Heterogeneous Software Integration for Intelligent Process Control

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THE AUTONOMOUS SYSTEMS LABORATORY

Ricardo Sanz

Heterogeneous Software Integrationfor IPCThe HINT Project

ASLab v 0.0 Draft | 1998-08-13

Contents

1 Introduction: Intelligent Process Control 2

2 Heterogeneous Problems and Heterogeneous Solutions 3

2.1 Complex Process Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Heterogeneous Software Solutions . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 The Need for Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3 Life cycles for Intelligent Process Control 7

4 HINT Objectives 8

5 HINT Components and Technology 9

5.1 HINT Integration Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

5.2 HINT Integration Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

5.3 HINT Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

6 HINT Demonstration 12

7 HINT Now and Tomorrow: DIXIT 14

8 Conclusions 15

9 Acknowledgments 16

1


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10 References 16

1 Introduction: Intelligent Process Control

In complex continuous process plants there has been a traditional gap between controlrequirements and control capabilities of control systems. Conventional controllers wereunable to cope with all the problems that lead to bad operation of the plant. Dependabilityand safety were the main criteria that led to the use of humans to perform control tasks inthese systems.

The introduction of artificial intelligence [Boullart 92] was seen as a major step towardsplant autonomy, because of the potential capability of emulating high level human behav-ior. Expert systems were the tools mostly used to put intelligence in process controllers.

Intelligent process control is the subarea of automatic control that deals with the useof advanced computer technology to attack control problems in a process plant. This istraditionally related with complex process control and artificial intelligence (AI), but -forsure- a control system is more intelligent if it uses the best solution to a problem and notonly AI based solutions.

Our group has been involved in the development of control applications for complexprocesses for several years: cement, pharmaceutical, plastics, petroleum, chemical, etc.

The architectural complexity of the systems varied from simple expert systems runningon small computers controlling batch fermentation processes (SECOFE) to heterogeneous,multilayered, distributed applications controlling cement kilns (CONEX [Sanz 91]).

The activities in CONEX [Sanz 90] and the other systems gave rise to our participationin the HINT project (Heterogeneous INTegration architecture for intelligent control sys-tems). HINT was an ESPRIT project (#6447), partially funded by the Commission of theEuropean Communities, which has produced a coherent framework for integrating differ-ent techniques, in particular AI ones, in order to overcome the obstacles mentioned in theabove paragraphs and to provide solutions to process control problems which require thekind of intelligent supervision that is presently carried out by human operators.

The HINT Project tried to cope with a recurring problem in complex process control:the need of integration of heterogeneous software components in a complex process control system .These components are new or legacy components performing activities in restricted areas:covering partial domains, performing simple functions and using single technologies.

The results from the HINT Project were:

An integration methodology

An integration architecture

A set of problem solving components

In this paper we present an overview of the HINT project, explaining its objectives andanalysing the integration technology developed. First we will try to characterise the ap-plication domain of HINT. Next a life cycle for intelligent controllers is presented. HINTobjectives and technology are analysed afterwards. Finally a brief assessment and a viewof the immediate future is presented with some conclusions.

ASLab.org / Heterogeneous Software Integration for IPC / v 0.0 Draft 2ASLab.org / Heterogeneous Software Integration for IPC / v 0.0 Draft 2ASLab.org / Heterogeneous Software Integration for IPC / v 0.0 Draft 2


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Figure 1: sample intelligent process control application architecture (From [MITA 1994]). It shows the needof integrating heterogeneous control processors. Fuzzy, neural and expert in this case, with the added difficultyof learning.

2 Heterogeneous Problems and Heterogeneous Solutions

When trying to assess HINT for this paper -in relation with COSY- we found the followingstatement in the COSY web pages:

?In particular, the COSY Programme will have the goal to study tools which are capable ofanalysing control systems with the increased complexity and hybrid nature resulting from compat-ible, consistent use of combined heuristic, quantitative and qualitative information, together withexpert knowledge, in a supervised control system architecture?

As we will see, HINT objectives are strongly related with COSY objectives.

2.1 Complex Process Problems

In big, complex plants, there exist lots of operation and control problems. The origins ofthe problems are diverse, but the final issue is that conventional control technologies arenot capable to solve them.

