COMBUSTION BOILER CONTROL METHOD, COMBUSTION BOILER, AND BOILER COMPUTATION SYSTEM

Abstract

A combustion boiler control method includes steps of (a) monitoring the current load of a combustion boiler, (b) finding a numerical value for a current computational maximum boiler momentary load for which at least one flue gas factor computed using currently monitored process data with a numerical model of the boiler fulfills an acceptance condition, and selecting the numerical value as the current computational maximum boiler momentary load, (c) indicating the current computational maximum boiler momentary load to an operator and/or, if the current load is (c1) less than the current computational maximum boiler momentary load, (c1i) indicating to the operator that the boiler load may be increased, and/or (c1ii) automatically increasing the boiler load, and/or (c2) greater than the current computational maximum boiler momentary load, (c2i) indicating to the operator that the boiler load exceeds the current computational maximum boiler momentary load, and/or (c2ii) automatically reducing the boiler load.

Description

BACKGROUND OF THE INVENTION

Field of the Invention

The invention relates to control of combustion boilers, in particular fluidized bed boilers, such as circulating fluidized bed (CFB) boilers or bubbling fluidized bed (BFB) boilers.

Technical Background

Combustion boilers, such as grate boilers and fluidized bed boilers are commonly utilized to generate steam that can be used for a variety of purposes, such as for producing electricity and heat.

In a fluidized bed boiler, fuel and solid particulate bed material are introduced into a furnace. The bed material and the fuel is fluidized by introducing fluidizing gas from a bottom portion of the furnace. Burning of fuel takes place in the furnace. In BFB combustion, fluidization gas is passed through the bed such that the gas forms bubbles in the bed. The fluidized bed can, in a BFB, be rather conveniently controlled by controlling the fluidization gas feed and fuel feed. In addition to fuel, certain additives such as aluminum silicates (such as, non-hydrated clay) and alkali alkaline earth metal carbonates and mixtures thereof (such as, limestone or calcium carbonate) may be added to the combustion to improve sorption of possible heavy metals, sulfur, and also to improve alkali sorption.

In CFB combustion, fluidization gas is passed through the bed material. Most bed particles will be entrained in the fluidization gas and will be carried with the flue gas. The particles are separated from the flue gas in at least one particle separator and circulated, returning them back into the furnace. It is common to arrange a fluidized bed heat exchanger downstream of the particle separator(s) to recover heat from the particles before they are returned into the furnace.

In all boilers, regardless of the combustion technology, the combustion conditions, such as the mixing of air and fuel, may not be ideal.

International application published under WO 2016/202640 A1 of Improbed AB discloses a thermal load control method for a combustion boiler. In the method, the thermal load of a combustion boiler is reduced if monitored flue gas velocity in at least one location of the boiler exceeds a predetermined maximum flue gas velocity limit. The flue gas velocity is computed from volume flow of flue gas divided by the cross-sectional area of the flue gas duct in the location just downstream the cyclone using an equation group.

Objective of the Invention

A combustion boiler traditionally is designed for a given load that is the respective boiler maximum continuous rating (BMCR) of the boiler. This is sometimes called the design load level.

It is an objective of the invention to improve performance, profitability, and flexibility of the boiler, and also to improve control of the boiler load. This objective can be achieved with the combustion boiler control method and with the combustion boiler defined by the claims.

A further objective of the invention is to reduce complexity of a control system of a combustion boiler. This objective can be met with a combustion boiler computation system defined by the claims.

The dependent claims describe advantageous aspects of the combustion boiler control method, of the combustion boiler, and of the combustion boiler computation system.

