Monitoring of Oxygen Content in the Flue Gas at a Coal-Fired Power Plant Using Cloud Modeling Techniques

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 63, NO. 4, APRIL 2014 953

Monitoring of Oxygen Content in the Flue Gasat a Coal-Fired Power Plant Using

Cloud Modeling TechniquesXiaojuan Han, Yong Yan, Fellow, IEEE, Cheng Cheng, Yueyan Chen, and Yanglin Zhu

Abstract The accurate measurement of oxygen content inthe flue gas at a coal-fired power plant is important for theplant operators to realize closed-loop and optimal control. In thispaper, eight zirconium oxygen analyzers were used to measurethe oxygen content in the flue gas under real plant conditions.A cloud model is incorporated into the measurement system. Inconsideration of the temporal and spatial characteristics of theoxygen sensors, a quantitative transformation fusion model basedon the cloud model theory is established. The oxygen content inthe flue gas is calculated using mean value, space fusion, andspacetime fusion methods, respectively. The temperatures ofboth flue gas and cold air are also measured to calculate the heatloss of the flue gas and the combustion efficiency of the boiler.On-plant demonstration results show that the proposed methodproduces more accurate measurements than those from the meanvalue method, leading to increased combustion efficiency andreduced heat loss.

Index Terms Cloud model, combustion efficiency, flue gas,heat loss, oxygen content, space fusion, spacetime fusion.

I. INTRODUCTION

THE combustion efficiency of a boiler is a measure of howeffectively the heat content of a fuel is converted to usableheat, which shows the extent of its complete combustion [1].If the combustion efficiency is to be improved, combustionconditions in a furnace must be adjusted so that the fuel burn-ing is optimized. Thus, optimizing the efficiency of a boileris important to minimize fuel consumption and atmosphericemissions [2], [3].

Existing approaches to optimizing combustion efficiencycan be grouped into three categories. The first categoryincludes analysis models based on thermodynamics and chem-istry [4]. Since the combustion process is extremely complex,it is difficult to establish a comprehensive simulation model.The second category is based on soft computing techniques

Manuscript received April 14, 2013; revised July 22, 2013; acceptedSeptember 23, 2013. Date of publication November 8, 2013; date of currentversion March 6, 2014. This work was supported in part by the ChineseMinistry of Science and Technology, and in part by the Chinese Ministry ofEducation, as a part of the 973 Project under Grant 2012CB215203 and partof the 111 Talent Introduction Project under Grant B12034. The AssociateEditor coordinating the review process was Dr. Salvatore Baglio.

X. Han, C. Cheng, and Y. Chen are with the School of Control and ComputerEngineering, North China Electric Power University, Beijing 102206, China.

Y. Yan is with the School of Control and Computer Engineering, NorthChina Electric Power University, Beijing 102206, China, and also with theSchool of Engineering and Digital Arts, University of Kent, Kent CT2 7NT,U.K. (e-mail: [email protected]).

Y. Zhu is with the Department of Thermal Control, Panshan Power Gener-ation Limited, Tianjin 301900, China.

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TIM.2013.2287117

such as neural networks [5], predictive control model [6],and fuzzy logic [7]. The third category includes hybrid sys-tems combining analysis models with soft computing tech-niques [8]. Soft computing techniques [9] have limitations ina practical application, which requires further analysis of theprocess being measured and related sensors so as to find suit-able secondary variables through the mechanism analysis. Inaddition, the number of secondary variables and the selectionof the sensor locations also affect the measurement accuracy.

However, the temperature and oxygen content of the flue gasare primary indicators of the combustion efficiency. To ensurecomplete combustion of the fuel, the furnace is supplied withexcess air. The combustion efficiency will be improved asthe excess air coefficient increases, until the heat loss in theexcess air is greater than the heat provided by more efficientcombustion. Thus, the correct level of the excess air isdetermined by analyzing the oxygen content [10]. At present,zirconium oxygen analyzers [11] are commonly used to mea-sure the oxygen content in the flue gas with specific require-ments such as accuracy, stability, response time, and durability.Zirconium oxygen analyzers are normally calibrated priorto their dispatch from the manufacturer, although periodicrecalibration on site of installation may be necessary [12].

