechicera, Mérida, VenezuelaE-mails: [email protected], [email protected] , [email protected]

Abstract: This work presents a methodology for the design of an intelligent supervisory system that combines theprinciples of fuzzy logic, the Internal Model Control (IMC) architecture and the paradigm of Multi-AgentSystems (MAS). The methodology has been conceived to be applied in an intelligent supervisory system,specifically for two kinds of complex petroleum industrial processes: the gas-oil separation process and the oil-heating process. The supervision proposal takes into account the fact of using standard local supervisorsschemes connected between themselves and to a global supervisor so that local objectives in each process canbe met, thereby letting the global or social objective be obtained through the application of basic mechanism ofcommunication, cooperation and coordination; where these objectives have been previously defined andstructured in a hierarchical manner. The paper includes some computational simulations performed underMATLAB / SIMULINK and the results obtained show a good overall system performance.

Keywords: Multi-Agent System, event detectors, intelligent supervisor system, internal model control.

1 Introduction

Intelligent supervision of industrial processes is anarea of intense study and many approaches have beenused based on artificial intelligence techniques;where most of the resultant methods are based onhybrid systems. Supervisory control is typically basedon a global assessment of the current operation point[1], that leads to system parameters characterizationwith the intention of providing diagnosis andanticipating possible changes that affect its currentstate of operation through the adjustment of scalarparameters of the controller or changes in the setpoint.

Among the broad variety of intelligent supervisorysystem schemes proposed in many publications,[2][3][4][29], there exist three basic elements: theevent detector, the model of the process and thedecision system. The event detector may beimplemented in many different ways; in particular,some common approaches are based on fuzzy logicand neural network; which are used to detect possibleoperation states in a particular process. There aremany alternatives for the determination of the modelof the process such as qualitative models, broadlyrecommended in supervisory scheme and non linearmodels based on local models such as the Takagi-Sugeno fuzzy model [6]. In addition,

multiresolucional modeling is another alternative thatintegrates a mathematical model, a knowledge basedmodel and a qualitative model [1]. Finally, thedecision system guided by a set of objectives andpriorities of the process makes some decisions thatresult in changes of the reference point of the processor changes in the scalar parameters of the controller.It is important to highlight that in a generalperspective, the supervision process implies thefollowing connotations: i) detection of the plant´sparameters, deviations and instability, ii) guidance ofcontroller tuning, adaptation and synthesis; iii)identification and diagnosis of process faults [5].

This paper attempts to define an intelligentsupervisory scheme to be applied in two petroleumprocesses connected in cascade: the gas-oil separationprocess and the oil-heating process. These particularprocesses have been studied from the point of view ofmodeling and control in [6][7][8]. The proposalconsists of using standard local supervisory schemesinterconnected between themselves and to a globalsupervisor, thus communication and cooperationmechanisms are needed. This is, therefore, a clearcase for the use of a supervisory scheme based onmulti-agent systems. The rest of the paper isorganized as follows. In section 2, the fundamentalsof Multi-agent systems are described. Section 3presents the supervisory scheme formal proposalbased on Multi-agent systems. Section 4 describes

WSEAS TRANSACTIONS on SYSTEMS and CONTROLMiguel Angel Ramirez Canelon, Jacinto Davila, Eliezer Colina Morles

ISSN: 1991-8763 435 Issue 9, Volume 4, September 2009

the case of study showing the results of thecomputational implementations inMATLAB/SIMULINK. Conclusions and future workare given in section 5.

2 Fundamentals of Multi-AgentSystems.

In order to design an intelligent supervisory controlsystem under the conception of multi-agent system, itis necessary to define the following aspects:

• The definition of a distributed platform,normally determined by the system to besupervised.

• The type of intelligence and knowledgepresentation strategy that will be used.

• The development of a communication,coordination and cooperation protocolbetween the different intelligent modules.

Multi-agents systems technology addresses some ofthe before mentioned difficulties. In particular, theprocess control area is a natural one for theapplication of intelligent agents because controllersmay be regarded by themselves as autonomousreactive systems [9].

Fig.1 Generic scheme of an agent according to Russel’sdefinition.