Advantages of the Invention

The combustion boiler control method comprises the steps of (a) monitoring the current load Q_h of a combustion boiler, (b) finding such a numerical value for a current computational maximum boiler momentary load for which at least one flue gas factor computed using currently monitored process data with a numerical model of the boiler fulfills an acceptance condition, and selecting the numerical value as the current computational maximum boiler momentary load Q_{h, max}, (c) indicating the current computational maximum boiler momentary load Q_h,max to the operator and/or, if the current load Q_h is

• • (c1) less than the current computational maximum boiler momentary load:
    • (c1i) indicating the boiler operator that the boiler load may be increased, and/or
    • (c1ii) automatically increasing the boiler load, and/or
    • (c2) greater than the current computational maximum boiler momentary load:
      • (c2i) indicating the boiler operator that the boiler load Q_h exceeds the current computational maximum boiler momentary load, and/or
      • (c2ii) automatically reducing the boiler load Q_h.

With the method, instead of having a fixed boiler maximum load, with the method of computing a flue gas factor and selecting suitable acceptance conditions, it is possible to safely operate the combustion boiler at or closer to its current computational maximum boiler momentary load that at times may be higher than the fixed boiler maximum load would be. The current computational maximum boiler momentary load can be higher than the design load level. Therefore, the overall performance of the boiler may be improved and enable increased power/heat production. Further, since the current computational maximum boiler momentary load may occasionally be less than the design load level, boiler wear resulting from exceeding the current computational maximum boiler momentary load may be better reduced. In other terms, the current computational maximum boiler momentary load can be considered to be a maximum allowable boiler load and/or a preferable boiler load.

The present applicant has been able to obtain, in the tests performed, in average, power output from a combustion boiler that exceeds the fixed boiler maximum load. The present applicant could, in the tests, demonstrate that for a combustion boiler the improvement potential may lie between 2.5 to 5% which corresponds, for example, 3 to 6 MW_th for a 120 MW_th combustion boiler.

Preferably, in the method

• • (i) the currently monitored process data of the boiler includes
    • (ia) current flue gas exit temperature in a flue gas flow channel and
    • (ib) heat duty for each heat transfer surface in the flue gas flow channel and further:
  • (ii) monitored process data from both ia) and ib) is used in computation of the flue gas factor and when finding the numerical value for the current computational maximum boiler momentary load Q_h,max.

Computation of heat duty of a heat exchanger is known for a skilled person in the art and heat duty can be obtained, for instance, by using the following equation:

$Q_{\mathrm{fluid},i}=q_{m,\mathrm{fluid},i}*\left(h_{\mathrm{fluid},\mathrm{out}}-h_{\mathrm{fluid},in}\right)$

wherein q_m,fluid,i is the fluid flow in an ith heat transfer surface, h_fluid,in is the enthalpy of fluid entering to the ith heat transfer surface, and h_fluid,out is the enthalpy of fluid exiting from the ith heat transfer surface.

The finding may be performed such that, if the at least one flue gas factor computed using currently monitored process data with a numerical model of the boiler fails to fulfill an acceptance condition, a next numerical value is automatically selected. Preferably, the next numerical value is selected iteratively. This may enable the use of computational library functions, and, in particular, of an iterative solver (such as, Python FSOLVE function, which solves roots of function).

The finding may be carried out with performing the computational steps of:

• • I: computing an estimate for boiler flue gas exit temperature that results in a computational boiler model when the thermal load of the boiler corresponds to the numerical value;
  • II: computing flue gas mass flow;
  • III: computing a heat duty for each heat transfer surface in the flue gas flow channel with its current heat duty that is corrected by using a numerical boiler model;
  • IV: using the computed heat duties for each heat transfer surface in the flue gas flow channel to compute flue gas temperatures at each heat transfer surface in the flue gas flow channel in the upstream direction of flue gas flow, starting from the heat transfer surface that is closest to the flue gas exit using the estimate for the boiler flue gas exit temperature; and
  • V: computing a flue gas factor for each heat transfer surface in the flue gas flow channel.

