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Progress in Nuclear Energy, Vol. 46, No. 2-3, pp. 206-225, 2005 Ava i lab l~ o n l i n e at www.sciencadirect.corn_7 © 2005 Elsevier Ltd. All rights reserved

c ~ E N C E ( ( / ) o ~ R E C "r o Printed in Great Britain 0149-1970/$ - see front matter

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EXPERIENCES AND APPLICATIONS OF PEANO FOR

ONLINE MONITORING IN POWER PLANTS

PAOL0 F. FANTONI

OECD Halden Reactor Project, Halden, Norway. Phone: +47 69 21 22 00 E-mail: Paolo.Fantoni@hrp.no

ABSTRACT This paper describes the motivations, ideas, work and lesson learned that resulted in the development of the software product called PEANO, for signal validation of process sensors in a dynamic system. The focus of this report is more on the process that started in 1994 and brought to the realization of the current system in 2003 (PEANO ver. 4.1).

KEYWORDS neural networks, fuzzy classification, signal validation, process monitoring, calibration reduction.

© 2005 Elsevier Ltd. All rights reserved

pattern recognition,

1. INTRODUCTION

PEANO is the result of the work of many persons. During these 10 years, many scientists from many countries have contributed to the development of the system in all its phases, from the original ideas to the current mature state. The actual state of the system is the result of user feedback, lesson learned from installations and applications, both in Europe and USA, and discussions during meetings, conferences and workshops where this project was presented and discussed. This paper presents an overview of all these valuable lessons and the results of some applications where PEANO was successfully used.

2. EARLY MODELS

The work on signal validation using empirical models began at the Halden Project in 1994 in the context of the Computerised Accident Management Support (CAMS) project (Fantoni et al., 1994,1995,1996,1997). The CAMS system was supposed to give advice to the plant operators during an accident progression, in order to identify the problem and apply countermeasures to bring the plant in a safe state. One of the modules in the CAMS system design was the Signal Validation Module, to validate the plant signals needed to perform the diagnostic and predictive tasks.

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The need for a fast, real-time validation of a set of process signals encouraged the idea of using empirical models of the process and in particular a special architecture of neural networks called Autoassociative networks. Research work in the signal validation area using autoassociative networks was already in progress in those years, mainly at the University of Tennessee and the results achieved with this technique were claimed to be very promising (Upadhyaya et al., 1990, Bartlett, 1991, Hines, 1996).

The first models and tests have been performed at the HRP using simulated data obtained by the APROS simulator code that was run to get a set of process signals during power ramps in a BWR nuclear plant (Forsmark II). APROS and the BWR model was the same used for the CAMS project.

The tests performed with these data were presented at the IAEA Specialist Meeting on Advanced Information Methods and Artificial Intelligence in Nuclear Power Plant Control Rooms, Halden, 13-15 September 1994 (Fantoni, 1994).

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Figure 1 Feedwater flow validation during a power ramp (BWR), 1994

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The ANN model was a single autoassociafive network with 3 hidden layers, an input layer with 14 process signals and an output layer with 14 output nodes. Robust training, which means the use of artificially added faults in the training set, was already implemented in this model.

Although these results were already promising, it is evident from Figure I that the ANN model experiences a visible error when the sensor fails (although the ANN value is closer to the real process value). Moreover (see Figure 2), failures in one signal (feedwater temperature in this example), had strange effects in other signals, as shown for the feedwater flow in the figure.

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3. THE PARTITION MODEL

This preliminary work suggested that using only one ANN for the process validation in the whole process operating range could not achieve the necessary accuracy. The idea developed at the end of 1994 consists in partitioning the process input space (n-dimensions space, where n is the number of monitored signals) and then assigning to each process cluster (partition region) a dedicated ANN. Each ANN would then be trained using only data belonging to its assigned cluster. During the validation, a classifier detects the operating cluster, depending by the process state, and triggers the corresponding ANN. Note that, with process state, we do not mean just power level, but the state defined by the complete set of input process variables, where power can be one of them.

