Intelligent flame analysis for an optimized combustion

Stephan Peper Dirk SchmidtSenior Consultant Sales ManagerCombustion OptimizationABB UTD/PGA Powitec Intelligent TechnologiesKallstadter Street 1 Im Teelbruch 134bMannheim, Germany Essen, Germany

KEYWORDS

Combustion Optimization, power plant, camera, image processing, neural net

ABSTRACT

One of the primary challenges in the area of process control is to ensure that many competingoptimization goals are accomplished at the same time and be considered in time. This paper describes asuccessful approach through the use of an advanced pattern recognition technology and intelligentoptimization tool modeling combustion processes more precisely and optimizing them based on aholistic view.

INTRODUCTION

Within the combustion process to date camera systems are mainly applied for visualization purposesonly [1]. This video technology provides the operator with online information of the flame behavior, butis limited to the operator’s capability to interpret the complex visual information and to react on it in anappropriate way.For this reason, in recent years intelligent flame analysis systems have been developed to extractadditional flame information characterizing the combustion in far better way [2,3]. Furthermore, thisinformation, available in a digital format, allows an intelligent processing in conjunction with the othermeasurements of the plant.The efficient use of this large set of information is crucial for a successful and continuous combustionoptimization. The proposed system uses a computer-aided significance analysis where, as a first step, theparameters dominating the process are identified. These parameters are the primary sources ofinformation for the optimization software that is structured in such a way that it automatically adapts toconstantly changing process conditions (adaptivity). The optimizer can recognize, among other things,the effects that wear and tear have on machines and plant components, or changes in the fuel used in theprocess, and can readapt its optimization strategy as necessary.

The paper describes the hierarchy of the system and the different steps of data collection, modelbuilding, and navigation for optimizing the combustion process. Finally, results of implemented systemswill be presented showing the potentials in saving operational costs for coal, ammonia, energy (selfconsumption), etc.

REQUIREMENTS

The efficient combustion is primarily defined by the air to fuel ratio and the proper distribution of the airwithin the furnace zones [4]. High excess air increases the energy due to a higher volume of hot flue gasleaving the stack. Additionally, the self-consumption of the related fans will be increased and alsocontribute to a higher spending of fuel. In contrary, low excess air will possibly result in highersecondary NOx, higher carbon in ash, and problems regarding water wall corrosion. For this reason theproper balance of fuel and air is crucial for minimal operational and maintenance cost.However, in case of a pulverized coal boiler such balance is hard to find. The information of the fuelflow broken down to each burner in general is not directly available. The fuel flow to the pulverizers isoften very coarsely given by feeder speed, whereas the fuel flow from the pulverizer to the burner is onlycalculated under the assumption of an equal split of the coal input between the corresponding burnerpipes. Thus an exact air/fuel control is not possible.Varying coal quality is gaining more importance since coal is obtained worldwide for varying prices.The combustion of a wider bandwidth can considerably increase the flexibility in this process ofprocurement. However, the coal type very much influences the combustion. Attributes such as calorificvalue, volatile matter, sulfur content, hard grove index, etc. have a great impact on the process.For these reasons, measures have to be applied, (I) from which the above information can be derivedand, (II) which optimize the combustion based on this information automatically. The system should

• equalize air/fuel ratio and flow,• allow a wider fuel bandwidth,• allow alternative fuel (e.g. sewage sludge),• stabilize flame and combustion,• reduce emissions, and• guarantee safe water wall atmosphere

at different loads and adapting to changing plant conditions.In the following a system will be explained – starting with the flame sensor - fulfilling such requirements[5]. It obtains this information through the analysis of individual flames by digital image processing.

FLAME SENSOR

The sensor, shown in Figure 1, uses an air-cooled, rigid endoscope with two optical outputs. This highlyrapid optical system has been designed especially for applications in furnaces.The endoscope can be used in any combustion medium. Special bands of wavelengths (such as UV, VIS,or NIR) can be selected.