The problems can span from the lower control levels (sensors and actuators) to thehigher ones (advanced controllers, reliability of models, human misoperations, etc.).

We are talking about problems, but in some cases, there are other type of tasks that arenot problems, but should be solved in order to get a better behaviour of the plant.

The complexity of the control of this plants is reflected in the structure of a complexprocess control system. Figure 2 shows a pyramidal view of complex process control. Theabstraction level increases when going upwards, at the same time that speed and -moreimportant- responsiveness decreases.



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Operational Layer

Strategical Layer

Tactical Layer

Advanced Control

Complex Loops

Single Loops

Sensors & Actuators

Management

Monitorization and Control

Plant Action

ActionPerception

Perception

Management Information System

Continuous Process Plant

Optimization

Plan Execution

Reactiveness

hours-days

UserInterface

1 sec

100 msec

10 msec

msec

Conventional

Control

secs-mins

mins-hours

Figure 2: Control layers in complex process control systems. Intelligent components are used in all the layersof the control pyramid. HINT problem solving components are targeted at reactive an tactical layers (Adapted

from [HINT 94]).



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Conventional process control spans from layer one to layer four (sensors to advancedcontrol). Intelligent control is usually viewed as part of the upper layers, but, dependingon the technology, it can be used also in lower layers.

By example, we have been using fuzzy inference technology in the sensor layer to per-form sensor validation. This is possible because speed of our fuzzy inference engines andpredictability of the fuzzy validator makes possible its use in fast real-time layers.

The complex process control is so complex that, in most cases, human supervision mustbe done for all layers.

2.2 Heterogeneous Software Solutions

If you browse one volume of proceedings from an IEEE Intelligent Control Symposium orIFACs AIRTC, you will see lots of demonstrations of software solutions to complex plantproblems or tasks; but what you will not see is a clear map from problems to solutions. It

seems possible to use all the techniques in all the problems.For a hammer everything seems like a nail. For a neural networker every problem is

obviously better solved using a neural network.

In fact it is true that you can use whatever technique you want. At the end all you haveis the Pentium or SPARC instruction set to do things1.

The hammer-nail problem is so extended that we have found expert systems writtenin FORTRAN and conventional procedures implemented by means of rules and artificialstate variables using an expert system shell.

The software toolbox of the intelligent control systems engineer is big; it contains lotsof software technologies sometimes complementary and sometimes diametrically opposedone to other. There have been efforts to characterise pairs problem-solution in restricted ar-

eas (See by example [Leitch 93]) and even more globally [Alarcon 1995]. But in fact the finaltool chosen depends on the persons that will construct the solution more that it dependson the problem to be solved. This effort has been done even in HINT [Alarcon 93].

Sample Tasks Sample Software SolutionsProcess interfaces Conventional programmingSituation assessment Expert systemsDirect control Neural networksSimulation Fuzzy LogicDiagnosis Model based reasoningData validation Genetic algorithmsData visualization GUIsData estimation Databases

. . . and many more. . . . ad infinitum.

What is the solution ?. Perhaps what we need is a better assessment of the technologiesin relation with problems of a domain. We call it a domain-task-technology (DTT) theory.

1 From our point of view what is critical is not technology capability but personal capability. At the end, whenyou are running an intelligent control project, you have persons working towards the solution. The technicalknowledge of these persons is a good point to start searching for a solution. Sometimes it is best to use a notso suitable technology because you have personnel knowledgeable in technologies away from the most suitable.The problem in this case is for your contractor, not for you; because he will reach a not so suitable solution butyou will maximise your profit.



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Perhaps the bets way is to leave the developers the technological decision. Perhaps theydont select the best technology, but the will select the technology the are more confidentwith.

In any case, when confronting heterogeneous problems, we will end with a set of het-erogeneous technologies to solve them. If we use a DTT theory because the set of taskswill conform the set of technologies. If we dont use such a theory, the set of persons in theproject team will conform this set of software technologies based on technologies in whichthey are knowledgeable.

As an example, the following table shows part of a DTT theory developed as part of theHINT Methodology. We will se details of this table later, when referring to HINT Technol-ogy.