The aim of this paper was to make full use of the existingsensors and adopt the idea of data fusion to further improve theaccuracy of oxygen content measurement, thereby improvingthe combustion efficiency of the boiler. Multiple identicalzirconium oxygen analyzers are used to achieve oxygen con-tent measurement in the flue gas. The cloud modeling theoryis incorporated into the measurement system to fuse the datafrom the analyzers. On the basis of statistical mathematics andfuzzy mathematics, cloud modeling presents a new approachto represent the randomness and fuzziness between uncertainlanguage and precise numerical value so that a quantitativetransformation fusion model based on the cloud modelingtheory is established. Practical tests were undertaken on a300-MW coal-fired power plant in China. The oxygen contentin the flue gas was determined using three methods based onmean value, space fusion and spacetime fusion, respectively,for the improvement of combustion efficiency and reductionof heat loss due to incomplete combustion.

II. FUSION THEORY BASED ON CLOUD MODELINGA. Numerical Characteristics of Cloud Model

The cloud modeling method [13] is used to model theuncertain transition between a linguistic term of a qualitative

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954 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 63, NO. 4, APRIL 2014

Fig. 1. Normal cloud with Ex = 0, En = 0.5, and He = 0.03.

Fig. 2. Forward cloud generator.

concept and its numerical representation. In recent years, thecloud modeling theory has been applied to data mining [14],decision analysis [15], intelligent control [16], and imageprocessing [17]. The model describes a qualitative conceptusing a set of three parameters that well integrate the random-ness and fuzziness of the concept. The numerical character-istics of the cloud model are expressed by expectation Ex ,entropy En , and hyperentropy He. Ex is the expectation ofthe cloud droplets distribution and the best position repre-senting the qualitative concept corresponding to the centerof the clouds gravity. En is the measurement uncertaintyof the qualitative concept determined by its randomness andfuzziness. The randomness represents the discrete degree ofcloud droplets that can represent the concept. The fuzzinessreflects the interval of cloud droplets accepted by the concept.He is the uncertainty measurement of En , i.e., the entropy ofthe entropy En , which is determined by the randomness andfuzziness of En . Fig. 1 shows an example of a normal cloudwith 2000 droplets [18], [19].

B. Cloud GeneratorsThe forward and backward cloud generators are the most

important algorithms in cloud modeling. Considering that apractical problem obeys or approximately obeys the normaldistribution, the normal cloud generator can be regarded asa basic cloud generator. The forward cloud generator isthe uncertainty transition model between a basic conceptand its value, mapping from qualitative to quantitative data.According to the three digital characteristics (Ex , En, He),the forward cloud generator can generate Drop(xi , ui ), asshown in Fig. 2, where xi is the quantity value and ui isthe membership degree of xi .

The algorithm of the forward cloud generator can be sum-marized as follows.

1) Generate a normally distributed random number xi withmean value Ex and standard deviation En

Ex = 1n

n

i=1xi (1)

En = 1

n 1n

i=1(xi Ex )2. (2)

2) Calculate ui

ui = exp((xi Ex )2

2(En)2

). (3)

3) The Drop(xi , ui ) reflects the whole content transformingfrom qualitative data to quantitative data.

4) Repeat steps 1)3) until the N th cloud droplet isgenerated.

The backward cloud generator (CG1), as shown in Fig. 3(a),is a conversion model, which converts quantity data to aquantitative concept.

Data xi with membership degree ui can be converted tothe quality cloud concept (Ex , En, He) through the backwardcloud generator. In practice, the backward cloud generatorwithout membership degree ui [as shown in Fig. 3(b)] canbe used as there is usually no clearly defined degree ui . Thealgorithm is as follows.

1) The sample mean value Ex and standard deviation Enare calculated from (1) and (2).

2) Calculate the entropy E n

En =

2 1

n

n

i=1|xi Ex |. (4)

3) Calculate hyperentropy

He =

(E n)2 (En)2. (5)

C. Space Fusion Structure Based on Cloud ModelingData fusion [20] is generally defined as the use of techniques

that combine data from multiple sources and gather that infor-mation into discrete, actionable items to achieve inferences,which is more efficient and narrowly tailored than achievedby means of disparate sources. Data fusion processes are oftencategorized as low, intermediate, or high level, depending onthe processing stage at which fusion takes place [21]. In recentyears, multisensor data fusion has received significant attentionfor a wide range of applications. Data fusion techniquescombine data from multiple sensors and related informationfrom associated databases to achieve improved accuracies andmore specific inferences than those could be determined by asingle sensor [22], [23].

