Download pdf - On-line monitoring of power-plant performance, using exergetic cost techniques

~ Pergamon Applied Thermal Engineering Vol. 16, No. 12, pp. 933-948, 1996

Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved

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ON-LINE MONITORING OF POWER-PLANT PERFORMANCE, USING EXERGETIC COST

TECHNIQUES

A. Valero , M. A. L o z a n o and J. L. Bar to lom6 CIRCE Research Center for Power Plant Efficiency, Department of Mechanical Engineering,

University of Zaragoza, Zaragoza, Spain

Abstract--Among the possible applications of thermoeconomics, the most promising one is perhaps the diagnosis of the operation of actual energy systems. Diagnosis can be considered as the art of discovering and interpreting signs of malfunction and of quantifying their effects in terms of additional consumption of resources. In the sphere of energy systems a good diagnosis requires: (i) the application of regulatory procedures and performance tests codes, in order to determine the state of the system with precision (clinical diagnosis) and (ii) a good theory which would provide concepts to aid the comprehension and to explain the causes of such a state (etiological diagnosis). The problem to solve can be formulated as follows: where, how and which part of the consumed resources can be saved by keeping the quantity and specifications of the final products constant? This paper briefly explains the thermoeconomic approach to solve this problem and presents a supervisory system running in a 350 MW coal power-plant which uses these ideas to diagnose in real time the causes of heat rate deviations. Copyright © 1996 Elsevier Science Ltd.

Keywords---Thermoeconomics, exergy analysis, performance tests, power-plant supervisory systems.

I N T R O D U C T I O N

Cos t accoun t ing is the field o f accoun t ing that records, measures and repor ts i n fo rma t ion a b o u t how much things cost. P rob lems general ly occur because companies do not have a good unde r s t and ing o f their costs. Business managers use cost d a t a for dec i s ion-making and pe r fo rmance eva lua t ion and control . They have techniques for p roduc t s and services cos t ing and use differential costs for es t imat ing how costs differ a m o n g al ternat ives. M a na ge r i a l cost account ing became a profess ion m a n y years ago, and a lmos t every o rgan iza t ion uses it.

In a para l le l to m o n e t a r y cost accoun t ing energy cost account ing for energy systems has been developed. However , energy cost accoun t ing is someth ing more than a manage r i a l technique for keeping down the c o n s u m p t i o n o f energy resources. I t p rov ides a ra t iona le for assessing the cost o f p roduc t s in terms o f na tu ra l resources and their impac t on the env i ronment , and helps to op t imize and synthesize very complex energy systems.

This technique has been called the rmoeconomics . It deals with costs, e i ther m o n e t a r y (S/k J) or pure energy costs (kJ o f resources /kJ o f p roduc t ) and it is main ly used for the cost account ing, d iagnosis , improvemen t , design and op t imiza t ion o f thermal systems. In a b r o a d sense t he rmoeconomics is the name o f a new science in which the second law o f t he rmodynamics meets economics . T h e r m o e c o n o m i c analysis techniques combine the first and second laws o f t h e r m o d y n a m i c s with m o n e t a r y cost ba lances conduc ted at the system c o m p o n e n t level and help unde r s t and the cost f o rma t ion process , minimize the overal l p roduc t costs and assign costs to the different p roduc t s p roduced in the processes.

N o t e tha t no o the r technique ever devised can go f rom physics to economics at the system c o m p o n e n t level. Conven t iona l s imula tors can answer 'wha t i f ' ques t ions that p rovide in fo rma t ion a b o u t how m a n y resources are needed to ob ta in some add i t iona l unit o f some flow under specified c i rcumstances but they d o n ' t p rov ide an in tegra ted answer o f the economic and the energy effects o f any mal func t ion .

The only way is t he rmoeconomics , because conven t iona l s imula tors add economics as a whole

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934 A. Valero et al.

and once the physical problem has been solved. The thermoeconomic approach is really very powerful and many of its applications are still in their infancy.

The application that most attention has been paid to up to now is perhaps cost accounting; however, it leaves theoretically unsolved problems like developing criteria for a good disaggregation level. Or in other words how much accounting information is enough? What type of information provided is best? Under what criteria? The answers to these questions need additional research.

However, the cost-accounting technique is developed enough to go one step further, to on-line cost accounting. In other words, with the rough data provided by a control room of an energy system, like pressures, temperatures, mass flow rates, electrical production, fuel consumption, oxygen excess and so on, let us convert this information plus the economic one into costs; thus giving the first real-time thermoeconomic diagnosis of a complex energy system ever made.

This idea is quite simple, instead of using costs obtain differential costs and get them on real time, using the same conceptual procedure that costs accountants developed many years ago. In the long run the idea is to integrate the energy control of a unit with the cost-accounting department at any level of information desired.

