Christophe Kolski

Mustapha Tendjaoui

Patrick Millot

A PROCESS METHOD FOR THE DESIGN OF "INTELLIGENT" MAN-MACHINE INTERFACES: Case study: "The Decisional Module of Imagery"

A new concept is appearing in the field of Man-Machine interaction: the design of "intelligent" interfaces. The aims of this paper are threefold, first to show what an "intelligent" interface is and present a five step design process for such an interface -specifically: (i) Man-Machine system analysis, (ii) operator, task and process modelling, (iii) specifying those assistance tools that form an "intelligent" interface, (iv) realization of the assistance tool, and (v) evaluation. The second part describes the structure of an "intelligent" interface, called D.M.I. (Decisional Module of Imagery), that is currently being developed, and the final part explains the D.M.I.'s development, using the five process steps outlined above. * Identification of aims, stuct ural and functional aspects of both the system and it s sub-systems. * technical constraint s (Dynamics, safety, production…). * Study of different functioning modes and their effects. * Operat or's tasks definit ion, according to different operational context of t he process and to it s functioning modes. * Identification of software aids, able to increase operat or's efficiency and to decrease his workload. * Definition of an hierarchy of failures that lead t o an operator intervening. * Definition of requirement s in term of information predict ion. * Knowledge and experience level function definition. * Make a study of different terms used by operators. * Make a study of tools prefered by the operators.

I. INTRODUCTION

The increasing complexity of industrial processes necessitate the design of control, supervision and decision support tools that are able to evolve along with the control system. Such man-machine interfaces play a vital role where the information being manipulated becomes more and more complex i.e. safety systems, production control systems and environment protection systems. Current literature on the subject of man-machine interfaces provides the designer with support in a number of ways:

• Ergonomic guidelines such as those of Scapin (86), Shneiderman (87) or Gilmore et al. (89),

• Interface guidelines associated with specific machines such as the Macintosh. These guides give some recommendations aimed at ensuring consistent representation between interface components for different applications,

• General books in the field of Man-Machine interaction such as those of Helander (88), Coutaz (90), Falzon (90) or Weir & Alty (91),

• Interface design and evaluation models that can (i) be adjusted on the task as, for example, KLM & GOMS (Card et al.,83) models, (ii) grammatically describe the interface such as ALG (Action Language Grammar) presented by Reisner (82) or CLG (Command Language Grammar) presented by Moran (81), or (iii) be adjusted to an agent (or object), as MVC (Model, View, Controller) in Smalltalk (Goldberg,84) or PAC (Presentation, Abstraction, Control) described by Coutaz (90),

• Literature dealing with Human Work Analysis and Evaluation. Such work paved the way for the attempts at improving Man-Machine interaction (see Wilson & Corlett,90),

• or finally, pandemic methods of interface design, from man-machine system analysis to the evaluation of implemented interfaces: see for example Kolski et al. (90), Millot (90) or Tendjaoui et al. (to be published).

Some of these works display a new trend in the field of Man-Machine interaction that of the "intelligent" interface concept (Zachary,86;Rouse et al.,87;Hancock & Chignell,89).

This concept is displayed in many different forms, for example: the adaptable or adaptive interface (Edmonds,81;Rouse,88;Hefley,90;Browne et al.,90), the expert interface (Brajnik et al.,90) or the human error tolerant interface (Rouse & Morris,85). In most cases these approaches use Artificial intelligence techniques to improve the interaction between man and machine and indicate in general the concepts of Assistant Operator and Intelligent Assistant System (Boy,88;90) or Intelligent Agent (Mandiau et al.,91a;91b).

Primarily, our work concerns us with the study of the "intelligent" interface, and is aimed at realizing such an interface supervised by an "intelligent" manager called the D.M.I.

(Decisional Module of Imagery). This paper is divided into three principal sections:

• The first section defines the "intelligent" interface concept with respect to the complex process control domain and is followed by a description of an "intelligent" interface design process.

• The second section presents a general framework for the "intelligent" image manager.

• Finally, the last section is devoted to the application of the "intelligent" interface design process in an experimental environment.

II. "INTELLIGENT" INTERFACE DESIGN METHODOLOGY -CONCEPTS AND TECHNIQUES

Our research works are orientated towards complex industrial processes and in this domain, our definition of an "intelligent" interface is: " a self-governing device, able to adapt itself to the operators informational needs". Consequently, it uses expert knowledge on (i) the different operating contexts of the process to be supervised and/or controlled, (ii) the characteristics of the operators using the interface according to the kind of task that they have to perform, and using the general model of problem solving (Rasmussen,80;83).

Our approach consists of using an expert system to improve the interaction between the process, the assistance tool and the operator (Tendjaoui et al.,90). As for all "intelligent" systems, such an interface design must be based on previous assistance tools and techniques whereupon Chignell & Hancock (88) stress three major criteria: task analysis, the use of expert systems and interface design tools.

We are in agreement with these authors, and describe in this section an "intelligent" interface design process. It is composed principally of five connected phases: (i) Man-Machine system analysis, (ii) operator, task and process modelling, (iii) the specification of the assistance tool incorporating the "intelligent" interface, (iv) implementing the assistance tool, and (v) evaluating the assistance tool. For each one of these five phases, we underline many obvious sub-problems. Table 1 shows our objects and examples of the techniques and tools used.

The science of "intelligent" interface design has only recently become one that is fully controlled, however, we aim at providing some points of reference in this field. Our design process is summarized below (see also figure 1 and table 1):

• The first phase begins with an analysis of the process, and outlines both normal and abnormal function modes allowing the definition of operator prescribed tasks. These tasks must consider all the operators resources and limits and outline all his assistance requirements. Thus, to perform his task, the operator may need specific tools for example: diagnosis support system, failure prediction support system, situation evaluation support system, and so on.

• In the second phase, we use artificial intelligence techniques to build three models: (i) an operator model, inspired from the general problem solving model of Rasmussen (80;83), (ii) an assisted task model, using prescribed task definition (we must consider here the assistance brought by the assistance tools that were specified in the first phase) and, (iii) a process model. Techniques from the field of qualitative physics (Kuipers,85;Caloud,88;88) may be of particular interest during this phase for building the process model.

• The third phase aims at specifying both the "intelligent" interface and its software environment. Operator and assisted task model analysis lead to an "intelligent" graphics display specification (this point will be detailed below). For each display, we must define all the presentation modes and graphical attributes whilst still respecting the ergonomics of the presentation and is why we are working on the design and evaluation of graphic displays, using Artificial Intelligence techniques (see Moussa et al.,90;Kolski,89;91 or Kolski & Millot, to be published).

• The fourth phase is the implementation of the "intelligent"display manager using the operator model, assisted task model and process model. This display manager will be connected both to an ergonomically arranged graphics library and the" specific treatment manager". It will have to take on a decision support system role in order to optimize data emanating from the assistance tool. This phase gives us a complete assistance system that incorporates the "intelligent" interface. • The fifth and final phase concerns the evaluation of the assistance system that has been realized. Evaluation will be considered firstly in a simulated process environment and secondly in an actual industrial setting. Literature on the subject of ergonomics is profuse especially in connection with the evaluation of interfaces, see for example the works of Wilson & Corlett (90), Abed (90), Millot (88) or Senach (90).

MODELLING

• Cognit ive t ask analysis techniques (Hollnagel,89;Norman,86).

• P roblem solving model of Rasmussen (80;83).

• Books on ergonomics

• Analysis of data from steps 4 and 5 • Using Artificial Int elligence techniques such as : object, rules… • M odelling tools P et ri network (see Abed,90 ;Abed et al. 90).

• Analysis of data issued from steps 2 and 3 • Using Artificial Int elligence techniques such as : object, rules… • M odelling tools P et ri network (see Abed,90 ;Abed et al. 90).

• M any modelling approaches exist in litt erat ure : t he reductionist approach (Raulefs,87), the confluence method (De Kleer, 84), the process t heory (Forbus,84) • Using operator activities evaluation technics, see (W ilson & Corlett,90;Abed,90;Millot,88;etc…).

• Using operator activities evaluation technics, see (W ilson & Corlett,90;Abed,90;Millot,88;etc…). • Creat ing a system to use knowledge issued from st ep 10.

PHASE SUB -PRO B LEMS O B JEC TIVE EXAMP LES O F TO O LS AND TEC HNIQ UES

• Using Artificial Int elligence technics.

• Formalizing algorit hms issued from st ep 11.

• Using existing tools : Diagnosis expert systems,…

We are, at present, implementing an "intelligent" interface that we call the Decisional Module of Imagery (D.M.I.) whose framework we will discuss below.

III. OVERVIEW OF THE D.M.I.'S FRAMEWORK.

The Decisional Module of Imagery (D.M.I.) is a so called "intelligent" interface whose design concerns in particular the sub-problems 1 to 7, 9, 10, 12, 13, 15 & 16 shown in table 1.

The D.M.I. with its integrated assistance tool is shown in figure 2 where it is incorporated into a process control room and overall data acquisition is centralized by the data acquisition module (Kolski et al.,90). Data is accessible by both the decision support system and the D.M.I. and is used by the decision support system for prediction, diagnosis or recovery procedures. This information is transmitted to the D.M.I. which then determines "what" can be presented to the operator, "when" it can be presented and "how" it can be presented. (Tendjaoui et al.,91a;91b; to be published):

1. What to present to the operator (we consider here that "what" contains the "why" by justifying the information displayed); 2. When shall we display it;

3. How shall we display it.

An ergonomical database centralizes all the presentation modes that can be selected and displayed by the graphics module. This graphics module is controlled by the inference mechanism through a shared memory whose access is controlled by a supervisor. The D.M.I. is developed using the "C" language on a VAX/VMS. The software hierarchy is described in more detail in a paper by Tendjaoui et al. (to be published).

