EP1364161A1

EP1364161A1 - Method for the production of a burner unit

Info

Publication number: EP1364161A1
Application number: EP02715666A
Authority: EP
Inventors: Rolf Dornberger; Peter Stoll; Christian Oliver Paschereit; Bruno Schuermans; Dirk Büche; Petros Koumoutsakos
Original assignee: Alstom Technology AG; Alstom Schweiz AG
Current assignee: General Electric Technology GmbH
Priority date: 2001-01-30
Filing date: 2002-01-30
Publication date: 2003-11-26
Anticipated expiration: 2022-01-30
Also published as: DE10104151A1; DE50212601D1; US7137809B2; US20050084811A1; EP1364161B1; WO2002061335A1

Abstract

The invention relates to spin-stabilised pre-mix burners (1), in which an axial mass flow distribution of the introduced fuel is set, such as to give favourable values for properties such as the output of NOx? and maximum amplitudes for apparent pulsations. Pareto solutions are thus determined for the said properties, whereby a distribution device (5), with control valves is represented by a tree structure with distribution parameters and iterative values for the distribution parameters are generated in a data processing unit by means of an evolutionary algorithm, by means of which the distribution device (5) may be set using a controller (10). Solutions are selected based upon the values determined by a measuring unit (11) which are particularly favourable relative to the said properties and are, in particular pareto-optimal. The distribution devices or the premix burner on the burner unit are then embodied according to such a solution.

Description

DESCRIPTION

METHOD FOR PRODUCING A BURNER PLANT

Technical field

The invention relates to a method for producing a burner system according to the preamble of claim 1. Burner systems of this type are used primarily in gas turbines.

State of the art

It is known that generic burner systems with conventional swirl-stabilized premix burners, in which the fuel is usually introduced more or less uniformly over the length, have problematic properties in various respects that have to do with the combustion process. Above all, the exhaust gases often contain a considerable amount of pollutants, especially NO _x . Pressure waves caused by pulsating combustion also often cause difficulties because they expose the gas turbine to high mechanical loads and reduce its service life.

To reduce these problems, it has been proposed to stabilize the combustion by influencing the pressure in the burner system by means of feedback. For this purpose, the pressure was measured there and the measured signal was fed back into the phase via loudspeakers. In this way, a more stable combustion and thus a reduction in the formation of pressure waves and also NO _x and CO emissions could be achieved. See also C. 0. Paschereit, E. Gutmark, W. Weisenstein: 'Structure and Control of Thermoacaustic Instabilities in a Gas Turbine Combustor ', Combust. Be. and tech. 138 (1998), pp. 213-232. However, the outlay on equipment required is very considerable.

Presentation of the invention

The invention is based on the object of specifying a method for producing generic burner systems which are of simple construction and in which the combustion proceeds favorably, in particular with regard to the reduction of pulsations and low emissions of pollutants, in particular NO _x . It has been found that the combustion process is strongly influenced by the mass flow distribution of the fuel introduced into the premix burner.

According to the burner systems are designed so that the fuel with a certain

Mass flow distribution is introduced into the premix burner, which ensures favorable properties of the combustion, in particular with regard to pulsations and pollutant emissions.

Brief description of the drawings

In the following, the invention is explained in more detail with reference to figures, which only represent an exemplary embodiment. Show it

1 schematically shows a premix burner with an upstream distribution device,

Fig. 2 shows schematically a structure of a test facility with a premix burner according to Fig. 1 and a distribution device and a data processing system for determining favorable mass flow distributions,

3 shows a diagram of a tree structure as a simplified model for the mass flow distribution;

4, 5a, b generally the optimization method used in determining favorable mass flow distributions, wherein

Fig. 4 shows the determination set of a typical optimization problem and its mapping to the corresponding target set and

5a, b steps in the selection of new parameters from previously generated test parameters in the target area;

6a, b the target area of the present optimization problem after 20 or 64 iteration steps,

Fig. 7 mass flow distributions according to selected solutions of the _. Optimization problem.

Ways of Carrying Out the Invention

A premix burner 1 (FIG. 1) of basically known construction, as used in a burner chamber of a gas turbine, has the shape of a truncated cone with an outflow opening 2 at its wide end. Air inlet slots 3a, b are provided along two diametrically opposed surface lines, on the outer sides of which there are 16 inlet openings 4 for the fuel supply are arranged, which form the burner end points of a distribution device 5.