Suppose the goal of a plant employee is to drive the unit to the limiting conditions of materials in order to achieve as much efficiency and availability as possible. If the age of the plant is not reflected in conventional accounting, this plant employee will receive congratulations from his managers in contrast to another more prudent operator.

Also differential exergy costs help to understand and correct the causes of malfunctions of a unit. According to the theory of exergetic cost each malfunction of a component has a cause and an impact on additional consumption of resources. If we know this information we can isolate the causes and concentrate on the important ones in order to solve them in real time wherever possible. This application of thermoeconomics is really very important in the sense that it is unique because no other technique developed until now permits one to isolate and quantify the causes of deviation of a unit from its design conditions.

B A S I C C O N C E P T S

Generally speaking, the structure of energy systems can be viewed as a set of 'n' subsystems, connected among themselves and with the exterior by means of 'm' energy/mass flows (Fig. 1).

From this representation, we can infer some obvious characteristics. There are flows which cross the boundaries of the system, taking resources, F, (flows 1 and 2), and giving up products, P, and residues, R, (flows 3 and 4), to the exterior or environment. These flows will be denoted as external, and their number is determined once the boundaries of the system have been defined. In contrast,

I I I I 4 I

Fig. 1. Example of the structure of a general energy system.

On-line monitoring of power-plant performance 935

the number of internal subsystems and flows will depend on the disaggregation level considered, since clearly each subsystem can, in principle, be decomposed into a number of devices and processes or subprocesses which interact by means of another set of energy flows. Theoretically, this successive disaggregation appears to have no limits, but in practice, either due to the lack of data or to the excessive complexity of the analysis, or to other reasons, one reaches a reasonable disaggregation level. For example, in a conventional thermal plant, this level would go so far as to detail the basic components and/or processes making it up.

One vital question we can now ask ourselves is: what is the cost of producing any given flow of our now-defined energy system? Let us solve the problem by successive approximations. First, let us set the maximum aggregation level, the overall system in Fig. 1. To produce the plant 's product, P, identified as flow 3, it is necessary to consume resources, F, identified as flows 1 and 2, and to dispose of residues or byproducts, R, identified as flow 4.

The cost of producing P will be the cost of the resources, F, minus what we get from selling R, as a byproduct, or alternatively, plus what we must pay to dispose of R as a residue to the environment. In either case, the cost of R will have a sign. For this simple analysis we do not consider amortization costs.

The specific or unit consumption of producing P is commonly defined as:

K p - - F - R physical units

P physical units

The physical units which define the numerator and denominator can vary in practice: energy/mass, mass/mass, energy/energy, etc.

We see that the unit cost of producing P is intimately linked to the concept of specific consumption, since if we have the unit prices of F and R (say CF and cR), we can get it from

C p m CF'F- cR'R monetary units

P physical units "

From this simple study we can now draw some important conclusions. The price of energy flows is something which is formed in the exchange, in the market, and it will never have any bearing on our analyses, simply because we are not interested in it. What we are interested in is the objective search for the production costs. Prices are external to the system.

The internal reasons for the production cost are due exclusively to the quality of the production process, defined by the specific consumption, or its inverse, the efficiency:

P 1 q - F - R

For our analysis, it is necessary and reasonable that ~c or r/should be dimensionless, and that they should objectively express the degree of quality of a process in a way which allows processes to be compared. For this purpose F, P and R must be evaluated in terms of a property which quantifies thermodynamic equivalence.

Obviously, the second law of thermodynamics gives us that property: exergy, or availability, which measures the amount of useful energy contained in a given flow with respect to specific exhausted environment conditions.

Using exergy to define F, P and R also guarantees that (F -- R) -- P -- I > 0 defining I as the process' irreversibility (i.e. the number of units of useful energy destroyed by its inefficiency). Alternatively, it also guarantees that in any real process, the following holds: K > 1 or t / < 1.

To define efficiency, it is first necessary to identify fuels or resources, products and residues. We cannot just associate the fuels with the input flows, nor the products with the output flows. We need to have a clear idea of what we want to produce, before defining efficiency.

In an Aristotelian sense, F is the 'causa materialis', or that from which something else arises, and P is the 'causa finalis', or the end, the reality towards which something tends. The principle of change, or 'causa efficiens' is in the inexorable degradation of natural resources, quantified in the term I.


These ideas lead to another no less important one: to know the costs, it is not enough to know the objective values of the exergies of the flows entering and leaving the system, together with the price of resources. Correct cost assignment will need our concept of efficiency.

Thus, we can construct the following chain of reasoning. The second law gives a function which quantifies, in an objective and general way, the thermodynamic importance of a plant's flows: their exergy. This allows us to define the true efficiency of processes in the plant, once we have identified the flows we wish to produce, and those which will consequently be consumed. The accumulated performance over the production process will measure the exergy expense required in the plant in order to produce a given flow in it. Finally, we can interpret the prices as weighting factors which externally modify the thermodynamic importance of the different flows. Thus, we naturally lead from exergy expense to economic costs. That is

Second law ~ Efficiency (unit consumption) --, Expense --* Cost.