The "What", "When" and "How" problems are described below.

III.1. The "WHAT" problem

The problem concerning what is to be displayed to the operator depends essentially on three criteria:

1. Operator requests: if the operator, when performing his supervisory tasks, requests information on for example, the state of a variable or to justify an action, then the D.M.I. has to supply this information.

2. Operator classification: if the decision support tools perceive an error in the system and propose advice or action, the operator can (i) be in agreement with this advice and act accordingly or, (ii) disagree and request some justification for the advice. The level of detail in this justification will depend on the operators skill level, e.g., a novice operator will tend to require more detailed justification than an experienced operator.

3. The operators task in relation to different process operations: the operators tasks and therefore his informational needs will change according to the state of the process (Rouse, 83;[START_REF] Rasmussen | Information processing and Human-Machine Interaction, An approach to cognitive engineering[END_REF]. Figure 4 shows the different cases where the D.M.I. can intervene and the different messages or displays that can be selected by the operator with respect to the different states of the process. For example, in a transition situation, the operator may need some advice on starting the process, whereas to assess the effects of a corrective action, the operator needs information about the progression of the correction. During an abnormal process state, alarms are automatically selected and displayed, but where an "uncommon" abnormal situation develops, the D.M.I. can focus on variables that can affect the system's production and/or security by suppressing all information or alarms that do not affect either production or safety.

HOW

III.2. The "WHEN" problem

The problem concerning "when" relevant information has to be presented to the operator depends on the nature of the information that he requested and also on the seriousness of any impending situation. Severity evaluation is bound by production targets and security constraints. To know when the D.M.I. has to display information, we first have to know what this information actually represents (the "WHAT"). For example, alarms are displayed to the operator as soon as they occur, whereas information on action or advice proposed by the decision support tools is only displayed to the operator when requested or when a process situation becomes hazardous.

The D.M.I. has, however, to evaluate the mental workload of the operator in order to determine whether any additional information could be adequately assimilated by him. Mental workload depends on many factors, e.g., the number of potential hazardous situations encountered, their severity, the operators skill level, etc.

III.3. The "HOW" problem

To know how to present a piece of information to the operator, we first have to know what this information represents and the severity of the process fault at that moment.

Information is then displayed in accordance with predefined ergonomic modes (figure 3). If several different presentation modes are available to the operator which can be used with the same efficiency, then he can configure the interface according to personal preference. Some examples of "HOW" are:

-To indicate the progression of a variable during the process, graphical curves might be appropriate. They inform the operator about trends in the variables history.

-Colour, red for example, can be used to indicate process status.

III.4. The inference mechanism

All the information described above is presented in rule form (see examples in paragraph IV). These rules are exploited by a data-driven inference mechanism integrated into the "intelligent" manager (Le Strugeon,91). The mechanism's operation is represented in figure 4.

The facts considered by the mechanism are: the process condition, the severity of the situation, the operator's task, the operator classification (or skill level), his informational requests and the previous "WHAT"event. These factors constitute the initial fact database and this data is centralized in the shared memory. The mechanism uses this data for deducing the value assigned to each of the three required conclusions: "WHAT", "WHEN" and "HOW". To verify the validity of these deductions, another database called the "possible fact base" is used.

It contains all the acceptable facts, and for each fact its possible values. Naturally, this database is instructed in accordance with the application concerned and its constraints.

It's conclusions are accessible by the supervisor which has to display information in accordance with the ergonomic graphics database containing all the possible presentation modes, such as curve, "star", text, mimic, bar-graph, counter, and so on. All these modes are evaluated by experts in the field of man-machine communication for their suitability and user friendliness. This section describes the computational method which is used to construct the D.M.I.'s knowledge database for a given application (figure 5).

Possible fact database

Conclusions

• The analysis phase (sub-problems 1 to 4 of the design process) gives an inventory of all possible values of decision criteria used by the D.M.I. for imagery management for example, the degree of severity associated with the process, the process situation and any assistance required by the operator with respect to the tasks he has to perform. This information defines descriptor units characterizing the Man-Machine system; each descriptor is composed of two states (Attribute/Value), for example, the attribute "functioning situation" may be assigned many values such as "Normal", "Abnormal" or "Critical". • Using these descriptors, we can build relationships between the decision criteria concerning the D.M.I.'s conclusions of "What information is to be presented to the operator", "When shall we display it" and "How shall we display it". During this step an "example database" is created. This example database uses attributes corresponding to the problem being analyzed and each attribute has a finite number of possible values that are mutually exclusive.

• The example database uses information derived from the Learning Machine in order to generate an optimal decision tree. This is created by the Artificial Intelligence Tool 1stClass and is based on the ID3 (Interactive Dichotomizer 3) algorithm which generates a set of procedures that build up into a decision tree using a sample called a "learning group" [START_REF] Quinlan | Discovering rules by induction from large collections of examples[END_REF]1983).

• The final step compiles the decision tree so that the D.M.I. can exploit it using production rules set by the inference mechanism written with the "C" language.

Constructing the D.M.I.'s knowledge database in an experimental industrial context is dealt in the following part of this paper. In order to evaluate the D.M.I., we have developed an experimental platform of a process that simulates the behaviour of a simplified power plant. It comprises two circuits: a primary circuit and a secondary circuit containing a turbine and a water reservoir (see figure 6). Based on this platform we are applying the design process presented in the first part of this paper to implement the D.M.I.'s knowledge database and to integrate it into the control system. The operators main tasks are: (i) to ensure the safety of the system by avoiding all primary circuit malfunctions whilst, (ii) providing constant output. Thus, when a malfunction occurs, the operators task is to compensate for the failure and bring the process back to a normal operating level.Under normal operating conditions his aim is to both supervise and regulate process variable adjustments to optimize output.

IV. CASE STUDY W ater cir cuit

IV.1. Phase 1: Analysis

C-TVEEC

W ater cir cuit Sodium

According to ergonomical requirements we have outlined several areas of assistance that the operator must be provided with (Vittet,81;Daniellou,86;Rasmussen,86;Taborin,89):

-structured alarms; -variable prediction; -action advice tendered when malfunction occurs;

-justification of such action advice.

As we are working in a simulated process and do not have a "real" operator, we have made the following suppositions concerning operator limits and resources:

-The only role considered is one of "process controller" (Bainbridge,78) and we have discarded other roles such as "maintenance operator", "process engineer", or "occasional software system user"; -we suppose that the operator solves problems when he compensates for failures and that he is used to working with workstations containing a graphical display, a keyboard and a mouse; -we suppose that the operator is familiar with all the terms and graphical presentations used by the interface (thus, when evaluating the system, preliminary learning and training sessions will be done).

IV.2. Phase 2: Modelling

We have constructed a decision tree using: (i) information related to the limits and resources of the human operator, (ii) theorical knowledge about the human operator derived from literature and based on the different steps of Rasmussen's model (Rasmussen 80;83). This decision tree tries to answer the questions: "What" information to present to the operator, "When" to present it, and "how" to present it. This decision tree has to be generic without detailing all the variables that constitute the process. This tree constitutes the operator model (see explanations given in sections III.1, III.2. and III.3).

The general task model assisted by the decision tool is shown in a simplified way in figure 8. Tasks may include: (i) starting the process, (ii) supervising and optimizing the process, (iii) diagnosing and correcting malfunctions, or (iv) shutting down the system (particularly if the situation becomes dangerous). 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 In figure 8, we can see the situation severity concept which is evaluated using three different "levels", figure 9:

-System security level: It can have one of the three following values 0, 5 or 10 where the value 0 means that the system safety is very good;

-Abnormal energy production level: It can have one of the three following values 0, 1 or 2 where the value 0 means that energy output production is normal;

-the emergency level: When failure compensation is still possible the assigned value will be either 0, 1 or 2. The 0 value means that the operator has enough time to compensate for the failure. Notice here that time is directly linked to the process dynamics.

These parameters are classified into the three categories shown in table 2: It is possible to identify 15 situation severity levels numbered 0 to 14 (figure-9), using the following function:

Severity level:= Degree (Security) + Level (Abnormal production) + Level (Emergency)

This severity level is also used to establish 15 structured screens and to display them to the operator and also indicates to us five process situations (see table 3): optimized, normal, abnormal, critical and non recoverable.

Table 3: Process situation identification corresponding to severity level. Finally, we defined qualitative relationships between all process variables. Here, we create a propagation network, where a dot represents a variable, and a link represents the propagation effect from one variable to another. A link is represented by four parameters which are: gain, response time, delay and the variation in way (+ or -). This principle is discussed in more detail by Caloud (88) or Ferray-Beaumont (89).

IV.3. Phase 3: Specification

To specify different representation modes and graphical attributes, we have used both our personal experience in the field of interface design, and recent literature on this subject.

These presentation modes answer the question "How to present information to the operator" that was specified in the operator model. For example, displaying a variable in a curve form requires a graphical representation of both static and dynamic sections of this curve.

Where several graphical displays need to be chained together, a human-interface cooperation model derived from both the operator model and the assisted task model is used see figure-11. The model considers the three behaviour levels listed by Rasmussen: (i) Skillbased behaviour, (ii) Rule-based behaviour and (iii) Knowledge-based behaviour. For each of these three levels, the "intelligent" interface will have to provide different graphical means of assistance using knowledge contained in the "What", "When" and "How" databases. The object is to obtain agreement between the operator and the decision support tool when a malfunction occurs and when the operator has to make a decision. Thus, if the operator does not agree with the course of action advised by the decision tool then the interface must be able to explain and/or provide justification of its decision (Taborin,89;Taborin,Millot,89).