An integrated cloud can be used to express the conversionfrom quantitative to qualitative data as the fuzziness andrandomness mapping from the quantitative to the qualitativeare effectively integrated by the cloud modeling theory. Thespace fusion structure based on cloud modeling is shownin Fig. 4.

HAN et al.: MONITORING OF OXYGEN CONTENT IN THE FLUE GAS 955

Fig. 3. Backward cloud generator with and without membership degree. (a) With membership degree. (b) Without membership degree.

Fig. 4. Space fusion structure based on cloud modeling.

In Fig. 4, n represents the number of spaces that all thesensors are included in the space fusion structure. The datalevel consists of Drop(x1i ), . . . , Drop(xni ) (i = 2, 3, . . . , m),where m is the number of sensors in each space. The samplingvalues are simultaneously obtained from the multiple sensorsin different spaces. In the feature level, the cloud droplets faraway from the center value are eliminated while the othersare regarded as the input to be transformed into the finalcloud digital features (E px , E pn , H pe ), (p = 1, 2, . . . , n) by thebackward cloud generator. In the decision level, the fusionrule can be implemented in a practical algorithm based onvectorization using the weighted average method [24]

Ex = ET1 E2(ET2 E2)1 (6)En = (ET2 E2)

12 (7)

where E1 comprises the expectations in each space,E1 = [E1x , E2x , . . . , Enx ]T , E2 is the weight values of eachspace, E2 = [(E1n)1, (E2n)1, . . . , (Enn )1]T , Ex is the fusedexpectation obtained using the weighted average method, andEn is the fused standard deviation.

D. SpaceTime Fusion Structure Based on Cloud ModelingSpace fusion based on cloud modeling is mainly used to

fuse the data from the sensors located in different spacessimultaneously. However, the simple space fusion withoutconsidering the time-domain characteristics of the target is

insufficient for the description of its feature. From the per-spective of timespace fusion, not only the space informationof the sensors is fused, but also the time-domain information isconsidered to achieve more comprehensive identification anddecision making of the target, so that the loss of informationis reduced and the recognition rate of the data fusion systemimproved [25]. The spacetime fusion structure based on cloudmodeling is shown in Fig. 5.

As can be observed from Fig. 5, Drop(E1ix ) consists ofE11x , . . . , E1mx obtained by space fusion in the first measur-ing cycle and Drop(E2ix ) comprises E21x , . . . , E2mx obtainedthrough space fusion in the second measuring cycle, where mis the sample interval in a measuring cycle. Drop(E1ix ) andDrop(E2ix ) are transformed by the backward cloud generatorto obtain (E1x , E1n , H 1e ) and (E2x , E2n , H 2e ). The fused result canbe obtained from (6) and (7).

III. OXYGEN CONTENT MEASUREMENT USING DATAFUSION METHOD BASED ON CLOUD MODELING

A. Oxygen Content Measurement SystemEight zirconium oxygen analyzers were used to measure the

oxygen content in the flue gas of a 300-MW coal-fired boiler.The analyzers were mounted between the superheater and theeconomizer of the boiler where the flue gas temperature wasappropriate. The flue gas system of the boiler is schematicallyshown in Fig. 6 [26]. The flue gas flows from the lower levelof the furnace to the upper level along the solid lines (Fig. 6).All analyzers were all calibrated prior to despatch to the powerstation and further calibration should not be necessary [12].However, due to specific installation conditions (presence ofdust, gas corrosion, etc.) at the coal-fired power station, thesensors were recalibrated six monthly or sooner through astandard two-point (zero, full scale) calibration process. Whenthe calibration hole was connected to the air, the oxygencontent should suggest the oxygen content of the air. If not, anon-site recalibration was undertaken by connecting a standardgas with an oxygen content of 6% to the calibration hole.

It can be observed from Fig. 6 that there were four airpreheaters (air preheaters 14), four main motors, and fourstandby motors. The outputs of four main motors are 13.5,13.8, 13.8, and 14.0 A, respectively. The four standby motorswere not used during the tests. NR321, NR322, NR421, andNR422 are the flue gas baffles in the entrance of air preheaters.NR319, NR320, NR419, and NR420 are the flue gas bafflesin the outlet of air preheaters. S1, S2, . . . , S8 represent thezirconium oxygen analyzers, one on each side of each airpreheater (as shown in Fig. 7). A total of eight zirconium


Fig. 5. Spacetime fusion structure based on cloud modeling.

