prediction of hydrogen safety parameters using intelligent techniques

INTERNATIONAL JOURNAL OF ENERGY RESEARCHInt. J. Energy Res. 2009; 33:431–442Published online 16 December 2008 in Wiley InterScience(www.interscience.wiley.com). DOI: 10.1002/er.1485

SHORT COMMUNICATION

Prediction of hydrogen safety parameters using intelligent techniques

Tien Ho1,�,y, Vishy Karri1 and Ole Madsen2

1School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001, Australia2Department of Production, Aalborg University, Fibigerstraede 16, DK-9220 Aalborg, Denmark

SUMMARY

With increase in the use and application of hydrogen for stationary and mobile applications, there is an increasedpressure to ensure the safety handling and monitoring of this combustible gas. The associated equipment to monitor andmeasure explosion limit of any leakage together with the pressure and flow rate is very expensive. Any reliablemathematical or empirical means to estimate and predict those safety features of hydrogen will greatly assist in avoidingexpensive instrumentation. In this paper predictive model for accurate estimation of hydrogen parameters such aspercentage lower explosive limit, hydrogen pressure and hydrogen flow rate as a function of different input conditions ofpower supplied (voltage and current), the feed of de-ionized water and various Hogens20 electrolyser system parametersis carried out. In addition, the percentage contributions of the input parameters on each hydrogen production parametersand optimum network architecture to minimize computation time and maximize network accuracy are presented. It isshown that output from the neural network predictive models of the hydrogen safety features agree well with itsexperimentally measured values. The hydrogen production parameters and predicted safety explosive limit were found tobe less than 5% of average root mean square error. Copyright r 2008 John Wiley & Sons, Ltd.

KEY WORDS: hydrogen generator; hydrogen safety; performance prediction using neural networks; sensitivity analysis

1. INTRODUCTION

Hydrogen is neither more nor less inherentlydangerous to work with compared to commonfuels. However, in some aspects it has to be treatedwith special caution due to its special properties,which require special engineering controls toensure its safe use. Hence, it is important toobserve some recommendations when workingwith hydrogen gas such as: use adequateventilation, leak detection devices and designing

leak proof hydrogen systems, the possibility ofhydrogen embrittlement in design, etc. [1].

Hydrogen and Allied Renewable Technologiesresearch programme at the University of Tasmaniahas built a custom laboratory for applied H2

applications for stationary and mobile use [2].During the handling process of hydrogengeneration, the following Australian Standards wereused: AS 2430.1—1987: Classification of hazardousareas—Part 1: Explosive gas atmospheres [3]; AS1482—1985: Electrical equipment for explosive

*Correspondence to: Tien Ho, School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001, Australia.yE-mail: [email protected]

Contract/grant sponsor: Hydro Tasmania Pty Ltd

Received 28 April 2008

Revised 1 October 2008

Accepted 1 October 2008Copyright r 2008 John Wiley & Sons, Ltd.

atmospheres—Protection by ventilation—Type ofprotection [4]; AS 2380.1—1989: Electricalequipment for explosive atmospheres—Explosionprotection techniques—Part 1: General requirements[5]; AS 1432—1996: Copper tubes for plumbing, gasfitting and drainage applications [6]; AS 4041—1998:Pressure piping [7]; AS 2982—1987: Laboratoryconstruction [8]; AS 2508.2.002—1992: Safe storageand handling information card—Hydrogen(compressed) [9].

Hydrogen generation and subsequent ventingshould conform to certain requirements to ensuresafety of the operators. Depending upon the flowrate of hydrogen it is decided whether it is venteddirectly into the atmosphere or disposed off byflaring [10]. Several researchers have worked overthe years to accurately estimate H2 flow rates[11–13]. It is also shown in the literature thathydrogen deflagrations are most likely to occur forsmall flow rates, where significant air can be foundinside the stack [13]. These flow rates influence theexplosion limits of hydrogen. In addition, it hasbeen shown in the literature that the ultrasonic andresistive hydrogen sensors for inert gas–watervapour atmospheres are complex designs [14]that require extensive infrastructure and cost.The H2 detection sensors, using ultrasonictechnology, detect variations in the acousticvelocity with a 0.4mm palladium wire. Althoughthese sensors operate at high temperatures andmeasure concentrations from 1 to 100% ofhydrogen concentration, they are expensive toinstall for routine measurements.