The model presented in figure 11 shows an overview of the communication between the operator and the "intelligent" interface, when a malfunction occurs.

The decision support tool considers the situation and generates a series of structured screens. Knowing both the situation severity and the operator's skill, complementary information can be displayed by the interface for example, action advice, action justification and so on. The operator can agree with the assistance system and act accordingly which can be equated to Skill-based behaviour. However he can be undecided and try to evaluate the situation further in order to make a diagnosis and to take corrective action (his reasoning based on complementary information). If "surface" justification allows him to identify the process situation he can adopt the proposed actions. This behaviour can be equated to Rulebased behaviour and figure 10 shows an example of "surface" justification. justification helping him to make his own diagnosis. Figure 12 shows an example of "deep" justification support: a display curve represents the variables most significant to the prediction made by the assistance system. These variables are chosen and displayed using the following criteria (Taborin,89):

-to present those variables connected with any action advice given by the assistance tool,

-to present those variables affected by the change of any connected variables, -finally to present any other variables affected.

A maximum of three variables are displayed at the same time, a variable being represented by three curves (figure 12) -its history prior to the last fault prediction, the current prediction and, its evolution since the last prediction. The prediction time, PT, is indicated by a vertical axis which corresponds to the date for which the process state has been considered by the assistance tool during the last prediction. hi gh l eve l hi story before the l ast predi ctio n evo lu ti on s in ce the l ast predi cti on no rma l l evel PT Fig. 12. An example of "deep" justification support

We have to define classical algorithms or use artificial intelligence to specify the assistance modules -for example, algorithms able to perform surface diagnosis or algorithms able to predict variable history and evolution according to the qualitative process model, and so on.

It is now possible to perform an effective implementation of these different modules.

IV.4. Phase 4: Implementation

The realization of the presentation modes and graphical attributes used by the "intelligent" interface was done with a tool called DATAVIEWS (V.I. Corporation,88), on a VS-3100 workstation. Each display is divided into 5 zones (figure 13). Zone A is used to display alarm and action advice, zone B is used to display text providing justification of actions, zone C -the most important -contains graphical information, zone D is used for dialogue with the I.D.M, and Zone E is used for process dialogue. The "intelligent" imagery manager is developed using "C" language. It incorporates an inference mechanism able to exploit all the production rules contained in the three "What", "When" and "How" knowledge databases. At present, there are 80 rules in the "What"database, 73 rules in the "When" database and 108 rules in the "How" database.

After their creation, these rules were analyzed by the ID3 algorithm (Quinlan,79), and developed as a decision tree. Below are two examples of the "What" knowledge database.

They illustrate the two differing information displays produced when the skill levels of the operators differ. The process function and the level of situation severity remain the same. For each example, the severity value means that there is abnormal energy production but safefy is normal (please see below). After all the assistance system modules have been realized we can implement the final design process phase -that of evaluation.

IV.5. Phase 5: Evaluation

This phase is implemented exclusively in the laboratory and all the necessary modules must be integrated into the experimental platform. Our objective here is to compare the operators behaviour with his performance wether an ordinary or "intelligent" interface is being used (figure 14). This comparison is done by using a set of failure scenarios (Tendjaoui et al.,91a). The experimental results will be published at a later date.

We are concurrently integrating a man-machine cooperation model in an industrial process supervisory system, called PREDEX (Gambiez et al.,90;Gambiez et al.,91). This system uses a qualitative model of the process to be controlled (Tang & Schollkoepf,90). The integration into the PREDEX system of the D.M.I. concept, relative to the adaptation of the interface to both the operator and his tasks, depends on results gained from this experience.

V. CONCLUSION

This paper presents an "intelligent" interface design process in the field of complex industrial processes. This process includes five main phases: (i) Man-Machine system analysis, (ii) operator, tasks and process modelling, (iii) specifying the assistance system incorporating an "intelligent" interface, (iv) implementing the assistance system, and (v) evaluating the assistance system. This process necessitates the construction of three different models: an operator model, an assisted task model and a process model. Using artificial intelligence techniques, it is possible to combine knowledge released from these three models in order to decide: (i) "What" information to present to the operator, (ii) "When" to present it, and (iii) "How" to present it.

We are applying this process to the design of an "intelligent" interface called the D.M.I.

the considered application of which is a simplified nuclear power plant. This interface is being implemented onto an experimental platform.

Fig. 1 .

 1 Fig. 1. "Intelligent" interface design process

  Fig 2. Typical Man-Machine system incorporating the Decisional Module of Imagery

  Fig 3. The "What -When -How" concept

Fig. 4 .

 4 Fig.4. The mechanism's operation(Le Strugeon, 91)

Fig. 5 .

 5 Fig.5. Method for constructing the knowledge database(Tendjaoui et al., 91b)

  Fig. 6. The simulated process

Fig. 7 .

 7 Fig. 7. The location of, and relationships between, variables in the simulated process

  Fig.8. the task model

  Fig 9. Alarm and severity evaluation levels

  Fig. 10. An example of "surface" justification

Fig. 13 .

 13 Fig. 13. The 5-zone composition of a display

  request = No request and IF Operator task = Problem solving THEN WHAT:= Automatic display of actions advice Finally, for the specific treatment manager we have constructed a simulated aid tool composed of two separate modules. The first module uses cards containing four types of information: (i) failure identification, (ii) an associated course of action, (iii) surface justification of this action and (iv) deep justification of this action. The second module uses a rapid power plant simulation in order to identify variable trends. Both these two modules are developed with "C".

  Fig 14. comparison between classical and "intelligent" interfaces

Re aliz ation Evaluation

	Analys is
		Real H uman	P roces s
		Operator
		Limits & res ources	Fixed tas ks	as sistance requirements
	Mode l lin g
	Theoritical Human Operator Limits & res ources	Operator's model	Fixed tas k model	P roces s model (according to as sistance
				needs)
	Spe cification			Specific
	-Representation modes -Graphical attributes	"intelligent" chaining of views	treatement : prediction, diagnosis ... (Integ rated in th e
				de cisio n supp ort
				sy ste m )
		Ergonomic graphical library	"Intelligent" image manager	Specific treatment manager
		Simulation
		Integration
			on site
		Validation

Table 1 -

 1 a. Detailed description of the method of design

PHASE SUB -PRO B LEMS O B JEC TIVE EXAMP LES O F TO O LS AND TEC HNIQ UES USED

				• Normally functioning syst em analysis methods, as:
				SADT , MERISE (T ardieur et al., 89), Fluence
				diagram (Sinclair et al., 65).
		Process	1	• Degraded functioning system analysis met hods, as : FME CA = design analysis procedure for failure mode, E ffects and Crit icality Analysis (FMECA,
				67). FT M = Fault tree method (Robert et al., 81).
	ANALYSIS	Presc ribed tasks Assistance needs	2 3	• Analysis of data issued from step 1 • W ork analysis • Analysis of data issued from t he step 1 • Operator's needs collection techniques if t he process exist s
		Real Human operator limits and	4	• Operator's data collection techniques • Human reliability analysis and evaluation techniques (see Swain & Guttman, 83 ou W illemeur, 88), T HERP : T echnic for Human Error Rate Prediction (Swain, 64).
		resources		SHE RPA : Systematic Human Error Reduction and Prediction Approach (Embrey, 86).
		theoritical	* Description of cognitive
		Human operator limits and resources	mecanisms built by the operator t o perform his different t asks. * Make a study of fundamental Human operator charact eristcs
		Human	* Building problem solving trees that describe different steps used by
		operator	an operat or to solve a specific
		Model	problem. T hese trees differ according to operator knowledge and
			experience.
			* Building a tree that describes
		Assited task model	different t asks performed by an operator according to different process operating modes
			* Considering different assistance
			tools.
		Process	* Building a process description as a
		Model acc ording to assistance	set of objects t hat describe different sub-systems and variables, as well as the relationships between them. T his description depends on the
		requirements	assistance technics (diagnosis,
			predict ion…)

Table 1 -

 1 b. Detailed description of the method of design (contd.)

	5
	6
	7
	8	…

  Using guide on ergonomy(Scapin, 87; M ac Cormick, Sanders, 85 ; et c. • Using formal assistance t ools of presentation mode choice : see Moussa et al., 90 or Kolski, 91. • Intervention of expert in informat ion presentation.

					USED
	SPECIFICATION	Presentation mode and Graphical attributes Chaining view s "intelligently" Specific treatment : diagnosis… predic tion,	9 10 * Definition of how to "intelligently" * For each operational mode and for each component , an ergonomic choice of present at ion modes and graphical attributes. * Respect of operator's habits. prepare and chain views according to different process modes operator performed tasks and experience : Building a Man-M achine cooperation network. * Building software modules allowing an operator assist ance when 11 calculat ion, prediction modules… performing his problem solving t ask :	• • Using Artificial Int elligence techniques. • Chaining knowledge formalized in st ep 6, 7 and 8. • Calculat ion algorit hms • Using Artificial Int elligence techniques.
	REALIZATION	"Intelligent" Image Manager library Ergonomic graphical	* effective creation of different graphical presentation modes * Using existing libraries	13 12
		Specific
		treatment manager		14

* Realization of a module able t o "intelligently" act ivat e t he interface according t o the cont ext. * Software realizat ion of specific assistance modules. * Connection of t hese modules to the "intelligent" interface.