A first prototype of this architecture was developed between September 1994 and May 1995. The main characteristics of this model were:

• The clustering/classifier algorithm was the well known unsupervised Kohonen Self Organising Map (SOM), responsible to identify a crisp partition of the input space.

• Each cluster dataset was used to train an auto-associative neural network of the same type of that applied in the first phase of the project.

The architecture was the one shown in Figure 3. The BWR tests have been used again with this model and the results achieved were definitely better than before, see Figure 4 and Figure 5.

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Figure 3 Signal Validation architecture in 1995 (OECD Halden Reactor Project, 1994)

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Figure 4 BWR test with the ~risp partition model, 1995

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Figure 5 BWR test with the crisp partition model, 1995

The ongoing work and these results were presented in June 1995 at the SMORN VII conference in Avignon, France (Fantoni, MazTola, 1995). Other tests using the same partition model applied to art offshore platform were presented the same year at the A.I. Petro Conference in Lillehammer, Norway, published in a proceedings book edited by B.Braunschweig & B.Bremdal in 1996 (Fantoni, Mazzola, 1995).

The accuracy of this model was evaluated and reported as in Figure 6. Two articles describing this partition model and its accuracy appeared in 1996, the first in Nuclear Technology (Fantoni, Mazzola, 1996)and the second in Annals of Nuclear Energy (Fantoni, Mazzola, 1996).

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Figure 6 Accuracy estimate of the 1995 crisp partition model

4. THE FUZZY PARTITION CLASSIFIER

The crisp characteristic of the SOM classifier had some drawbacks that can be summarised as follows:

• The boundary problem. That model does not consider properly the transition area between clusters, where the input pattern could be considered a member of two or even three clusters at the same time. As a consequence of that, the switching mechanism changes the working ANN abruptly, when the input pattern moves in ambiguous areas, resulting in unexpected changes in performance.

• A crisp classifier always tries to find a reasonable membership cluster, even when the input pattern is far away from all the identified clusters. In rum, the activated ANN always gives a response, so that there is no way to have a reliability measure for the output. This is a very well known problem of every black-box approach (and there is nothing more than ANN modelling that can be called black- box).

In September 1995, the HRP attended the EUFIT 95' conference in Aachen, Germany, where an entire session on clustering techniques was organised. From this experience, the idea of developing a fuzzy- clustering model to solve the above problems, took shape. The resulting model was a 3-step clustering algorithm as follows:

• Step 1: A crisp clustering model using the well known algorithm ISODATA (Tou, Gonzalez, 1974). This step is needed to create a rough partition to initialize the second step.

• Step 2: Fuzzy clustering, using the FuTzy-C-Mean (FCM) algorithm (Bezdek, 1981), where the starting partition is the output from step 1.

,, Step 3: Possibilistic clustering, using the Krishnapuram's algorithm (Krishnapuram, Keller, 1993). This step addresses the second problem above.

The difference between fuzzy clustering (step 2) and possibilis#c clustering (step 3) is that the fuzzy approach assumes that all the input patterns belong to the partition and must be classified in all the existent clusters at some degree (fuzzy membership). The possibilistic approach assumes instead that outlier patterns could possibly enter the classification algorithm and could be classified as outsiders (not belonging to any existent cluster). In the hypothesis that all the input dataset contains good data (which is the case of a training dataset), the fuzzy clustering model produces a better partition than the possibilistic model. This fact led to the actual 1996 architecture, where the possibilistic model is in parallel to the fuzzy model and it is used only to assess the validity of the input pattern.

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Figure 7 Recireulation Pump Trip with core flow AND feedwater flow failure (Fuzzy partition model, 1996)

The simulated BWR data were used to test this architecture, with very good results, see Figure 7 and Figure 8. This model and the corresponding results were presented at the NPIC&HMIT conference, Pennsylvania State University, 1996 (Fantoni, 1996) and the EHPGMeeting in Loen, Norway (Fantoni, 1996).