In the course of the manufacture of a burner system, mass flow distributions are initially determined which are as favorable as possible with regard to a target variable, the components of which are formed by certain properties, in particular the emission of NO _x and the maximum of the pressure surges occurring. This is done by means of a test setup (FIG. 2), in which a pre-mixing burner 1 designed as described in connection with FIG. 1 is preceded by a distribution device 5 which is suitable for test purposes and which can be designed, for example, as shown in FIG. 1.

The input of the distribution device 5 is formed by a feed line 6 which is connected to a fuel source, e.g. B. a stationary gas line (not shown) is connected and provided with an inlet valve 7, which limits the fuel supply. The main line 6 then branches into two branch lines 8a, b, from each of which four feed lines branch off, in each of which one

Control valve is. The control valves are labeled V_ to V ₈ . Following the respective control valve, the supply line branches to two pairs of opposing inlet openings 4 in such a way that two axially successive groups of four inlet openings are each acted upon by fuel via one of the control valves Vi, ..., V ₈ . The control valves V _1; ..., V ₈ are designed so that certain mass flows mi, ..., m ₈ can be set with them. The two arranged on the same side

An on / off valve is arranged upstream of inlet openings 4. By means of the on / off valves V " _l7 ..., V" ₁₆ the fuel supply to two successive inlet openings 4 are specifically blocked.

The structure of the distribution device 5 can differ from the described in many respects. Thus, a larger or smaller group of inlet openings or only a single inlet opening can be assigned to each control valve. The on / off valves can be used elsewhere or can be omitted or only such valves can be used, e.g. B. one for each entry opening. The topology can also be different, e.g. B. correspond to the distribution device 5 'shown in Fig. 3 (Fig. 3), a tree structure of three-way valves, as will be described in more detail below. The tests, the results of which are given below, were carried out with a distribution device which corresponded to that shown in FIG. 1, but without the on / off valves V ' ¹ _! , ..., V " ₁₆ .

The control valves V _lf . , , , V _{8 of} the distribution device 5 are set by a control unit 10 according to values output by the data processing system 9. A measuring unit 11 supplies the measured properties of the burner system to the data processing system 9. For the representation of the mass flow distribution in the data processing system 9, the distribution device 5 is mapped onto the distribution device 5 '(FIG. 3), ie a model is used in which it is represented by a binary Tree structure from three-way valves V ¹ _! , ..., V ' ₇ and assume that the total mass flow has a fixed value M. The position of each of the three-way valves can be represented by a distribution parameter p, O≤p≤l, the proportion falling on the left outlet in the distribution of the mass flow between the left and the right outlet equivalent. If the individual mass flows at the outputs of the control valves V _1; ..., V ₈ with m _1; ..., m ₈ , the distribution parameter of valve V ¹ results _! to pι = (mι + ... + m ₄ ) / M, that of the valve V ' ₂ to p ₂ = (mι + m ₂ ) / (m _x + ... + m) etc. and vice versa calculate the distribution parameters p _x , ..., p slightly rriι, ..., m ₈ according to mι = Mpιp ₂ p ₄ , m ₂ = Mpιp ₂ (lp ₄ ), etc. Because the data processing system 9 with the model described works, only seven parameters are required, thus reducing the dimension of the determination space (see below) by 1.

If, as in the present case, optimization is carried out with regard to several independent properties, it is generally not possible to select a specific optimal solution, but a number of so-called Pareto-optimal solutions can be found, each of which is characterized in that they are not Pareto-dominated , d. H. that there is no other solution that would be cheaper in terms of one property and less unfavorable in terms of none of the other properties In other words, a solution that is cheaper in at least one property than a Pareto-optimal solution is inevitably less than that in at least one other property.

The target sizes of the Pareto-optimal solutions usually form a hyper-surface section in the

Target sizes spanned target area, the so-called Pareto-Front, which borders the target amount, ie the amount of target sizes of all possible solutions against areas of the target area that would be cheaper, but are not accessible. At the Pareto front, further hyper-surface sections bordering the target set are connected, which solutions included, which are not Pareto-optimal, but u. U. are still of interest.

For the search for Pareto-optimal solutions, semistochastic methods are available. B. based on the natural evolutionary process of living beings by crossing, mutation and selection and realized using so-called evolutionary algorithms. With the help of these, based on certain, e.g. B. randomly approximated output variables for a set of parameters iteratively approximates Pareto-optimal solutions, by the parameters z. B. varied by recombinations and random mutations and a new set of determinants is selected from the test variables thus produced by selection based on the corresponding target variables. As soon as a certain termination criterion is met, the iteration is terminated.