• M any int erface design t ools exist, for example : Dat aviews, X-Windows, Sl-Gms, … T hey are of different kinds : t ool box, application framework, interfaces generator,…(see

Coutaz, 90)

.

Table 2 :

 2 Values assigned to safety, production and emergency.

	Value In se curity le ve l
	0	Normal
	5	Abnormal
	10	Critical

ACKNOWLEDGEMENT

We would like to thank: two unknown reviewers for their constructive comments, and Martin O'Neill for his Anglicization of our paper.


Michèle Christophe Kolski

Michèle Duribreux

TOWARDS EXPLICIT CONSIDERATION OF TIME IN COGNITIVE ENGINEERING WORKBENCHES FOR "INTELLIGENT" ASSISTANCE SYSTEM DESIGN*

Keywords: Cognitive engineering workbench, time, process control, assistance system design, pluridisciplinarity

come

Towards explicit consideration of time in cognitive engineering workbenches for intelligent assistance system design

INTRODUCTION

As a consequence of technological development, the human operator of the industrial process control room is becoming less and less involved in manual control tasks, while at the same time, realizing ever more complex cognitive tasks to solve problems [START_REF] Rasmussen | Information processing and Human-Machine Interaction, an approach to cognitive engineering[END_REF][START_REF] Moray | Monitoring behaviour and supervisory control[END_REF]). In complex processes, the tendency has been to assist the operator by "assistance systems" (see for instance the works of Helander, 1988;[START_REF] Millot | Supervision des procédés automatisés et ergonomie[END_REF][START_REF] Boy | Operator assistant systems[END_REF]1990;[START_REF] Tendjaoui | An approach towards the design of intelligent man-machine interfaces used in process control[END_REF]Kolski et al., 1992, and so on). These new systems can be based -or not-on Artificial Intelligence approach, such as expert systems. To be really efficient, it is important that these tools consider how the operators use time in their different tasks. However, the acquisition, the structuralization and the formalization of knowledge and temporal strategies obviously pose a problem which has not been yet solved. The ergonomist and the knowledge engineer themselves have especially been lacking in this respect, although, at the present time, cognitive engineering workbenches are starting to appear. The main idea of the article is that, although, these workbenches are not really adapted to human tasks in the control room. Recent advances in temporal logics in Artificial Intelligence allow this approach to be possible.

The article consists of five main parts. After an initial description of assistance systems that can be found in control rooms of industrial processes, the problematic issues of knowledge and temporal strategies used by the human operator in such control rooms will be studied. The third part will discuss the different tools and the current knowledge engineering workbenches which ease the knowledge acquisition, structuralization and formalization for the ergonomist and the knowledge engineer, in spite of the lack of temporal aspects consideration. The fourth will emphasize certain temporal tools available in Artificial Intelligence which can contribute to the development of knowledge engineering workbench utilizing time explicitly. Finally, a multidisciplinary approach is proposed.

CLASSIFICATION OF ASSISTANCE TOOLS IN INDUSTRIAL CONTROL ROOMS

Presently, the human operator in industrial process control rooms can be assisted in problem solving tasks by systems which are generally based on Artificial Intelligence techniques.

These complementary tools can be classified according to their functionalities during the problem solving step, as defined by [START_REF] Rasmussen | The human as a system component[END_REF], used by a human operator in case of an abnormal situation. This step can be split up into several sequential stages : In case of alarm or of abnormal event detection, the operator observes information in order to identify the process state. Therefore, he uses predictions, evaluations and alternatives to define a general strategy. Then, he defines tasks and chooses some adequate improvements in the process. He has therefore to define the action procedures, to act on the process, and finally to keep up with the effects of the corrective actions.

Figure 1, modified from [START_REF] Taborin | Cooperation entre Opérateur et Système d'Aide à la Décision pour la Conduite de Procédés Continus: Application à l'Interface Opérateur[END_REF], classifies assistance tools for each of these stages. Note that the operator, facing familiar or preplanned situations, is not obliged to perform the complete process of problem solving.

Class 0 :

This class has no assistance system and therefore operator is not assisted. In case of dysfunctioning, the system just raises the alarms.

Class 1 :

This class consists of filtering alarms systems. In case of a defect, these systems present the most significant alarms of the ordinary state of the process. The sub-alarms are eliminated, because they are not the direct consequence of the defect. The integration of these systems furthers a preventive behavior and bases the observation and diagnosis stages on significant variables. Such systems are described by [START_REF] Andow | Real-time analysis of process plant alarms using a mini-computer[END_REF] and [START_REF] Lees | Process Computer Alarm and Disturbance Analysis : outline of methods for systematic synthesis of the fault propagation structure[END_REF].

Class 2 :

The second class groups together systems that search and synthesize significant information, i.e. selecting that information which is most indicative of the process functioning state. The operator must consider this information not only during the supervision tasks, but also during the problem solving ones. Furthermore, when useful information is not available, the system must be able to supply information issued from upper levels, for instance variables in relation with chemical reactions. These principles have been studied by [START_REF] Goodstein | Discriminative display support for processus operators[END_REF], [START_REF] Rouse | Design and evaluation of an on-board computer based in formation system for aircraft[END_REF] and [START_REF] Gambiez | Man-machine interface for PREDEX, an expert system shell for process control and supervision[END_REF] for instance.

Class 3 :

The third class groups the diagnosis aid systems able to locate defects and to define their causes, from the set of available information. Contrary to the class 1 systems, they do not provide any help concerning the fault detection. Generally, the reasoning period is too long to alert the operator quickly enough to the emergence of a problem and a diagnosis result. On the other hand, they provide him with interactive help for reasoning and evaluation of the situation, by presenting high level information on the process state.

To this effect, they must satisfy a additional constraint, the capacity to justify their results. The HORSES system, developed by [START_REF] Boy | an expert system for fault diagnosis in orbital refueling operations[END_REF] fits this class. Its originality consists in a knowledge base constituted by three levels : the first one permits a fast situation recognition, the second one analyses this situation and makes a diagnosis, the third and last level -called metaknowledge -controls the other levels. The advantage of this decomposition is to allow an adaptation of reasoning to the situation. This class groups the systems by alarms treatment. Their function consists in alerting the operator when a defect appears. They do not limit the selection to the most significant alarms -like in the class 1 -, but are able to make a first analysis of the situation in order to quickly present a set of information, especially concerning the state of the different sub-systems which are implicated in the perturbation. This first diagnosis is less complete than a diagnosis which could be provided by a class 3 system, but it helps the operator during his problem solving step, by presenting him a set of information to be considered. The interface design is very important, because the help provided by these systems covers a large part of the human problem solving step. Their main interest consists in limiting errors made during the situation evaluation. An example of such a system is described by [START_REF] Ancelin | Un système expert pour le traitement des alarmes d'un réacteur nucléaire[END_REF].

Class 5 :

In this class, a system has to : (i) estimate the reactions of the process consequent to the actions, (ii) predict the future state of the process, (iii) formulate hypotheses, and finally (iv) make decisions. In other words, it has to reason like a human, adopting a knowledge-based behaviour. But such truly intelligent and extremely complex reasoning has not yet been modelled in spite of the very active research in this field. Artificial Intelligence contributions to the design of such systems particularly include : the deep reasoning, the planning, the reasoning by analogy, the temporal logic, and the qualitative physics. The system PREDEX (Tang and Schoelkopf, 1990) provides several functionalities of the tools from this class.

Class 6 :

The systems of this class aid at a recovery. Unlike those of class 5, the knowledge of these systems corresponds to the one used by a well trained human operator, who adopts a procedural behaviour. These tools propose a diagnosis and suggest a procedure to correct the defect. So, they provide the operator with a great quantity of different information on several levels : significant information, diagnosis, corrective actions, etc. The problem is the choice and manner of presenting the information. These systems allow the operator to check, before acting on the process, if the defined procedure is the same that the one proposed by the assistance system. This condition limits the errors made during the evaluation of the situation, in particular (i) those related to the procedures, (ii) the errors due to the planning and correction. Several tools of this class, such as ESCORT [START_REF] Paterson | Escort : the application of causal knowledge to real-time process control[END_REF], PREDEX [START_REF] Tang | Un générateur de systèmes experts de sconde génération pour la supervision des procédés continus[END_REF] or DIAPASON [START_REF] Coudouneau | DIAPASON : un système d'aide à la supervision[END_REF], make deep and/or shallow diagnoses and propose advice so as to correct the situation.

Class 7 :

In this class, the intelligent systems automatically control the process. These are class 6 systems which perform actions based on reasoning. One such system, ARTIFACT, is based on a relational model linking the different sub-systems of the process [START_REF] Francis | ARTIFAC : a real-time shell for intelligent feedback control[END_REF]. It deduces "conditions/actions" rules from which a planning module analyses the best set of possible actions to reach the wanted state. Nevertheless, such a system does not take into account time in its reasoning process. Therefore, it can only be applied to processes whose inference duration is slow enough to be considered as static.

ARTIFACT has only been applied on very simple processes. It is not yet possible to let to such a system completely control complex processes because it still must integrate the knowledge related to all the possible situations. Few systems exist in this class.

Class 8 :

In this class, a system assists the operator when requested (Mandiau et al.,91). Using knowledge about (i) the tasks to be performed and (ii) data issued from the process, this system permanently generates action plans. These plans can either be used by the operator, or be applied directly by the system on the process at the operator request. These systems are called "Operator assistant systems" [START_REF] Boy | Operator assistant systems[END_REF] or "Assistant Expert Systems". They use certain functions of class 6 systems, providing the operator with recovery assistance, and acting directly on predefined processes.

