Annals of Nuclear Energy 54 (2013) 184–191

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Annals of Nuclear Energy

journal homepage: www.elsevier .com/locate /anucene

Effects of soft control in the nuclear power plants emergency operation condition

Mohamed Ali Salem Al Harbi a, Ar Ryum Kim a,b, Inseok Jang b, Poong Hyun Seong b, Shigenori Shirouzu c,Sotetsu Katayama c, Hyun Gook Kang a,b,⇑a Department of Nuclear Engineering, Khalifa University of Science, Technology and Research, P.O. Box 127788, Abu Dhabi, United Arab Emiratesb Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 373-1, Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Koreac Tokyo Office, International NGO, Global Strategic Institution, Advanced Radiation Medical Science, Tokyo, Japan

a r t i c l e i n f o a b s t r a c t

Article history:Received 23 August 2012Received in revised form 9 November 2012Accepted 10 November 2012Available online 20 December 2012

Keywords:StressHuman errorSoft controlElectroencephalogram (EEG)Electrocardiogram (ECG)Skin temperature

0306-4549/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.anucene.2012.11.014

⇑ Corresponding author at: Department of NucleaKorea Advanced Institute of Science and TechnoYuseong-gu, Daejeon 305-701, Republic of Korea. Tel42 350 3810.

E-mail addresses: uae.alharbi@gmail.com (M.A.S. A(A.R. Kim), nuclear82@kaist.ac.kr (I. Jang), phseoshirouzu_vista@live.jp (S. Shirouzu), so.katayama@hyungook@kaist.ac.kr (H.G. Kang).

In addition to the evolution from buttons and switches to the computer-based consoles, the operator mayinteract with the plant via soft controls. Soft controls are input interfaces connected with control and dis-play systems that are mediated by software, rather than by direct physical connections. However use ofsoft control may cause unknown difficulties of operation and provide new opportunities of human errors.This study is to investigate the effect of the new interface to human errors in the emergency operation.Based on the emergency operation procedure, the human error modes were identified by using system-atic human error reduction and prediction approach. Experiments with 21 graduate students in maincontrol room mockup in the nuclear engineering departments of universities in UAE and Korea were con-ducted to observe the operators’ behavior resulted from the use of new input interface (Emergency safetyfeature-component control system Soft Control Module, ESCM). Physiological parameters such as electro-encephalogram, electrocardiogram and skin temperature were measured to assess the stress level of thesubjects. The experimental results showed more human errors during ESCM tasks than non-ESCM tasks.The analysis of the physiological measurements also demonstrated that subjects were in high stress levelduring the ESCM tasks in comparison with non-ESCM tasks. It is notable that this study was performedwith graduate students without consideration of their expertise levels. Different behaviors of the noviceand the expert groups were also discussed.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

The main control room (MCR) is the bank of information regard-ing the plant status and provides means to control abnormaldeviations in a nuclear power plant (NPP). The operators in theMCR observe the process and maintain the desired plant status.Conventional type of MCR uses the physical and the hardwiredconnections. The advanced MCR utilizes the computer-based con-soles and the operators may interact with the plant via soft con-trols. In NUREG/CR-6635 (2000), soft controls are input interfacesconnected with display systems that are mediated by software,rather than by direct physical connections. Many types of inputinterfaces are used as soft controls including touch screen, lightpen, mouse, trackball and joystick.

ll rights reserved.

r and Quantum Engineering,logy, 373-1, Guseong-dong,.: +82 42 350 3830; fax: +82

l Harbi), arryum@kaist.ac.krng@kaist.ac.kr (P.H. Seong),

gmail.com (S. Katayama),

However, soft controls may provide new opportunities for oper-ators to make error. Lee et al. (2011) reported different types of hu-man errors caused by the use of soft control. It is well known thatone of the main reasons of nuclear accidents is the human errorcaused by the confusion of operators. For instance, the main rootof the Three Mile Island (TMI-2) accident was due to the operator’slack of ability to identify or react properly to the unexpected auto-matic shutdown of the reactor according to Lee et al., 2011. Humanerrors are investigated in many industries. Stanton et al. (2009)discussed the human error and their consequences. Within com-plex socio-technical systems, around 75% of all accidents andsafety compromising incidents are attributed, in part at least, tohuman error. Shorrocka and Kirwan (2002) presented a similarstudy and mentioned several near miss cases that occurred in UKairports due to human error. We need to know the human opera-tor’s way of thinking and reaction so that we can effectivelyaddress these critical problems.