A significant number of definitions of agents can befound in the literature and one of the simplest andmost accepted is the one presented by Russel [10]that considers an agent as an entity that acts andperceives in a particular environment. Nevertheless, aformal definition of agent was established in [11],where an agent is considered as a computationalsystem situated in an environment and is capable ofan autonomous and flexible actions in thatenvironment, in order to meet its design objectives.Communication, cooperation and coordination arethree key issues in multi-agent systems. Coordinationis the process by which an agent reasons about its

local actions and the anticipated actions of others totry to ensure the community acts in a coherentmanner. As Weiss explains in [27]: “The purpose ofcoordination is to achieve or avoid states of affairsthat are considered as desirable or undesirable by oneor several agents. To coordinate their goals and tasks,agents have to explicitly take dependencies amongtheir activities into consideration. Two basic,contrasting patterns of coordination are cooperationand competition. In the case of cooperation, severalagents work together and draw on the broadcollection of their knowledge and capabilities toachieve a common goal. Against that, in the case ofcompetition, several agents work against each otherbecause their goals are conflicting.” We add thatagents also perform a third type of coordinatingbehavior which is neither cooperation, norcompetition. It is collaboration: agents assist otherseven though they do not have common goals. This ispossible by means of negotiation. Coordination bymeans of cooperation or negotiation seems to requiresome form of communication.

In agent based systems, communication can occur atdifferent levels of sophistication ranging fromprimitive to high-level. Software agents can bedeveloped on different platforms and using differentprogramming languages. They usually interact byexchanging complex symbolic information andpossibly have to agree on complex interactionprotocols. Several proposals have been designed todevelop agent-communication languages such as theKQML (Knowledge Query Manipulation Language)[13] and FIPA-ACL (Foundation for IntelligentPhysical Agents - Agent Communication Language)[14].

On the other hand, it is imperative, that agents use acommon vocabulary to communicate where thesymbols used in messages have the same, predefinedmeaning for all of the agents. This fact reflects theimportance that agents share certain ontology. Acomputational ontology is an informational modelthat describes concepts and relations in some specificdomain [15].

The capacity of communication between agents canbe interpreted as an additional element of perception(receiving messages), of cognition (interpretation andactions to be executed) and actions (sending ofmessages). These aspects allow agents to conformsocieties where global objectives may exist apartfrom the individual objectives of each agent [16]. Inthis context, cooperation becomes relevant for thosesocieties. A cooperative situation is validated if



either adding a new agent could result in an increasein the performance level of a determined group oragent actions serve to avoid actual conflicts [11].

Finally, the area of agents-architecture considers theaspects related to the design of computationalsystems that satisfies the features of the agentstheories [17][18]. It specifies how an agent can bedecomposed into the construction of a set ofcomponent modules. There exist three kinds ofarchitectures: i) deliberative architectures (cognitiveagents) where the agent is designed according to theparadigm of the symbolic artificial intelligence (forinstance GRATE and IRMA [19][20]); ii) reactivearchitectures where the agents do not include anykind of central symbolic world model and do not usecomplex symbolic reasoning (A classical example isthe so called subsumption architecture of RodneyBrooks [21][22]) ; iii) hybrid architecture where theagent is the combination of two subsystem. Onesubsystem is the deliberative, which contains asymbolic model of the world while the other is thereactive. Classical examples are the TouringMachines, COSY and INTERRAP [23][24][25].Another example of hybrid architecture is the multi-agent simulation platform GALATEA whichprovides for the representation of agents that canbehave reactively and proactively, according to thecircumstances of (simulated) execution [26].

3 Supervisory Proposal Based OnMulti-Agent System.

The supervisory proposal based on multi-agentsystem is shown in figure 2. Local supervisors aregrouped depending on the class of process and areconnected each other to the global supervisor.

Fig.2 Supervisory Scheme based on Multi-AgentSystem for Industrial Processes.

It can be observed from figure 2, that there are Mclasses of processes identified as P_1, P_2, …, P_Mwhere M is the number of processes classes and N isthe amount of processes for each M class, so therewill be N supervisor local agent for each class ofprocess. For instance, supervisor local identified byS_Local P_1 1 y S_Local P_1 2 belong to the sameclass of process 1 and are connected each other and tothe global supervisor agent. Every process isgoverned by its respective local supervisor and at thesame time is connected to the global supervisor.Figure 3 shows the particular diagram of theprocesses that will become the case of study.