The implementation of each of these systems leads to problems related to (i) the dynamics and the complexity of the process to control and (ii) the objectives of the computing assistance. Indeed, these systems exploit heterogeneous and complex knowledge geographically distributed in the installations. Furthermore, these systems require functions dependent on the operator problem solving state. Field studies of control rooms show the existence of many poorly adapted "intelligent" assistance systems. One reason for their efficiency is that these tools do not consider how operators used time in their different tasks.

TEMPORAL KNOWLEDGE AND STRATEGIES OF THE HUMAN OPERATOR IN A CONTROL ROOM

Temporal problems have been studied for many years in psychology (see the works of [START_REF] Piaget | Le développement de la notion de temps chez l'enfant[END_REF][START_REF] Fraisse | Psychologie du temps[END_REF] quoted by [START_REF] Decortis | Dimension temporelle de l'activité cognitive lors des démarrages de systèmes complexes[END_REF]. The temporal dimension of human operator activities in complex industrial process control rooms, have been researched in work psychology for only about ten years. But a better understanding of the intervention of the time in the human operator's reasoning and knowledge and the specification of future assistance tools have already been identified (see De [START_REF] De Keyser | Les communications homme-machine dans les systèmes complexes[END_REF]Dorner, 1988;[START_REF] Decortis | Dimension temporelle de l'activité cognitive lors des démarrages de systèmes complexes[END_REF][START_REF] Grosjean | Elicitation of temporal reference systems[END_REF]Javaux, 1991 or De Keyser, 1990;1991).

For example, in a continuous steel casting control room [START_REF] De Keyser | Les communications homme-machine dans les systèmes complexes[END_REF] direct actions given to the operator are weak, but, on the other hand, the control room can change into a "dispatch" room. In addition to the supervision of process and actions in the form of orders which modulates the actions of other workers, the operator manages and co-ordinates all the activities surrounding the casting machine. Because of information exchanges with the staff, he detects or collects the technical failures, works out a pre-diagnosis and appeals to the department concerned, in addition to other administrative tasks. The operator must therefore be in interaction with many other workers. According to field studies, operators communicate verbally : dated or undated facts, numerical data, tendencies, estimations related to a chronology of actions, qualitative valuations, judgments, references to the past, reactions to certain operator's decisions. These different types of verbal communications are used for support of information and to make the estimation, the judgment and the diagnosis easier in some situations. They are used as a collective memory which can be refreshed by the operator and from which information can be drawn. They include different notions of time.

Many other studies show the cognitive wealth of the human operator's work. For example, in his study about the temporal dimension of operators cognitive activity in a start up of a thermal power station, [START_REF] Decortis | Dimension temporelle de l'activité cognitive lors des démarrages de systèmes complexes[END_REF] emphasizes the temporal aspects of operator, such as : (1) estimation of process state durations according to the content of events, temporal limits and variation speeds, (2) temporally reasoning on interdependent process elements, (3) estimation on the duration of the action and annex events, and on intervention time, (4) optimal management of simultaneous actions, (5) synchronization of collective actions of different intervening parties, (6) reduction of time for events in order to make up delays, (6) watching the evolution of events before intervening at the right time, (7) adaptation and development of chronological, sequential and simultaneous strategies, according to the situation of the process running.

With regard to such temporal aspects, De [START_REF] De Keyser | Fiabilité humaine et gestion du temps dans les systèmes complexes[END_REF] discussed the difficulties that the operator faces when evaluating, integrating, coordinating or planning the machine system, and the errors which can be associated with these situations.

Thus, the importance of the real needs concerning the human operator's assistance in control rooms is shown. In the development process of assistance tools in complex systems, computerized tools, particularly for acquiring and formalizing the knowledge are used. But these tools are yet not really adapted to the temporal problems of assistance. The following paragraph deals with one such study.

REVIEW OF THE METHODOLOGIES AND TOOLS FOR KNOWLEDGE ACQUISITION IN RELATION TO THE DEVELOPMENT OF ASSISTANCE TOOLS

Whatever the application, current approaches to knowledge acquisition can be based on a large variety of methods, techniques and tools, most of them imported from other sciences (psychology, linguistics, epistemology, software engineering, artificial intelligence...) and adapted for computerization.

The principle of a knowledge engineering workbench consists of a wide range of tools whose purpose is to support the knowledge engineer, in cooperation with an expert, in core knowledge engineering activities. Figure 2 shows a general architecture of the different functions of such a workbench. With an elicitation toolkit box, the expert's knowledge can be acquired in relation to a pre-existing model, which is including in a library of generic models, characterized at structural, functional and conceptual levels. Finally, a translator module uses a transcription of the high level model into lower level form (objects, rules...) directly executable via an inference engine integrated in the assistance system. Many elicitation tools, which are specific to one or several acquisition techniques, currently exist. For example : ETS [START_REF] Boose | A Knowledge acquisition program for expert systems based on personal construct psychologie[END_REF] based on repertory grids techniques, MACAO [START_REF] Aussenac | Conception d'une méthodologie et d'un outil d'acquisition de connaissances expertes[END_REF] based on protocol analysis, ROGET [START_REF] Bennet | A knowledge-based system for acquiring the conceptual structure of a diagnostic expert system[END_REF] and MORE [START_REF] Kahn | MORE : an intelligent knowledge acquisition tool[END_REF] based on interviewing techniques. Other such tools specific for a given task are described by [START_REF] Boose | A survey of knowledge acquisition techniques and tools[END_REF]. However, none of them explicitly represents the notion of time.

Current research is trying to combine knowledge acquisition techniques in a toolbox form, e.g. AQUINAS, a knowledge acquisition workbench including repertory grid, interviewing and scaling techniques [START_REF] Boose | A survey of knowledge acquisition techniques and tools[END_REF]. The Esprit project ACKnowledge is studying the integration of machine learning and the techniques and tools for knowledge acquisition, to result in a coherent computing environment called KEW (Knowledge Engineering Workbench). The workbench associates techniques for knowledge collection (card sorting, repertory and scaling grids) and machine learning techniques (e.g. generalization). The selection of these techniques depends on the type of knowledge and the way the knowledge is represented. At some point in the process, the elements of knowledge are included and the quality of the knowledge base is evaluated in order to establish what should be complemented, refined or made coherent (Julien, 1991). In comparison with the various temporal strategies brought into operation by a human operator in a control room of complex systems, these workbenches seem to be insufficient in spite of the variety of techniques that they offer to the designers. The first knowledge acquisition methods, associated with the life cycle of a knowledge-based system, were often based on the rapid prototyping. A prototype was first of all developed and then refined until the final knowledgebased system was obtained. This incremental approach has lead the development of many knowledge-based system methodologies [START_REF] Hayes-Roth | Building expert systems[END_REF][START_REF] Nassiet | Contribution à la méthodologie de développement des systèmes experts : application au domaine du diagnostique technique[END_REF][START_REF] Benkirane | Methodological knowledge elicitation approach. Application to the design and realization of a knowledge based system dedicated to the technical diagnosis[END_REF]. This process, also called structured acquisition of knowledge [START_REF] Johnson | Knowledge elicitation for second generation expert systems[END_REF] enables the collection of the knowledge and the development of field models. Extension of other scientific methods have resulted in the development of very formalized and powerful methodologies, such as KADS and KOD which are briefly described below. Such methodologies seem to be particularly attractive to the present research.

The KADS methodology, originating from epistemology, guides the knowledge based system development from the organization of the domain to the development of the whole system. Based essentially on the construction of several models, it breaks the domain organization into stages where the problem is decomposed into component parts and the analysis of knowledge is separated from its machine implementation. KADS has three modelling levels : (1) a language to describe the conceptual model, constituting an intermediate representation between the expert's data and the design/implementation of the knowledge based system ; (2) a design module describing the representation of the conceptual model to the external needs using an appropriate framework, and (3) a module of modalities to specify the co-operation and the communication between the knowledge based system and the user. The success of KADS has been represented by the marketing of a tool called OPEN KADS. The workbench has proved itself in large applications such as the SACHEM (Système d'Aide à la Conduite des Hauts fourneaux En Marche) project developed for SOLLAC. This project developed an intelligent system based on expert knowledge to supervise metallurgy blast furnaces. The KADS methodology does not explicitly mention temporal management, although it should be possible to take into account temporal constraints of a process, as is presently being done by the designers of KADS.

The KOD methodology (Knowledge Oriented Design) concerns the acquisition of an expert's knowledges (but only by interviewing techniques), their modelling based on the retranscription of the verbalizations, and finally a specification of the knowledge base according to a multiple step analysis of the verbal data. Also based on a knowledge modelling, the KOD methodology offers a cognitive model which is used as an intermediary between the natural language of the expert (practical model) and the computer language in which the knowledge based system will be implemented (computing model). On the cognitive level, the expert uses "taxonomies", "actinomies" and schemes of interpretation and reasoning [START_REF] Vogel | Génie cognitif[END_REF]. From this data acquired from the verbalizations of the expert, the practical mode allows to obtain a specification of the expertise, which states precisely the field and the phases of setting up the expertise. The practical model is leading the collect of data in order to make the future knowledge structuralization easier. Then, the collected data is reduced in order to recognize the manipulated units in the expert's speech (cognitive model). KOD is now called K-Station which is available on the market. The time notion is not explicitly considered in the K-Station. However, this notion is explicitly mentioned by Vogel when he deals with the "actinomy" by decomposing it in elementary sequences, which are combined in order to produce complex sequences. Vogel suggests that Allen's model (see the next paragraph) enables to calculate the temporal relations between the events. Despite promises of explicitly taking time into account, most assistance systems for human operators in the control rooms of industrial complex systems have not done so. Even if knowledge acquisition utilizes resources from different fields, the temporal tools of artificial intelligence are not utilized. Nevertheless, in the following paragraph, different temporal tools can be used to deal with problems related to dynamic complex processes.