Accordingly, it is important to examine the effect of soft controlduring emergency operation condition. The main purpose of thisstudy is to quantitatively investigate the effect of separate touchscreen known as ESCM (ESF-CCS Soft Control Module) to humanerrors. For this purpose, experiments were conducted in the mock-up of a NPP MCR called compact nuclear simulator (CNS). The

M.A.S. Al Harbi et al. / Annals of Nuclear Energy 54 (2013) 184–191 185

experiments utilized the actual emergency operating procedures(EOPs) and the derived human error mode table.

In order to measure the stress level of the operators in a non-intrusive manner, we applied three physiological measurements:Electroencephalogram (EEG), electrocardiogram (ECG), and skintemperature. Pascalis and Perrone (1996) and Murat et al. (2010)showed that EEG has proven its validity to recognize various hu-man mental conditions. Park et al. (2011) stated that EEG was ableto detect the emotion induced videos to a 34 healthy subjects byobserving the increase of beta waves. Sulaiman et al. (2012) iden-tified that alpha power represent the relaxation condition whilebeta power specifies the alertness condition. Another related study,Hamid et al. (2010), investigated the relation among human stressand EEG power spectrum of beta and alpha bandwidth. In healthscience field, Miyake et al. (2009) utilized ECG to measure theautonomic nervous system (ANS) to detect the physiological re-sponses to workload change. Or and Duffy (2007) presented theirresearch regarding facial skin temperature drop in relation withmental stress. Reyes et al. (2009) also demonstrated the develop-ment of a measure to detect the mental workload using human fa-cial skin temperature variance. They noticed that simulator drivingwhich imposed a higher subjective workload clearly caused thedramatic decrease of nose temperature.

Section 2 of this paper describes the task analysis process of thetarget EOP. Section 3 explains the experiment performed and itsresults will be analyzed in Section 4. Discussion and conclusionwill be presented in Section 5.

2. Task analysis

2.1. Scenario

This study aims to investigate the effect of ESCM to humanerrors in emergency operation of a NPP. Among many possibleaccident scenarios, the steam generator tube rupture (SGTR) wasselected as the primary accident scenario for the experiment sinceit involves some soft control actions and its occurrence frequencyand potential severity is high Free and Schor, 1986.

Pressurized water reactors utilize steam generators (SG). Thepurpose of SG is to produce steam by evaporating the water. ASG consists of thousands of pipes to transfer heat from the reactorcore to the cooling water located outside the tubes. The createdsteam in turn will turn the blades of the turbine and produce elec-tricity. Finally the steam will be condensed and go back to beheated in the steam generators (NRC library, Backgrounder onSteam Generator Tube Issues).

SGTR happens when a tube inside a SG is broken. This will causethe leakage of the primary coolant (radioactive water) to the sec-ondary side. There is a possibility that such an accident will resultin the radioactivity release to environment. It also caused the lossof coolant in primary side. The typical symptoms of the SGTR thatcan be noticed by the operator are.

� The Level of the Pressurizer (PZR) decreasing.� PZR Pressure decreasing.� PZR Temperature decreasing.� PZR Heater is in the [ON] status.� Failed SG Level increasing.� The variance of two SG Feedwater Flow.

2.2. Emergency operation procedure and task analysis

EOP is the procedure the operator must follow in the emergencyaccident condition. A detailed EOP is provided in nuclear field tohelp execute the various tasks required to mitigate all anticipated

accidents under the emergency condition. The experiment in thisstudy utilized the EOP for SGTR accident developed based on thereal one of existing NPP. Modification of EOP was prerequisite toconduct the experiments since the simulator is a compact simula-tor to show principles and not all detailed actions as in the controlroom. Thus, some components which are described in the EOP arenot modeled in the simulator and these tasks are excluded.

We applied systematic human error reduction and predictionapproach (SHERPA) to analyze the tasks in the modified EOP. SHER-PA is one of the human error identification techniques and suitableto classify the human errors resulted from the use of soft controls(Lane et al., 2006; Ha and Seong, 2007; Stanton, 2002; Harris et al.,2005). It demonstrated the highest overall rankings on a number ofassessment criteria for its performance such as comprehensive-ness, accuracy, consistency, theoretical validity, usefulness andacceptability (Kirwan, 1990).

In the task analysis, we divided all tasks in the EOP into unittasks which cannot be divided into sub-tasks as shown in Fig. 1and the tasks in the gray boxes are represented the unit tasks.According to Lee et al. (2011), these unit tasks can be rearrangedalong to its sequence: (1) operation selection, (2) screen selection,(3) control device selection, and (4) operation execution as shownin Fig. 2. Also, six types of human errors were selected among var-ious possible human error modes as shown in Table 1.