Fig. 6. Flue gas system of the boiler (flue gas moves from lower to upper level).

oxygen analyzers were installed, four (S1, S2, S3, and S4) onthe left side of the furnace while the other four (S5, S6, S7,and S8) on the right of the furnace.

The oxygen content in the flue gas was recorded over aperiod of 24 h. The scan cycle of the Distributed ControlSystem was 200 ms, the sampling frequency was 5 Hz, theresolution of the data acquisition system was 0.1%, and theuncertainty of the sensors was all 1.0%. Fig. 8 shows a typicalset of results over a period of an hour.

Fig. 8 shows, over this typical hour in time, the data onthe left of the furnace are lower than that on the right of thefurnace. The reason for this phenomenon is believed to bebecause the vertical section of the boiler flue is so large thatthe distribution of oxygen content is uneven. This slow fluctu-ation of the measurement from each analyzer was observed

during the plant demonstration trials. When the measure-ment data are obtained, their consistency should be checkedto eliminate the influence of spurious data on the oxygenmeasurement.

B. Data Consistency CheckTo ensure the accuracy of the measurement data from the

multisensors, the first step is to complete the selection ofinitial data and minimize the gross errors. Gross errors areundetected errors that will cause a measurement to deviatesignificantly from the mean value [27]. The data consistencycheck is undertaken as follows.

The oxygen content data collected from S1 to S8 arerepresented as x1, x2, . . . , x8, ordered from small to large,


Fig. 7. Installation of two zirconium oxygen analyzers in the air preheater.

Fig. 8. Oxygen content measured from the eight zirconium oxygen analyzers.

and set x 1 and x 8 as the lower and upper limits, respectively

x 1, x 2, x 3, x 4, x 5, x 6, x 7, x 8.

The median xM of quartile is defined as

xM = x4 + x 5

2. (8)

The median xup of quartile in interval [xM , x 8] is

xup = x5 + x 6 + x 7

3. (9)

The median xdown of quartile in interval [x 1, xM ] is

xdown = x2 + x 3 + x 4

3. (10)

The discrete degree of quartile is dx = xup xdown. Thedata greater than dx are regarded as gross errors, namely, thejudgment interval of invalid data is

x i xM > dx, (i = 1, 2, . . . , 8). (11)When x i xM is less than dx , the measuring data x i areconsidered to be effective consistency data. Random interfer-ence in each sensor can be effectively eliminated using thismethod. The data shown in Fig. 8 were tested using the abovemethod. Typical results illustrating the data consistency checkare shown in Fig. 9. In Fig. 9, data marked with L are obtainedby calculating dx + xM in the data consistency check. It canbeen seen that the data of S1S8 are mostly in the range of Land only several points in S7 are out of the range of L (calledspurious points) and hence have been eliminated.


Fig. 9. Data consistency check.

Fig. 10. Space fusion model of the oxygen content measurement in theflue gas.

C. Monitoring of Oxygen Content in the Flue Gas Basedon Space Fusion Method Using BackwardCloud Generator

In practice, the mean values of the data after the consistencycheck in Fig. 8 (spurious points have been removed) areregarded as the oxygen content measurement in the fluegas. The arithmetic mean value and standard deviation arecalculated using (1) and (2). However, only eight sensors areavailable in the oxygen content measurement system, whichgives a finite number of measurements. If the oxygen contentfrom the eight sensors is simply averaged by the mean valuemethod, it is not the best method to improve the measurementaccuracy. Therefore, fusion method based on cloud modeling isapplied to eliminate the uncertainty of measurement and obtainmore reliable results from a limited number of measurements.The calculation process of the cloud model based on spacefusion is described as follows.

The eight analyzers are divided into two groups accordingto their installation locations (Fig. 6). The data obtainedfrom the sensor groups on the left side of the furnace areregarded as the first group. The cloud droplets xi are writ-ten as Drop(xi), (i = 1, . . . , 4). The data measured from

the right side of the furnace are regarded as the secondgroup and the cloud droplets y j can be written as Drop(y j ),( j = 1, . . . , 4). The space fusion model based on the backwardcloud generator, as shown in Fig. 10, is simplified as follows.

The two groups of data are used to determine (Ex x,Enx , Hex) and (Exy, Eny, Hey), respectively. The combinedvalues of Ex and En are calculated from (6). A typicaldistribution of cloud droplets over a period of 6 s after beingspace fused in comparison with the mean value method isshown in Fig. 11.