Therefore, our,hypothesis is that the irreversibilities in the system, together with the subjective concept of production which we have for each and every one of the subsystems making up our plant, are what generate costs. Although to calculate this we will need extra information, such as the price of resources, the cost of amortization, maintenance, etc., for the subsystems, these data are external to the system, and do not alter the intrinsic physical processes.

When the flow is internal, it will leave one subsystem and enter another. The efficiency of these subsystems can also be defined such that the physical units of exergy consumed to produce this flow can be found, as before, but now by accumulating all the unit exergy consumptions of the previous processes, all the way back to the external flows.

Since 1986, Prof. A. Valero et al. [1-3] from CIRCE, University of Zaragoza, have been developing the Theory of Exergetic Cost and the Structural Theory of Thermoeconomics for assigning costs to plants and systems.

The concept of exergetic cost is still only within thermodynamics, but it already shares many of the characteristics of economics. It is clearly a conceptual link between these two disciplines. Thus the isomorphism between exergetic cost and economic cost let us straightforwardly convert thermodynamic costs into thermoeconomic costs, simply by adding the prices or resources (or) and the plant's depreciation costs (Z) (amortization costs) to the matrices found in calculating the costs:

Cp ~-

~ c r F + Z monetary units P physical units "

Note that vector Z can contain not only amortization costs but any depreciation cost incurred by the system due to bad operation maintenance costs as well as overheads. In other words, once we have a good costs structure we can charge to the costs scheme any type of direct and indirect costs, depending upon the controller who decides what information to get from the developed system.

Using the exergetic costs, it is also possible to find out which subsystems are malfunc- tioning, and to what extent they are doing so; how their failure affects other subsystems, to what extent these subsystems increase the plant's production cost and also what effect an improvement would have on the overall behaviour of the plant. Furthermore, by this approach it is possible to provide a tool for controlling economic costs and for plant management and maintenance.

D I A G N O S I S OF M A L F U N C T I O N S IN E N E R G Y SYSTEMS

Given an energy system [4], the manner in which its productive structure is defined is a key point in the thermoeconomic analysis of the system. The causal or productive structure is a way of disaggregating systems focusing on causes of irreversibilities rather than identifying actual physical


flows and devices (flow diagram). What is important in this representation is the amount which each local resource contributes to the formation of each product, since the cost of each product is the weight sum of the unit cost of internal resources, i.e. products from other units and of the external resources. Therefore we may focus our attention in the formation of each product and then interrelate the whole structure. The best productive structure will be that which explains in greatest depth and with greatest simplicity the productive function of the subsystems and flows present in the physical structure of the plant analysed.

The productive structure of some unit in a plant may differ depending on the type of plant and its productive purpose. As an example, consider an adiabatic compressor. The exergy increase of air, AB, may be decomposed into its mechanical, ABe, and thermal, ABr, components. There is no doubt that the former is the product of the compressor. However, the latter will be residue, byproduct, or product, depending on what the global objective of the plant is (a refrigeration cycle, a gas turbine system, etc.).

The internal parameters of the productive structure are the unit consumptions of local resources Kij which correspond to the productive units. It is evident that if we define productive units in such a manner that we identify one and only one significant exit flow per unit, and, each flow is characterized by its exergy, these parameters become simple exergy quotients:

F 2 y ]

FI = k 0 Pj ~ Pj F2 = k2i P j

In order to explain the methodology for the diagnosis of malfunctions in an energy system we have chosen the power-plant shown in Fig. 2. This plant has a boiler which supplies steam to a power cycle, which in turn supplies mechanical power to the generator. The electricity is used in part in the auxiliary devices of the boiler and cycle, and the rest is provided to the electrical network. Figure 2 gives the state of the plant under actual operating conditions and that of the target or design conditions for the same net power production, 350 MW. All numbers are given in megawatts of exergy.

As we can see, the additional fuel consumption of this plant in order to maintain the same production is

AF, = F,(actual conds) - F,°(target conds) = 1050 - 1000 = 50 MW.

Now we can obtain the unit exergy consumptions for each local resource for actual and target operating conditions, as shown in Table 1.

In any energy system the exergy of the resources is greater than or equal to that of the products. In this way we know that, for the plant as a whole as well as for any unit, F - P = I >_>_ 0. After this equation it is evident that the amount of exergy needed to obtain the products is equal to the exergy of the resources consumed. This idea permits the introduction of a thermodynamic function named exergetic cost that is defined as follows: given a system whose limits, disaggregation level and production aim of the subsystems have been defined, we call the exergetic cost, B*, of a physical flow the amount of exergy needed to produce this flow. We call the unit exergetic cost of a flow the exergetic cost per unit exergy,

k* B* Bt

B °, like B, is a thermodynamic function, and its definition encloses, or is closely related to, others which are commonly found in the literature, such as materials ' energy content, embodied energy, cumulative exergy consumption, etc.