The first optical output is used to monitor a defined area of a single flame through a CCD video camera.The second optical output is used for a CMOS camera, transmitting radiation data of the flame directlyto a field computer for flow condition analysis.

FIG 1 – FLAME SENSOR

Each sensor is assigned to its own field computer analyzing the flame data and extracting image criteria.The criteria or characteristics are transmitted, parallel with the video pictures, to a system computer,which is typically located in the control room.The CCD camera obtains 3 video images, individually for red, green and blue light (RGB) 25 times persecond. This camera system delivers the live color video images for the control room. This informationcan be used to determine the flame temperatures in a sufficient quality. At the same time, the secondcamera system (CMOS camera) determines flame characteristics highly spatially and temporallydiscretized. An image frame of up to 1280 x 1280 pixel can be obtained and be processed at a speed of10 Mpixel/sec. Flame characteristics, such as flow conditions (i.g. fluctuation), are analysed by afrequency analysis. The temporal and spatial behavior of fluctuations in flame intensities within theburner field is drawn upon as a measure of turbulence. This correlates to the turbulence of the flow field,to the particle load, and to the speed inside the single flame. Adjustments like fuel and air variations arereproducible attained.

A optical device with beam splitterB video cameraC CMOS camera

flow conditions

pattern recognitionvideo signal

A

C

B

A optical device with beam splitterB video cameraC CMOS camera

flow conditions

pattern recognitionvideo signal

A

C

B

TABLE 1 – TECHNICAL DATA OF THE FLAME SENSOR

DESCRIPTION MEASURE

DIAMETER (PROTECTION TUBE OF ENDOSCOPE) 1.7 inches (43 mm)

LENGTH (PROTECTION TUBE OF ENDOSCOPE) 31.5 inches (800 mm)

EXTENSION MODULE 11.8 inches (300 mm)

ENVIRONMENTAL TEMPERATUR, HEAD < 2370 F (1300 C)

ENVIRONMENTAL TEMPERATUR, MANTLING < 750 F (400 C)

MATERIAL Stainless steel

AIR CONSUMPTION 30 m3 / h

PRESSURE > 4 bar

COOLING AND PURGING AIR Oil- dust- and water free

FLAME CHARACTERISTICS

The visual information will be transferred to an image processing system. Here flame characteristicshighly correlating with

• temperatures,• intensity fluctuation,• light frequencies,• coal distribution, and• coal fineness

will be determined internally. Beside these explicit characteristics, implicit information additionally isextracted. Up to 25 different features will be generated. A high degree of spatial and temporaldiscretization provided by the camera system guarantees high information content of the received data.Parallel to the characteristic extraction the system includes a visualization module, which enables theoperator to get an online feedback of burner adjustments also on comfortable thermography tools.One example for the use of this thermography module is shown in Figure 2.

FIG 2 – THERMOGRAPHY MODULE ‘PIT INDICATOR'

The different temperatures are color-coded. Via the cursor measuring points, lines, or areas can bedefined according to locations of interest. In these locations calculations are performed to determineMIN/MAX or MEAN values over specific time intervals. Limits can be set and alarms are issued in casethe temperatures exceed or fall short of these limits. A special feature is the ’POLYLINE’-display. Twofreely positionable polygon lines (straight lines or curves) can be saved as chronological representations.The temperature differences in the observed area are continuously displayed on a time scale. Thetemperature distribution in this region can thus be displayed for a period of hours or days. In the exampleabove, the change in the flame shape is easily discernible from ’POLYLINE’ 0 and 1.Rapid process characteristics are required for online prognosis and as a basis for an automatic controlwith the system own optimization software based on neural nets.The characteristics are acquired via extraction of so-called moments by means of digital imageprocessing (high temporal and spatial resolution). The moments are image information, which describethe flame image with great detail and reproducibility and in a manner, which the evaluation software isable to understand.