THE POTENTIAL CONTRIBUTION OF TEMPORAL TOOLS IN RELATION TO KNOWLEDGE ACQUISITION AND MODELLING

In artificial intelligence, logical formalisms and tools for the explicit management of time can be used to solve problems [START_REF] Bestougeff | Outils logiques pour le traitement du temps[END_REF]. But, for any application, the most primitive adequate temporal entities should be chosen. Systems based on "points" are used the most, because they are easy to model. In fact, a representation based on punctual entities does not naturally represent the notion of duration. The need for extensive entities has led to different definitions of interval : sub-sets of points or pairs of points for instance [START_REF] Libert | Modèles de raisonnement temporel en I[END_REF]. Similarly, the relationship between these entities is important. To representat time, the notion of precedence is fundemental. This relationship can be expressed as : "has begun strictly after/before", "has started simultaneously at", "just before/after", and so on. When there are temporal intervals, the insertion between two time intervals is natural. Finally, the properties of the relationship between the temporal entities must be satisfied. Discrete modelling can be used, for others applications, a continuous point of view is necessary.

In artificial intelligence, the basic time models are those of [START_REF] Allen | Towards a general theory of action and time[END_REF][START_REF] Mcdermott | A temporal logic for reasoning processes and plans[END_REF]. These models have been at the origin of many studies, extensions and realizations leading to deduction systems. For example, based on the temporal model of Allen, the NEMO inference engine is used for the development of real-time knowledge-based systems [START_REF] Tang | Un générateur de systèmes experts de sconde génération pour la supervision des procédés continus[END_REF][START_REF] Gambiez | Man-machine interface for PREDEX, an expert system shell for process control and supervision[END_REF].

According to [START_REF] Allen | Towards a general theory of action and time[END_REF], time is an abstract notion which only exists with regard to the evolution of the universe, and is perceptible only due to environment changes. The practical representation of time is therefore linked to the evolution of the environment, i.e. to the concepts of event, state, situation, succession of states and so on. This model is essentially based on time intervals. A set of thirteen basic binary relations describes all the possible ways two intervals can be related to each other. Three entities are then taken into consideration : the properties about intervals, the events and the processes during an interval.

Figure 1 :

 1 Figure 1 : Classification of assistance systems (from Taborin, 1989) Class 4 :This class groups the systems by alarms treatment. Their function consists in alerting the operator when a defect appears. They do not limit the selection to the most significant alarms -like in the class 1 -, but are able to make a first analysis of the situation in order to quickly present a set of information, especially concerning the state of the different sub-systems which are implicated in the perturbation. This first diagnosis is less complete than a diagnosis which could be provided by a class 3 system, but it helps the operator during his problem solving step, by presenting him a set of information to be considered. The interface design is very important, because the help provided by these systems covers a large part of the human problem solving step. Their main interest consists in limiting errors made during the situation evaluation. An example of such a system is described by[START_REF] Ancelin | Un système expert pour le traitement des alarmes d'un réacteur nucléaire[END_REF].

Figure 2 :

 2 Figure 2 : Global principle of current computerized tools for knowledge acquisition

According to [START_REF] Mcdermott | A temporal logic for reasoning processes and plans[END_REF], the time can be considered in a continuous manner and basic entities of the model are a set S of states a set T of instants. This model represents the world state as a photograph of the world at a fixed time, and defines temporal points as elementary entities for the representation of time. Some facts are identified with subsets of the set of states. In this model, an event can have a duration, corresponding with a succession of states in which the event happens only once in time, and is missing in states near this succession. McDermott also introduced notions manipulating basic entities.

In spite of their limits, the McDermott and Allen's models have introduced a basic formalism, which is used as a reference for different models for an efficient management of the temporal relations [START_REF] Ghallab | Raisonnements temporels symboliques : représentations et algorithmes[END_REF][START_REF] Ermine | Raisonnement temporel dans les systèmes experts[END_REF].

Each of the temporal approaches contribute to time modelling in knowledge engineering workbenches and are used by the designers in developing assistance tools for operators in complex systems. Meanwhile, two crucial problems appear : (i) the specialists in temporal logics are not conscious of the real assistance needed in control rooms, while (ii) human factors specialists are not really knowledgeable concerning the study and development of knowledge engineering workbenches for assistance systems design in complex processes. Thus, the integration of these temporal aspects requires a close collaboration between work psychologists, ergonomists, knowledge engineers, automatists and computer scientists.

CONCLUSION : NECESSITY OF A MULTIDISCIPLINARY APPROACH FOR THE EXPLICIT CONSIDERATION OF TIME IN COGNITIVE ENGINEERING WORKBENCHES

Because of automation of complex industrial processes, human operators in control rooms require assistance systems considering explicitly time. The development of such assistance systems requires the use of improved tools, to acquire, structuralize and formalize expert knowledges and strategies. These tools are presently being studied, developed or validated in industrial and university research laboratories.

However, to obtain the most efficient tools for incorporating temporal needs of the human operator, a multidisciplinary approach between work psychologists, ergonomists, knowledge engineers, automatists and computer scientists is necessary. Each of these has complementary knowledges concerning : (1) the real needs of the human operators in control rooms, (2) the tasks to be performed, (3) the characteristics of the man at work, (4) different technical, ergonomical and methodological aspects related with knowledge acquisition, structuralization and formalization, (5) techniques, tools and methods issued from artificial intelligence, and so on.

The figure 3 proposes a multidisciplinary approach in this field. With Work Psychology, Sociology and Ergonomics experts, it is possible to take a census of temporal needs and strategies in control rooms of complex processes. A collaboration with Cognitive Engineering experts can lead to a census of the current knowledge acquisition tools and their characteristics and functionnalities. With Artificial Intelligence experts, it is possible to take the census of temporal logics tools. With all three, it is then possible to study the adequacy of identified models, methods and techniques. This analysis must lead to (1) a better specification of cognitive engineering workbenches usable for the development of knowledge bases integrated in assistance system(s), and also (2) a better specification of specialized workbenches (for instance real-time expert system shells) for the development of inference engine(s) integrated in assistance system(s).

ACKNOWLEDGEMENT

The authors would like to thank Dr. Andris Freivalds for his improvement of the manuscript.


C Aymonier

A Gratias

J Mercadier

F Cansell
email: cansell@icmcb.u-bordeaux.fr

Global reaction heat of acetic acid oxidation in supercritical water

Keywords: Hydrothermal oxidation, Temperature profile, Reactor simulation, Acetic acid, Global reaction heat

The scaling-up of hydrothermal oxidation process is performed by the use of simulation tools, which require an important experimental support. In this way, we have proposed a new experimental approach to determine the global reaction heats of hydrothermal oxidation reactions. A quasi-adiabatic tubular reactor was developed in order to follow the temperature profiles along the reactor during the oxidation reaction. From the experimental temperature profiles, a model of the reactor thermal behavior was proposed and gives access to the global reaction heats. The experimental setup and model have been validated for acetic acid oxidation in supercritical water with hydrogen peroxide as oxidant. The global reaction heat of acetic acid hydrothermal oxidation (P=25 MPa, 400 5T5570 °C) has been found equal to -925 kJ mol -1 .

Introduction

Industrial societies generate increasing quantities of organic wastewaters and sludges, which require disposal or destruction. Disposal has usually been achieved in landfills, but this practice will rapidly become quite expensive and very restricting. An important research work has been undertaken to develop new technologies in order to achieve four major aims: valorization, decom-position in environmentally acceptable end products, final volume reduction of wastes, and reduction of energy consumption. Among the alternative practices, hydrothermal oxidation, and more precisely Supercritical Water Oxidation (SCWO), is being investigated as a method for destroying liquid wastes and sludges [START_REF] Cansell | Hydrothermal oxidation of model molecules and industrial wastes[END_REF][START_REF] Modell | Processing method for the oxidation of organics in supercritical water[END_REF][START_REF] Gloyna | Progress in supercritical water oxidation: research[END_REF][START_REF] Schmieder | SCWO: facts and hopes[END_REF]. End products resulting from hydrothermal conversion are essentially water and mineral acids (HCl, H 2 SO 4 , H 3 PO 4 ,...) in liquid phase, carbon dioxide and molecular nitrogen in gas phase [START_REF] Aymonier | Hydrothermal oxidation of a nitrogen-containing compound: the fenuron[END_REF]. Numerous studies describe the reactive pathway of the hydrothermal oxidation of model molecules and industrial wastes: acetic acid [START_REF] Meyer | Acetic acid oxidation and hydrolysis in supercritical water[END_REF], methanol [START_REF] Tester | Revised global kinetics measurements of methanol oxidation in supercritical water[END_REF], phenol [START_REF] Gopalan | A reaction network model for phenol oxidation in supercritical water[END_REF], pyridine [START_REF] Crain | Kinetics and reaction pathways of pyridine oxidation in supercritical water[END_REF], pulp mill sludges [START_REF] Modell | Supercritical water oxidation of pulp mill sludges[END_REF], etc.