Valero et al. [1] gave a rational procedure for allocating costs of any system whose limits and disaggregation level, which specify its subsystems, have been defined.


1000 MW c ~ I Boiler

Fc "1 !

I0 I Pal

Pb Cycle

2 Alternator

Pm 3

101 Pa2

Data: Design conditions. Targeted performance

350 MW e

Pn

1050 MW c _ Boiler

Fe -I 1

I0 Pal

Pb Cycle

2

I01 Pa2

Pm Alternator

3

Data: Operating conditions. Observed performance

350 MW e

P°

, tt Design cond. - 1000

Result: Additional fuel consumption

1050 - Operating cond.

k !iiiii!!!!!!iiiiiiii ii i=i=i!i!i!!!!i!iiiiiiiiii i! ! !i!il 50 MW c

Fig. 2. Data for a 350 MWo power-plant.

For the case in hand four equations are obtained from the propositions:

PI . The exergetic cost is a conservative property. For each component of a system the sum of the exergetic costs of the inlet flows is equal to the sum of the exergetic costs of the exiting flows.

P2. The exergetic cost of the flows entering the plant equals their exergy. Or, in other words, the unit exergetic cost of resources is one.

P3. I f a unit has a product composed of several flows with the same thermodynamic quality, then the same unit exergetic cost will be assigned to all of them.

Table 1. Unit exergy consumptions.

Actual Target

k Exergy (MW) k Exergy (MW)

Boiler Product 520 500 Fuel 2.0192 1050 2.0000 1000

Electricity 0.0250 13 0.0200 10 Irreversibility 543 510

Steam cycle Product 390 380 Steam exergy 1.3333 520 1.3158 500

Electricity 0.0308 12 0.0263 10 lrreversibility 142 130

Alternator Product 375 370 Mechanical work 1.0400 390 1.0270 380

lrreversibility 15 10 Plant Product 350 350

Fuel 3.0000 1050 2.8571 1000 Irreversibility 700 650


Table 2. Local malfunction of components

Ak = k (actual conditions) - k ° (target conditions)

Boiler Fuel Electricity

Cycle Steam exergy Electricity

Alternator Mechanical work

0.0192 0.0050

0.1750 0.0045

0.0130

The equations obtained for target conditions are

P2 Cost of coal input P1 Boiler P1 Cycle P1 q- P4 Alternator

and solving for the unit costs:

Unit exergetic cost of coal: Unit exergetic cost of steam: Unit exergetic cost of mechanical work: Unit exergetic cost of electricity:

k,* = 1 MJ/MJ k,*.1000 + k*.10 = kb*-500 k*.500 + k*.10 = k*.380 k*.358 = k*(350 + 10 + 10)

k,* = 1 MJ/MJ. kb* = 2.0571 MJ/MJ. k* = 2.7820 MJ/MJ. k,* = 2.8571 MJ/MJ.

The unit exergetic costs and exergy consumptions are the key parameters when we try to assess the causes for the additional consumption of the plant. The methodology is as follows.

We attribute the local malfunctions of components to the positive deviations of their unit exergy consumptions, k,j, because it implies an additional consumption of fuel in order to produce a given amount of product. For the case in hand we obtain Table 2.

A component malfunction provokes an additional fuel consumption in the plant, we will name it as the impact on fuel consumption of this component:

F c ~ ' ~ k 1 Pn

It can be demonstrated [4] that the impact on fuel of the ' i ' component is (AF~)~ _-_ Zk*AkjP, J

from j = 1 to the number of local resources to component i. This expression is quite logical, whilst AkjP~ represents the local additional consumption of

resource j and k* is its unit cost in terms of external resources needed to produce it. This formula will be as exact as the values of the unit exergetic costs are stable. This is assured by the use of an appropriate disaggregation level and the application of exergy instead of energy in the system analysis.

As a result, the whole plant impact on fuel will be

/ 7 , - (F,)o = ~(AF,.)i. i

The assessment of the impact on fuel consumption to the diagnosis of the plant operation is shown in Table 3 and Fig. 3.

Therefore the thermoeconomic quantitative diagnosis of this plant assesses that the boiler is responsible for 17.4 MWc of additional coal consumption, the cycle for 19.1 MWc and the alternator for 13.6 MWc.

Note that neither the conventional energy or exergy analyses can produce this result.