With this approach, the situation of each heat transfer surface (here and hereafter, “heat transfer surface” means a heat exchanger, a heat exchanger tube, heat exchanger tube bundle, heat exchanger packages, and/or a constructive group of heat exchangers, such as an economizer) in the flue gas flow channel can be estimated numerically with the flue gas factor in a situation when the thermal load of the boiler corresponds to the numerical value. Preferably, the term “heat transfer surface” means a constructive group of heat exchangers, such as an economizer. So, we can now test whether a given numerical value that is a candidate for a current computational maximum boiler momentary load would produce an acceptable situation at the heat transfer surface.

According to an embodiment of the invention, in step (III) the numerical boiler model is of the form Q_{fluid, i, candidate}=Q_{fluid,i,current}+Σαa_j,i (Q_h,candidate)^j−Σpar_j,i (Q_h,current)^j.

The fitting of the parameters (par_j,i) can be done manually by a human or automatically by a computer utilizing historical data. Automatic update of the parameters may be done, e.g., once per month. AI and neural network based algorithms can be utilized in an automatic update.

On one hand, this enables predicting the maximum computational allowable current boiler momentary load without going to the limit with the current boiler load, in contrast to the method disclosed in WO 2016/202640 A1, and, on the other hand, and even more importantly, enables going to the limit without exceeding the maximum computational allowable current boiler momentary load.

Preferably, the flue gas factor includes or is:

${\mathrm{df}}_i={k_i\left(q_{m,\mathrm{fluegas}}/\left({\rho }_{\mathrm{fluegas},i}*A_{cross,i}\right)\right)}^n$

• • where:
  • k_i is a non-zero parameter that may be chosen combustion-boiler specifically, preferably positive (non-zero) number;
  • q_m,fluegas is a flue gas mass flow;
  • n is a model parameter that may be chosen combustion-boiler specifically, preferably positive (non-zero) number;
  • p_fluegas,i is the density of the flue gas at the i^th heat transfer surface; and
  • A_cross,i is the cross-sectional area of the flue gas flow path at the i^th heat transfer surface.

This is particularly convenient since choosing this functional form for the flue gas factor, becomes very flexible and can be easily adapted to suit different combustion boiler needs, such as, based on the conditions in the current fuel.

Particularly, advantageously, the model parameter n may be selected to include at least one of the following:

• • (i) in the range of 0.9 to 1.1, preferably equivalent or about 1.0, for using computed flue gas velocity;
  • (ii) in the range of 2.9 to 3.5, preferably between 3.2 and 3.35, for using computed flue gas caused erosion; or
  • (iii) in the range of 1.8 to 2.2, preferably equivalent or about 2.0, for using pressure loss.

The value for n may be changed over time. This is advantageous for the reason that the flue gas flow conditions at the heat transfer surfaces may change over time, such as because of slagging, ash agglomeration, or fuel, or bed conditions. Thus, the flue gas factor may be shifted over time, to better reflect the actual boiler situation.

According to an embodiment of the invention, when n=2 and the flue gas factor represents a pressure loss, the comparison between the flue gas factor df_i and a predetermined maximum value for the flue gas factor df_max,i can be carried out for each heat transfer surface. According to an embodiment the acceptance condition is substantially df_i=df_max,i.

According to an embodiment of the invention, when n=2 and the flue gas factor represents a pressure loss, the comparison can be done between the sum of the flue gas factors df_i

dp _tot =df _i

and the sum of the predetermined flue gas factors df_max,i or simply predetermined flue gas factor represents total pressure drop and, hence, the comparison represents the comparison of total pressure drops between the furnace and stack. According to an embodiment, the acceptance condition is substantially dp_tot=dp_max,tot.

According to an embodiment of the invention, the flue gas factor represents an ash loading factor and can be written in the form:

df _i =k _ph C(d)q_{m_faVp}ⁿ

where k_ph is particle hardness factor, C(d) is particle diameter function, q_{m_fa} is fly ash mass flow rate, v_p is particle velocity and n is exponent (0.3 to 4). In such a case, the predetermined flue gas factor represents maximum ash loading value, and can also be adjustable based on the ash properties (softness, etc.).