Due to the supercritical water properties [START_REF] Cansell | Thermodynamic aspects of supercritical fluids processing: applications to polymers and wastes treatment[END_REF][START_REF] Frank | Calculation of the dielectric constant of water to 1000 °C and very high pressures[END_REF][START_REF] Marshall | Ion product of water substance, 0-1000°C, 1 -10000 bars -new international formulation and its background[END_REF], both organic matter and oxidant (air, oxygen or hydrogen peroxide) are completely miscible with water [START_REF] Japas | High pressure phase equilibria and PVT -Data of the water -oxygen system including water -air to 673 K and 250 MPa[END_REF][START_REF] Connolly | Solubility of hydrocarbons in water near the critical solution temperatures[END_REF], so that interfacial transport phenomena are suppressed. In this way, high destruction rates of wastes can be obtained in relatively short reaction times with a Chemical Oxygen Demand (COD) reduction greater than 99.9% [START_REF] Cansell | Hydrothermal oxidation of model molecules and industrial wastes[END_REF]. However, the solubility of inorganic salts drops to the parts per million (ppm) range [START_REF] Armellini | Precipitation of sodium chloride and sodium sulfate in water from sub-to supercritical conditions: 150 to 550 °C, 100 to 300 bar[END_REF]. So, inorganic salts precipitate and can deposit on reactor and heat exchanger surfaces. Moreover, corrosion of SCWO reactor materials by highly corrosive aqueous environment is also a major factor, which must be taken into account, for the development of SCWO processes [START_REF] Kritzer | Investigations of the corrosion of reactor materials during the process of Supercritical Water Oxidation (SCWO)[END_REF]. In these conditions, many new concepts of reactors have been developed to overcome salt precipitation and corrosion [18 -23].

In parallel, for scaling-up hydrothermal oxidation process [START_REF] Oh | Numerical analysis and data comparison of a supercritical water oxidation reactor[END_REF][START_REF] Dutournie | Simulation of a tubular reactor for supercritical water oxidation[END_REF][START_REF] Petrich | Model and simulation of supercritical water oxidation[END_REF], numerous research teams have worked on the development of simulation tools. In fact, two parts of the process require a precise model: the reactor and the heat exchanger. However, the knowledge of parameters, which characterize the process operation, is necessary for the development of simulation tools. These data are essentially hydrothermal oxidation chemical pathway, reaction kinetic and thermal parameters (reactive mixture thermodynamic properties (fluid specific calorific capacity) and heat transfer coefficients). Even if chemical pathways and kinetic parameters of hydrothermal oxidation reactions are well documented [START_REF] Meyer | Acetic acid oxidation and hydrolysis in supercritical water[END_REF][START_REF] Savage | Kinetics of acetic acid oxidation in supercritical water[END_REF][START_REF] Li | Kinetic model for wet oxidation of organic compounds in subcritical and supercritical water[END_REF], only few works concern thermal parameters. Chen et al. [START_REF] Chen | Simulation of a concentrictube reactor for supercritical water oxidation[END_REF] proposed an average reaction heat of -435 kJ mol -1 for most organic compounds and -870 kJ mol -1 for acetic acid. Similar result of reaction heat ( -847 kJ mol -1 ) was found with Prophy Plus, a simulation computer software package, for the acetic acid hydrothermal oxidation with oxygen [START_REF] Prosim | Prosim user's guide, 132 route d'Espagne[END_REF]. However, the equation of state used was established for pressures lower than 15 MPa. So, we propose a new experimental approach to determine global reaction heats of hydrothermal oxidation reactions. The global reaction heat eval-uation is based on: (i) a quasi-adiabatic tubular reactor developed at ICMCB, which allows to measure the temperature distribution along the reactor, (ii) a modeling of this reactor to exploit the experimental temperature profiles and so to calculate the global reaction heat.

In this paper, the global reaction heat of acetic acid hydrothermal oxidation has been determined.

After a description of the quasi-adiabatic tubular reactor, the obtained temperature profiles for acetic acid hydrothermal oxidation are discussed. A model of the reactor is proposed to calculate acetic acid global reaction heat.

Experimental section

Quasi-adiabatic tubular reactor

The quasi-adiabatic tubular reactor was developed to determine the heat quantity produced by hydrothermal oxidation reactions of model molecules and industrial wastes. The quasi-adiabatic tubular reactor (Fig. 1) was made of Inconel 718 tube of 2.4 mm inside diameter and 8.4 m length (volume of 38 ml). It was equipped with 28 thermocouples (type K) distributed along the reactor. Generally, the temperature increase takes place in the first part of reactor. In this way, more thermocouples were distributed in the first part of T injection =the temperature at the quasi-adiabatic tubular reactor inlet (just after the mixing of acetic acid and hydrogen peroxide feed solutions), D =the mass flow rate at the reactor inlet, t r =the residence time in the reactor, A.Ac initial =the weight concentration of acetic acid at the reactor inlet, and X= the acetic acid conversion rate.

the reactor than in the second part. The tube was isolated with ceramic fiber and surrounded with a thermal shield. The thermal shield constituted a copper sheet surrounded with electrical heating in order to reduce thermal loss. The quasi-adiabatic tubular reactor allowed to obtain a temperature profile along the tube which characterized the exothermic oxidation reaction. This continuous reactor was built to work up to 50 MPa and 600 °C.

The quasi-adiabatic tubular reactor was adapted to the ICMCB continuous pilot plant facility, described elsewhere [START_REF] Aymonier | Hydrothermal oxidation of a nitrogen-containing compound: the fenuron[END_REF], whose the capacity reaches 3 kg h -1 . The residence time in the reactor was calculated from the software Labview.

Acetic acid feed solution (CH 3 COOH at 99-100% from J.T. Baker in de-ionized water) and hydrogen peroxide feed solution (H 2 O 2 at 35% from Fluka in de-ionized water) were pumped with high pressure pumps (LEWA). Acetic acid feed solution was pre-heated and mixed to a hydrogen peroxide feed solution (not pre-heated) at the reactor inlet. When it left the reactor, the reactive mixture was quenched in a water-cooled heat exchanger and the pressure reduced to atmospheric pressure. Liquid and gas phases were separated in a gas/liquid separator.

Analytical material and method

Acetic acid concentration in both feed and effluent liquid phase was measured by High Performance Ionic Chromatography (HPIC) with a Dionex DX 120 apparatus (column system: ion Pac AS 12A 4 mm+ion Pac AG 12 4 mm). Elution was performed at a flow rate of 1 ml min -1 using the following mobile phase: 2.7 mmol l -1 Na 2 CO 3 /0.3 mmol l -1 NaHCO 3 . The measure accuracy was evaluated at 9 3%.

A COD measure was also performed in feed and effluent liquid phase. The sample was heated two hours with a powerful oxidant (potassium dichromate). Oxidable organic matter reduced Cr 6 + ions into Cr 3 + ions. The quantity of produced Cr 3 + ions was measured by colorimetry. The measure accuracy was equal to 95%.

The gas effluent composition was analyzed on line by Gas Chromatography (GC) with a Varian star 3600CX apparatus (column system: Porapaq Q+ molecular sieve 13× ). Helium was used as carrier gas (He flow= 30 ml min -1 ). The apparatus was equipped with a thermal conductivity detector. The temperatures of injector, column and detector were respectively 150, 40 and 180 °C.

Results and discussion

Acetic acid oxidation with hydrogen peroxide as oxidizer was studied at 25 MPa. H 2 O 2 quantity was brought in stoichiometry, as calculated from the following equation:

CH 3 COOH + 4H 2 O 2 2CO 2 + 6H 2 O (1)
Oxidation runs were performed with a solution injection temperature at the tubular reactor inlet close to 400 °C. The concentration of acetic acid at the reactor inlet, just after the mixing of acetic acid and hydrogen peroxide feed solutions, ranged between 3.35 and 4.90% w/wt. The run conditions and acetic acid conversion rates are summarized in Table 1. The conversion rate is defined from the following equation:

X = F-F% F (2)
where X, the conversion rate, F and F%, the initial and final acetic acid molar fluxes. We only present the acetic acid conversion rate because the results obtained with COD analysis were well correlated.

In fact, byproduct formation in liquid phase during acetic acid hydrothermal oxidation was negligible and acetic acid was transformed into H 2 O and CO 2 [START_REF] Meyer | Acetic acid oxidation and hydrolysis in supercritical water[END_REF].

Experimental temperature profiles

The temperature profiles obtained with the quasi-adiabatic tubular reactor for the different runs are reported in Fig. 2.

The injection temperature at the reactor inlet, just after the mixing of acetic acid and hydrogen peroxide feed solutions, was maintained to 400 °C. The quasi-adiabatic tubular reactor is surrounded with a thermal shield (see Section 2.1). The temperature of the thermal shield was uniform for the run A1 and equal to 400 °C. A temperature gradient of 100 °C between the reactor inlet and the reactor outlet (400 °C at the reactor inlet and 500 °C at the reactor outlet) was imposed for the runs A2 and A3 to reduce the gradient between the reactive mixture temperature and the thermal shield temperature at the reactor outlet.

