Research Article Experimental Determination of Heat

Hindawi Publishing CorporationJournal of EngineeringVolume 2013 Article ID 719296 8 pageshttpdxdoiorg1011552013719296

Research ArticleExperimental Determination of Heat TransferCoefficient in Stirred Vessel for Coal-Water Slurry Based onthe Taguchi Method

C M Raguraman1 A Ragupathy1 and L Sivakumar2

1 Mechanical Engineering Annamalai University Annamalai Nagar Tamil Nadu 608 002 India2 Vice Principal Sri Krishna college of Engineering and Technology Coimbatore India

Correspondence should be addressed to C M Raguraman cmrrmechgmailcom

Received 31 October 2012 Revised 3 May 2013 Accepted 8 May 2013

Academic Editor You-Rong Li

Copyright copy 2013 C M Raguraman et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Heat transfer in stirred vessels is important because process fluid temperature in the vessel is one of the most significant factors forcontrolling the outcome of process In this study the effects of some important design parameters for coal-water slurry in agitatedvessel used in coal gasification such as stirrer speed location of stirrer Dd ratio and coal-water ratio were investigated and opti-mized using the Taguchi methodThe experiments were planned based on Taguchirsquos 119871

9orthogonal array with each trial performed

under different levels of design parameter Signal-to-noise (SN) analysis and analysis of variance (ANOVA) were carried out inorder to determine the effects of process parameter and optimal factorrsquos level settings Finally confirmation tests verified that theTaguchi method achieved optimization of heat transfer coefficient in agitated vessel

1 Introduction

Research on heat transfer coefficient in agitated vessel is stillcritical and ongoing Heat transfer in stirred vessel is impor-tant because process fluid temperature in the vessel is oneof the most significant factors for controlling the outcome ofprocessMechanically agitated vessels arewidely used inmin-ing food petroleum chemical pharmaceutical pulp andpaper industries and are also used in coal gasification powerplant [1]The intensity of heat transfer duringmixing of fluidslike coal slurry depends on the type of the stirrer the designof the vessel and condition of the processes [2] In this studythe effects of some important parameters such as stirrerspeed location of stirrer Dd ratio and coal-water ratio wereinvestigated and optimized

Performing an experiment is more suitable for determi-nation of the real performance characteristics of a systemHowever to prepare an experimental setup is very expensiveand some systems cannot be constructed and tested in a lab-oratory Also preparing an experimental setup is a very time-consuming procedure because of the high trial numbers

Because of these difficulties the modeling and then testingthe system using numerical analysis ANN (artificial neuralnetwork) or optimizing the trial numbers according to theTaguchi method is more appropriate and very popular nowa-days [3]

Heat transfer rates in agitated vessel have been investi-gated for coal-water slurry in a flat bottom vessel equippedwith flat-blade impeller making an angle of 45 degree to theaxis of the shaft Also the heat transfer coefficients of the flat-blade impeller parallel to the axis of the shaft results [4] werecompared

2 Experimental Detail

21 Experimental Setup The schematic diagram of the exper-imental setup is shown in Figure 1 An agitator is driven bya vertically mounted motor which is used for stirring Theentire vessel is completely insulated The inlet of the jacket isconnected to an electrically operated boiler which producessteam continuously at constant pressure A pressure gaugeis connected to the jacket for monitoring the jacket steam

2 Journal of Engineering

Pressuregauge

Temperature indicator

Electrical supply

VariacMotor

Pressure gauge

Spring loaded safety valve

Water inlet

GVGV

Steam strainer

Stirrer

Jacketed vessel

Insulation

Heating coil

Water outlet

GV1

GV2

GV3

Figure 1 Experimental setup

pressure A vent is provided for releasing noncondensable gasand to maintain the pressure in jacket A steam strainer isplaced at the bottom of the vessel to collect the condensedsteam

A jacketed mild steel cylindrical vessel of 210mm diam-eter 6mm thick and 160mm height with a flat bottom wasused Similarly the cylindrical inner vessel of 110mm diame-ter 6mm thick and 110mm height with similar flat bottomwas used This assures that the height of vessel is alwaysequal to diameter of the vessel (119867 = 119863) [4]The impeller usedin the jacketed vessel is having four flat blades (Figure 2) andis driven by a variac controlled motor The impeller makesan angle of 45∘ to the axis of the shaft The thickness of eachblade is 16mm and having a width of 175mm Outlets aregiven at the bottom side of the agitated vessel for collectionof samples

The cylindrical vessel of 210mm diameter 6mm thickand 160mm height with a flat bottom was used which isnamed as jacketed vessel Similarly the cylindrical inside ves-sel of 110mm diameter 6mm thick and 110mm height withsame flat bottom was used So the height of vessel is equal todiameter of the vessel (119867 = 119863) [4] The impeller used is fourflat blade whose details as given in Figures 2(a)ndash2(c)

22 Experimental Procedure Initially the drain valve pro-vided at the bottom of the jacketed vessel and gate valve 1was closed The vessel was first filled with a known volumeof water and mix with coal particle (size BSS 36) The initialtemperature of the slurry (t