3. Experiment

3.1. Subjects

Twenty-one graduate students of nuclear engineering depart-ments participated in this experiment from Khalifa University ofScience, Technology and Research (KUSTAR) in UAE and KoreaAdvanced Institute of Science and Technology (KAIST) in Korea.The subjects were 9 females and 12 males aged ranging from 21to 34 years.

3.2. Environment

The experiment took place in the mockup of MCR whichincludes the compact-type plant simulator called CNS. The experi-ment equipment in KUSTAR is shown in Fig. 3 and this is similar tothe one in KAIST. The reference plant of this simulator is Kori 3&4which is Westinghouse three-loop type pressurized water reactor.The mockup includes a large display panel, computer-based con-soles with separate touch screen known as ESCM. Each subjectwas asked to operate the simulator and control any safety equip-ment in the plant using ESCM. The experiments were conductedin identical laboratories in KUSTAR and KAIST.

3.3. Procedures

Firstly, the subjects had to complete 2 h simulator training tocertify that they were able to navigate the screens of CNS to findrequired information and to manipulate the equipment. Thebackground information related to SGTR and the EOP training weredelivered to enhance their understanding. The subjects in thisexperiment played the role of the reactor operator while theexperiment conductors covered the other roles such as supervisoryreactor operator, electrical operator and turbine operator.

Sensors for measuring the physiological signals were attached30 min before the experiment starts for their stabilization. Duringthe experiment, by utilizing the developed human error modetable, the conductors marked the errors of subjects. Video ofexperiments was also recorded to the use of analysis. The plantoperation required to be done by the subjects took around 1 h.

Fig. 1. The example of task analysis.

Fig. 2. The example of sequence analysis and its related error modes.

Table 1The possible human error modes (Lee et al., 2011).

Error mode Cases

E1: Operation omission Omission of a step in a procedureOmission of an instruction in a stepNonrecognition of unexecuted action

E2: Wrong object Right operation on wrong objectWrong operation on wrong object

E3: Wrong operation Wrong operation on right objectOperation in wrong direction

E4: Mode confusion Right operation on wrong modeWrong operation on wrong mode

E5: Inadequate operation Operation too long/shortOperation too much/littleOperation mistimedOperation incomplete

E6: Delayed operation Too late operationFig. 3. The mockup of MCR installed in KUSTAR.

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Fig. 4. EEG electrodes positions.

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3.4. Instruments

3.4.1. ElectroencephalogramEEG was recorded to analyze the brain wave of subjects during

the experiment. Park et al. (2011) illustrated that the power spec-trum analysis is expected to provide a measurement to know if thesubject is facing any alertness condition, especially by using beta

Fig. 5. The typical example of task analys

waves. The device used in this experiment consists of eight elec-trodes which are attached on the proper position on the scalp usinga special paste. The attachment positions are shown in Fig. 4. Thesignals were recorded and analyzed using software calledTELESCAN.

3.4.2. ElectrocardiogramECG sensor detects the electrical excitation of the human heart

as difference of voltage between two electrodes patches attachedon the surface of subjects’ chest. The ANS is known to show humanconditions like sleep, tense or relax. The ANS is divided into twodivisions the parasympathetic nervous system (PSNS) and thesympathetic nervous system (SNS). There are two types of sensors.The first one is a wireless sensor. By analyzing the online signalfrom this wireless sensor, the best location of electrode attachmentcan be determined. The second one is a memory type sensor. It canbe used to record the signals for longer time in remote place.

3.4.3. Skin temperatureThermal image camera is used for detecting any deviation in

skin temperature. It is able to take high resolution infrared images(320 � 240 pixels) with temperature range of �20 �C to 1200 �C.The thermal sensitivity of the recorded image is 50 m�K. The reso-lution should be adjusted accordingly to the distance between thecamera and the subject. Nose skin temperature changes extractedfrom thermal images are expected to provide objective informationabout the workload of an operator and it can be obtained relativelyeasily. In this experiment the thermal images were taken every30 s.

is for both ESCM and N-ESCM tasks.

Table 2Group statistics of human errors regarding the use of ESCM.

Groups N Mean Std. deviation Std. error mean

ERRORS RELATED TOESCM

21 0.004169 0.0040973 0.0008941

ERRORS RELATED TON-ESCM

21 0.001800 0.0016000 0.0003500

Fig. 6. Distribution of error rate of ESCM and N-ESCM task groups.