The year 1996 is a milestone for the PEANO system (at that time it was the Signal Validation Module of the CAMS system), because the base architecture never changed in the following years, although many interesting improvements and refinements have been implemented in the model in the years 1999-2003. The main blocks of this architecture can be summarised as follows:

* Process inputs: the input space is defined by the current value AND a number of past values for each monitored signal. This is the auto-regressive portion of the validation mechanism embedded in this model.

• Fuzzy classifier: the input space is partitioned in a suitable number of clusters (the number of clusters is defined by the model, not by the user). Each cluster drives an ANN auto-associative network trained with the input patterns belonging to that cluster.

• Possibilistic clustering: this is used to verify that the input pattem belongs to the input space used for the training (in other words: the process state is within the model knowledge)

• A bank of ANN: auto-associative neural networks trained with each duster data. All the ANN in the bank run against the input pattern, producing a validation pattern.

• Output selector: The result of each ANN is weighted according to the fuzzy membership value in the corresponding cluster. In the real case, only 2 or 3 clusters have a membership value larger than 0.

• In-step feedback: After a mismatch correction, the corrected pattern is fed again into the ANN. This is to compensate for induced errors in not affected signals.

• Back-step feedback: When an error gets larger, the corrected pattern is fed back again into the classifier module, to avoid false classifications due to large drifts or failures.

• One-step ahead feedback: Each input pattem is compared to the previous (validated) pattern. As a function of the acquisition scan rate, the system knows the maximum stepwise signal change. Big sudden signal failures can be trapped this way.

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Figure 9 The Fuzzy Partition Model

Figure 9 shows the main idea behind this fuzzy partition model.

5. THE EDF TESTS

In 1997, the French EdF decided for a co-operation with HRP to test the recently developed signal validation model, as described in Chapter 2.

They supplied 5 files with simulated data representing process data (14 signals) of a PWR 900 Mw in different operating conditions. They also prepared 5 test files with data from the same plant, where they had put unknown drifts or failures in one or more of the 14 monitored signals.

The selected signals are shown in Figure I0.

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Figure 10 The EDF tests ofa PWR 900 (1997-98)

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The results of these tests were presented in 1998 at a workshop in Lyon (Fantoni, Renders, 1998), at the Water Reactors Safety Meeting, USA (Fantoni, Roverso, Owre, 1998) and FLINS 98' in Belgium (Fantoni, Figedy, Racz, 1998). Examples of the test results are shown in Figure 11 and Figure 12.

For the first time in this project, the concept of accuracy bands was used. These bands track the accuracy of the model in calculating the true value of the signal. Accuracy values are calculated by the model during the training and are cluster dependent. Normally 2 bands are displayed in mismatch plots, like in Figure 11 and Figure 12, one representing 1 standard deviation of the accuracy value and the other one 2 or 3 (user selectable) standard deviations. The introduction of the accuracy bands in 1997 (also called tolerance bands or error bands at that time) is an important milestone in the development of the PEANO system.

6. PEANO VER. 1.0 AND 2.0

At the beginning of 1997 the signal validation model, as described above, was completely programmed in the Matlab environment (the 2 plots in Figure 11 and Figure 12 are in fact Matlab plots). At that time HRP started the development of a more friendly and efficient user interface, leaving the calculation kernel in Matlab. The interface was developed using the National Instruments development tool LabWindows/CVI.

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Figure 13 The first software architecture of PEANO (1997)

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The system was developed in modules, according to the structure shown in Figure 13, where the Training Unit and the Data Validation Unit were developed in Matlab and the rest in LabWindows. The first design of user interface is shown in Figure 14 and the system took the name of PEANO, from the name of Giuseppe Peano, the Italian mathematician who pioneered the set theory in the XIX century (PEANO is also an acronym for Process Evaluation and Analysis through Neural Operators):

Figure 14 PEANO user interface (1997)

It is worth to note that this interface is the first design of a PEANO model development tool, including the pre-processing unit, the classification unit and the training unit. The Monitoring unit was not included in this interface (1997) and it was still coded in Matlab, for off-line use.