1

∘C) can be noted through thedigital temperature indicator (Micro sensor Make T-type)

whichwas connected to a thermocouple (T-type copper con-stant) The boiler was filled with required amount of waterNow the gate valve 2 was closed and the boiler was switchedon After steam was produced gate valve 3 was closed Thevariac (Continuously variable auto transformer AutomaticElectric Ltd Range 0 to 270V) was used to vary the stirrerspeed When the boiler steam pressure reaches 25 kgcm2(gauge)-(Bourdon type Gurn Make) open the gate valve 2and allow the steam into the jacketed vessel Now keep thepressure gauge at 20 kgcm2 (gauge) by adjusting the gatevalve 2 and start the stopwatch When the temperaturereaches the saturation temperature of the water (100∘C) thetime period (120591 sec) was noted and this period is called heatingperiod (unsteady state) The above procedure was repeatedfor other sets of readings

3 Preparation of Coal-Water Slurry

Coal gasification is a key process in integrated and coal gasifi-cation combined cycle (IGCC) power plant Being one ofthe most competitive and promising coal gasification tech-nologies the slurry feed-type entrained-flow coal gasificationprocess is shown in Figure 3 which has been extensively usedin the foreign countries Coal-water slurry (CWS) is amixtureof water and coal First pulverize coal mix with water accord-ing to proper proportion (coal water = (40 to 70) (60 to 30)and then add a little additive (about 1 of the total soiledweight the coal-water two-phase flow is formed after strongagitation [6] In an entrained-flow gasifier using coal-waterslurry it should be necessary to uniformlymix the coal-water

Journal of Engineering 3

(a) 275mm (b) 550mm (c) 825mm

Figure 2 Details of Flat-blade impeller All dimensions are in ldquommrdquo

Slurry pump FlowmeterLock hopper

CW CW

CWM tank

Coalcrushing

CoalBurner

Oxygen

Comb of syngas

Dataacquisition

Scrubber

Gasifier

Gas analyser

Water in

CW

LC valve

Figure 3 Entrained-flow coal gasification processmdashslurry feed type [5]

slurry with oxygen to obtain the higher carbon conversion inshort residence time (04ndash5 s)

The proximate and ultimate analysis of the Indian coalused in the preparation of the slurries is given in Table 1

Additive is one of the key factors affecting CWS qualityUsually additive dosage accounts for about 1 of the totalweight Based on function additives can be used like dis-persant and stabilizing agents which are used in this projectthat is sodium carbonate and sodium salt of carboxyl methylcellulose (Na-CMC) respectivelyThe best dosages of disper-sant and stabilizers are 075 by wt of solids and 01 by wtof total solids [7]

The concentration of coal slurry could be maintainedfrom 40 to 70 which is able to feed into the gasificationwithout any feeding problem [8] so that the concentrationschosen are 40 50 and 60The coal particle size used forcoal-water slurry preparation is BSS 36 (0422mm)

Table 1 Coal analysis details

Proximate analysis Weight basis in Moisture 664Ash 4871Volatile matter 1912Fixed carbon 2553Gross calorific value in KcalKg 3491Ultimate analysis Weight basis in Carbon as C 3453Hydrogen as H 181Nitrogen as N 105Sulphur as S 047Oxygen by difference 192


4 Uncertainty Analysis

The objective of a measurement is to determine the value ofthe measurand that is the value of the particular quantity tobemeasured Ameasurement therefore beginswith an appro-priate specification of the measurand the method of mea-surement and the measurement procedure In general theresult of a measurement is only an approximation or estimateof the value of the measurand and thus is complete onlywhen accompanied by a statement of the uncertainty ofthat estimate [9] Through there are many factors in themeasurement uncertainty in this study it is assumed that themajor factors of resolution or detection of sensors and thevariation of the measured data during repeated tests at thetest condition It defined the measurand the output quantityas a function 119884 = 119891(119883) of the input quantities119883

The uncertainties of the measured data were calculatedby combining the type A and B [10] The type A uncertaintywas evaluated by statistical analysis of series of observationof the 50 times sampled data and the type B uncertainty wascalculated by previousmeasurements specifications from themanufacturer hand-books calibration certificates and soforth [11] The standard uncertainty was computed as rootmean square error (RSM) of the type A and B uncertaintiesTable 2 Shows the uncertainty estimation of each measuredparameter and 50 samples were collected in each test [11]

5 Application of Taguchi Method

The quality engineering methods of Taguchi employingdesign of experiments (DOE) is one of the most importantstatistical tools for designing high quality system at reducedcost Taguchi methods provide an efficient and systematicway to optimize designs for performance quality and costOptimization of process parameter is the key step in theTaguchi method to achieve high quality without increasingcost [12] Taguchirsquos parameter design method is a powerfultool for optimizing the performance characteristics of aprocess [13 14] The experiments were designed based on theorthogonal array technique Using the Taguchi experimentaldesign an orthogonal array was selected for four factors andthree levels for each factor The orthogonal array selectedfor this study is the 119871