940 A. V a l e r o et al .

Boiler Fuel Electricity

Cycle Steam Electricity

Alternator Mechanical work

Plant

Table 3. Impact on fuel consumption

(k,* = l).(Ak, = 0.0192).(Ph = 520) = 9.98 (20%) (k* = 2.8571).(Ak,. = 0.0050).(Ph = 520) = 7.43 (15%)

17.41 (35%)

(k~* = 2.0571).(Ak~ = 0.0175).(P,,, = 390) = 14.04 (28%) (k* = 2.8571).(AL = 0.0045).(P,,, = 390) = 5.03 (10%)

19.07 (38%)

(k,* = 2.7820).(Ak,, = 0.0130).(P,, = 375) = 13.56 (27%) 50.04 (100%)

O N - L I N E M O N I T O R I N G SYSTEM

The plant and its productive structure

It is in very complex plants where thermoeconomics can provide indisputable results because the matric organization of the information is intrinsic to the nature of the theory itself. The matric treatment of thousands of data, as well as the costing computation, makes the complexity of the plant a minor problem. In contrast, any non-generalized method for calculating costs could be used for small energy plants. Therefore, to demonstrate the power of the method it is necessary to choose a sufficiently complex plant to check the technology.

The theory has been applied to a 350 MW unit of the lignite-fired Teruel power-plant, whose flow diagram is shown in Fig. 4.

The first requirement for a study of a plant's performance is to know the number of subsystems and flows which it comprises. A coal-fired electricity-generating station, can be broken down into, for example, six main elements: coal handling, boiler, steam cycle, cooling system, electric system and auxiliary systems.

The plant has been decomposed above into six subsystems. Exergetic analysis of processes establishes that the exergy loss (in kW or tons of coal/h) of any plant is equal to the sum of the losses of each of the subsystems which it comprises.

Each of these subsystems can, in turn, be decomposed into other suitable subelements. For example, the steam cycle is made up of the water-steam system inside the boiler, and the high (and low) pressure zones. The high-pressure zone comprises the medium- (and high-) pressure turbines, the associated heaters, the deareator and piping. We can finally decompose the basic elements of the system and even reduce the aggregation so far as to identify the thermodynamic irreversibilities which take place in each single component or element in the plant. We can have as many disaggregation levels as required.

Operating conditions 1050 MW c ~ .......

Design conditions 1000 MW c

Additional fuel consumption 50 MW c

Impact on fuel provoked by the component malfunctions

Boiler Cycle Alternator 17.4 MW c 19.1 MW e 13 .6MW c

Fig . 3. R e s u l t s fo r a 350 MW~ p o w e r - p l a n t .

On-line monitoring of power-plant performance

O

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941

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A. Valero et al.

i ~ i t ~ l

1


I DATA A C Q U I S I T I O N S Y S T E M (DAS) J

l AvallaNe signal /

(Buffer of the DAS ( , / ~ / Power output Computer // pressure vsc

'1 I processed ! Performance load signal / Operating mode /

~ Di~rlmir~ted load step

Automatic sarnpling J~ (Limits ( Mean and variance

Mean test (Conditions ( Variance test Conditions rating

• Computed processed signal

proce

J Simul~ YES ~ Validated signal ~

(Buffer of SIVAS (

I Calculation algorithms I

Simulated signal

J~ J OUTPUT EVALUATION I-

_1

Fig. 6. Validation system for recorded data (S1VAS).

Each component is connected to the others by energy flows of mass (coal, air, flue gas, water, steam), heat or work (mechanical, electrical). These flows can be identified thermodynamically: pressure, temperature, flow, enthalpy, entropy and exergy. These are, in many cases, easily measured.

The lower the base-line disaggregation level of the analysis, the greater the number of flows to be measured. All the data which finally reach the control room, or are collected in a performance evaluation, measure some property of some energy flow in the plant. The more data available, the more useful the information is, and all the data can be combined in terms of performance in the light of exergetic analysis.

Now the causal or productive structure can be obtained from the plant. Figure 5 shows this structure for the case at hand.

This is composed of productive and dissipative units (squares), junctions (rhombs) and branching points (circles). Each significant flow, of a different nature or cost, is assigned with a branching point where the flow is distributed. Each productive or dissipative unit is characterized by its own product or significant flow. It can also have other outgoing flows or byproducts. In order to produce these, it will consume external resources or products belonging to other units. In every junction, a significant flow is obtained as the sum of the others of the same nature but of a different origin. The rules for representing the productive structure shown in Fig. 5 are similar to those suggested by Frangopoulos [5].

The process of systematic identification of malfunctions is not evident at all, it depends on the number of reliable data, the depth of analysis and the allocated causality.


For the plant studied we have taken to diagnose 40 components and 120 flows, which can be assessed from 120 data obtained from the supervisory system of the plant plus the thermal balance design conditions and the technical data of the components.

Modular steady-state simulator

Obtaining differential costs requires a base case for comparison, and targeting in conventional cost accounting is difficult because differences between the actual cost and the target could also be due to a target which is set too low or too high. Differences between the target and actual costs are indicators that things are not as we expected; they do not necessarily imply that something is wrong.