This paper describes a neural network approachto estimating the hydrogen production parameterssuch as percentage of explosive limits, hydrogenpressure and flow rates from a Hogen20electrolyser based on the input parameters suchas the voltage, current, water quality, watertemperature and water pressure. To cater forvarious test rigs, a hydrogen generator, Hogens20 Series 2 Hydrogen Generator, from ProtonEnergy Systems was set up for H2 production. Theproduction of H2 is measured using several sensorsto observed varied hydrogen concentration in air,various production pressures and flow rates. Theaims of this study were to build a betterunderstanding of various production properties

of the hydrogen as a function of input parameters.In addition, the percentages contributions of theseinput parameters on each hydrogen productionparameters and optimum network architecture tominimize computation time and maximizenetwork accuracy are presented in terms ofsensitivity analysis.

2. EXPERIMENTAL TEST RIG ANDSENSORS

The experimental test rig involved building sensorsfor Hogen20 electrolyser with a view to accuratelymeasure both the input and output parameters.The de-ionized water purification system sensorsincluded, a flow-rate sensor (Gem sensor low flowseries 155421, RFA type), a pressure sensor (Gemsensor GM-2200B-6, two-wire transmitter) and atemperature sensor (MIR-HPC-TX RTD Probedirect process mount) with fully programmablePR-5333A transmitter. The hydrogen productionperformance parameters sensors included, com-bustible gas sensor commercially availablepalladium wire resistive type sensor (e2v techno-logiesVQ603/2), a system pressure transducers(PT307), a back pressure regulator (BPR310) andproduct pressure transducer (PT312), Flow rate(LPM) are obtained by using a system of Z350hydrogen gas management.

The conversion of analog to digital signal iscarried out using computer interface, particularlyserial communication interface RS232 with the aidof LABVIEW software capable of multifunctiondata acquisition card (DAQ) NI 6025E PCI(Figure 1). A Windows Diagnostic Software

12

3 4

Figure 1. Temperature sensor (1), pressure transducer(2), flow sensor (3) and DAQ card (4).

T. HO, V. KARRI AND O. MADSEN432

Copyright r 2008 John Wiley & Sons, Ltd. Int. J. Energy Res. 2009; 33:431–442

DOI: 10.1002/er

Operation to monitor data, data log and modifyconfiguration settings for Hogens 20 electrolyserwas supplied by the Hogen20 electrolyser.

Therefore, the generic inputs for electrolysersare cell stack voltage (V), cell stack current (A),de-ionized water quality (MO), water temperature(1C), water pressure (kPa), water flow rate (GPM),electrolyser system temperature (1C), stack currentPWR102, system pressure (PSI) and boardtemperature (1C). The hydrogen productionperformance as output is the measurement ofpercentage lower explosive limit (%), hydrogenpressure (PSI) and flow rate (LPM).

It is important to note that the methodologyproposed here could be a generic application forall hydrogen production involving electrolysisprocess where prediction of safety features ofH2 can be carried out without expensiveinstrumentation. This predictive tool for H2

safety is seen as an aid for building mathematicalmodels based on neural network principleswithout having to invest on prohibitive sensors.

3. NEURAL NETWORKS AS PREDICTIVETOOLS FOR H2 PERFORMANCE

The developed system will be capable of predictingaccurate hydrogen production parameters includ-ing percentage of lower explosive limit, hydrogenflow rate, hydrogen pressure as a function ofdifferent input conditions such as power feed, feedwater and electrolyser system parameters. Artifi-cial neural networks (ANNs) were chosen aspredictive models because of their inherent tomodel non-linear process, adaptive learning, self-organization, real-time operation, ease of insertioninto existing technology [15–18]. Neural networkshave been extensively used for performanceestimation of several manufacturing processes inthe literature [15,19].