9orthogonal array which is shown in

Table 2Based on orthogonal arrays the number of experiments

that may cause an increase in time and cost can be reducedby means of the Taguchi technique It uses a special design oforthogonal arrays to learn the whole parameter space with asmall number of experiments [15 16]

The selection of control factors and their levels is madeon the basis of some preliminary trial experiments conductedin the laboratory and also from the literature review on thesubject The choice of three levels has been made because theeffect of these factors on the performance characteristics canbe estimated simultaneously whileminimizing the number oftest runs An 119871

9(34) standard orthogonal array [14] as shown

in Table 3 was employed for the present investigation This

Table 2 Uncertainty estimation of variables

Measurements Unit A-type B-type 119906(119910)

Temperature (119879) ∘C 151119890 minus 2 100119890 minus 2 101119890 minus 2

Pressure Kgcm22522119890 minus 2 150119890 minus 2 225119890 minus 2

Speed rpm 105119890 minus 2 112119890 minus 2 102119890 minus 2

Table 3 Factors and their specified levels

Sl no Variables LevelsI II III

1 Stirrer speed in rpm 100 200 300

2 Stirrer location from freesurface in cm 275 55 825

3 Dd ratio 40 20 134 Coal-water ratio 40 60 50 50 60 40

Table 4 1198719(34) standard orthogonal array

Experimentno Stirrer speed Stirrer location Dd ratio C-w ratio

1 1 1 1 12 1 2 2 23 1 3 3 34 2 1 2 35 2 2 3 16 2 3 1 27 3 1 3 28 3 2 1 39 3 3 2 1

array is most suitable to provide the minimum degrees offreedom as 9 required for the experimental exploration

The experimental layout for the process parameter usingthe 1198719(34) orthogonal array is shown in Table 4 The appli-

cation steps in the Taguchi method are given in Figure 4

6 Results and Discussion

The heat transfer coefficient was determined as per exper-iment runs Three readings (corresponding to the threereplications) are recorded for each experimental condition asshown in Table 5

61 Statistical Analysis Statistical analyses (over all lossfunction SN ratio ANOVA) are carried out for a significancelevel of 005 that is for a confidence level of 95 Thestatistical errors in the experiment are normally distributedin normal probability plot as shown in Figure 5

62 Overall Loss Function A loss function is then definedto calculate the deviation between the experiment value andthe desired value The control factors that may contribute toreduce variation (improved quality) can be quickly identified


Determining the factors

Determining the levels of each factor

Choosing an appropriate orthogonal array (OA)

Assigning the factors to column of the OA

Conducting the experiments

Determining the optimal levels of factors

Conducting the confirmation experiments

Analyzing the data

Figure 4 Application steps in the Taguchi method

Table 5 Experimental layout using an 1198719(34) orthogonal array

Sl no Stirrer speedin rpm

Stirrerlocation fromfree surfacein mm

Ddratio C-w ratio

1 100 275 40 402 100 55 20 503 100 825 13 604 200 275 20 605 200 55 13 406 200 825 40 507 300 275 13 508 300 55 40 609 300 825 20 40

by looking at the amount of variation present as a responseTaguchi recommends the use of the loss function to measurethe deviation of the quality characteristics from the desiredvalue The four control variables belong to the higher-the-better quality characteristic The loss function of the-higher-the-better quality characteristic can be expressed as follows

The higher is better

119871119894119895= [

1

119903sum

1

1199102

119894119895119896

] (1)

where 119871119894119895is the loss function of the 119894th quality characteristic

in the 119895119894119895 experiment 119903 is the number of tests and 119910

119894119895119896is

99

95

90

80706050403020

10

5

1

minus40 minus30 minus20 minus10 0 10 20 30 40

()

Residual

Normal probability plot(responseis HT coefficient for 45∘ angle blade)

Figure 5 Normal probability plot (response is HT coefficient)

the experimental value of the 119894th quality characteristic in the119895th experiment at the 119896th test [9] As a result four qualitycharacteristics corresponding to the four control variablesand heat transfer coefficient are obtained using (1)

63 Signal-to-Noise Ratio In Taguchi technique the varia-tion of the response is also examined using an appropriatelychosen SN ratio Broadly speaking the SN ratio is theratio of the mean (signal) to the standard deviation (noise)These SN ratios derived from the quadratic loss functionare expressed on a decibel (dB) scale The formula used tocompute the SN ratio depends on the objective functionThe overall loss function is further transformed into thesignal-to-noise (SN) ratio [9] In the present study heattransfer coefficient is a ldquothe higher the betterrdquo type of qualitycharacteristic since the goal is to maximize the heat transfercoefficient The SN ratio 120578

119895in 119895th experiment can be

expressed as follows

120578119895= minus10 log (119871

119894119895) (2)