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4. Results of experiment

The analysis was done with 21 experimental data sets whichinclude video recording, checklist of errors, EEG, ECG and thermalimages. Among them, ECG of two subjects and thermal image ofone subject were not included in the analysis due to the heavynoise of measurement.

The plant operation based on EOP consists of many unit tasks.As mentioned before, these unit tasks were identified by taskanalysis. We categorized them to several task groups for the conve-nience of analysis. The task groups which utilize ESCM are namedas ESCMi. The other tasks groups are named as N-ESCMi. The typ-ical example of task analysis for both ESCM and N-ESCM tasks areshown in Fig. 5. However, there is not any difference betweenESCM task and N-ESCM task because of a limitation of CNS. Inthe existing plants, operators should navigate screens and select

Fig. 7. Averaged error rat

target devices on the ESCM, whereas this is not modeled in theCNS. Based on the timing of each task group, the recorded mea-surements were synchronized.

4.1. Human error analysis

There are different numbers of unit tasks in each of task groups.In order to compare the human performance of task completion,we use the error rate of each task group as a measure which canbe defined as follows:

error rate of task group i ¼ Number of errors in task group i by all subjectsðnumber of subjectsÞ � ðnumber of unit tasks in task group iÞ

SPSS (Statistical Package for the Social Sciences) was used asstatistical program to perform the test. First issue we addressedwas whether there exists distinguishable human performance dif-ference between ESCM and N-ESCM task groups. Table 2 shows ba-sic group statistics. The averaged error rate of the ESCM taskgroups is 0.42% while that of N-ESCM task groups is 0.18% andthe p value is lower than 0.05 (p = 0.014) as the result of Wilco-xon–Mann–Whitney (Kruskal, 1957). As shown in Fig. 6, the differ-ence is significant with 95% confidence level. Thus we can concludethat the human performance of ESCM task groups is statisticallydifferent from that of N-ESCM task groups.

The comparison of error rates of individual task groups is ex-pected to provide more insights. The number of N-ESCM taskgroups is larger than that of ESCM task groups. Thus for the com-parison purpose, we arbitrary selected one N-ESCM task groupwhich must be performed between two ESCM task groups. Fig. 7shows the error rates in the ESCM and N-ESCM groups along withsequence of tasks.

4.2. EEG analysis

The EEG data were processed and analyzed using TELESCANsoftware package. In order to remove noises, EEG signal below4 Hz was filtered out using Fast Fourier Transform. Using powerspectrum analysis, the power ratio of brain wave was obtained.Beta power ratio is the ratio of beta bandwidth (13–30 Hz) powerover the sum of all alpha (8–13 Hz), beta, theta (3–8 Hz), and gam-ma (0.5–3.5 Hz) bandwidth powers. We utilized the average valueof beta power ratios of eight channels’ measurements as the mea-sure of relative strength of beta power among the others. The for-mula can be mathematically expressed as the following equation:

b power ratio ¼ VðbÞVðhÞ þ VðaÞ þ VðbÞ þ VðcÞ ð1Þ

e in each task group.

Fig. 8. Averaged beta power ratio of EEG spectrum in each task group.

Fig. 9. The result of statistical analysis between beta power ratio and error rate.

Fig. 10. Averaged PSNS activity

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Fig. 8 shows the averaged beta power ratio of each task groups.The data shown in the figure are averaged on all the subjects andall channels. As explain Section 1, higher beta power ratio indicatesalertness of subjects and it represents that the alertness of subjectswas increased when they conducted ESCM task groups. Also, betapower ratio is positively related to the error rate (R2 = 0.4715) asshown in Fig. 9. During ESCM task groups, the subjects made morenumber of errors under relatively higher alertness condition.

4.3. ECG analysis

ECG sensor detects the electric excitation of heart. When elec-tric excitation get across to the whole cardiac muscle, the voltagelevel of ECG becomes the maximum and this peak is called R wave.R–R interval implies the time interval from R wave to the followingR wave. Time series data of R–R interval were transformed to fre-quency domain in this analysis. The ECG data were processed

level in each task group.

Fig. 11. The result of statistical analysis between the level of PSNS and error rate.Fig. 13. The result of statistical analysis between the number of subjects whoshowed nose temperature drop and error rate.

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and analyzed using NEW_WORLD software package. ObtainedR–R interval is used to calculate the activity of PSNS and SNSactivity. PSNS activity can be measured by the area of the high fre-quency components of the frequency spectrum of R–R interval. Thenoises from improper recording were filtered out.