As it can be observed from figure 3, the output of oilof the two production separator is connected to bothfired-heaters. However, depending on the physicaldimensions and the specifications of functioning of afire-heater, the output of one production separatorcould be connected only to one fire-heater.

Fig.3 Schematic diagram of the separation process andoil heating process of oil.

3.1 Design of the local supervisor agent.

Taking into account the considerations of thearchitecture ICM (Internal model control) establishedin [7] [8] and based on the criteria in [6] for thedetermination of fuzzy models, the schematicdepicted in figure 4 is proposed as the localsupervisor agent. It may be observed from figure 4that the regulatory level is composed by the internalmodel control architecture, which has the followingsub-elements: i) the singleton fuzzy model of theplant determined from input-output data; ii) theinverse of the fuzzy model which is used as thecontroller; iii) The filter F that acts as an integratorand adds robustness to the feedback control systemagainst the presence of disturbances; iv) The plant tobe controlled which, for simulation purposes, consistsof a linearized mathematical first order model aroundan operating point.



Fig. 4 Supervision local scheme for each process.

The internal model control structure (IMC) isintroduced as an alternative to the feedback structure.Its main advantage is that closed-loop stability isassured simply by choosing a stable IMC controller[28]. Also, closed-loop performance characteristics,for instance settling time, are related directed to thecontroller parameters, which make on-line tuning ofthe IMC controller very convenient.

In the supervisory level, the first element is the eventdetector. Once this block detects some change in thestate of the process, the information is sent to thedecision system.

The decision system converts the informationreceived by the event detector in an indexperformance and based on the states of other similarprocesses and a generic knowledge base; it can eitheradjust the consequent of the fuzzy model (therebyadjusting the controller) or change the reference ofthe process. Furthermore, if an unacceptable changein the operation point occurred then the systemdecision would declare a fail in the process.

Finally, the element called “Interaction with otherprocesses” notifies the state of the local process toother similar processes and at the same time receivesthe states of similar processes. The decision system,based on the state of the local process and the statesof the remote similar processes must decide if a localaction is taken or if the action has cooperativecharacteristic.

3.1.1 Design of the event detector

The event detector is based on the linguisticMamdani model for MISO system (Multiple InputSingle Output). A general rule of this kind of modelis given by:

(1) isy Then is and...and is : 11)( kk

nnkk GAxAxIfR

Where R(k) denotes the k-th rule, Aik y Gk are fuzzysets en Ui ⊂ ℜ y V ⊂ ℜ, respectively, “y” is the output;meanwhile x = (x1,…xn)T is the vector of inputvariables. The event detector has two inputs: themodeling error and the derivative of the output(dy(t)/dt). The combination of these inputs isconsidered as the more adequate combination ofinputs for characterizing the events of the processboth for the oil separation process and the heating oilprocess. Indeed, it will be taken a sample of values ofthe modeling error and the derivative of the output yand the calculation of the average of the samples isdone in each time window r1, r2,…, rn. Figure 5illustrates this idea. Therefore, the input variables arethe average modeling error (emp) and the averageoutput derivative for each time window.

The possible events according to the variation of theinput vector are the following: normal condition, littleincrement of the operation point, significantincrement of the operation point, decrement of theoperation point, significant decrement of theoperation point, fail↑ (this can be caused by a strong



increment of the operation point) and fail↓ (this canbe caused by a null operation point).

These events can be conceived as operations regionsof a determined process and each event is representedas a fuzzy set, so the transition of one event toanother is done of a gradual manner. However, it isimportant to know that the fail state can be caused bymalfunctioning of a sensor or an internal fault in thevalve control.