The signal-to-noise ratios corresponding to overall lossfunction are computed using (2) for each of the nine exper-imental conditions and are reported in Table 5 Since theexperimental design is orthogonal the factor effects can beseparated out in terms of the SN ratio and in terms ofthe mean response The average values of SN ratios of thefour control factors at each of the four levels are shown inFigure 6 and from which the levels corresponding to thehighest SN ratio values are chosen for each parameter rep-resenting the optimum condition Here the optimum condi-tion corresponds to the maximization of the heat transfer co-efficient It is clear from Figure 6 that the optimum levels arestirrer speed (300 rpm) stirrer location (550mm) Dd ratio(2) and C-w ratio (60 40) respectively

In addition to SN analysis main effects of the processparameters on themean response are also analyzedThemeanresponse refers to the average value of the quality characteris-tic for each factor at different levels Thus the average valuesof the heat transfer coefficient for each factor at the threelevels have been calculated and also indicated the same opti-mum level of the parameters as obtained in SN ratio analysis


Table 6 Results for quality characteristics and signal-to-noise ratio

Experiment no Heat transfer coefficient Wm2 K SN ratio (dB)Reading 1 Reading 2 Reading 3 Mean

1 52186 52547 54009 52914 54472 61536 62627 62560 62241 55883 60848 61296 62449 61531 55784 65278 65489 67512 66093 56405 62412 62938 64025 63125 56006 57641 58394 58457 58164 55297 67983 67614 67980 67859 56638 63082 63415 64051 63516 56069 67883 68433 68023 68113 5666

64 Analysis of Variance (ANOVA) ANOVA is a methodmost widely used for determining significant parameterson response and measuring their effects Table 6 shows thecomputed results of the ANOVA with 95 confidence InANOVA the ratio between the variance of the processparameter and the error variance called as119865-ratio determinedwhether the parameter has significant effect on the qualitycharacteristic This process is carried out by comparing the119865-ratio value of the parameter with the standard 119865 tablevalue (119865

005) at the 5 significance level If the 119865-ratio value

is greater than 119865005

the process parameter is consideredsignificant Depending on it it can be seen that the effects ofall the factors on heat transfer coefficient are significant Thelast column of Table 6 indicates the percentage contribution(significance rate) of each process parameter of the totalvariation indicating their degree of influence on the resultsAccording to Table 6 Dd ratio (119875 = 4760) has the mostdominant effect on total variation and it is followed by stirrerspeed (119875 = 4565) C-w ratio (119875 = 416) and stirrer loca-tion (119875 = 017) However C-w ratio yields the most signi-ficant effect on the measured response as shown by the muchhigher 119865-ratio (25651) and also percent contribution (4760)The percentages of contribution of each variable are shown inFigure 7

65 Prediction of Optimum Heat Transfer Coefficient Fromthe analysis of SN ratio and themean response characteristicthe optimum levels of the control factors are determined asstirrer speed

3 stirrer location

2 Dd ratio

2 and C-w ratio

3

Hence the predicted mean of the quality characteristic (heattransfer coefficient) has been computed as shown by [17] asper (3)

120578opt = 120578119898+ (Speedopt minus 120578

119898) + (Locationopt minus 120578

119898)

+ (Dd ratioopt minus 120578119898) + (C-w ratiopot minus 120578

119898)

(3)

where 120578opt = predicted optimum heat transfer coefficient and120578119898is the overall average of all the experimental data for heat

transfer coefficientThe calculated values of various response averages are

120578119898= 62617Wm2K speedopt = 6650Wm2K locationopt

= 6296Wm2K Dd ratioopt = 6548Wm2 K and C-w

ratiopot =6371Wm2K So substituting these values in (3) themean optimum value of the heat transfer has been predictedas 120578opt = 70799Wm2K

66 Confidence Interval (CI) for Predicting a ConfirmationExperiment A confirmation experiment is used to verifythat the factors and levels chosen from an experiment causea product or process to behave in a certain fashion Aselected number of tests are run under constant specifiedconditions to observe results that the experimenter hopesare close to the predicted value Thus a 95 confidenceinterval (CI) for the predictedmean of optimumheat transferon a confirmation test is estimated using (4) [18]

CI = radic119865 (120572 sdot 1 sdot 119891119890) 119881119890[

1

119899eff+1

119903] (4)

119873eff =119873

1 + 119879DOF (5)

where119865(120572sdot1sdot119891119890) is the119865-ratio required 100 (1minus120572) percent con-

fidence interval 119891119890is DOF for error 119881

119890is the error variance

119903 is number of replications for confirmation experiment (=3)and 119899eff is effective number of replication119873 is total numberof experiments (=27 (9 times 3)) and 119879DOF is the total degreesof freedom (=8 (2 times 4)) associated with the estimate of meanoptimum FromTable 6 the values are119881

119890= 15101119891

119890= 18 and

from standard statistical table in (15) the required 119865-ratio for120572 = 005 119865 (005 1 18) = 441 Substituting these values in (4)and (5) the calculated confidence interval is CI = plusmn1247Thus the 95 confidence interval of the predicted optimalheat transfer coefficient is obtained as 70799Wmminus2Kminus1 thatis 69552Wm2K lt 70799Wm2K lt 72046Wm2K

67 Confirmation Experiment In order to test the predictedresult confirmation experiment has been conducted byrunning other three replications at the optimal settings of theprocess parameters determining the analysis