According to an embodiment of the invention, the acceptance condition is substantially df_i=df_max,i but in practical circumstances the acceptance condition can be defined as:

$d{f}_{\max ,i}-\delta <d{f}_i\le d{f}_{\max ,i}$

wherein δ>0 and depends on the numerical accuracy and/or method. When df_max,i−δ<df_i≤df_max,i, this means that at least one flue gas factor computed using currently monitored process data with a numerical model of the boiler fulfills the acceptance condition and, in such a case, maximum allowable boiler load has been found and so the numerical value Q_{h, candidate} is selected as the current computational maximum boiler momentary load Q_{h, max}.

According to an embodiment of the invention, the acceptance condition is substantially S (df_i)=S (df_max,i) but, in practical circumstances, the acceptance condition can be defined as utilizing the following sums:

$S\left(d{f}_{\max ,i}\right)-\delta <S\left({\mathrm{df}}_i\right)\le S\left(d{f}_{\max ,i}\right)$

wherein δ>0 and depends on the numeric accuracy and/or method. When S (df_max,i)−δ<S (df_i)≤S (df_max,i), this means that at least one flue gas factor computed using currently monitored process data with a numerical model of the boiler fulfills the acceptance condition and, in such a case, maximum allowable boiler load has been found and so the numerical value Q_{h, candidate} is selected as the current computational maximum boiler momentary load Q_{h, max}. According to an embodiment, the summation index i goes over all of the heat transfer surfaces. According to another aspect of the invention, the summation index i goes over only a part of the heat transfer surfaces, preferably, in a flue gas channel.

It may be particularly useful if the value for n is determined from a group of boilers comprising at least two separate boilers using operational data monitored for each of the boilers. Using a greater number of boilers (two, three, four, . . . ) gives a larger data set. Hence, there will be more operational data monitored. This may produce better results, which may be especially good in a situation when the determination uses interpolation and/or extrapolation of experimental data.

For the computation in step (I), the flue gas exit temperature may be substantially estimated by equation:

$T_{boiler,\mathrm{exit}}=a_0+S{a}_i{Q}_{h,\mathrm{candidate}}^i$

or, preferably, its first, second, or third (or higher) degree approximation. The coefficients a may be obtained by fitting after measuring flue gas exit values for a number of discrete steam load values. This data may be collected over time and refreshed from time to time, such as, periodically. Alternatively, or in addition, it may be collected in one or more calibration runs of the combustion boiler.

The fitting of the coefficients (a) can be done manually by a human or automatically by a computer utilizing historical data. Automatic update of the coefficients may be done, e.g., once per month. AI and neural network based algorithms can be utilized in automatic update.

According to an embodiment of the invention, in step (I), the flue gas exit temperature may be substantially estimated by utilizing artificial intelligence tools. According to another embodiment of the invention, in step (I), the flue gas exit temperature may be substantially estimated by utilizing a neural network.

According to an embodiment of the invention, in step (I), the flue gas exit temperature may be estimated by equation:

$T_{\mathrm{boiler},\mathrm{exit}}={\alpha }_0+{\alpha }_1*Q_{h,\mathrm{candidate}}+{\alpha }_2*Q_{h,\mathrm{candidate}}^2$

wherein α₀, α₁ and α₂ can be predefined constants. Alternatively or in addition, the fitting of the coefficients (a) can be done manually by a human or automatically by a computer utilizing historical data. Automatic update of the coefficients may be done, e.g., once per month. AI and neural network based algorithms can be utilized in an automatic update.

According to an embodiment of the invention, α₀ term may be solved based on the current state values:

${\alpha }_0=T_{\mathrm{boiler},\mathrm{exit},\mathrm{current}}-{\alpha }_1*Q_{h,c\mathrm{urrent}}-{\alpha }_2*Q_{h,\mathrm{current}}^2$

wherein T_{boiler,exit,current} represents measured flue gas exit temperature.

According to an embodiment of the invention, in step (II), the flue gas mass flow is computed using boiler mass and energy balance equations.