4 shows a situation in which the determination space is 3-dimensional with parameters i, x ₂ and x ₃ . The determination quantity B over which the

Determination variable varies, is limited by the fact that the variables are each between zero and an upper limit X _if X ₂ or X ₃ and therefore forms a cuboid, the product of the intervals [0, Xχ], [0, X ₂ ] and [0 , X ₃ ]. By means of a known functional relationship f, which can be given by a mathematical model or by an experimental setup, each target quantity x = (xι, x ₂ , x ₃ ) is assigned a target quantity γ_ = f (x) which lies in a target set Z. , It is a subset of the 2-dimensional target space in this case, ie = (ι, y ₂ ) / where y _x and y _{2 represent} two properties that are to be optimized. The target set Z can be the complete image set of the Determination set B under figure f or a part of it restricted by constraints.

The target sizes of the solutions sought form a so-called Pareto front P (solid line), which plots the target quantity Z against small, i.e. H. favorable values of

Properties γ, y ₂ bordered. Solutions connect to the side of the Pareto front, which are also on the edge of the target set Z. They are not Pareto-optimal, since for each of these solutions a solution can be found on the Pareto front, in which both properties are cheaper, but they can May also be of interest.

It is now primarily a matter of finding parameters x for which the associated parameters y = f (x) are as close as possible to the Pareto front P. In addition, they should be distributed somewhat evenly over the entire Pareto front P and, if possible, also over the border areas of the target quantity Z adjoining the same. Such solutions are generated using an iterative evolutionary or genetic algorithm, which forms the basis of a program that runs on a

Data processing system expires. As a rule, each variable is characterized by a bit vector of a length L, which, for. B. is 32, coded.

To find approximately Pareto-optimal solutions, output quantities lying in the determination set B are first generated, which form the starting point of the iteration as the first set of determination quantities. You can e.g. B. be distributed regularly or randomly over the determination quantity B. Then so many iteration steps are carried out until an abort criterion is met. It may be that a certain maximum number of Iteration steps have been carried out or a certain computing time has been consumed or also in that the change in the target variables has remained below a certain minimum during a certain number of iteration steps.

The following substeps are carried out for each iteration step:

Recombination: New sizes are generated by combining parts of several parameters from the current set. For example, either all possible ordered pairs of determination variables are first formed or only a few are determined by means of a random generator. Each parameter determines a vector from n real parameters. A number 1 with O≤l≤n is now also generated by means of a random generator and two new variables are then formed by taking the first 1 parameters from the first determination variable and the others from the second determination variable and vice versa.

Mutation: For the sizes generated in the recombination step, z. B. added sizes generated according to a normal distribution. Of course, several sizes can be generated from one size in this way.

Selection: The two above-mentioned steps result in a set of test parameters, which is usually larger than the original set of test parameters. A new set of determinants, which are particularly favorable on average, is now selected from this usually relatively large quantity of test variables. The The selection procedure is of great importance for the development of the iteration. To control the approach to the Pareto front P and to adjacent regions of the edge of the target set Z, in particular to achieve a broad approach, the procedure is preferably as follows:

In a first selection step, the hyperplane of a part of the target space, which is characterized by the condition yι = 0 and which comprises the target set Z and which coincides with the y ₂ axis in the two-dimensional case shown (FIG. 5a), is subjected to a partition in subsets which form intervals Ix ¹ in this case. Proceeding from this, the said part of the target space is subdivided into subsets W ^x , which are the archetypes of the orthogonal projections of the same along the positive yχ ~ axis onto the said intervals Ix ¹ . In other words, the subset x ¹ for a given i is the set of all points in said part of the target space for which yχ> 0 and y ₂ lies in Ix ¹ . In FIG. 5a it forms a strip parallel to the coordinate axis yx.

For each of the non-overlapping subsets x ¹ , the test size for which yx is optimal, ie minimal, is now determined and selected. In FIG. 5 a, the target sizes of all test sizes are marked with a circle o, those of the test sizes selected in the individual Wx ¹ are marked with a superimposed mark x.

In a second selection step, the part of the target area containing the target set Z is subdivided into partial sets W2 ³ in a completely analogous manner, and the test size for which y _{2 is} optimal, ie minimal, is determined and selected for each partial set. These solutions are marked with a superimposed plus sign + in FIG. 5b. The new set of parameters, with which the next iteration step is then tackled, is made up of the test parameters selected in both selection steps.