The results are shown in Table 7 and it is observed thatthe mean heat transfer coefficient obtained from the confir-mation experiments is 71287Wm2K which falls within thepredicted 95 confidence interval


300200100

564

561

558

555

552

564

561

558

555

552

825550275

1324 605040

Stirrer speed Stirrer location

C-w ratio

Main effects plot for SN ratios

SN ra

tios o

nH

T co

effici

ent

SN ra

tios o

nH

T co

effici

ent

Signal-to-noise larger is better

Signal-to-noise larger is better Signal-to-noise larger is better


Dd ratio

(flat-blade impeller-HT-45∘ to axis of the shaft)

Figure 6 Main effects plots for SN ratios of coal-water slurry

Table 7 Analysis of variance (ANOVA) for heat transfer coefficient

Factor Sum of squares(SS)

Degrees offreedom Variance (119881) 119865-ratio 119865

005

Pure sum ofsquares (SS1015840)

Percent contributionin (119901)

Stirrer speed 2602970 2 1301485 24602lowast 355 2592389 4565Stirrer location 20350 2 10175 192 355 9770 017Dd ratio 2713922 2 1356961 25651lowast 355 2703342 4760C-w ratio 246707 2 123354 2332lowast 355 236126 416Errors 95223 18 5290 mdash mdash 137545 242Total 5679172 26 mdash mdash mdash 5679172 100Tabulated 119865-ratio at 95 confidence level 119865 (005 2 18) = 355lowastSignificant at 95 confidence level

0

10

20

30

40

50

Stirrer speed C-w ratio

Independent variables

Con

trib

utio

n of

HT

co-e

ffici

ent (

)

Stirrerlocation

Dd ratio

Figure 7 of contribution of each design variables

7 Comparison between BladeGeometry of Stirrer

The optimal parameter of heat transfer coefficient from theprevious literature study in agitated vessel with flat bladeimpeller parallel to the axis of the shaft was determined andoptimized as 25823Wm2K based on the Taguchi method[4] But in this study the blade angle is changed making 45∘angle to the axis of the shaft As per comparison between

Table 8 Results of confirmation experiments

Heat transfercoefficient Wmminus2 Kminus1

ReplicationsMean

Reading 1 Reading 2 Reading 371378 71521 70962 71287

these two types of blades the flat-blade impeller making45∘ angle to the axis of the shaft is performed as the bestfor getting higher heat transfer coefficient (71287Wm2K)during the agitation using coal slurry

8 Conclusions

The purpose of the investigation is to determine the heattransfer coefficient for stream augmented flows in agitatedvessels with flat blade impeller making 45∘ angle to the axisof the shaft The rate of heat transfer is influenced by anumber of physical and geometrical factors such as vesselconfiguration impeller type and process fluid like coal slurryThis study has shown the application of the Taguchi methodon the performance evaluation of heat transfer coefficient foragitated vessel using coal slurry in coal gasification power


plant The following conclusions may be drawn form thepresent investigation work

(i) Based on the ANOVA results all control factorshave significant effect on the quality characteristicsstatistically except stirrer location

(ii) Dd ratio (119875 = 4760) has the most dominant effecton total variation and it is followed by stirrer speed(119875 = 4565) C-w ratio (119875 = 416) and stirrerlocation (119875 = 017)

(iii) The optimal levels of the process parameters werefound to be stirrer speed of 300 rpm stirrer locationof 550mm Dd ratio of 2 and C-w ratio of 60

(iv) The optimized value of the heat transfer coefficient fora 95 interval has been predicated as 70799Wm2Kthat is 69552Wm2K lt 70799Wm2K lt 72046Wm2K

(v) From confirmation experiments the mean value ofthe heat transfer corresponding to the optimum con-ditions was obtained as 71287Wm2K (Table 8)which fell within the predicated value

(vi) The jacketed vessel was heated by waste steam whichis exhaust from the steam turbine in the coal gasifica-tion power plant

(vii) The Taguchi method can successfully be applied toheat transfer investigation in agitated vessel using coalgasification plant to save energy time andmaterial inexperimentation

Acknowledgments

The authors are sincerely thankful to the authority of Depart-ment of Mechanical Engineering Annamalai University forextending their support in carrying out this project Thecooperation extended by all technical staffs of steam labora-tory during experimentation is highly appreciated

References

[1] C L Rai I Devotta and P G Rao ldquoHeat transfer to viscousNewtonian and non-Newtonian fluids using helical ribbon agi-tatorrdquo Chemical Engineering Journal vol 79 no 1 pp 73ndash772000

[2] J Karcz and F Strek ldquoHeat transfer in jacketed agitated vesselsequipped with non-standard bafflesrdquoThe Chemical EngineeringJournal vol 58 no 2 pp 135ndash143 1995

[3] AM Pinar O Uluer and V Kirmaci ldquoOptimization of counterflow Ranque-Hilsch vortex tube performance using Taguchimethodrdquo International Journal of Refrigeration vol 32 no 6 pp1487ndash1494 2009