In the literature survey related to somehydrogen demonstration projects from the pastdecade, most of the relevant electrolyser modelswere used conventional approaches, empiricalmodelling with different mathematical modelsin some simulation software [20–22]. Theconventional approaches, however, were found

to be unsuitable for subsequent online applicationand control [23–25]. During hydrogen production,the percentage of lower explosive limit, hydrogenflow rate and hydrogen pressure will reach acertain level whereby the developed backpressurecan cause all of the electrolyser systems to stop.This condition is usually call ‘critical state’ andmust be avoided to reduce the safety margin onhydrogen generation. An intelligent neuralnetwork is required to model the relationshipbetween chosen input and output variables ofhydrogen production parameters so that the H2

production pressure is monitored as a digitaloutput. ANNs will automatically predict andaccurately estimate results for the chosenoutput parameters and will prevent unexpecteddangerous working environments as well as obtainhydrogen flow rate and pressure parametersas required. Moreover, the knowledge base,found from extensive experimentation covering acomprehensive range of input parameters, willassist in understanding the effect of major processvariables on electrolyser performance.

3.1. Brief description of UTAS ANNs software andoptimized layer by layer (OLL) neural networkarchitecture

The University of Tasmania’s (UTAS), ANNssoftware package was developed in-house at theUTAS following research into artificial intelligenttools. The package contains several types of neuralnetworks. Initially, the programmed software usesMicrosoft Excel as its front-end GUI for userinteraction with the actual training and executionof the neural network utilizes Visual Basic forApplication scripting to link with externals filesprogrammed in Pascal. The final version has beenreprogrammed within MatLab environment. Thesoftware facilitates the data set to be broken upinto training and testing data sets. The differencebetween the two data sets is that the training dataset influence the weights of the interconnectionbetween nodes of the network, while the test dataset do not. Output predictions are given for bothdata sets. Once the appropriate neural networkfrom a suite of networks is chosen, the softwarethen provides a summary screen informing the

PREDICTION OF HYDROGEN SAFETY PARAMETERS 433


DOI: 10.1002/er

user of the options chosen for training the networksuch as the number of inputs, outputs, total datapatterns, iterations and threshold functions. Theuser is able to change some of these options ifrequired before iterative training commences.Training the network requires an intensive dataprocessing depending on the number of inputs/outputs as well as data patterns, neural networktype and iterations. Once the network is success-fully trained, the results of the training processincluding the predicted outputs, difference be-tween predicted and actual values for each data setas well as root mean square (RMS) errors, averagemeasured value, standard deviation are displayedin the workspace screen. The user is also able touse the trained neural network for new data sets byentering the input values to obtain the correspond-ing neural network predicted output values. In thispaper, extensive experimentation of hydrogensafety parameters are predicted using UTASoptimization layer by layer neural network, whichhas been the subject of significant investigation byKiatcharoenpol [26] and David Butler [27]. Assuch, these three references are used heavilythroughout this section.

The OLL ANN, which was introduced is of thefeed-forward type, but is considered to yield‘results in both accuracy and convergence rates,which are the orders of magnitude superiorcompared with back-propagation learning’ [28].The weights of OLL are updated duringtraining and observing the dynamics of thehidden and output layers to solve the

weights exactly. Therefore, the linearization ofthe interconnection weights of each layer isoptimized with iteration process, which is notneed to be tuned by the user [27].

While the specified literature provides adequatetheory on the neural network model studied in thispaper, it is useful here to consider the basic theoryassociated with this neural network. It isimportant to note that while the objective ofeach neural network is to predict the values of H2

safety performance as outlined above, thearchitecture and algorithms used by eachnetwork to achieve this are significantly different.A brief note on the OLL network is discussedbelow.

The architecture of an OLL network shown inFigure 2 consists of an input layer, one hiddenlayer and an output layer. All input nodes areconnected to all hidden nodes through weightedconnections, and all hidden nodes are connected toall output nodes through weighted connections.

The basic ideas of OLL learning algorithm arethat the weights in each layer are modifieddependent on each other, but separately from allother layers and the optimization of the hiddenlayer is reduced to a linear problem. The weightsare adjusted on the basis of minimizing the costfunction, which is the error between the targetoutput and the network output as shownbelow [27]

EðR;SÞ ¼1

P

Xpp¼1

1

2ðtp � ypÞ2

Figure 2. Basic structure of an OLL network with one hidden layer and scalar outputs [26].