The same applies to exergetic and thermoeconomic costs, however, an additional complexity appears in the analysis. The dynamic nature of the plant, i.e. the weather conditions, the coal quality, the load, etc., may change during the operation. Therefore, we must filter out these external influences in order to compare the production costs which are really due to internal malfunctions.

This addresses the necessity of developing a base-case simulator rather than having a simple base case for the plant. Therefore an important step is the development of a 'steady-state' rather than a dynamic simulator.

~ . . 0 _ _ W E R P L A N T ~

Signals acquisition ]

( Signals (10") (

[Validation 1~---( l)atabase (

-( Data (5') (

t Standard modules Input data [ ~ OfsimulationCOmponents Model structure

Modules of Convergence criteria thermodynamic

properties calculation

I State vector (target conds.) State vector (actual conds.)

Efficiencies and variables of components

I ' i I .

' i -, |calculation of

I [sp ecific components I diagn°sis I fuel consumptionl',

Fig. 7. System structure of the plant diagnosis.


Table 4. Boiler: overall energy balance (performance test)

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Test 1 Test 2 Test 3

Target Actual Target Actual Target Actual conditions conditions conditions conditions conditions conditions

Mass load 103.812 103.395 101.005 102.752 102.691 102.294 Inlet energy (MW) 1008.820 1025.160 985.060 1018.100 999.340 1021.570

Coal (%) 99.054 98.794 99.049 98.800 99.052 98.852 Natural gas (%) Electricity (%) 0.946 1.206 0.951 1.200 0.948 1.148

Net energy transferred to 844.610 845.250 825.930 839.130 837.170 840.610 the cycle (MW) Energy efficiency 83.720 82.450 83.850 82.420 83.770 82.290 Losses (MW) 164.210 179.910 159.130 178.970 162.170 180.960

Blowdown (%) 0.207 0.204 0.202 0.198 0.205 0.200 Steam from drain tank (%) 0.000 0.074 0.000 0.049 0.000 0.081 Unburned coal (%) 0.503 0.126 0.503 0.088 0.503 0.107 Slags (%) 0.078 0.129 0.078 0.125 0.078 0.112 Gases (%) 15.245 16.753 15.126 16.856 15.196 16.954 Heat from auxiliaries (%) 0.066 0.084 0.067 0.084 0.066 0.080 Heat from boiler wall (%) 0.180 0.180 0.180 0.180 0.180 0.180

Gases Fuel moisture (%) 7.878 6.898 7.868 7.070 7.874 7.154 Hydrogen moisture (%) 0.649 0.640 0.629 0.630 0.641 0.632 Air moisture (%) 0.086 0.121 0.085 0.118 0.086 0.112 Fly ashes (%) 0.105 0.248 0.104 0.229 0.105 0.209 Dry gases (%) 6.471 8.793 6.386 8.753 6.436 8.794

Achieving such a target is not simple. Firstly, a validated model of the plant is necessary, in order to determine the thermodynamic state of reference at design conditions for any load point and ambient conditions; then software for calculating the state of the plant from data obtained from performance tests is required and then these values are used to provide actual design values of the most characteristic parameters defining the plant; finally the exergy analysis of the plant based on this corrected state of reference is obtained.

The simulator is based on the SICIVEX program[6] and uses the General Electric methodology [7] as guidelines for predicting the expansion lines of the high-pressure and condensing sections. Then the values are corrected from the heat rate tests data. The pressure-flow rate relationships are determined from the ellipse law of Stodola. The throttle-flow ratio performance correction curve has been specifically used for partial prediction purposes. The feedwater heaters are modelled from manufacturer's data using the N T U method.

Table 5. Boiler: overall exergy balance (performance test)



Total inlet (MW) 1065.370 Coal (%) 99.104 Natural gas (%) Electricity (%) 0.896

Net exergy transferred to 464.790 the cycle (MW) Exergy efficiency 43.630 Irreversibility (MW) 600.580

Unburned coal (%) 0.491 Slags (%) 0.028 Gases (%) 7.095 Blowdown (%) 0.177 Boiler wall (%) 0.139 Air-steam heater (%) 0.000 Air-gas heater (%) 1.903 Fire chamber (%) 9.846 Superheater-reheater (%) 6.513 Economizer (%) 0.967 Water-steam driving (%) 0.427 Air driving (%) 0.411 Gases driving (%) 0.209 Combustion chamber (%) 27.450 Blowing (%) 0.253 Temperating (%) 0.327 Drum (%) 0.196