The raw data is further processed to characteristics specially adapted to the navigation software. Besidesgeometrical and statistical characteristics, neuronal characteristics form the basis to predict NOx andother production results. An example is given in Figure 3. Here no explicit physical characteristics but“raw signals” are shown and correlated with NOx concentration before SCR. The good correlation isgiven with the advantage, that these raw signals are immediately available at combustion. Thus they caninfluence the NOx control much quicker than the (extractive) measurement can do. The signal ‘NOxbefore DENOX’ is corrected regarding time delay.

FIG 3 – NOX CHARACTERISTICS

SIGNIFICANCE ANALYSIS

The main objectives of the significance analysis are to

• identify those process parameters which have largest impact on a specific optimization variable, andto

• model the relationship between the measured variables or camera-based characteristics and theresulting controlled variables.

Crucial for this analysis is the joint processing of the visual and conventional plant characteristics, sincethe combination (subsets) of characteristics often indicate highest optimization potentials.

In order to minimize the required computation work and to avoid ambiguity, the analysis only usesrelevant data records of a period of several months. The data records are imaged via neural networks andare used for weighting the process parameters in accordance with their relevance to a specificoptimization objective.Based on the results of the weighting procedure, the “ranking”, the potential for improving thecombustion conditions can be assessed.A possible ranking is shown in the following table. The characteristics or channels are split into threegroups: significant, redundant, and excluded channels. According to a given optimization goal only thetop four channels are most relevant, whereas the other channels only provide redundant or noinformation. Consequently, in the neural net model of the boiler behavior only the top four will beincluded.

TABLE 2 – RESULTS OF A SIGNIFICANCE ANALYSIS

X PIT-ANALYZER

channel ranking of significance analysisdatafile: power 146 date: 23/05/01significant channelsrank channel single error incremental error0 PLA_BD_ZUT30 0,087 0,0901 REG_KL_KALU_M30 0,131 0,0882 MOMENT R02Z1 0,116 0,0993 MOMENT B02Z1 0,121 0,110

redundant channels4 CO2 S2 0,116 0,1075 SELU_KL_2_BR32 0,118 0,1006 CO2 S1 0,120 0,1067 TERT_LU BR31 0,134 0,0968 MOMENT B01Z1 0,135 0,1199 AIR 1 0,136 0,11010 TERT_LU BR32 0,137 0,10511 CO S2 0,137 0,10312 CO S1 0,139 0,10513 MOMENT R01Z1 0,139 0,11014 AXIAL 0,140 0,10815 RADIAL 0,140 0,11316 NOX_V_DENOX 0,141 0,11117 MOMENT B03Z1 0,141 0,101

excluded channels18 MOMENT B01Z219 MOMENT R05Z1

As an example, the result of the significance analysis – the ranking – is shown in TABLE 2: In contrastto the redundant channels (rank 4 - 17), the significant channels are suitable as inputs for the

optimization of control. With an alteration of the “PLA_BD_ZUT30” (dispatcher load mill 30) and“REG_KL_KALU_M30” (controller flap cold air mill 30) as process input values, the definedoptimization target can now be influenced. The two important online characteristics “MOMENT...”(ranks 2, 3) help to precisely determine the intervention quantity. It is just these moderate interventionsthat account for the adaptivity of the process control! At any given time, the optimization strategy adaptsto the current process situation.At the end of the significance analysis a complete model is built mapping the controlled actuators andtheir influence on the combustion process in a neural network.

NAVIGATOR

The boiler model is the basis for optimizing the combustion process. A program called “PiT navigator”performs this optimization.

Traditional neuro/fuzzy models are generated once during a training phase. In contrast, this system learnsautonomously, on a continual basis. It is able to recognize, among other things, changes induced by thewear and tear on machines and plant components or any fluctuating calorific values in the primary andsecondary fuels. The navigator independently readjusts its optimization strategy.

The process characteristics gathered by the system are ideally used as new process control variables. Inthe past, it was only possible to use dead-time-based variables for controlling. As online characteristicscan now be collected by the system itself, the new data allow the process to be adequately controlledmuch more quickly and at a greater accuracy. Figure 4 shows the navigator as the center of theoptimization process.