DOI: 10.1002/er

where E is the cost function, p the current trainingdata, tp the target output of the training data p, yp

the network output of the training data p, P thenumber of training data, R the hidden-inputconnections weight matrix, and S the output-hidden connections weight matrix.

The weights are adjusted in two groups, whichare output-hidden weight matrix and hidden-inputweight matrix as the name ‘Optimized Layer byLayer’. First, the gradient of the cost function withrespect to output layer weights is set to zero todetermine the optimum output layer weight matrix(Sopt) as the formula below

dE

dS¼

1

P

Xpp¼1

ðsTzp � dpÞzp ¼ 0

where S is the individual output layer weight, sTzp

are equivalent to the network output y, zp thescalar output of hidden neuron at training data p,d p the target/desired output at the training data p.

The results of a set of linear equations that canbe used to find the optimum output layer weightmatrix Sopt as below

A � S ¼ b

Sopt ¼ A�1 � b

A ¼ matrix½ah;j�; ah;j¼Xpp¼1

zphz

pj ; h; j ¼ 0 . . . J

b ¼ matrix½bk;j�; bk;j ¼Xpp¼1

tpkz

pj ; j ¼ 0 . . . J;

k ¼ 1 . . .K

Secondly, the optimal hidden layer weightmatrix (Ropt) is determined by using similarapproach as above and transform non-linearsigmoid functions to linear equations usingTaylor series expansion. The linearized weightmatrix slin is calculated as below

slinj ¼ f 0ðnetjÞ � sj ; j ¼ 1 . . . J

where slin is the linearized weight matrix, f 0(netj)the derivative of weighted sum input to hiddenlayer neuron j and sj the output layer weight fromhidden layer neuron j.

The linearized weights now depend on thetraining pattern being processed. The resultlinearized network structure as shown below(Figure 3).

Now a new cost function for the hidden layeroptimization is derived as below

Ehidden ¼ Elinear þ mEpen

where Ehidden is the overall error for hidden layer,Elinear the error for linearized activation functions,Epen the penalty term to account for linearizationerror and m the penalty constant.

Then using the chain rule to take the partialderivatives of Elinear and Epen with respect toindividual hidden layer weight Drji so that thechange required to achieve the optimumhidden layer weights (DRopt) can be derived asfollows:

@Ehidden

@Drji¼@Elinear

@Drjiþ m

@Epen

@Drji¼ 0

Figure 3. Linearized networks for the optimization of hidden layer weights at output neuron k [27].



DOI: 10.1002/er

The optimal change to the hidden layer weightmatrix is now calculated as

D�Ropt ¼ ~A�1 � ~b

A ¼ matrix½aji;hm� for j 6¼ h

aðji;hmÞ ¼Xpp¼1

Xkk¼1

½ðslinpkj� xpi Þðslinpkh Þ � x

pm�

i;m¼ 0 . . . I ; j; j¼ 0 . . . J; k¼ 1 . . .K for j ¼ h

aðji;hmÞ ¼Xpp¼1

Xkk¼1

ðslinpkj� xpi ÞðslinpkhÞ � x

pm

h

þmJjSkj j � jf 00ðnet

pj Þj � x

pi x

pm

i;

i;m ¼ 0 . . . I ; j; j¼ 0 . . . J; k¼ 1 . . .K

~b¼ vector½bji�; bðjiÞ ¼Xpp¼1

Xkk¼1

½ðtpk� ypkÞ � ðslinpkj Þ � x

pm�

where slinpkj, slinp

khare the linearized weight from

neuron k at output layer to hidden neuron j, h fortraining data p, xi

pxmp are the input of neuron i, m at

the input layer, skj the output weight from hiddenneuron j to output neuron k and f 00(netj

p)5 secondderivative of f(net) at hidden neuron j.

Then the hidden layer weight matrix is updatedas in the formula shown below

Rnew ¼ Rold þ DRopt

An iterative procedure to alternatively optimizethe output and hidden layer weights has to beincorporated into training algorithm to the finalweight matrices S and R as well as obtain theminimum error. The training algorithm issummarized as below.

1. The weights of the network are initialized withrandom values.

2. The ANN is used in a feed-forward manner, byexposing it to certain process inputs (withknown process outputs and with networksweights R and S).