In a relatively large number of cases, especially in the vicinity of the central area of the Pareto front P, the same test sizes are determined in both cases, so that a selection step is usually sufficient to determine these test sizes. In the side

However, this is generally not the case in the edge areas and especially in the part of the edge of the target quantity Z adjoining the Pareto front P. If you also value the determination of solutions in these areas, it is necessary to carry out both selection steps.

Of course, there is also the possibility to select not only one test variable in each of the subsets, but also a selected set of test variables, e.g. B. the k cheapest in terms of the remaining component with k> l.

The described procedure for the selection can easily be transferred to cases in which the dimension m of the target area is greater than 2. In this case, one will preferably form all m hyperplanes, which are characterized in that one of the coordinates yχ, ..., y _{m is} equal to zero and each carry out a partition thereof in subsets. This can be done by dividing each of the coordinate axes into intervals from the start and then using all products of intervals as subsets of a hyperplane which are divided into the coordinate axes spanning the hyperplane.

In each of the subsets that are formed by the archetypal orthogonal projections onto the subsets of the hyperplanes, the most favorable trial size with regard to the remaining component is then selected and finally the selected trial sizes are combined to produce the new set of determinants over the subsets and hyperplanes. Depending on whether you are interested in the most comprehensive possible determination of solutions or, above all, want to ascertain those lying in certain areas, the selection can also take into account only a part of the hyperplanes, especially since, as explained above using the example, the central areas of the Pareto Front are usually pretty well grasped during the first selection step.

The specific procedure determined by the algorithm can, of course, differ from the above by a different summary of individual steps, etc. In particular, the ones described do not necessarily need to be described

Selection steps to be carried out one after the other.

The division into intervals can be even or logarithmic, but it can also be finer, for example, in areas of particular interest. The partitions in subsets can be held or changed throughout the iteration, e.g. B. to be adjusted to the distribution of target sizes. Instead of or in addition to hyperplanes, subspaces of lower dimensions can also be used, but then each subset has to be optimized with regard to several properties, which requires further specifications or a recursive procedure. Various selections of the described procedure are conceivable in the selection. The procedure described has the advantage that the determination of the solutions is controlled in each case by the specifications with regard to the location of the target variables such that the target variables derived from the same are finally distributed in a desired manner over an edge region of the target quantity. Of course, various modifications are also possible for recombination and mutation. These sub-steps are also not necessary in both cases.

In the present optimization problem, the determination space is spanned by the distribution parameters, ..., p ₇ , which can each vary over the interval [0,1], the target space, on the other hand, by emissions and pulsations, in the example the two properties NO _x content and maximum Amplitude A of the pressure waves that occur. The target space is shown in Fig. 6a, 6b, with the target sizes of the 100 solutions determined after 20 iteration steps (Fig. 6a) and the 320 solutions determined after 64 iteration steps (Fig. 6b). The two figures clearly show how More and more, especially cheap solutions are being determined and gradually the limit of the amount of target values against the favorable values of the properties - the Pareto front - is emerging.

A specific solution is now selected from the determined solutions, and further, possibly more intuitive criteria can be included in the decision. The size of the selected solution is then used as the basis for the manufacture of the burner system, in particular the manufacture or adjustment of the distribution device 5. A burner system is therefore manufactured in which the Distribution parameters p_, ..., p ₇ and thus the mass flows mx, ..., m ₈ were determined so that they correspond to the size of the selected solution.

If, in addition to the control valves, the distribution device 5 also contains on / off valves, as shown in FIG. 1, the determination space must be supplemented by corresponding binary switching parameters, each of which is represented by a bit that has the values 0 for 'closed' and 1 for 'open'. The occurrence of these parameters changes practically nothing in the optimization process described above. A change is only necessary for the mutation. Here z. B. be provided that each switching parameter, that is, each bit, with a certain, for. B. fixed probability is inverted, that is, 0 changes to 1 and 1 changes to 0.