[4] CM Raguraman R Ramkumar L Sivakumar andA Ragupa-thy ldquoAn effect of blade geometry on heat transfer performancein stirred vessel-coal water slurry system using coal gasifica-tionrdquo International Journal of Engineering Science and Technol-ogy vol 2 no 4 pp 587ndash594 2010

[5] Y CChoi X Y Li T J Park JHKim and JG Lee ldquoNumericalstudy on the coal gasification characteristics in an entrainedflow coal gasifierrdquo Fuel vol 80 no 15 pp 2193ndash2201 2001

[6] CoalWater Slurry TechnologymdashTangshanKeynan Environmen-tal Protection Technology and Equipment Co Ltd Japan

[7] E S Mosa A M Saleh T A Taha and A M El-Molla ldquoEffectof chemical additives on flow characteristics of coal slurriesrdquoPhysicochemical Problems ofMineral Processing vol 42 pp 107ndash118 2008

[8] T J Park J H Kim J G Lee J C Hong Y K Kim and Y CChoi ldquoExperimental studies on the characteristics of Entrainedflow coal gasifierrdquo Energy Conversion Research DepartmentKorea Institute of Energy Research Jaejon Korea

[9] ISOIEC Guide Uncertainty of Measurement Part-3 Guide tothe Expression of Uncertainty in Measurement Joint committeefor guides in Metrology (JCGM) 2008

[10] C G Persson Guide to the Expression of Uncertainty inMeasurement (GUM) and Its Possible Use in Geo Data QualityAssessment Q-KEN Riga Lativa 2011

[11] L Jinkun Y Inyoung Y Sooseoki and K J Su ldquoUncertaintyanalysis andANOVA for themeasurement reliability estimationof altitude engine testrdquo Journal of Mechanical Science andTechnology vol 21 no 4 pp 664ndash671 2007

[12] S Kumanan J E R Dhas and K Gowthaman ldquoDeterminationof submerged arc welding process parameters using Taguchimethod and regression analysisrdquo Indian Journal of Engineeringand Materials Sciences vol 14 no 3 pp 177ndash183 2007

[13] A E LanceQuality by Design TaguchiMethod and US IndustryIrwin Professional Sidney Australia ASI Process HoustonTex USA 2nd edition 1994

[14] D C Montgomery Design and Analysis of Experiments Johnwiley New York NY USA 4th edition 1997

[15] W H Yang and Y S Tarng ldquoDesign optimization of cuttingparameters for turning operations based on the Taguchimethodrdquo Journal of Materials Processing Technology vol 84 no1ndash3 pp 122ndash129 1998

[16] H Oktem T Erzurumlu and I Uzman ldquoApplication of Taguchioptimization technique in determining plastic injection mold-ing process parameters for a thin-shell partrdquo Materials andDesign vol 28 no 4 pp 1271ndash1278 2007

[17] V Parashar A Rehman J L Bhagoria et al ldquoInvestigation andoptimization of surface roughness for wire cut electro dischargemachining of SS 304L using Taguchi dynamic experimentsrdquoInternational Journal of Engineering Studies vol 1 pp 257ndash2672009

[18] P J Ross Taguchi Techniques for Quality Engineering McGraw-Hill New York NY USA 1989

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Pressuregauge

Temperature indicator

Electrical supply

VariacMotor

Pressure gauge

Spring loaded safety valve

Water inlet

GVGV

Steam strainer

Stirrer

Jacketed vessel

Insulation

Heating coil

Water outlet

GV1

GV2

GV3

Figure 1 Experimental setup

pressure A vent is provided for releasing noncondensable gasand to maintain the pressure in jacket A steam strainer isplaced at the bottom of the vessel to collect the condensedsteam

A jacketed mild steel cylindrical vessel of 210mm diam-eter 6mm thick and 160mm height with a flat bottom wasused Similarly the cylindrical inner vessel of 110mm diame-ter 6mm thick and 110mm height with similar flat bottomwas used This assures that the height of vessel is alwaysequal to diameter of the vessel (119867 = 119863) [4]The impeller usedin the jacketed vessel is having four flat blades (Figure 2) andis driven by a variac controlled motor The impeller makesan angle of 45∘ to the axis of the shaft The thickness of eachblade is 16mm and having a width of 175mm Outlets aregiven at the bottom side of the agitated vessel for collectionof samples

The cylindrical vessel of 210mm diameter 6mm thickand 160mm height with a flat bottom was used which isnamed as jacketed vessel Similarly the cylindrical inside ves-sel of 110mm diameter 6mm thick and 110mm height withsame flat bottom was used So the height of vessel is equal todiameter of the vessel (119867 = 119863) [4] The impeller used is fourflat blade whose details as given in Figures 2(a)ndash2(c)

22 Experimental Procedure Initially the drain valve pro-vided at the bottom of the jacketed vessel and gate valve 1was closed The vessel was first filled with a known volumeof water and mix with coal particle (size BSS 36) The initialtemperature of the slurry (t