2.3 The Need for Integration

If we have a set of technologies, there appears a problem when building the intelligent con-troller. The application architecture for each technology, its data sources and products, thedata formats and abstraction levels, will produce a software engineer nightmare if tryingto design a single, compact application.

If using a shell to build this application we need something like a Swiss-army-knifeshell. Gensym is trying to do this with G2.

The other alternative is to employ several tools to build application components andintegrate them in a single application. The keyword is integration. This word is a typicalcandidate for the lots-of-uses/no-meaning syndrome. When we use this term we meanmakingseveral things work together. And, when referring to software applications it means makingseveral portions of code work together.

Software integration is what a linker does with program parts. But our problem is morecomplex, because the parts are built in different ways and the input/output mechanismsare not as simple as parameter passing in a programming language.

When trying to identify the integration needs and issues we will talk about:

Core Technologies

Integration Methodology

Integration Architectures

Core technologies specify the way components are built. Each technology opens a pathto specific code and data structures. By example, talking about expert systems -a core

technology- it is assumed that there will be a explicit representation of application depen-dent knowledge -the knowledge base- that will be used by an application independentcode -the inference engine.

Integration methodology specify how to perform the conceptual integration of the com-ponents.

Integration architecture is software design -or even implementation- to provide mecha-nism to translate the conceptual integration specified by the methodology into real, work-ing code.



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In HINT core technologies are technologies suitable for use in intelligent process con-trol applications. Some components based on these technologies were developed. Some asreusable packages (fuzzy, neural, user interface) some as demonstrator specific code (ex-

pert, simulation, data capture).The integration methodology is the HINT Methodology. The integration architecture

built to support the methodology was a blackboard based monohost architecture.

3 Life cycles for Intelligent Process Control

From the inner point of view, intelligent control systems design must begin with a care-ful operator task analysis and correct balance of responsibility. At the end humans areresponsible of the correct operation of the whole system, so they must be confident withthe automated activities of the control system. Success of advanced technologies in processcontrol should not be measured by the economy gains or the reliability enhancements but

by their operation time. The time that operators let them do their work.

Finding a right way to build intelligent process control systems is a no hope task be-cause of heterogeneity in process problems leads to a high degree of variety in applicationstructure. Some of the reasons for the complexity of the search of a life cycle are:

Use of artificial intelligence technologies, which are inherently unpredictable and un-planificable2.

Non determinist computational methods.

Knowledge based processing.

Knowledge extraction problems.

Knowledge representation problems.

Application structure dynamics.

Exploratory programming.

High level of novelty

Strong coupling between development phases: inherent feedback

Postponed specifications and designs

Complex non-hierarchical development teams

All these things lead to spiralled or prototype based development life cycles. But thereexist another problem and is the problem of technology heterogeneity and the need ofintegration.

This problem discovers a need for analysis of control problems and technology selectionthat must be done before any other work can start, because technologies selected will guidethe design of the integrated application. This is where a DTT theory is involved.

2 Despite the efforts put on formalising life cycles for knowledge based systems like KADS [Schreiber 93] andsimilars.



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Figure 3: A view of the intelligent process control life cycle.

In Figure 3 a way from a problem to a solution is depicted. We start at the left node, witha problem in a domain. And want to reach the lower node; a solution in that domain. Thehammer-nail way is straightforward; directly from problem to solution without botheringabout what is the best way to do that. The monotechnology, non integrated application isthe easiest solution.

We promote the other way round. We need a brief -or not so brief- stay in the twoother nodes: task and component. In task we analyse and decompose the problems inconceptually elemental tasks. In Component we select the technology or technologies thatoffer better solutions for this tasks.

The objective of HINT is to support this approach by means of providing mechanisms

-conceptual and software- to perform the integration of heterogeneous components

4 HINT Objectives

The objective of HINT was provide technology for the three top layers of the control pyra-mid. These technology should provide support for integration of heterogeneous compo-nents. This was achieved by means of blackboard structuring around task oriented objects:The Basic Control Processes (or BCPs for short).

The proposed pyramidal structure means:

1. The information available in each layer, as well as the control procedures present init, become progressively more complex as we ascend.