In step (II), the computation of flue gas mass flow may include taking into account mass flow of components CO₂, H₂O, N₂, SO₂, O₂. The concentration of these components can be measured reliably with rather simple equipment.

In step (II), the component values may include fuel parameters. This enables reflecting changes in the fuel properties or/or in the kind of fuel that is used in the combustion boiler. For example, for fuels that tend to cause more erosion, the acceptance condition may be stricter, while a more relaxed acceptance condition may be used for fuels that tend to cause less erosion.

The step (b) may be performed remotely to the combustion boiler, preferably, in a cloud-based computation service. This helps to simplify the maintenance of the combustion boiler, since the remote computation equipment, such as configured to run the cloud-based computation service, can be maintained separately from the combustion boiler. The computational software updates, for example, can thus be performed centrally at one or a few locations, instead of updating software at each combustion boiler.

Alternatively, the step (b) may be performed locally at the combustion boiler, preferably, at an edge server. This may speed up the computation since no data needs to be transferred to a remote computation location.

Any of the currently monitored process data and/or current load may be obtained from real-time measurements. Instead of this, or in addition to it, the currently monitored process data and/or current load may be treated by filtering, treated by averaging, computing trends or any combination of these. This helps to avoid noise or outlier measurements to impact the outcome of the computation, and thus facilitates operation to increase stability of the current computational maximum boiler momentary load.

The acceptance condition may include a hysteresis condition, requiring a predefined minimum change before changing the current computational maximum boiler momentary load. This may increase the stability of the current computational maximum boiler momentary load, preferably, helping to avoid changing the current computational maximum boiler momentary load up and down within a short period of time.

Even though the method can be utilized in any sort of combustion boiler, the present applicant finds it particularly useful if the combustion boiler is a circulating fluidized bed (CFB) or a bubbling fluidized bed (BFB) boiler, and the step (b) is carried out for the combustion boiler heat transfer surfaces. The method is particularly convenient for CFB or BFB boilers.

According to an embodiment, the step (b) is carried out for the combustion boiler heat transfer surfaces between a furnace and stack.

A combustion boiler comprises a furnace and associated passes defining a flue gas flow path a flue gas flow path and having a number of heat transfer surfaces, measurement instrumentation to monitor current load of the combustion boiler, further measurement instrumentation to currently monitor process data, and a control system configured to carry out the boiler control method.

According to an embodiment, the combustion boiler comprises a furnace and associated passes defining a flue gas flow path a flue gas flow path and having a number of heat transfer surfaces in the flue gas flow path.

In such a combustion boiler, the boiler control can be improved. The advantages are the same as the advantages of the method.

The control system may comprise an edge server that may be configured to process the real-time measurement results for currently monitored process data and/or current load, namely, by filtering, averaging, and/or computing trends. The edge server will facilitate cutting down the amount of currently monitored process data. In certain installations, this may be particularly useful, especially, in view of the fact that there may be sixty to ninety gigabytes of monitored process data each day.

The control system may be configured to carry out the method step (b) to determine the current computational maximum boiler momentary load locally. This facilitates to have fast decision making at the combustion boiler since little or no data may need to be transferred from the combustion boiler system.

Alternatively, or in addition, the control system may be configured to send data to a remote, preferably, a cloud-based, computing system that may be configured to carry out the method step (b) and return the current computational maximum boiler momentary load to the control system. This facilitates to have a combustion boiler simpler and makes updating the computing system easier. The updating can in this situation be performed centrally and not at each and every combustion boiler.

The edge server may be configured to reduce an amount of measurement data that is passed to the remote computing system. In this manner, a narrower bandwidth for transferring data may suffice. In certain installations, this may be particularly useful especially in view of the fact that there may be sixty to ninety gigabytes of monitored process data each day.