1

∘C) can be noted through thedigital temperature indicator (Micro sensor Make T-type)

whichwas connected to a thermocouple (T-type copper con-stant) The boiler was filled with required amount of waterNow the gate valve 2 was closed and the boiler was switchedon After steam was produced gate valve 3 was closed Thevariac (Continuously variable auto transformer AutomaticElectric Ltd Range 0 to 270V) was used to vary the stirrerspeed When the boiler steam pressure reaches 25 kgcm2(gauge)-(Bourdon type Gurn Make) open the gate valve 2and allow the steam into the jacketed vessel Now keep thepressure gauge at 20 kgcm2 (gauge) by adjusting the gatevalve 2 and start the stopwatch When the temperaturereaches the saturation temperature of the water (100∘C) thetime period (120591 sec) was noted and this period is called heatingperiod (unsteady state) The above procedure was repeatedfor other sets of readings

3 Preparation of Coal-Water Slurry

Coal gasification is a key process in integrated and coal gasifi-cation combined cycle (IGCC) power plant Being one ofthe most competitive and promising coal gasification tech-nologies the slurry feed-type entrained-flow coal gasificationprocess is shown in Figure 3 which has been extensively usedin the foreign countries Coal-water slurry (CWS) is amixtureof water and coal First pulverize coal mix with water accord-ing to proper proportion (coal water = (40 to 70) (60 to 30)and then add a little additive (about 1 of the total soiledweight the coal-water two-phase flow is formed after strongagitation [6] In an entrained-flow gasifier using coal-waterslurry it should be necessary to uniformlymix the coal-water


(a) 275mm (b) 550mm (c) 825mm



CW CW

CWM tank

Coalcrushing

CoalBurner

Oxygen

Comb of syngas

Dataacquisition

Scrubber

Gasifier

Gas analyser

Water in

CW

LC valve
































1 1 1 1 12 1 2 2 23 1 3 3 34 2 1 2 35 2 2 3 16 2 3 1 27 3 1 3 28 3 2 1 39 3 3 2 1
















Analyzing the data





Ddratio C-w ratio

1 100 275 40 402 100 55 20 503 100 825 13 604 200 275 20 605 200 55 13 406 200 825 40 507 300 275 13 508 300 55 40 609 300 825 20 40



119871119894119895= [

1

119903sum

1

1199102

119894119895119896

] (1)



119894119895119896is

99

95

90

80706050403020

10

5

1


()

Residual







120578119895= minus10 log (119871

119894119895) (2)






1 52186 52547 54009 52914 54472 61536 62627 62560 62241 55883 60848 61296 62449 61531 55784 65278 65489 67512 66093 56405 62412 62938 64025 63125 56006 57641 58394 58457 58164 55297 67983 67614 67980 67859 56638 63082 63415 64051 63516 56069 67883 68433 68023 68113 5666






3 stirrer location

2 Dd ratio

2 and C-w ratio

3




119898)


119898)

(3)








1

119899eff+1

119903] (4)

119873eff =119873

1 + 119879DOF (5)





119890= 15101119891

119890= 18 and





300200100

564

561

558

555

552

564

561

558

555

552

825550275

1324 605040


C-w ratio


SN ra

tios o

nH

T co

effici

ent

SN ra

tios o

nH

T co

effici

ent




Dd ratio






005




0

10

20

30

40

50



Con

trib

utio

n of

HT

co-e

ffici

ent (

)

Stirrerlocation

Dd ratio






ReplicationsMean



8 Conclusions











Acknowledgments


References





















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) 275mm (b) 550mm (c) 825mm



CW CW

CWM tank

Coalcrushing

CoalBurner

Oxygen

Comb of syngas

Dataacquisition

Scrubber

Gasifier

Gas analyser

Water in

CW

LC valve
































1 1 1 1 12 1 2 2 23 1 3 3 34 2 1 2 35 2 2 3 16 2 3 1 27 3 1 3 28 3 2 1 39 3 3 2 1
















Analyzing the data





Ddratio C-w ratio

1 100 275 40 402 100 55 20 503 100 825 13 604 200 275 20 605 200 55 13 406 200 825 40 507 300 275 13 508 300 55 40 609 300 825 20 40



119871119894119895= [

1

119903sum

1

1199102

119894119895119896

] (1)



119894119895119896is

99

95

90

80706050403020

10

5

1


()

Residual







120578119895= minus10 log (119871

119894119895) (2)






1 52186 52547 54009 52914 54472 61536 62627 62560 62241 55883 60848 61296 62449 61531 55784 65278 65489 67512 66093 56405 62412 62938 64025 63125 56006 57641 58394 58457 58164 55297 67983 67614 67980 67859 56638 63082 63415 64051 63516 56069 67883 68433 68023 68113 5666






3 stirrer location

2 Dd ratio

2 and C-w ratio

3




119898)


119898)

(3)








1

119899eff+1

119903] (4)

119873eff =119873

1 + 119879DOF (5)





119890= 15101119891

119890= 18 and





300200100

564

561

558

555

552

564

561

558

555

552

825550275

1324 605040


C-w ratio


SN ra

tios o

nH

T co

effici

ent

SN ra

tios o

nH

T co

effici

ent




Dd ratio






005




0

10

20

30

40

50



Con

trib

utio

n of

HT

co-e

ffici

ent (

)