This study will take into account the average PSNS activity. Thehigher value of PSNS activity shows a relaxed condition while thelower value indicates that the subject is under tense condition.As shown in Fig. 10, it demonstrates that the PSNS activities ob-served in the ESCM task groups are generally lower than those inthe N-ESCM task groups. That is, subjects were under tense condi-tion when they performed the ESCM tasks. However, the one of theN-ESCM task groups showed lowest PSNS activity. N-ESCM 2 is thetask for ‘Stop RHR pump’. This task involves checking activity of theRHR pump and many subjects got confused because they thoughthis is execution activity instead of checking activity. However, itis difficult to observe the strong relationship between the level ofPSNS and error rate (R2 = 0.1882) because of N-ESCM 2 as shownin Fig. 11.

4.4. Skin temperature analysis

As pointed in the studies of Or and Duffy (2007) and Reyes et al.(2009), the stress is correlated with the drop of the nose

Fig. 12. The number of subjects who showed nos

temperature. The FLIR Quick Report software was used to analyzethe nose temperature of subjects during experiments. Using theflying spot meter on the thermal pictures which are taken every30 s, we measured the temperature of nose. This measurementwas plotted in time domain. The nose temperature difference be-tween beginning and end of each task group was identified.

Fig. 12 shows the number of subjects who showed the temper-ature decrease during each task group. The results obtained fromthe thermal images analysis shows that more subjects showed atemperature drop at the ESCM task groups and it indicates mostsubjects got stressed when they conducting ESCM tasks. Also, thenumber of subjects who showed nose temperature drop is signifi-cantly related to the error rate (R2 = 0.8846) as shown in Fig. 13.During ESCM task groups, the subjects made more number of er-rors under relatively higher alertness condition.

5. Discussion and conclusion

In advanced MCR, the plant systems that are upgraded withdigital technologies are likely to have soft controls. However, softcontrols have characteristics that provide new opportunities foroperators to make errors. Previous studies including Lee et al.(2011) also pointed out there is a difference in human error modes

e temperature drop during each task group.

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when soft control by observing the emergency operation experi-ments with expert operators. This study aims to evaluate thehuman performance change due to the application of ESCM withquantitative measure. We tried to check it quantitatively by theexperiments in a MCR mockup using physiological assessmentinstruments.

The results of experiments showed the deteriorated humanperformance during ESCM tasks. There was obvious differencebetween the error rates of ESCM tasks groups (0.42%) and thoseof N-ESCM groups (0.18%). The higher error rates of subjects inESCM tasks indicate that separate touch screen may cause addi-tional stress and therefore the subjects were confused.

The results were consistently supported by physiological mea-surements. We analyzed the EEG signals from eight channels re-corded during experiment. Generally speaking, the overall trendof the beta power ratio of EEG tends to be higher at ESCM taskgroups. It implies that the subjects had a higher level of stress atthose points. PSNS activity from ECG measurement also demon-strates that at ESCM task groups the subjects tend to have higherlevel of stress (lower values of PSNS activity). Nose temperaturesof the subjects extracted from the thermal images taken every30 s show that the more subjects tend to have a decrease in tem-perature level at ESCM task groups comparing to N-ESCM taskgroups. Based on the previous studies such as Or and Duffy(2007) and Reyes et al. (2009), this means that the subjects tendto have higher level of stress at ESCM task groups.

It is notable that we performed the experiments with graduatestudents while the operators are very skillful experts in actualnuclear power plants. The effect of expertise or familiarity was alsoobserved in the experiments of this study. Some of the subjects arevery familiar with ESCM or the operation of simulator since theywere involved in the design of MCR mockup which was used in thisexperiment. Their stress levels measured by all three physiologicalparameters at ESCM task groups are quite similar to those ofN-ESCM task groups. And their error rates are very low in compar-ison with the others. Their error rates in ESCM task groups are notdistinguishable with those in N-ESCM task groups. One more con-siderable point is the difference of task natures. That is, the higherstress level might be caused by the higher difficulty of ESCM tasksnot by the separate touch screen itself. In this study we did not per-form the analysis on the difficulties of individual tasks.

In summary, the error rate and the three physiological instru-ments provided a proof of higher level of stress of the subjects dur-ing the ESCM task groups at least for novice operators. This impliesthe enhanced supports for the human operators are required in theMCR design. The experiments were performed in a mockup MCR

with the limited number of subjects whose expertise levels aredifferent. The extended experiments for more realistic and practi-cal results are recommendable. The number of subject, the varietyof scenario, the realistic replication of emergency operation pres-sure and the grouping with expertise level could be considered.

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