3. Compute the optimal output layer weights Sopt

as below

Sopt ¼ A�1 � b

Then updating the ANN with these values.4. The ANN is used in a feed-forward manner

again with the new Sopt weights, and calculatingRMS error (RMS1).

5. Then the optimal hidden layer weight changeDRopt is computed as below

DRopt ¼ ~A�1 � ~b

Then updating the ANN with Rtest based onfollowing equation

Rtest ¼ Rold þ DRopt

6. The ANN in a feed-forward manner is usedagain with the new Sopt and Rtest weights, andcalculating RMS error (RMS2).

7. The following conditions will be checked

7.1. If RMS2oRMS1 then:a. Updating the hidden layer weight

matrix; therefore, R5Rtest.b. Let RMS1 5RMS2.c. Decreasing the constant b by:

mnew ¼ mold � b

d. Go to step 8.7.2. If RMS2XRMS1 then:

a. Decreasing the constant g by:

mnew ¼ mold � g

b. Then repeat the process from step 5.Note that

0obo1 ðnormal b ¼ 0:9Þ

1og ðnormal g¼ 1:2Þ

Within this case study, the values of b5 0.9and g5 1.2 were found to be the best effectiveconstant over extensive experimental trainingprocesses.

8. Training process will be ceased in the caseRMS1 error is within a tolerable range, other-wise repeat from step 2.



DOI: 10.1002/er

4. APPRAISAL OF THE PREDICTIVEMODELS

The key parameters for the hydrogen predictivemodel using ANNs as the function of differentinput conditions of power feed, feed water andelectrolyser system parameters were chosen as theinput parameters. The designed networks use 10basic process parameters as inputs. Those well-defined inputs were: cell stack voltage (V), cellstack current (A), de-ionized water quality (MO),water temperature (1C), water pressure (kPa),water flow rate (GPM), electrolyser system tem-perature (1C), stack current PWR102, systempressure (PSI) and board temperature (1C). Theoutput is also well defined with percentage lowerexplosive limit (%), production hydrogen pressure(PSI) and hydrogen flow rate (LPM) to assist theoperators to monitor potential hazardous flowsand that cause production failure.

In particular, the used learning rules can have agreat effect on how quickly the ANN learns theprocess. The training data were representation ofall the operating conditions of the system, as wellas being presented to the network in a particularway. There is one hidden layer and the bestnumber of neurons within the hidden layer is 10 asshown in Figure 4. The sigmoid function was usedas the activation function in the hidden layer.

It is important that randomizing the order oftraining patterns sufficiently represents all

operational aspects of the system to be modelled.The training set for the neural networkprogramme is carried out via data acquisition.The first 700 elements of data are randomlyacquired as the training variables for thenetwork, and the last 60 data are acquired fortesting the network. These data represent variousH2 safety performance parameters for variedinput.

In addition, the number of input layer neuronswill affect the accuracy of the network. The inputparameters that have little or no influence on thesystem outputs will reduce the network accuracy.Therefore, sensitivity analysis is used to identifythe minimum number of inputs required to haveoptimum performance of the chosen ANN [27]. Asan indication of how the input parameters areinfluencing the network prediction, it is useful touse RMS error and the calculation of percentagecontribution of each input parameters to eachhydrogen product parameters using the followingformula [29]:

Percentage contribution ¼Derrrmsi � 100Pi

1 Derrrms

with

Derrrmsi ¼ errrmsi � errrmsorig

where i is the number of input parameters, errrmsi

the RMS error associated with input i omitted

Figure 4. Network RMS error and computation time with changing architecture.



DOI: 10.1002/er

errrmsorig the original RMS error without any inputvariable omitted.

Note that

Derrrmsi ¼Derrrmsi if DerrrmsiX00 if Derrrmsio0

�

Tables I–IV show the results from predictiveimportant analysis, highlighting the train andtest RMS error and percentage contributionobtained when each of the 10 input variablesare alternatively omitted from the predictivemodel. The major objective of this particular

numerical investigation is to observe thebehaviour of the OLL network with changingarchitecture. From this study some usefulinformation from a ‘process control’ point of viewon which input is most influencing the hydrogenproducts prediction is obtained. The influence ofinputs over each observed output hydrogen productvariable are consistent for both the training andtesting data sets.