Two kinds of temperature profiles were obtained (Fig. 2). For runs A2 and A3, the evolution of temperature profiles versus reactor length can be divided in three parts. The first one (T injection 5 T5460 °C) corresponded to the reaction initiation phase. The hydrogen peroxide decomposition and the first percent of acetic acid conversion (exothermic reaction) could explain the temperature increase. In a second part (T]460 °C), the temperature increased rapidly. This temperature increase was explained by the combined effects of the water specific heat capacity decrease and the reaction kinetic rate acceleration. In fact, the water specific heat capacity decreased from 13.5 kJ kg -1 K -1 at 400 °C to 4.6 kJ kg -1 K -1 at 460 °C (25 MPa) [START_REF] Schmidt | Properties of Water and Steam in SI-Units[END_REF]. Furthermore, the reaction kinetic rate increased by a factor 14 between 400 and 460 °C (activation energy of acetic acid oxidation reaction of 180 kJ mol -1 [START_REF] Meyer | Acetic acid oxidation and hydrolysis in supercritical water[END_REF]). The maximal temperatures reached are reported in Table 2. When the maximal temperature was reached, acetic acid conversion into CO 2 and H 2 O was quasi complete and the heat quantity released by the reaction was quasi null. So, in a third phase, the temperature decreased due to thermal loss according to the temperature gradient between reactive mixture and thermal shield.

Concerning the run A1, the temperature did not reach 460 °C during the reaction initiation phase because acetic acid concentration in the feed solution was too small.

Finally, these experimental temperature profiles locate the hot spot in the reactor and give the maximal temperature reached versus the initial acetic acid concentration. More the acetic acid concentration decreased, more the distance of the hot spot position from the reactor inlet increased.

A reactor model has been proposed to calculate the acetic acid global reaction heat from these experimental temperature profiles.

Reactor modeling

The quasi-adiabatic tubular reactor operated at steady state. The energetic balance of an open reactor in permanent working conditions is expressed as [START_REF] Villermaux | Ge ´nie de la Re ´action Chimique[END_REF]:

Á Ã Ã Ã Ä power received by the system  à à à Š= Á Ã Ã Ã Ä heating of mixture during the reactor crossing  à à à Š+ Á Ã Ã Ã Ä heat adsorbed by chemical reactions  à à à Å
From this energetic balance, we have evaluated the global heat quantity released by the oxidation reaction in the experiment conditions studied. This global reaction heat takes into account the reaction enthalpy and the mixing enthalpies of the different compounds in the reactive mixture. With hydrogen peroxide as oxidant, the global reaction heat also includes the heat released by the hydrogen peroxide decomposition. In this paper, the global reaction heat will be noted DH g .

The decomposition of this global reaction heat in its different terms (reaction enthalpy and mixing heat) was not investigated because the system was too complex even for the study of a model molecule like acetic acid. In fact, the reactive medium for the hydrothermal oxidation of acetic acid with hydrogen peroxide is already composed of numerous compounds (H 2 O, CH 3 COOH, CO 2 , H 2 O 2 , byproducts of hydrogen peroxide decomposition [START_REF] Croiset | Hydrogen peroxide decomposition in supercritical water[END_REF], etc.). Furthermore, the scale up and the work of the process require only the knowledge of a global reaction heat.

A simulation work has shown that the quasiadiabatic tubular reactor can be assimilated to a plug flow reactor in the studied conditions [START_REF] Dutournie | Simulation of a tubular reactor for supercritical water oxidation[END_REF]. For a plug flow reactor, the energetic balance is performed on a slice of reactive mixture (Fig. 3) [START_REF] Villermaux | Ge ´nie de la Re ´action Chimique[END_REF]:

dq dV R =U dA dV R (T p -T) =F 0 G p dT dV R +F A dX dV R DH g (3)
where dq =power received by the reactor slice of volume dV R from the exterior, U=global thermal transfer coefficient, dA=exchange surface of the reactor slice with the exterior, T p = exterior temperature, T= temperature of the reactive mixture in the slice, F 0 = total molar flux, F A = reagent molar flux, G p = specific calorific capacity of reactive mixture, X=conversion rate of the reagent, and DH g = global reaction heat. First, we assumed that the thermodynamic properties of the reactive mixture could be assimilated to those of pure water. In fact, the concentration of organic load at the reactor inlet was lower than 5% in weight. That means:

G p = Cp water (T) (4)
where Cp water (T) = specific calorific capacity of pure water versus temperature at constant pressure.

Second, the configuration of the reactor tube was such as only the half of the tube surface exchanged heat with exterior:

dA dV R = PRdL PR 2 dL = 1 R (5)
where R= tube radius, and dL= studied slice length. With these assumptions, the energetic balance on a reactive mixture slice in the quasi-adiabatic tubular reactor was:

U R (T p -T)= F 0 Cp water (T) dT dV R + F A dX dV R DH g (6)
(dV R = PR 2 dL) Integrating Eq. ( 6) on the quasi-adiabatic tubular reactor length, we obtained: hydrogen peroxide was evaluated at -925 kJ mol -1 . The heat quantity released by the hydrogen peroxide decomposition was included in this value and was equal to -15 kJ mol -1 with water as solvent.

The validation of this result was performed by a simulation of the reactor temperature profiles by a combination of the determined global reaction heat with kinetic data of the hydrothermal oxidation reaction of acetic acid [START_REF] Dutournie | Simulation of a tubular reactor for supercritical water oxidation[END_REF].

The one-dimensional program solves the oxidation of model compounds in an adiabatic reactor at constant pressure or in a reactor cooling by convective heat transfer. The simulation results are shown in Fig. 2. The kinetic parameters were adjusted to fit the simulated temperature profiles to the experimental temperature profiles: preexponential factor equal to 85×10 9 s -1 and activation energy equal to 170 kJ mol -1 . These kinetic parameters are closed to those previously published: preexponential factor of 3.6× 10 11 s -1 and activation energy of 180 kJ mol -1 (22.55 P5 31 MPa and 415 5T 5525 °C) [START_REF] Lee | Efficiency of H 2 O 2 and O 2 in supercritical water oxidation of 2,4-dichlorophenol and acetic acid[END_REF]. The simulation proves the validity of the obtained global reaction heat and the requirement of a such experimental reactor to scale up hydrothermal oxidation process.

Conclusions

Some parameters are necessary for scaling-up hydrothermal oxidation process (chemical pathway, reaction kinetic and thermal parameters, fluid thermodynamic properties, etc.). In this way, this paper was dedicated to the determination of hydrothermal oxidation global reaction heat from experimental investigations.

We have built a quasi-adiabatic tubular reactor to determine the heat quantity produced by the oxidation reaction. This reactor allows to obtain a temperature profile along its tube. A thermal modeling of the quasi-adiabatic tubular reactor was proposed to calculate global reaction heat from the experimental temperature profiles.

The experimental setup and the model were evaluated for acetic acid oxidation in supercritical water with hydrogen peroxide as oxidant. Besides

UPR & L 0 (T p -T)dL =F 0 & L 0 Cp water (T) dT dL dL +F A DH g & L 0 dX dL dL (7)
where L=the reactor length. Finally, the global heat quantity released by the reaction can be calculated from Eq. ( 7):

DH g = UPR & L 0 (T p -T)dL -F 0 & L 0 Cp water (T) dT dL dL F A X (8)
A software has been developed to calculate the global reaction heat from this equation and the experimental temperature profiles.

Determination of acetic acid global reaction heat

For all experiments, the Reynolds number was greater than 2200; so, the flow was turbulent in the reactor [START_REF] Villermaux | Ge ´nie de la Re ´action Chimique[END_REF]. In these conditions, the quasi-adiabatic reactor can be assimilated to a plug flow reactor and Eq. ( 8), which is deduced from Eq. (3), can be used to calculate acetic acid global reaction heat.

The thermal transfer coefficient U was determined experimentally from runs performed with pure water and found equal to 26.5 W m -2 K -1 . It was assumed constant in our working conditions. The water specific calorific capacity function, Cp water (T), was determined from data base [START_REF] Schmidt | Properties of Water and Steam in SI-Units[END_REF].

Starting from the experimental three temperature profiles and the reactor modeling, acetic acid global reaction heat was calculated. The results are reported in Table 3. Thus, the global reaction heat of acetic acid hydrothermal oxidation with the calculation of global reaction heats, the experimental temperature profiles allow to locate the hot spot in the reactor and to determine the maximal temperature reached versus the initial acetic acid concentration.

The reaction heat of acetic acid hydrothermal oxidation with hydrogen peroxide was evaluated at -925 kJ mol -1 . A simulation of the temperature profiles along the reactor is in good agreement with the experimental temperature profiles. The main interest of the system, quasi-adiabatic tubular reactor and temperature profile analysis procedure, is the global reaction heat determination of complex wastes, which can not be evaluated by simulation.

To improve the simulation tool performances, we work on the determination of heat transfer coefficient in the reactor and the heat exchanger.

Fig. 1 .

 1 Fig. 1. Quasi-adiabatic tubular reactor.

Fig. 2 .

 2 Fig. 2. Experimental and simulated temperature profiles for acetic acid hydrothermal oxidation with hydrogen peroxide at 25 MPa.

Fig. 3 .

 3 Fig. 3. Reactor model.

Table 1

 1

	Hydrothermal oxidation of acetic acid at 25 MPa
	Run	T injection (°C)	D (kg h -1 )	t r (s)	A.Ac. initial (% w/wt)	X (%)
	A1	395	0.5	34.2	3.35	30.5
	A2	392	0.5	26.4	3.92	98.4
	A3	403	0.5	24.6	4.90	96.9

Table 2

 2

	Maximal temperature reached
	Run	T max (°C)
	A1	438
	A2	558
	A3	568

Table 3

 3 Global reaction heat of acetic acid hydrothermal oxidation

	Run	DH g (kJ mol -1 )
	A1	-904.5
	A2	-936.7
	A3	-933.5

Acknowledgements

The authors thank the ECODEV program (CNRS) and the societies l'Electrolyse, Anjou Recherche and CNIM for their financial support.