Stirrerlocation

Dd ratio






ReplicationsMean



8 Conclusions











Acknowledgments


References





















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation

































1 1 1 1 12 1 2 2 23 1 3 3 34 2 1 2 35 2 2 3 16 2 3 1 27 3 1 3 28 3 2 1 39 3 3 2 1
















Analyzing the data





Ddratio C-w ratio

1 100 275 40 402 100 55 20 503 100 825 13 604 200 275 20 605 200 55 13 406 200 825 40 507 300 275 13 508 300 55 40 609 300 825 20 40



119871119894119895= [

1

119903sum

1

1199102

119894119895119896

] (1)



119894119895119896is

99

95

90

80706050403020

10

5

1


()

Residual







120578119895= minus10 log (119871

119894119895) (2)






1 52186 52547 54009 52914 54472 61536 62627 62560 62241 55883 60848 61296 62449 61531 55784 65278 65489 67512 66093 56405 62412 62938 64025 63125 56006 57641 58394 58457 58164 55297 67983 67614 67980 67859 56638 63082 63415 64051 63516 56069 67883 68433 68023 68113 5666






3 stirrer location

2 Dd ratio

2 and C-w ratio

3




119898)


119898)

(3)








1

119899eff+1

119903] (4)

119873eff =119873

1 + 119879DOF (5)





119890= 15101119891

119890= 18 and





300200100

564

561

558

555

552

564

561

558

555

552

825550275

1324 605040


C-w ratio


SN ra

tios o

nH

T co

effici

ent

SN ra

tios o

nH

T co

effici

ent




Dd ratio






005




0

10

20

30

40

50



Con

trib

utio

n of

HT

co-e

ffici

ent (

)

Stirrerlocation

Dd ratio






ReplicationsMean



8 Conclusions











Acknowledgments


References





















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation

















Analyzing the data





Ddratio C-w ratio

1 100 275 40 402 100 55 20 503 100 825 13 604 200 275 20 605 200 55 13 406 200 825 40 507 300 275 13 508 300 55 40 609 300 825 20 40



119871119894119895= [

1

119903sum

1

1199102

119894119895119896

] (1)



119894119895119896is

99

95

90

80706050403020

10

5

1


()

Residual







120578119895= minus10 log (119871

119894119895) (2)






1 52186 52547 54009 52914 54472 61536 62627 62560 62241 55883 60848 61296 62449 61531 55784 65278 65489 67512 66093 56405 62412 62938 64025 63125 56006 57641 58394 58457 58164 55297 67983 67614 67980 67859 56638 63082 63415 64051 63516 56069 67883 68433 68023 68113 5666






3 stirrer location

2 Dd ratio

2 and C-w ratio

3




119898)


119898)

(3)








1

119899eff+1

119903] (4)

119873eff =119873

1 + 119879DOF (5)





119890= 15101119891

119890= 18 and





300200100

564

561

558

555

552

564

561

558

555

552

825550275

1324 605040


C-w ratio


SN ra

tios o

nH

T co

effici

ent

SN ra

tios o

nH

T co

effici

ent




Dd ratio






005




0

10

20

30

40

50



Con

trib

utio

n of

HT

co-e

ffici

ent (

)

Stirrerlocation

Dd ratio






ReplicationsMean



8 Conclusions











Acknowledgments


References





















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












1 52186 52547 54009 52914 54472 61536 62627 62560 62241 55883 60848 61296 62449 61531 55784 65278 65489 67512 66093 56405 62412 62938 64025 63125 56006 57641 58394 58457 58164 55297 67983 67614 67980 67859 56638 63082 63415 64051 63516 56069 67883 68433 68023 68113 5666






3 stirrer location

2 Dd ratio

2 and C-w ratio

3




119898)


119898)

(3)








1

119899eff+1

119903] (4)

119873eff =119873

1 + 119879DOF (5)





119890= 15101119891

119890= 18 and





300200100

564

561

558

555

552

564

561

558

555

552

825550275

1324 605040


C-w ratio


SN ra

tios o

nH

T co

effici

ent

SN ra

tios o

nH

T co

effici

ent




Dd ratio






005




0

10

20

30

40

50



Con

trib

utio

n of

HT

co-e

ffici

ent (

)

Stirrerlocation

Dd ratio






ReplicationsMean



8 Conclusions











Acknowledgments


References





















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










300200100

564

561

558

555

552

564

561

558

555

552

825550275

1324 605040


C-w ratio


SN ra

tios o

nH

T co

effici

ent

SN ra

tios o

nH

T co

effici

ent




Dd ratio






005




0

10

20

30

40

50



Con

trib

utio

n of

HT

co-e

ffici

ent (

)

Stirrerlocation

Dd ratio






ReplicationsMean



8 Conclusions











Acknowledgments


References





















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation


















Acknowledgments


References





















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Documents

Research Article Experimental Determination of Heat