This architecture was described in HWR-515 (Fantoni, Figedy, Racz, 1998) and presented at the EHPG Meeting in Lillehammer, Norway ( May 1998).

The years 1998 and 1999 were devoted to the development of the software around the fuzzy partition model designed in 1995-96. Although the calculation kernel remained the same, the software architecture made significant steps towards the realization of a complete software package for signal validation. In PEANO ver. 2.0 the architecture was that shown in Figure 15, where the system became a Client-Server system where the monitoring modules (clients) were connected in TCP/IP to the server. The calculation kernel was installed in the Matlab engine, both in version 2 and 3. A new user interface was developed for the monitoring station, see Figure 16.

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Figure 15 The Client-Server architecture of P EANO (1998)

Other new features in PEANO 2.0 were:

• A PEANO database, containing all the process and model information necessary to the validation task, connected to the server through the ODBC protocol.

• Improved data acquisition unit, to acquire process samples from the plant in real-time.

Figure 16 User interface for the monitoring display (1998)

During this period, 2 main application tests were conducted: The tests at the Full Scope Simulator in Madrid (Fernandez IUobre, Carrasco, 1998), performed by Tecnatom SA, and the installation of the system at the I-IBWR in Halden (Hoffmann, Figedy, 1999).

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6.1 The Installation of Peano at the Halden HBWR ~The HBWR is a natural circulation boiling heavy water reactor. The maximum power is 25 MW (th), and the water temperature is 240°C, corresponding to an operating pressure of 33.3 bar. Selected operating data is given in Table I.

Table I Nominal Reactor Operating Data

Power Level Reactor Pressure Heavy Water Saturation Temperature Maximum Subcoolmg Primary Steam Flow (both circuits) Return Condensate Temperature Subcooler Flow Plenum Inlet Temperature

up to 20 MW (th) 33.3 bar 240°C 3.0 MW 160 ton/h 238"C 160 ton/h 237"C

The reactor pressure vessel is cylindrical with a rounded bottom. It is made of carbon steel and the bottom and the cylindrical portion are clad with stainless steel. The flat reactor lid has individual penetrations for fuel assemblies, control stations and experimental equipment. The vessel is designed for a maximum operating pressure of 40 bar, 250°C.

14 tons of heavy water acts as coolant and moderator. A mixture of steam and water flows upwards inside the shroud tubes which surround the fuel rods. Steam is collected in the space above the water while water flows downwards through the moderator and enters the fuel assemblies through the holes in the lower ends of the shroud. The steam flows to two steam transformers where heat is transferred to the light water secondary circuit. Condensate from the steam transformers returns to the reactor by gravity. An external subcooler loop is installed to provide experimental variation of void fraction in the fuel assemblies and the moderator, and is also used for heating and cooling purposes.

In the secondary circuit, two circulation pumps pass the water through the steam transformers, a steam drum and a steam generator where steam is produced in the tertiary circuit. The tertiary steam is normally delivered as process steam to the nearby paper mill, but may also be dumped to the river.

There is generally no access to the reactor hall when the reactor is operational, and therefore all control and supervision is carried out from the control room.

During the first 3 months of operation at the I-IBWR, no sensor failures or calibration drifts have occurred. To test drift detection capabilities, off-line tests have been prepared. For this experiment a start-up cycle was used, starting at low power at around 1 MW going up to nominal power at 18 - 19 MW. A drift of - 3% per hour was added to signal F41 (STA Steam Flow), starting at the 8 th pattem. The prepared data file was then used as input to the PEANO server to test the performance at a time of sensor failure. The results are presented in Figure 17, with top graph showing the measured and validated values and lower graph indicating the mismatch with its corresponding error bands.