A combustion boiler computation system comprises a group of combustion boilers, each boiler comprising a boiler control system comprising an edge server system that is configured to process the real-time measurement results for currently monitored process data and/or current load, namely, by filtering, averaging, and/or computing trends, and to send the processed real-time measurement results to a remote computing system, a remote computing system that, preferably, is a cloud-based computing system, configured to receive data processed from real time measurement results and to compute data using a numerical boiler model for each of the boilers, and to return computation results for each of the boilers.

Further, in the combustion boiler computation system, the boiler control system is configured to adapt its function based on the computation results.

The advantage for this arrangement is that the need of computation devices at the combustion boiler can be reduced, still obtaining effective and fast computation results from the remote computing system.

The computing system may be configured to find such a numerical value or a current computational maximum boiler momentary load for which at least one flue gas factor computed using currently monitored process data with a numerical model of the boiler that fulfills an acceptance condition and selecting the numerical value as the current computational maximum boiler momentary load. This basically enables using the method of the invention also in a distributed environment.

The boiler computation system may be configured to adapt or to calibrate a numerical model, such as, the flue gas factor numerical model, for a boiler using processed measurement data for the boiler. This makes it easier to remotely adapt or to calibrate the numerical model for boiler control.

The boiler computation system may be configured to adapt or to calibrate a numerical model for a boiler using processed measurement data collected also from other boilers. This enables using a greater collection of data to adjust the numerical model for boiler control.

BRIEF DESCRIPTION OF THE DRAWINGS

The combustion boiler and its control method are explained in more detail below in the context of the embodiments shown in the appended drawings in FIG. 1 to FIG. 8, of which:

FIG. 1 illustrates a CFB boiler;

FIG. 2 illustrates a BFB boiler;

FIG. 3 illustrates the flow of measurement data from sensors;

FIG. 4 is a flow diagram illustrating a first method for finding the current computational maximum boiler momentary load Q_{h, max};

FIG. 5 is a flow diagram illustrating a second method for finding the current computational maximum boiler momentary load Q_{h, max};

FIG. 6 illustrates how the current computational maximum boiler momentary load Q_{h, max} can be presented to the boiler operator;

FIG. 7 shows boiler momentary load Q_h and computed current computational maximum boiler momentary load Q_{h, max}, as well as the effect of using the method according to the invention during a test period;

FIG. 8 shows a closer look at the data of FIG. 7, showing boiler momentary load Q_h computed current computational maximum boiler momentary load Q_{h, max} where the effect of using the method according to the invention during the 10 day test period is better visible.

The same reference numerals refer to same technical features in all FIG.

DETAILED DESCRIPTION

FIG. 1 shows a combustion boiler 10 that is a CFB boiler and comprises a furnace 12 that has tube walls 13 connected to water-steam circuit of the combustion boiler 10. Water is fed from water tank (not shown) to an economizer and, from the economizer, via a steam drum to evaporative heat transfer surfaces such as the tube walls 13, and then guided via the steam drum to superheaters and then to a turbine. A flue gas channel may be provided with the economizer and/or superheater/s.

Fluidization gas (such as, air and/or oxygen-containing gas) is fed from fluidization gas supply 153 to below the grate (the grate not shown in FIG. 1) via a windbox (not shown), wherefrom the primary fluidization air enters into the furnace through nozzles (not shown) (to fluidize the bed), and secondary fluidization gas feed 152 (to feed oxygen containing gas to control combustion). The effect is that the bed materials will be fluidized and also oxygen required for the combustion is provided into the furnace 12. Further, fuel is fed into the furnace 12 via the fuel feed 22. The combustion can be adjusted by controlling the fuel feed 22 (such as, by reducing or increasing fuel feed), and by controlling the fluidization gas feed (such as, by reducing or increasing the amount of oxygen supply into the furnace 12). Fuel can be fed together with additives, in particular, with such additives that act as alkali sorbents, such as CaCO₃ and/or clay for example. In addition or alternatively, NOx reduction agents, such as ammonium or urea can be fed into the combustion zone of the furnace 12, or above the combustion zone of the furnace 12.