It has been shown in this work that animportant consideration when designing network-training data is to select carefully those variablesthat are to be used as inputs. Only those

Table I. Predictive important results for lower explosive limit.

Lower explosive limit RMS error (%)

Input variable omitted from training and test data sets Train Test Percentage contribution

No input variables omitted 2.18 3.891. De-ionized water quality 2.07 2.65 0.002. Water pressure 2.12 2.92 0.003. Water flowrate 2.23 5.07 6.814. Water temperature 2.35 8.37 25.825. Cell stack voltage 2.21 4.88 5.726. Cell stack current 2.14 3.54 0.007. Stack current PWR102 2.29 6.92 17.468. Electrolyser system temperature 2.32 7.70 21.949. System pressure 2.33 7.75 22.2410. Board temperature 2.17 3.25 0.00Non-contribution variables omitted 1.94 2.12

Table II. Predictive important results for hydrogen flow rate.

Hydrogen flow rate RMS error (%)


No input variables omitted 3.07 3.161. De-ionized water quality 3.74 4.91 0.002. Water pressure 2.62 1.95 0.003. Water flowrate 2.49 2.94 6.814. Water temperature 3.58 4.63 25.825. Cell stack voltage 4.32 6.92 5.726. Cell stack current 4.54 7.10 0.007. Stack current PWR102 3.56 4.54 17.468. Electrolyser system temperature 4.56 7.36 21.949. System pressure 3.96 5.80 22.2410. Board temperature 2.71 2.43 0.00Non-contribution variables omitted 1.98 1.62



DOI: 10.1002/er

parameters that contribute towards improving theaccuracy of the network prediction should beincluded as input parameters.

Figures 5(b, d and f) highlight the erroroccurrence and the histograms of the errorfor testing conditions. The average results ofneural networks testing were achieved. In orderto provide a measure of accuracy of thepredictions as well as provide a means ofcomparison between each of the different neuralnetworks, several parameters have been calculatedand also displayed to the screen. These aredefined as: average measured value, averageerror (value), average standard deviation (value)and average RMS error (%) as shown inTable V.

4.1. Percentage of lower explosion limit

Figure 5(a) shows the actual values of the predictedand experimental values of the explosion limits. Itcan be seen that very good prediction results wereobtained for percentage of lower explosive limitwith the percentage average RMS error being 1.8%and average deviation being 0.69 as shown inTable V and the histogram of Figure 5(b).

4.2. Hydrogen pressure

The testing results for prediction of hydrogenpressure were obtained as shown in Figure 5(c)with the percentage average RMS errorbeing 2.73% and deviation being 4.5. The histo-gram of testing prediction result is shown inFigure 5(d).

Table III. Predictive important results for hydrogen pressure.

Hydrogen pressure RMS error (%)


No input variables omitted 1.60 2.191. De-ionized water quality 5.82 7.55 0.002. Water pressure 7.12 9.28 0.003. Water flowrate 3.96 5.16 6.814. Water temperature 4.23 5.61 25.825. Cell stack voltage 2.31 3.13 5.726. Cell stack current 3.45 4.66 0.007. Stack current PWR102 1.58 1.70 17.468. Electrolyser system temperature 2.68 3.67 21.949. System pressure 3.32 4.34 22.2410. Board temperature 1.51 1.57 0.00Non contribution variables omitted 1.48 1.70

Table IV. Summary of non-contribution variable ofeach predictive hydrogen product.

Non-contribution variable

Lower explosive limit 1. De-ionized water quality2. Water pressure6. Cell stack current10. Board temperature

Hydrogen flow rate 2. Water pressure3. Water flowrate10. Board temperature

Hydrogen pressure 7. Stack current PWR10210. Board temperature

Table V. Summarize prediction results for hydrogensafety parameters.

Hydrogen safety parameters

LeLHydrogenpressure

Hydrogenflow rate

Average measured value 38.62 95.16 7.53Average error (value) 0.01 0.18 �0.03Average STD (value) 0.69 4.5 0.2Average RMSE (%) 1.8 2.73 2.71



DOI: 10.1002/er

4.3. Hydrogen flow rate

The testing results for prediction of hydrogen flowrate, via histogram are shown in Figure 5(f). The

prediction result was obtained for prediction of

hydrogen flow rate with the percentage average

RMS error being 2.71% and deviation being 0.2.