7 shows as examples five different solutions, ie mass flow distributions, the abscissa showing the numbers of the control valves V _x , ..., V ₈ and the ordinate the mass flows ιτiχ, ..., m ₈ . The properties achieved with this are shown in the following table:

Solution sign NO _x content Maximum amplitude

[ppm] [mbar]

1 (equal distribution) circles 2, 5 3, 12

2 diamond 3, 0 2, 92

3 triangle 4, 0 2, 83

4 cross 5, 0 2, 80

5 square 2, 0 3, 37

Solutions 3 and 4 offer particularly favorable values in terms of the pressure surges that occur, while solution 5 the shows the best exhaust gas values, but with high values for the pressure maxima. Solution 2, on the other hand, again offers very good properties in this respect, for which only a slightly increased NO _x emission has to be accepted.

Of course, different deviations from the example described are possible. So additional properties or other than those described such. B. CO emissions, average amplitude of the sound generated u. a. the optimization will be based. Also the

Optimization method may differ from what is described. It is also possible to search for Pareto-optimal solutions with different loads and corresponding values of the total mass flow M and thus to determine solutions which have favorable properties over a larger working range.

Finally, the solution that best meets the requirements is selected and a burner system is produced in which the corresponding premix burner used in the test arrangement each have a fixed axial mass flow distribution which corresponds to the size of the selected solution. The desired mass flow distribution can be set in different ways. So z. B. can be used in the burner system distribution devices with chokes or switches, which generate the desired fixed mass flow distribution in the simplest and most reliable way. However, the mass flow distribution can also be set very easily by dimensioning, in particular the diameter of the inlet openings. In this case, the Distribution device each consist of a pipe system that connects the entrance to the inlet openings.

LIST OF REFERENCE NUMBERS

1 premix burner 2 opening

3a,. b Air inlet slots

4 entry openings

5 distribution device

6 main line 7 inlet valve

8 branch lines

9 data processing system

10 control unit

11 measuring unit

Claims

PATENT CLAIMS

1. A method for producing a burner system with a fuel source and at least one swirl-stabilized premix burner (1), and a distribution device (5), via which several

Inlet openings (4) in the premix burner (1) are connected to the fuel source, characterized in that a desired mass flow distribution in the at least one premix burner (1) is determined by successively determining a number of determinants defining the mass flow distribution, vectors from a determination quantity, which are a subset of one is n-dimensional space, can be generated by means of a data processing system (9) and, in a test arrangement with at least one premix burner (1) and at least one adjustable distribution device (5), the mass flow distribution is set according to the determined variable and a target variable, a vector from a target quantity , which is a subset of an m-dimensional space, is measured on the test arrangement and finally a suitable determination variable is selected on the basis of the target variables and the at least one premix burner or the at least one distribution device of the burner is designed in such a way that the mass flow distribution corresponds to that which is determined by the selected parameter.

2. The method according to claim 1, characterized in that the components of the determinants at least in part from the distribution parameters (p _X; ..., p ₇ ) of Branching points of a tree structure are formed, by means of which the distribution of the mass flow over inlet openings or groups of inlet openings (4) of the at least one premix burner (1) is determined.

3. The method according to claim 1 or 2, characterized in that Pareto solutions, which are characterized in that for each solution, in which a component of the target size has a more favorable value, at least another inevitably

Component has a less favorable value, can be determined at least approximately by means of the data processing system (9) and the suitable determining variable is selected from the Pareto solutions.

4. The method according to claim 3, characterized in that the approximate determination of the Pareto solutions is carried out by determining output variables serving as a set of determination parameters and performing iteration steps until the completion of an abort criterion by means of the data processing system (9), in each case from a set a new set of determinants is determined from determinants by generating from the set of determinants a set of test variables each in the determinate set, from which the new set of determinants based on the target variables measured with the mass flow distribution determined by the determinants is selected.

5. The method according ^'to claim 4, characterized in that the generation of the test variables from the set of

Determinants through random mutation or The parameters are recombined by means of the data processing system (9).

6. The method according to any one of claims 1 to 5, characterized in that the concentration of at least one pollutant, for. B. the NO _x concentration in the exhaust gas forms a component of the target size.

7. The method according to any one of claims 1 to 6, characterized in that a measure of the pulsations occurring in the burner system, preferably their maximum amplitude forms a component of the target size.

8. The method according to any one of claims 1 to 7, characterized in that the inlet openings (4) are at least partially axially successively made.

9. The method according to any one of claims 1 to 8, characterized in that the desired mass flow distribution is at least partially achieved by dimensioning the inlet openings (4).

10. The method according to any one of claims 1 to 9, characterized in that the desired mass flow distribution is at least partially achieved by throttling and / or switching the distribution device.