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Figure 17 Drift test on signal F41, STA Steam Flow (-3% per hour)

This installation at the Halden Reactor was the first time that PEAN0 has been connected in real-time to an actual physical process. A lot of experience has been gained from preparing the system with recorded process data and dealing with the related problems (e.g. process faults and earlier sensor failures). Suggestions for program improvements coming from this experience have been taken into account in later

versions of PEANO. The field tests only covered the first two months of the installation. No real sensor drift or other problems were experienced or detected during that time. Nevertheless, it has already become clear that the validation task is performed properly throughout the whole operating range of the reactor and that false incoming patterns are detected correctly. The tests performed with artificial sensor failures give good confidence in the future performance of the system in real-time situations.

7. PEANO VER. 3.0

The development of PEANO had another major step in 1998-1999, that resulted in the release of ver. 3.0. The main features in this release can be summarised as follows:

• Complete redesign of the server/development module. 0 Redesign of the inter-module communications ) Design of a new wavelet denoising module • Redesign of the database module • Implementation of a new, more efficient, training algorithm

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The calculation kernel was still implemented in Matlab. PEANO 3.0 design is reported in HWR-585 (Fantoni et al., 1999).

PEANO ver. 3.0 was presented in many conferences around the world (Fantoni, Hoffmann, Roverso, 1999) The presentation of the system at the EPRI Maintenance Conference in Atlanta, 1999, gave an important strategic shift to the design objectives of PEANO. As we already noticed, the project begun as a validation method for CAMS, in accident conditions. When the project was given an independent life in 1996, it continued to be thought as a validation tool in real-time. From Atlanta 1999 the attention was posed to an emerging problem in USA that addressed the need to monitor on-line the calibration state of plant safety instrumentation in order to relax the TechSpec requirement to verify all the instruments during an outage. The peculiarities of this problem, technically called "the calibration reduction topic" and the requirements that come from that, are the basis for all the design objectives of the following years.

8. RECENT ISSUES (2000 - 2004)

8.1 PEANO ver. 4.0 PEANO ver. 4.0 is the first version that does not require Matlab to run the calculation kernel. All the features and interfaces of version 3.0 were ported to version 4.0. From a user standpoint, the differences can be summarised as follows:

• Higher speed in model development and monitoring • No need for third party software

Version 4.0 is also the first PEANO version to have a complete user manual.

The calculation kemel had some improvements in the year 2000, coming from the user experience in the years 1997-1999. These changes have been documented in HWR-641 (Hoffmann, Fantoni, 2001).

PEANO ver. 4.0 and numerous applications developed for EPRI (USA), Foxboro Scada, ENEA and CESI (Italy), have been presented in several conferences, workshops and meetings (Fantoni, Hines, Shankar, 2001,2002).

In September 2001, The Halden Project organised a workshop on "Power Plant Surveillance and Diagnostics" (Ruan, Fantoni, 2001), where the current state of PEANO was presented and then published in a book edited by Dr. Da Ruan (SCKoCEN, Belgium) and the author (Ruan, Fantoni, 2002).

8.2 The EPRI Experiment (2001-2002) In 2001, in a project funded by EPRI, PEANO 4.0 was tested against real data coming from several US nuclear plants, in the framework of the EPRI On-line Monitoring Implementation Plan (Fantoni, Hoffmann, 2002, 2003).

Figure 18 to Figure 20 describe some results achieved with PEANO, using real data from a US PWR nuclear plant. PEANO was used to monitor 55 process signals up to the secondary side of the steam generators. The data was taken from 3 months of operation (March to May 2000) at different operation conditions.

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Figure 18 shows the re-calibration of a steam flow sensor while the plant was in operation. It is visible that after this intervention, the mismatch was well within the 2 standard deviation limit.

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Figure 19 shows the monitoring of a feedwater flow sensor during the power-up ramp. While the mismatch between PEANO and the instrument is always within the limits, it is interesting here to note that the tolerance band width changes with the plant operation conditions, because the accuracy of the system cannot be the same in all the possible conditions.