0 10 20 30 40 50 6026

28

30

32

34

36

38

40

42

44

46

Comparison of Actual and NetworkPredicted Values for % LEL

Number of Testing data

% L

ower

exp

losi

ve li

mit

ActualPredicted

-15 -10 -5 0 5 100

1

2

3

4

5

6

7Testing histogram for hydrogen pressure

Error (actual - predicted values)

Fre

quen

cy

0 10 20 30 40 50 6020

40

60

80

100

120

140

160

180

200

220

Comparison of Actual and NetworkPredicted Values for hydrogen pressure


Hyd

roge

n pr

essu

re (

PS

I)

ActualPredicted

0 10 20 30 40 50 60-2

0

2

4

6

8

10

Comparison of Actual and NetworkPredicted Values for hydrogen flowrate


Hyd

roge

n flo

wra

te (

LPM

)

ActualPredicted

-15 -10 -5 0 5 100

10

20

30

40

50

60Testing histogram for hydrogen flowrate


Fre

quen

cy


-15 -10 -5 0 5 100

2

4

6

8

10

12

14

16

18Testing histogram for %LEL

Fre

quen

cy

(a) (b)

(c) (d)

(e) (f)

Figure 5. Comparison of actual and predicted values of: (a) percentage lower explosive limit; (b) hydrogen flow-rate;(c) hydrogen pressure; (d) percentage lower explosive limit testing histogram; (e) hydrogen flow-rate testing histogram;

and (f) hydrogen pressure testing histogram.



DOI: 10.1002/er

Figure 5(e) shows the actual values of thepredicted and experimental values of hydrogenflow rate.

It can be seen that the prediction of hydrogenperformance and safety parameters are carried outsuccessfully using optimized layer by layer withsensitivity analysis of network architecture. Theprediction of these parameters based on theelectrolyser’s input parameters can be a replacementfor expensive sensors and instrumentation.

5. CONCLUDING REMARKS

In conclusion, this paper has presented theprocess of building up hydrogen predictive modelsusing optimized layer-by-layer neural network.The predictive models using ANNs are createdto predict the production pressure, flow rateand the explosive limits of hydrogen and theassociated parameters of Hogen20s hydrogengenerator.

From the results of graphs and data for thetraining and testing, it can be seen that the createdneural networks were trained well. When lookingat the percentage RMS error and deviation forevery test case, they are very small and the resultswith average error values always approximatelyzero. The explosive limits of hydrogen werepredicted to be 72%, the H2 pressure and flowrates were predicted to be 75 and 3%,respectively. It can be seen that the networkprediction results compare with its actual outputtarget values, acquired from expensive sensors,extremely closely. This is particularly encouragingsince based on the input parameters of theHogen20 electrolyser; the hydrogen outputperformance can be predicted without installingexpensive experimentation. This is also seen as ageneric intelligent tool that can be applied to anyH2 production unit to monitor safety features ofH2 production.

NOMENCLATURE

ANN 5 artificial neuron networkAS 5Australian Standard

DAQ 5 data acquisitionHART 5Hydrogen & Allied Renewable

Technology research programmeLPM 5 litre per minuteOLL 5 optimized layer by layerRMS error 5 root mean square errorRAPS 5 remote area power systemsUTAS 5University of Tasmania, Australia

ACKNOWLEDGEMENTS

The authors are deeply grateful to Hydro Tasmania PtyLtd for financially supporting the project as well as all ofthe Hydrogen & Allied Renewable Technology researchmembers for sharing ideas and concept along the way.

REFERENCES

1. Petersen MG. Installation of a hydrogen generator.Research Report, University of Tasmania, 2004.

2. Karri V. Design and development of hydrogen laboratory.Research Report, vols. 1–3. University of Tasmania, 2004.

3. Australian Standard 2430.1. Classification of hazardousareas—Part 1: explosive gas atmospheres. AustralianStandard 2430.1, 1987.

4. Australian Standard 1482. Electrical equipment for ex-plosive atmospheres—protection by ventilation—type ofprotection. Australian Standard 1482, 1985.