2. The BCPs represent control procedures instantiated according to the objectives ofeach layer. These procedures are, as well, progressively more complex.

3. The development procedure operates under the exhaustive elaboration of the lowerlevel layers and its installation and previous tests before taking into account thehigher layers.



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4. Each layer is conceptually independent of higher layers, and in their absence a mini-mum control functionality is assured by default.

5. The information flow (BCPs input) is from lower layers to higher layers. Higher lay-

ers are responsible for gathering information at the right moment. The responsibilityof the lower layers is to assure the availability of that information.

6. The flow of activities (BCPs output) is moderated by the deposition of information(tuning control loop parameters, for example) in accessible positions of the same orlower layers.

The HINT project finished by the end of 1994 with partial success. This success wasonly partial because:

The reusability of the code developed was not high. It is difficult to build a newapplication using HINT software.

The strategic layer was not demonstrated.

Distributed applications were not possible under HINT.

5 HINT Components and Technology

The main HINT components are: the HINT Integration Methodology, the HINT IntegrationArchitecture and the HINT Components

5.1 HINT Integration Methodology

The HINT integration methodology:

Defines when specific control problems are suitable to be solved by the cooperationof the different techniques.

Specifies the different phases to follow when integrating different AI based technolo-gies, namely the definition, analysis, knowledge acquisition, conceptualization anddesign phase.

Defines the vehicles means by which this integration can be carried out: The BCPs.

It is important to notice that the BCP structure is able to represent and support bothautomatic tasks already being performed in present control systems and those taking placein more intelligent layers.

BCPs can be classified as one of the following four types according to their intrinsicfeatures and objectives:

Preventive : aiming to avoid possible problems before they appear by manipulating thecorresponding variables or suggesting recommendations.



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Corrective : aiming to resolve undesirable situations once a problem is detected by per-forming or recommending appropriate actions.

Optimization : aiming to carry out enhancements which improve the process.

Modal : causes a change in the processes functioning, i.e., in the way that the operationteam or the higher level staff carry out the process, usually in response to orders fromhigher levels.

At present, these four types constitute all the possibilities from our point of view. Theycover all the functionalities that might be needed to govern a process. Consider: at anymoment, the process will be in a certain state and we might set three possible classifica-tions of the state, good, normal and bad. In the next moment the same classifications areapplicable, and we might postulate that the state transitions between these two instants areperformed by a BCP. The transition from a normal state to a good one will be realized viaan optimization BCP. A bad state passes to a normal one via a corrective BCP, and in orderto avoid a change from a normal state to a bad one, a preventive BCP will be fired. Noother transitions are to be feasible within one layer. These are then the functions of the op-timizing, corrective and preventive kinds of BCP. When an order or an action is performedin an upper layer, however, a BCP might be created in a lower layer as a result in order todo the bidding. The latter is the modal kind of BCP.

5.2 HINT Integration Architecture

The architecture for heterogeneous integration developed in HINT is based on the black-board concept [Engelmore 88]. The HINT blackboard is structured in three layers (opera-tional, tactical and strategical) according with the three conceptual layers proposed by the

methodology.

The contents of the blackboard are described using an specifically developed languagecalled the HINT blackboard Generation Language (GL) which describes the objects con-tained in each layer, its behaviour an the relations between objects in different layers. TheGL compiler compiles the GL file into C code that implements blackboard data and black-board manager procedures.

The main object contained in the blackboard are HINT BCPs. They are constructedusing the HINT generation language and based on the methodological design done forintegration of heterogeneous knowledge sources based on the components.

The knowledge sources access blackboard data using an interface library. The black-board supports dynamic creation of objects, but the methodology recommends not to use

it because of predictability penalties.

The structure of a BCP object is shown in Figure 4. It is composed of:

Start: the start point determines the activation of the BCP. It can be fired automati-cally or by a trigger condition (e.g. a changing of a variable) or by an order from ahigher level.

Control phase, including:



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Start End

Control Phase Follow - Up

Detection

M11

Message

M12

Message

M2

Sugges

tion

M31

Ac

tions

Mon

itore

d

M32

Pre

dictions

M33

Reeva

lua

tion

A1

Ac

tion

Diagnosis Suggestions&

Actions

ActionsMonitoring

PredictiveReasoning

HypothesisReevaluation

andEnd Condition

Presentation Stress

Figure 4: Basic Control Process structure (From [HINT 1994]).