Fig. 5 Time Windows of the Event Detector

Three fuzzy sets were defined for each input variableand output variable. Figure 6 shows the fuzzy sets.The resultant base rule (with its respective weight) isthe following:

R1 (w1 = 1): If dyp is Null and emp is Null Then theprocess is in normal condition.R2 (w2 = 0.5): If dyp is Null and emp is High Then theprocess has a little increment in the operation point.R3 (w3 = 0.5): If dyp is Null and emp is low Then theprocess has a little increment in the operation point.R4 (w4 = 1): If dyp is High and emp is Null Then theprocess has a little increment in the operation point.R5 (w5 = 1): If dyp is High and emp is High Then theprocess has a significant increment in the operationpoint.R6 (w6 = 1): If dyp is High and emp is Low Then theprocess is in fail ↑.R7 (w7 = 1): If dyp is Low and emp is Null Then theprocess has a significant decrement of the operationpoint.R8 (w8 = 1): If dyp is Low and emp is Low Then theprocess has a little decrement of the operation point.R9 (w9 = 1): If dyp is Low and emp is High Then theprocess is in fail ↓.

3.1.2 Design of the decision system

The decision system agent is capable of takingappropriate decision based on the output of the eventdetector and the operation state of other similarprocesses. The following expert system with itsrespective generic rules is proposed:

Fig. 6 Fuzzy sets of the input and output variables.

R1: If the local process is in normal condition andsome remote process has a change in its operatingpoint Then update the status of availability forcooperating and inform to the global supervisoragent.R2: If the local process has some change in itsoperation point and at least there is at least onesimilar remote process in normal condition Then takeits own corrective action, ask for cooperation andinform to the global supervisor agent.R3: If the local process is in normal condition and allthe remote processes are in normal condition Theninform to the global supervisor agent.R4: If the local process is in fail Then inform theglobal supervisor agent.

It is important to highlight that in R1, the definitionof the cooperation actions will be established by theglobal supervisor. Essentially, the cooperation dealswith the change in the setpoint of a similar process ifit is considered in normal condition. For instance, ifthe separation process 1 is in normal condition andthe separation process 2 has some change in itsoperating point or has some perturbation, the localsupervisor agent of the process separation 1 canchange its reference in order to try to guarantee thatthe total flow of oil of the two separators does not



have a significant variation. Nevertheless, if forinstance, the two separators have some problem thenit is no possible to change the reference for this kindof process. In this case, a change in the setpoint inone of the fire-heaters should be done.

With respect to rule 2, R2, the following actions weredefined: adjusts the fuzzy model, thereby adjustingthe fuzzy controller (the purpose is to decrease thesteady state error), changes the set point and declaresa process fault. A fault can be caused by drasticincrement of the operation point that overcomes thelimits of the functioning of the production separator.The reason for changing the set point is to guaranteethe fulfillment of a global objective (A cooperationaction).

3.2 Design of the global supervisor agent

The function of the global supervisor agent is toestablish some mechanisms in order to guarantee theachievement of the global objective of the wholeprocess. For complex industrial processes, we haveestablished that the global objective should be thefollowing: To obtain a better quality product withmeasured specifications inside a definite range. Inthis particular case, the temperature of the oil at theoutput of a fired-heater should have a value between180 and 190 degree Fahrenheit.

According to the construction specifications of thetwo separators, the total flow of oil at the output ofthe two separators should have a value between 45Barrels/Hour and 51 Barrels/Hour of oil in order toguarantee the optimum range of temperaturementioned.

We have used the principle of energy conservation(Whenever it is permitted), for changing the setpointand guaranteeing all processes outputs of a specificclass do not overcome the maximum limit of theinput variable of the next process. For instance, in ourparticular case of study, we have to guarantee that theflow of oil at the output of the production separatordo not exceed the maximum limit of flow of oilpermitted at the input of the fired-heater.

In order to achieve this, we have defined thealgorithm depicted in figure 7, where ∆pot is the totalvariation of the operation point of all the N processesassociated to a specific class of process, j is theamount of processes whose condition is normal, ∆S isthe sum of all the changes in the setpoint of eachspecific process and ∆Sp1, ∆Sp2, …, ∆Spj are theindividual changes of each setpoint.

Fig. 7: Algorithm for the global supervisor agent.

4 Case of Study: implementation ofthe supervision proposal based inSMA.

The implementation of the supervisory proposal wasdeveloped in MATLAB / SIMULINK using the S-Functions blocks either in C or m language. Theseblocks permit the incorporation of customizedalgorithms inside SIMULINK environment leadingthe creation of simulation schemes of highcomplexity. In addition, a user graphical interfacewas developed using GUIDE in MATLAB.