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Figure 20 is an interesting example of span drift of a steam flow sensor. Span drifts are difficult to detect because they show up only at some location of the instrument range. In this real-life example, the instrument was perfectly inside the calibration range until the power level came close to the rated level (high end of the instrument range). At this point the instrument started to drift and eventually finished outside the allowed tolerance band. At the plant, this drift was discovered only one month later.

8.3 The CESI project (2002-2003) In 2002, CESI (Italy) funded a project to evaluate the capability of PEANO to monitor a rather large set of signals in a fossile plant (Fantoni, Zanetta, Gregori, 2004).

PEANO has been applied to a steam-water cycle of a 320 MW fossil power plant in Italy. Data in normal operating conditions have been acquired by CESI. The set of data included 66 sensors, a remarkable number for this kind of application, whose signals were sampled every one minute. Data were collected on the plant for 8 months. Load variations were included in between 10% and 100% of the full range and data were collected not only in stationary conditions but also during load or thermal transients. A scheme of the monitored process is reported in Figure 21.

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Pcc ~ A L D A I A I ~ - - ]

"c~a

"~_~.~, I ~-~,> i,...~ . . . . . . . . . . . . . . ~ . . . . . . . . . . . =. . . . . . . . . . = , , - ,

Figure 21 The 66 sensors CESI project

r ~ ~*aSta

Figure 22 - Curve-fitting and residual errors during monitoring

Figure 22 is an example of residual monitoring for 2 channels, without the use of real-time filters. Figure 23 is an interesting example of complete loss of 2 signals, where PEANO was used to supply correct estimated values for both of them, in transient conditions.

PEANO 223

P£,pil l l f,~ r~gnal 6

5 ~ - " . . . . . . . . J- . . . . . . .

2 0 f - . . . . . - . . . . . . t - . . . . . . . . . . . . .

0 . L t

~ s i d ~ (2 s~gme)

2 . . . . . "I . . . . . . r - . . . . . . . . . . . .

- 2 . . . . . . . . . . . . / . . . . . . . . . . . .

27-M~q-2001 ~B - I~01 ~9-May-2001

T~n~eaaOut r,, signal20

5or)I . . . . . . . . . . . 1 !1 . . . . . . . . . . . . . r'e~dU~ 12 slgrnal

. . . . . . . . . . . . I - . . . . . . . . . . . . .

. . . . . . . . . . . . I . . . . . . . . . . . . . r

Figure 23 Loss of signals and PEANO data reconciliation

8.4 PEANO ver. 4.1 and the NNLPS algorithm PEANO ver. 4.1 was released in 2003 and it contains the features and requirements needed for the

.... calibration reduction" problem. All these features are a consequence of the discussions with the US utilities during the EPRI On-Line Monitoring User Group meetings in the years 2000, 2001 and 2002. The main new features in this version are:

! i • A completely new pre-processing unit, that runs as a stand-alone product • An off-line monitoring module, useful for non real-time applications (as it is the case for the

calibration reduction problem)

PEANO ver. 4.1 has been documented in HWR-742 and it was presented at the EHPG meeting in Sandetjord, Norway in May 2004 (Hoffmann et al., 2004). The NNLPS approach is a substitute of the ANN bank in the fuzzy partition model. It is the result of a co-operative work with the University of Tennessee in 2002. The main advantage of this option is the reduction of training time. The implementation and test of this algorithm has been reported in (Fantoni, Hines, Kirschner, 2002), HWR- 690 (Hoffmann, Kirschner, 2002) and HWR-742 (Hoffmann, Kirschner, 2004).

9. CONCLUSIONS

The PEANO project is still going on at the HRP. Although the current product is at the cutting edge in the field of signal validation and the original ideas, algorithms and technologies that constitute the core of the system make it a high-tech system, more work is foreseen to comply with the ever increasing and demanding requirements of the process industry.

In 1999, during the EHPG Meeting at Loen, Norway, while PEANO was remotely connected to the Halden HBWR and presented in real-time to the audience, a poster was visible on the wall of the conference room, with this message: "The adventure is not over ye f ' .

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