1. Detection: this may coincide with the start point, and usually depends on a condi-tion involving several variables. During the BCPs lifetime, this step can be initiatedseveral times.

2. Diagnosis: a diagnosis of the problem is provided.

3. Suggestions and Actions: according to detection and any diagnosis (if it exists), anaction is performed or a recommendation is generated (if the loop is not closed).

Presentation Stress: this determines the current priority, seriousness and state ofstress of the BCP, affecting its presentation on the user interface.

Follow-up phase, composed by:

1. Monitoring: the actions realized are detected and monitored, and the progress of theBCP and the evolution of the process are evaluated.

2. Prediction of the evolution of the BCP in order to determine whether to terminate theBCP or to continue.

3. Hypothesis re-evaluation and End conditions: the hypothesis (formed at the diag-nosis stage) is reviewed and the decision is taken whether or not to return to the

control phase (detection, diagnosis or action) or to go to the end step.

End: this terminates the BCP. It initiates terminal processing.

5.3 HINT Components

Five components were planned as demonstration. The technologies for these componentswere:



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Neural networks

Fuzzy logic

Expert systems

Model based reasoning

Graphical user interfaces

All they were shown in the demonstrator performing some tasks: assessment, valida-tion, prediction, diagnosis.

Some of the components that make up the HINT demonstrator can be customized inorder to develop a new application. The HINT customization toolkit -or HINT Toolkit-provides the system developer with an integrated environment in which the customiza-tion and the test of the HINT system can be carried out. It allows the customization of the

individual modules of the HINT application but also provides common features to coordi-nate the development and to test the applications. By means of this HINT Toolkit a systemdeveloper can customize a HINT application straightforwardly. The system developer isguided by means of a system developers manual -including the integration methodology-which describes the complete development process - from knowledge acquisition to theactual implementation by means of the HINT Toolkit.

The reusability of the demonstrator components were quite variable. The operator as-sessment expert system and neural netrok predictor were built for demonstration exclu-sively, but the fuzzy module provided a tool for building general fuzzy processors. Thespecific component part for the demonstrator was a knowledge base file. The rest (knowl-edge base editor and fuzzy knowledge source engine) were totally reusable. Usability -notreusability- of the model based reasoner was low even in HINT.

The user interface builder was a very reusable and effective tool for building user inter-faces based on a HINT blackboard.

6 HINT Demonstration

It is very important to note that, as part of the HINT project, a demonstrator has beenimplemented and installed in a petrochemical plant owned by Repsol S.A (one of the majorSpanish firms and a member of the HINT consortium) in Cartagena, Spain, in order to besure of the suitability of the approach. This demonstrator is currently being used by thecontrol team of the plant and it is already showing very promising results.

Several AI based modules have been developed in this demonstrator as an exampleof the various techniques that can be used in a HINT-like application, where the wholedevelopment -architecture, methodology and components- is oriented towards integratedman-machine systems.

The HINT methodology provides a good way to achieve proper balancing between manand machine: the maquette approach to development. It is a type of rapid prototyping, butwhat provides the prototype is mostly look and feel and some little functionality. Themaquette is developed in parallel with the real final system -with reduced effort providingearly feedback to get a better integration with plant systems and operators.



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Figure 5: A component customization tool: the Fuzzy Logic Shell. This tool is used to edit fuzzy knowledgebases used by the run time fuzzy knowledge source.

From FL

From PI

From ES

Figure 6: Sample screen from Repsol Cartagena demonstrator. It shows and advanced energy view of thefiltering process section. It includes data from several knowledge sources.



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A big effort was put in the integration of human operators in the control system thiswas the main objective of the demonstrator. The components used were:

A decision support system was the main component of the demonstration application(based on expert system technology).

An advanced user interface based on abstract views of the plant were developed.

A raw data validation and estimation system was done using fuzzy logic technology.

A neural network based predictor was used to reason about future behaviour of theplant.

A model based reasoner performed limited diagnosis of some subsystems.