Each subplot in Fig. 11 represents the distribution of clouddroplets over 1 s. According to the description of Ex , En , He,as shown in Fig. 1, when the drops near Ex are the mostintensive, the more dilute those away from Ex are, the higherthe possibility of Ex close to the real oxygen value. The fusionresults of Ex and En over a period of 60 min are plotted inFig. 12.

The standard deviation En obtained by the space fusionmethod is smaller than that by the mean value method, whichis consistent with the distribution of cloud droplets (Fig. 11).The overall errors obtained by the space fusion method andthe mean value method are 0.0919 and 0.3649, respectively.In repeated experiments, En represents the discrete degreeof the drops, which can be used to evaluate the reliabilityof measurement results, that is, the smaller En , the higherthe reliability of measurement results. Therefore, using thespace fusion method can improve the measurement accuracy,compared with the mean value method.

D. Monitoring of Oxygen Content in the Flue Gas Basedon SpaceTime Fusion Method Using BackwardCloud Generator

Fig. 8 shows that the oxygen content in the flue gas hasalmost no change in 1 min, thus the average value of the


Fig. 11. Distribution of cloud droplets obtained by mean value and space fusion methods.

Fig. 12. Comparison of oxygen content measurements obtained by mean value and space fusion methods.

oxygen content can be regarded as the measured oxygencontent (the measuring cycle of the oxygen sensors takes1 min). According to the spacetime fusion model (Fig. 5),for the same sensor, the oxygen content measurements fromthe first and second measurement cycles are fused to obtainmore accurate oxygen content.

Fig. 13 shows the cloud droplets over a period of 6 min gen-erated by the mean value and spacetime fusion. Each subplot

represents results over a period of 1 min. En obtained by thespacetime fusion method is less than that obtained by thespace fusion. The fusion results of Ex and En over a periodof 60 min are plotted in Fig. 14.

It can be observed from Fig. 14 that the standard deviationobtained by the spacetime fusion method is only 0.01,smaller than the space fusion method and significantly smallerthan the mean value method. Therefore, the uncertainty of the


Fig. 13. Distribution of cloud droplets obtained by mean value and spacetime fusion methods.

Fig. 14. Oxygen content measured using the three methods.

oxygen content measurement obtained through the spacetimefusion method is improved significantly.

IV. COMBUSTION EFFICIENCY IMPROVEMENTTo improve combustion efficiency and reduce heat loss due

to incomplete combustion, the actual amount of air fed tothe furnace is greater than the theoretical value. Because a

slight change in oxygen content in the flue gas will affect theboiler efficiency significantly, the excess air must be strictlycontrolled. The excess air can be estimated from the measuredoxygen content in the flue gas [1], [2]

= 2121 O2% (12)


Fig. 15. Combustion efficiency and flue gas heat loss obtained by spacetime fusion and mean value methods.

Fig. 16. Differences in combustion efficiency between the spacetime fusion and the mean value methods.

where O2 is the measured oxygen content from the zirconiumoxygen analyzers.

For a coal-fired boiler, heat loss q2 of the flue gas canbe calculated using the following equation, according to theenvironment conditions:

q2(%) = 0.741 T21 O2% (13)

where T is the temperature difference between the flue gasafter the economizer and the cold air.

The combustion efficiency () of the boiler is thus

(%) = 96.7165 0.741 T21 O2% 0.0045 T . (14)

The temperature difference between the flue gas and the coldair, and the flue gas oxygen content obtained using the spacetime fusion and mean value methods are substituted into (13)and (14) to obtain the combustion efficiency and flue gas heatloss. The results are plotted in Fig. 15.

It is evident that the combustion efficiency obtained by thespacetime fusion method is increased in comparison with themean value method. In addition, the flue gas heat loss obtainedby the spacetime fusion method is also reduced in comparisonwith the mean value method. The difference in combustionefficiency between the spacetime fusion method and the meanvalue method is plotted in Fig. 16.

Fig. 16 shows that the hourly average combustion efficiencyobtained using the spacetime fusion method is improved by


0.0887% compared with that from the mean value method, i.e.,the average heat loss obtained using the spacetime fusionmethod is also reduced by 0.0887%. If the calorific valueof standard coal is set to be 29 307 kJ/kg with the cost of796 per ton, the annual savings of standard coal wouldbe about 398 580 tons per generation unit, which is equiva-lent to 301 330 according to the cost saving model givenin [28].