Coefficient of availability (%) 55.030

1071.770 1040.270 1066.240 1055.360 1067.310 98.846 99.099 98.854 99.102 98.901

1.154 0.901 1.146 0.898 1.099 464.790 454.240 454.240 460.590 460.590

43.360 43.670 42.600 43.640 43.150 606.980 586.040 612.000 594.770 606.730

0.125 0.491 0.087 0.491 0.106 0.047 0.028 0.044 0.028 0.040 7.363 7.062 7.250 7.082 7.410 0.112 0.115 0.112 0.116 0.113 0.141 0.139 0.140 0.139 0.141 0.150 0.000 0.099 0.000 0.164 2.101 1.868 2.213 1.889 2.133 9.870 10.006 9.670 9.911 9.629 6.829 6.384 6.854 6.461 6.653 0.728 0.928 0.727 0.951 0.751 0.522 0.413 0.711 0.421 0.736 0.521 0.413 0.516 0.412 0.517 0.237 0.212 0.241 0.212 0.210

26.936 27.514 27.722 27.476 27.261 0.247 0.245 0.241 0.250 0.243 0.513 0.303 0.566 0.318 0.540 0.192 0.215 0.199 0.204 0.201

54.990 55.000 54.130 55.020 54.790

ArE ]S/I2-B

946 A. Va |e ro et al.

Table 6. Performance test: air-gas heaters



Combustion air excess, eac (%) 25.00 19.94 25.00 20.31 Total air excess, eat (%) 38.96 37.47 38.98 38.42 Air leakage, xaf (%) 11.17 14.62 11.18 15.05 Idem ASME PTC 4.3 AL (%) 9.69 12.63 9.71 13.03 Inlet air temperature, t~.0 ( C ) 24.19 32.22 24.24 32.78 Outlet air temperature, t,.s ( C ) 342.26 269.97 337.38 282.57 Inlet gases temperature, tg., (' C) 417.82 413.00 412.05 416.00 Outlet gases temperature, tg.~ (cC) 151.50 204.00 149.61 197.07 Outlet gases temperature (without 163.15 224.00 161.10 217.23 correction), t~.~ (%) Efficiency ASME PTC 4.3, q~ 64.70 49.53 64.71 51.87 Fuel, F (kW) 65215 51247 62234 5511 I Product, P (kW) 44940 28726 42805 31511 lrreversibility, 1 (kW) 20275 22521 19429 23601 Exergy efficiency, qb (%) 68.91 56.05 68.78 57.18 Pressure loss in CAGP air side ~ 95 250 ~ 95 250 (mm H_,O) Pressure loss in CAGP gases side ~ 55 170 ~ 55 180 (mm H_~O) Pressure loss in CAGS air side ~ 125 220 ~ 125 210 (mm H~O) Pressure loss in CAGS gases side ~ 120 195 ~ 120 190 (mm H~O)

25.00 24.22 38.96 38.35 11.17 11.37 9.69 9.96

24.21 32.22 340.31 281.14 415.51 416.00 150.74 199.98 162.32 215.70

64.70 52.19 64015 55351 44081 32587 19934 22764

68.86 58.87 95 240

55 175

125 220

120 180

Table 7. Performance test: feedwater heater

Results: Heater ASME PTC 12.1 Group: I (350 MW) Unit: Teruel Powerplant (ENDESA) Device: Heater A.P. No. 5

Desuperheat section Ads = I 18 (m ~-) Uds = 697 (W/m-'.K) NTUds = 0.0620 (ad.) Eds = 0.0245 (ad.)

Condensing section Ac = 685 (m -~) Uc = 3260 (W/m-'.K) NTUc = 1.7078 (ad.) Ec = 0.8187 (ad.)

Drain cooling section Adc = 211 (m-') Udc = 2331 (W/m'-.K) NTUdc = 0.3795 (ad.) Edc = 0.1352 (ad.)

Heat exchanger surface Overall heat transfer coefficient

Heat transfer units Effectiveness





A comparison of the predicted exergy efficiencies of the main subsystems with those found by the heat rate tests reveals differences of the order of 1% in the least favourable cases.

The simulator is therefore a tool quite well adapted to the plant under steady-state design conditions.

Instrumentation stud), and data validation

Another important difference with conventional cost accounting arises from the fact that the rough data from the plant are 'measured' values, not actual values, which have an accuracy and precision that depends upon the measuring instrument whose calibration may degrade throughout time.

Table 8. Performance test: feedwater heater. Temperature profile

Actual conditions Target conditions

tl 172.2 172.2 t2 211.6 211.8 Table 9. Performance test: feedwater heater. Pressure losses (bar) ts 446.2 446.2 th 212.1 212.2 Actual conditions Target conditions

tod 177.8 176.3 DP 12 3.00 0.85 TTD 0.540 0.401 DPds 0.30 0.26 DCA 5.600 4.065 DPdc 0.30 0.30


Table 10. Impact on fuel due to individual components (%)

947

C o n d e n s e r ~

Low pressure feedwater heaters

Deaerator

High pressure feedwater heaters

Steam pipes

Ai r -gases heater

Air -s team heater

Boiler

Low pressure pump

High pressure pump

Low pressure turbine

Medium pressure turbine l High pressure turbinei

I Alternator

Others'

]

+++!+++i+i+~i++++++!+i++++;++++:+++2+++++++]

+i+++%;i++++++++++~+i+~++;+i+++++++++++~!~+j~++++++++!++i+++!;!++++++i++~+!;:++++!~i++++~

+~;+:i+++++++i++++i++ii+~i++++i!+~!+++++~+i+++i++++;;i+l

t 1 I I 1 5 10 15 20 25 30

1 35

Thus we need to develop a robust measurements database which contains acceptable maximum, minimum, average, standard deviation and variance values, as well as methods for substituting every incorrect value for a reasonable one in order to proceed with the costs calculation.