7 HINT Now and Tomorrow: DIXIT

The architecture was a fairly simple monohost blackboard. In it a centralized and activedata structure (the blackboard) is the only means of communication among problem solv-ing modules. It is also the vehicle for cooperative problem solving and it is responsiblefor data coherence within the whole system. The interaction between knowledge sourcesand the blackboard is done using shared memory. It provides maximum speed but limitsdistributability.

From a first point of view we can say that HINT technology was -at least- immature,and that the HINT Product was unclear and barely useful. If we try to build -today- anapplication using HINT software we need a lot of information that is not really available.The HINT Developers Manual says quite a few about the real structure and use of thesoftware.

As a basic objective of DIXIT, reusability must be reached, and it must start from HINTtechnology reuse. So the first work is the analysis of HINT products in order to get areusability perspective of them.

Another drawback of HINT is that id did not demonstrate the use of a strategical layer.This layer is mainly comprised by its global objectives. These global and strategic objectivesof a plant can be divided into the following categories:

Continuity: the objectives define criteria which assure the overall continuity of theproduction process, as well as best alternative options.

Maintenance: this includes objectives which evaluate operations that generate wearwith the aim of reducing it, and the detection of possible problems (in the process) asa consequence of maintenance operations.

Production: these objectives govern the net quantity of product obtained, using nu-merical measures.

Quality: this class defines and establishes both internal and external criteria for qual-ity and sets goals related to contractual constraints and sales requirements.

Efficiency: these objectives relate to the analysis of production costs. Structural andstaff costs are not considered.



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Safety: these objectives touch on all safety criteria affecting the integrity of equipmentand components, the industrial installation, the plant staff, and third parties.

The problem is how to represent these objectives, how to provide reasoners on them andhow to integrate them in the multilayer structure of the advanced control system.

These three points (reusability, distributability and strategy) are the main targets of thenew ESPRIT project DIXIT.

8 Conclusions

The main results of the project can be summarised in a methodology, an architecture and

some problem-solving software components. The components developed were based onartificial intelligence technologies and were situated in the full spectrum of automationlayers.

The development of an integration methodology was the main point in HINT. This de-velopment was aimed at facilitating and guaranteeing the coherent interaction betweenmultiple heterogeneous techniques in the task of solving industrial control problems. Thisintegration methodology (i) Defines when specific control problems are suitable to be solvedby the co-operation of the different techniques, (ii) specifies the different phases to followwhen integrating different AI based technologies and (iii) defines the vehicles means bywhich this integration can be carried out.

The main concepts that appear behind the HINT architecture are the blackboard as an

architectural schema and the concept of multilevel blackboard. We must change this twoconcepts in order to give them a wider meaning, fundamentally due to the system trans-formation from a centralized backboard to a distributed system.

In the HINT architecture the higher part of the work was performed sequentially: onlythe modules having enough information were able to access common blackboard data. Therest of the knowledge sources expect patiently their turn to modify or read the backboardinformation.

The main concept: the HINT backboard, continues being valid, given it is the moreflexible mechanism for interchanging information between components that use differentsoftware technologies, such us neural networks, fuzzy logic, expert systems, model basedreasoning, etc.

In spite of the valid HINT blackboard schema, it is clear that for the distributed appli-cation development, it must be complemented with an agent oriented system. In a black-board system, the knowledge bases must accomplish all their communications using theblackboard. In a mixed system, usually the most of the transactions flow through the back-board but other kind of communications are allowed. Now the knowledge bases are calledagents and the direct communication between agents are supported by the system.

The concept of a leveled control hierarchy continues been perfectly valid. This hierarchyreflect a clear reality in manufacturing plants. The control is made in different levels ofincreasing speed and decreasing intelligence as we go up to the higher levels.



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9 Acknowledgments

We would like to acknowledge the funding from the Commission of the European Union

through ESPRIT Project 6447 HINT.

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Title Heterogeneous Software Integration for IPCSubtitle The HINT ProjectAuthor Ricardo Sanz

Date 1998-08-13Reference v 0.0 Draft

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(C) THE AUTONOMOUS SYSTEMS LABORATORY

Heterogeneous Software Integration for Intelligent Process Control

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