V. CONCLUSION

The cloud modeling fusion method has been adopted in thefusing calculation to obtain the data from multiple oxygen ana-lyzers without any prior knowledge about the oxygen content.The oxygen space fusion and spacetime models based oncloud modeling have been established to achieve more accu-rate measurement of the oxygen content. The method has beenused to determine the combustion efficiency of the boiler andthe heat loss of the flue gas. On-plant demonstration resultshave shown that the hourly combustion efficiency has beenimproved by 0.0887% using the spacetime fusion techniquecompared with that obtained by the mean value method, whichcould result in the annual savings of standard coal of 398 580tons. The improved measurement of the oxygen content in theflue gas has led to the increased combustion efficiency andreduced heat loss.

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Xiaojuan Han received the B.Eng. and M.Sc.degrees in thermal automation from Northeast Elec-tric Power University, Jilin, China, in 1993 and1996, respectively, and the Ph.D. degree in thermalpower engineering from North China Electric PowerUniversity (NCEPU), Beijing, China, in 2008.

She was an Assistant Engineer with Nengda Com-pany, NCEPU, in 1996. She was a Lecturer atNCEPU in 2000. She has been an Associate Pro-fessor of measurement and control engineering withthe School of Control and Computer Engineering,

NCEPU, since 2007. She has published over 60 research papers in journals andconference proceedings. Her current research interests include the applicationof multisensors information fusion technology in thermal power plant, sensorfusion, fault diagnosis, and renewable energy power control technology.


Yong Yan (M04SM04F11) received the B.Eng.and M.Sc. degrees in instrumentation and con-trol engineering from Tsinghua University, Beijing,China, in 1985 and 1988, respectively, and the Ph.D.degree in solids flow measurement and instrumenta-tion from the University of Teesside, Middlesbrough,U.K., in 1992.

He was an Assistant Lecturer with Tsinghua Uni-versity in 1988. In 1989, he joined the University ofTeesside as a Research Assistant. He was a Lecturerwith the University of Teesside from 1993 to 1996,

and then as a Senior Lecturer, Reader, and Professor with the University ofGreenwich, Greenwich, U.K., from 1996 to 2004. He is currently a Professorof electronic instrumentation, the Head of the Instrumentation, Control andEmbedded Systems Research Group, and the Director of Research with theSchool of Engineering and Digital Arts, the University of Kent, Canterbury,U.K. He has published over 280 research papers in journals and conferenceproceedings in addition to 12 research monographs.

Dr. Yan currently serves as an Associate Editor for the IEEE TRANSAC-TIONS ON INSTRUMENTATION AND MEASUREMENT. He was the TechnicalProgram Co-Chair for I2MTC in 2011 and I2MTC in 2012. He is a fellow ofthe Institution of Engineering Technology (IET, formerly IEE), the Instituteof Physics, and the Institute of Measurement and Control. He received theAchievement Medal by the IEE in 2003, the Engineering Innovation Prize bythe IET in 2006, and the Rushlight Commendation Award in 2009.

Cheng Cheng received the B.Eng. degree in mea-surement and control technology and instrumenta-tion from Yanshan University, Qinhuangdao, China,in 2004. He is currently pursuing the Master ofEngineering degree in measurement technology andelectronic instrumentation with North China ElectricPower University, Beijing, China.

His current research interests include measurementtechnology for coal-fired power plants and otherindustries, sensor fusion, soft measurement, andlarge-scale energy storage technology.

Yueyan Chen received the B.Eng. degree in automa-tion from North China Electric Power University,Beijing, China, in 2011, where she is currentlypursuing the M.Sc. degree in automation.

Her current research interests include measurementtechnology for coal-fired power plants and otherindustries, sensor fusion, soft measurement, andlarge-scale energy storage technology.

Yanglin Zhu received the B.Eng. degree in mea-surement and control engineering from North ChinaElectric Power University, Beijing, China, in 2005.

He is currently an Engineer of thermal automationwith Guohua Panshan Power Generation CompanyLtd., Tianjin, China. He is mainly engaged in ther-mal maintenance work. His current research interestsinclude the measurement and control technology forcoal-fired power plants.

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Documents

Monitoring of Oxygen Content in the Flue Gas at a Coal-Fired Power Plant Using Cloud Modeling Techniques