This imposes another important step in the project: a thoughtful analysis of each and every measurement in the plant in order to discriminate its usefulness for the costing process. If the answer is yes, then analyse the instrument's precision, accuracy, reliability against decalibration and the typical behaviour of the measuring system when decalibration occurs (for instance if this occurs smoothly or suddenly), in order to detect symptoms of bad results. Then, create the database of actual and expected values as explained. This database must be active in real time, in order to convert all the rough data from the plant into a set of acceptable values to compute costs.

To get this, one more step is also needed: not all the working conditions of the plant are in steady-state, due to load variation, coal composition changing, unstable functioning of some subsystem, etc., the rough values from the plant cannot and should not be used for providing costs because we would get mindless results. Therefore, it is necessary to develop a computer system for discriminating stationary conditions of the plant. This means keeping all the data for sufficient time and analysing them to guarantee a good costing analysis.

Figure 6 depicts the validation system developed. Data are obtained from the data acquisition system every 10 s. Two reference variables are of key importance in this step: the power load and the superheated steam pressure, which provide the operation mode with either a fixed or sliding pressure. These variables, together with the coal and ambient conditions, are the input simulator variables which provide the target conditions.

Automatic sampling provides the average and variance calculations of all the data. Then the recorded data are tested in order to keep their values inside the limits for the particular operation mode. If the variance of the signal is greater than some specified limits the value is replaced using a set of replacement rules. The variance test is also used in the analysis of the load, in order to discriminate whether the unit is in steady-state condition or not. If this is the case the diagnosis procedure will not proceed, and will wait for a stable condition.

This process is carried out every 5 min and all the processed data are stored in a SIVAS database, which will provide the data for the next step.

Thermoeconomic diagnosis

Once we have a set of complete and filtered data, we can proceed to the thermoeconomic analysis itself.


This quant i ta t ive diagnosis is made every 5 min and consists of the compar ison of the actual performance with target operat ing condit ions. Then an assessment of componen t malfunct ions and an evaluat ion of the impact this mal func t ion produces on fuel in the plant takes place.

The system structure is depicted in Fig. 7 and follows the theoretical procedure given in the previous sections.

R E S U L T S

Tables 4-10 show some results obta ined from the system. Tables 4-6 present the target /operat ing condi t ions compar isons for the boiler, a i r -gas preheaters and a steam preheater f rom performance tests. Table 10 shows a typical ou tpu t displaying the impact on fuel due to individual components in a given state of the plant.

The system started in June 1995. A complete report of the practical results will be given in a for thcoming article.

Acknowledgements--The authors would like to acknowledge the ENDESA technicians for their support and interest in developing the system. This project was sponsored by the Programa de Investigaci6n Electrot6cnica PIE no. 131083 and coordinated by OCIDE as a part of the national electricity research plan.

R E F E R E N C E S

1. A. Valero, M. A. Lozano and M. Mufioz, A general theory of exergy saving. In Computer-aided Engineering and Energy Systems. Second Law Analysis and Modelling, Vols 2-3, pp. 1-21. AES, ASME Book H0341C (1986).

2. A. Valero, L. Serra and M. A. Lozano, Structural theory of thermoeconomics. In Thermoeconomics and the Design Analysis and Improvement of Energy Systems (Edited by H. J. Richter), pp. 189-198. ASME Book No. H00874 (1993).

3. M. A. Lozano and A. Valero, Energy 18, 939-960 (1993). 4. M. A. Lozano, J. L. Bartolom6, A. Valero and M. Reini, Thermoeconomic diagnosis of energy systems. In A Future

for Energy, Proceedings of the International Symposium FLOWERS '94, Florence, Italy (Edited by E. Carnevale), pp. 149-156 (1994).

5. C. A. Frangopoulos, Energy 12, 563-571 (1987). 6. J. A. Alconchel, A. Valero and J. Abadia, Energy simulation of real operating steam power-plants. In Thermoeconomic

Analysis of Improl,ement of Energy Systems (Edited by C. Ruixian and M. Moran), pp. 485-492. Pergamon Press, Oxford (1989).

7. R. C. Spencer, K. C. Cotton and C. N. Cannon, A method for predicting the performance of steam turbine generators 16,500 kW and larger, GER-2007C (1974).