5. Australian Standard 2380.1. Electrical equipment forexplosive atmospheres—explosion protection techniques—Part 1: general requirements. Australian Standard 2380.1,1989.

6. Australian Standard 1432. Copper tubes for plumbing, gasfitting and drainage applications. Australian Standard 1432,1996.

7. Australian Standard 4041. Pressure piping. AustralianStandard 4041, 1998.

8. Australian Standard 2982. Laboratory construction.Australian Standard 2982, 1987.

9. Australian Standard 2508.2.002. Safe storage and handlinginformation card—hydrogen (compressed). AustralianStandard 2508.2.002, 1992.

10. Safety Standard for Hydrogen and Hydrogen Systems(NSS 1740.16). Office of Safety and Mission Insurance,NASA, 1997.

11. Murav’ev LL. Model for calculating and optimizing gasflow rates in the hydrogen-permeable capillaries of permea-tors. Theoretical Foundations of Chemical Engineering 2002;36(3):235–240.

12. Sokolovskii VI, Makarov VM. Improving permeators forproducing ultra-pure hydrogen. Khim Neft Mashinostr1998; 6:3.

13. Bernard P, Mustafa V, Hay DR. Safety assessment ofhydrogen disposal on vents and flare stacks at high flow



DOI: 10.1002/er

rates. International Journal of Hydrogen Energy 1999;24:489–495.

14. Lomperski S, Anselmi M, Huhtiniemi I. Ultrasonic andresistive hydrogen sensors for inert gas–water vapouratmospheres. Measurement Science and Technology 2000;11:518–525.

15. Huang SH, Zhang HC. Artificial neural networks inmanufacturing: concepts, applications and perspectives.IEEE Transactions on Components, Packaging and Manu-facturing Technology, Part A 1994; 17(2):212–228.

16. Zurada JM. Introduction to Artificial Neural Systems. WestPublishing Company: St. Paul, 1992.

17. Khanna T. Foundations of Neural Networks. Addison-Wesley: MA, 1990.

18. Caudill M, Butler C. Understanding neural networks—computer explorations. Basic Networks, vol. 1. Massachu-setts Institute of Technology: MA, 1992.

19. Karri V, Frost F. Need for optimisation techniques toselect neural network algorithms for process modelling ofreduction cell. Proceedings of the International Conferenceon Artificial Intelligence in Science and Technology(AISAT), Hobart, TAS, Australia, December 2000;134–140.

20. Divisek J, Steffen B, Schmitz H. Theoretical analysis andevaluation of the operating data of a bipolar waterelectrolyser. International Journal of Hydrogen Energy1994; 19(7):579–586.

21. Steffen B. Calculation of potential field and heat distribu-tion in a bipolar electrolyser. Proceedings of the 2nd IEA

Technical Workshop on Hydrogen Production, Julich,September 1991; 4–6.

22. Crocket RGM, Newborough M, Highgate DJ, Probert SD.Electrolyser-based electricity management. Applied Energy1995; 51:249–263.

23. Crocket RGM, Newborough M, Highgate DJ. Electro-lyser-based energy management: a means for optimising theexploitation of variable renewable-energy resources instand-alone applications. Solar Energy 1997; 61(5):293–302.

24. Dutta S. Technology assessment of advanced electrolytichydrogen production. International Journal of HydrogenEnergy 1990; 15(6):379–386.

25. Oystein U. Modeling of advanced alkaline electrolyzers: asystem simulation approach. International Journal ofHydrogen Energy 2003; 28:21–33.

26. Kiatcharoenpol T. Drilling performance using artificialneural networks. Ph.D. Thesis, The University of Tasma-nia, Hobart, 2004.

27. David Andrew Butler. Enhancing automotive stabilitycontrol with artificial neural networks. Ph.D. of EngineeringScience Thesis, University of Tasmania, 2006.

28. Hecht-Nielsen R. Neuro Computing. Addison-Wesley Pub-lishing: University of California, 1989.

29. Ergezinger S, Thomsen E. An accelerated learning algo-rithm for multilayer perceptrons: optimization layer bylayer, University of Hannover. IEEE Transactions onNeural Networks 1995; 6:31–42.



DOI: 10.1002/er

prediction of hydrogen safety parameters using intelligent techniques

Documents