In order to test the intelligent supervisory scheme twoscenarios were established. The first scenarioconsisted in introducing a drastic change from 24barrels/hour to 29 barrels/hour in t = 180 seconds as itcan be observed in figure 8a. The perturbation lasted20 seconds.

Another sudden change was introduced in the processseparation 2 in t = 40 s (a change in the flow input ofoil from 24 barrels/hour to 32 barrels/hour) and thetime of the perturbation lasted 20 seconds. Figure 9ashows the output y2(t) of the second separator. Figure8b and figure 8c shows the behavior of the valveposition and the error modelling in the processseparation 1.



Fig. 8a Output of separator 1

Fig. 8b Valve position of separator 1

Fig. 8c Error modelling of the first separator

In figure 8a, the output y1(t) of separator 1 has achange in its reference in t = 45 seconds, five secondsafter the perturbation occurred in the processseparation 2. This change in the reference ofseparator 1 means a cooperation action as itcompensates the increasing of total flow of oil of thetwo separators because separator 1 is reducing itsoutput flow of oil while separator 2 is increasing itsoutput flow of oil because of the presence of theperturbation. A similar behavior is presented in t =185 seconds in the output of process separation 2. Inthis case, as it may be observed in figure 9a, the localsupervisor of the process separation 2 does a

cooperation action because of its change in itsreference in t = 185 seconds.

Figure 9b and figure 9c shows the behavior of thevalve position and error modelling of separator 2. Asit may be observed in these figures, both behaviorsare very similar to the one presented in separator 1but in different ranges of time.

Fig 9a Output of separator 2

Fig. 9b Valve position of separator 2

Fig 9c Error modelling of separator 2

In the second scenario, various changes of thecurrent operation point were simulated in bothseparators in the range of time defined between t= 100 s and t =250 s. Figure 10 shows threechanges introduced to the process separation 1. A



similar behavior is shown in figure 11 for theprocess separation 2.

Fig 10 various changes in the current operation point ofseparator 1

In this particular case, the global supervisor sendsa message to both local supervisor agents of theheating processes so that they can change theirrespective reference.

Fig 11 various changes in the current operation point ofseparator 2

Fig 12 Output temperature of fired-heater 1

Fig 13 Output temperature of fired-heater 2

As a result, both references in the fired-heaterswere changed while changing in the operationpoint happened in the separation processes.Figure 12 and figure 13 shows the behavoiur ofthe temperature of oil at the output of each fired-heater.

Fig 14 Total Flow of Oil at the Output of the twoSeparators (Proposed Methodology)

Fig 15 Total Flow of Oil at the Output of the twoSeparators (Local Supervisor Systems working without

connection)



The importance of our proposal can be demonstratedif we analyze the behavior of total flow of oil at theoutput of the two separators using the methodologydescribed before. As it may be observed, the range ofvariation of the total flow of oil is between 46barrels/hour to 50 barrels/hour. On the other hand,figure 15 shows the total flow of oil at the output ofthe two separators with the local supervisors workingwithout any connection. In this case, the range ofvariation of the total flow is between 47 barrels/hourto 52.5 barrels/hour of oil. Although the differenceseems insignificant, however, when a drastic changein the current operation point lasts a considerableperiod of time, then the range of variation of the totaloutput may affect considerably the next processes ifthere is no any cooperation between the localsupervisors systems.

5 Conclusions and Future Works

In this paper, a supervisory scheme based on multi-agent systems was proposed as a methodology for theintelligent supervision of industrial processes withmultiple feedback controls. It is important tohighlight that this methodology can be applied toother sort of processes connected in cascade.

The use of the ICM structure in the regulatory levelhas been an interesting alternative for supervisiontasks. The error modelling is a key information fordetecting some deviation in the model of the process.

The computational simulations were done inMATLAB SIMULINK using S-functions for theemulation of the agents: local supervisor agent andglobal supervisor agent. This is a significantcontribution of this paper because there exist manyplatforms for developing agents and few efforts havebeen done in Matlab software. The results of thesimulations demonstrate that the cooperation betweenlocal supervisor agents may permit that the globalobjective of the whole process can be reached.

Future works contemplate the inclusion inside thelocal supervisor scheme of a module that can predicta change of event, so the mechanism of cooperationcan be enhanced notably.

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