FIG 4 – NAVIGATOR OPTIMIZES THE COMBUSTION PROCESS BASED ON PLANTINPUTS

SYSTEM OVERVIEW

The final configuration of the system is shown in Figure 5. The navigator is installed on a LINUX PC inthe control room and directly communicates with the DCS. A large set of interfaces for different controlsystems exist.For safety reasons, the navigator does not overwrite set points in the DCS but provides set point changesto the control. In case of large load ramps, invalid load ranges, or set point limit violations its input tothe DCS is not considered.The navigator receives the flame characteristics from a field PC associated to each camera. The camerainformation is directly processed and compressed on the field PC in order to transfer the highlydiscretized spatial and temporal signals in the appropriate time frame.

FIG 5 – SYSTEM CONFIGURATION

SYSTEM APPLICATION

Highest benefits are to reach in coal [6] and oil fired power plants. However, first implementations inwaste incineration plants also show positive results. The potentials here are an increase of wastecombustion with a decrease of steam variation and at the same time with reduced pollutant and corrosiveemissions. Furthermore, in rotary kilns of cement plants the system is of a high benefit [7]. It reduces theprimary fuel up to 5% and, at the same time, reduces the NOx emissions at the primary combustionaround 20%.

Übertragung der

DCS

Rückzugs-einrichtung Feldschrank ‚ME‘

Multisensorincl. video - and CMOS camera

Field controller with cabinet

Transfer of image characteristics

System PC with visualization software

Cabinet Retraction module

Video signal Ethernet

Process coupling with setpoint correction

The first complete installation in a coal-fired power plant comprises of six sensors, each for one burner.The total air volume decreases through a locally optimized fuel-air ratio. The excess air in the flue gaswas reduced in load variations between 70% and 100 % in an average value of 0,8%. Parallelmeasurements show a substantially lower CO and NOx concentration than without using the system. Theboiler operation proved to be clearly more positive regarding residual oxygen at the combustion chamberwalls helping to prevent corrosion.Other advantages, which could be gained with the system included:

• Increased boiler efficiency (up to 1%)• Optimization of the fuel-air ratio for individual burner, burner level and total combustion system• Reduction of excess air• Reduction of NOx• Reduction of CO• Control over extinguishing of the flame on an individual burner and coal mill operation; correction

of negative effects on the combustion process• Cessation of discontinuous check of the fuel distribution

CONCLUSION

A novel system has been proposed which provides an integrated solution for combustion optimization. Asophisticated camera system extracting flame characteristics of each individual burner coupled with aself adapting neural net based navigator allow the increase of efficiency, reduction of emissions, and thecombustion of a wider fuel bandwidth.The direct availability of additional up to 25 different characteristics of the flame allows a quick reactionon changes in the process. General changes of boiler behavior will continuously result in modeladaptations and thus allow the navigator to accurately find the optimal combustion state.Finally, a number of system applications even in different industrial processes prove the relevance ofsuch solution.

REFERENCES

1. Kaiser, Engineering Office, “External Furnace Television System IMA 011/210-211”, technicaldescription

2. Siemens AG, Power Generation Group, “Instrumentation &Control in the Power Plant: AutomaticCombustion Diagnostics”, product brochure, 1999

3. Käß, M. et al., “Verbrennungsdiagnosesysteme zur Optimierung von Kohlenstaubfeuerungen”,VGB-Fachtagung “Feuerungen 2001 – Effizienter Feuerungsbetrieb im deregulierten Strommarkt –“,VGB PowerTech e.V., Kassel, November 2001

4. Singer, Joseph G., “Designing for boiler performance”, Combustion Fossil Power, Forth Edition,Combustion Engineering, Inc., 1991, 6-5 ff

5. ABB Utilities, “Combustion Optimization – Intelligent Flame Analysis”, product brochure, 2001

6. D. Schmidt & S. Sauer et al., Lignite and Low Rank Coals, VGB & EPRI Conference,Erbach/Eltville, May 17-18, 2001

7. Powitec Intelligent Technologie, “Navigation systems for the cement industry”, product brochure,2001