Bed material is also fed into the furnace, which bed material may comprise sand, limestone, and/or clay, that, in particular, may comprise kaolin. One effect of the bed and, generally, of the combustion, is that, in the water-steam circuit, water and steam is heated in the tube walls 13 and water is converted to steam.

Ash may fall to the bottom of the furnace 12 and be removed via an ash chute (omitted from FIG. 1 for the sake of clarity) and part of the ash, so-called fly ash, will be carried along the flue gas.

Combustion products, such as flue gas, unburnt fuel and bed material proceed from the furnace 12 to a particle separator 17 that may comprise a vortex finder 103. The particle separator 17 separates flue gases from solids. Especially, in larger combustion boilers 10, there may be more than one (two, three, . . . ) separators 17 preferably arranged in parallel to each other.

Solids separated by the separator 17 pass through a loop seal 160 that preferably is located at the bottom of the separator 17. Then, the solids pass to fluidized bed heat exchanger (FBHE) 100 that is also a heat transfer surface so that the FBHE 100 collects heat from the solids to further heat the steam in the water-steam circuit. The chamber in which the FBHE 100 is located may be fluidized and the FBHE 100 itself comprises heat transfer tubes or other kinds of heat transfer surfaces. FBHE 100 may be arranged as a reheater or as a superheater. From the FBHE outlet 101, steam is passed into a high-pressure turbine (if the FBHE 100 is a superheater) or medium-pressure turbine (if the FBHE 100 is a reheater). For the sake of clarity, the turbines are not illustrated in FIG. 1. The solids may be returned from the FBHE 100 via a return channel 102 into the furnace 12. Especially, in larger combustion boilers 10, there may be more than one (two, three, . . . ) loop seals 160 and FBHE 100, and return channels 102, preferably, arranged in parallel to each other, such that for each separator 17, there will be respective loop seal 160, FBHE 100 and return channel 102. In practice, some of the FBHE 100 may be arranged as superheaters while some others may be arranged as reheaters.

The flue gases are passed from the separator 17 to horizontal pass 15 and, from there, further to backpass 16 (that, preferably, may be a vertical pass) and from there via flue gas conduit 18 to stack 19.

The backpass 16 comprises a number of heat transfer surfaces 21_i (where i=1, 2, 3, . . . , k, where k is the number of heat transfer surfaces). In FIG. 1, heat transfer surfaces 21₁, 21₂, 21₃, . . . , 21_k-1, 21_k are illustrated. Heat transfer surface 21_k depicts air preheater. Heat transfer surfaces 21_k-1, 21₂ depict superheaters and heat transfer surfaces 21₁, 21₃ depict reheaters. The actual number of different heat transfer surfaces in each of these components, for example, may be selected for each combustion boiler differently according to actual needs. And, there may be further components as well, comprising a heat transfer surface 21.

Flue gas exiting the last heat transfer surface 21_k will be at the flue gas exit temperature T_{FG, exit}. This temperature is measured with temperature sensor 20_k.

According to one aspect, the temperatures before and after each heat transfer surface 21_i (T_FG,in,i, T_FG,in,i+1, respectively) can be measured with respective temperature sensors 20_i (where i=1, 2, 3, . . . , k−1, k).

According to another aspect, and, preferably, these temperatures, however, do not necessarily need to be measured. It will suffice to know the flue gas exit temperature T_{FG, exit}. The temperatures before and after each preceding heat transfer surface 21_i (T_FG,in,i, T_FG,in,i+1) can be obtained numerically. This will be explained further below.

A combustion boiler 10 is equipped with a plurality of sensors and computer units. Actually, one middle-size (100-150 MW_th) combustion boiler 10 may produce one hundred million measurement results/day, which needs 25 GB of storage space. FIGS. 1, 2, and 3 illustrate some of the sensors and computer units. Examples of sensors are combustion gas (usually combustion air) volume flow sensors 30 (for measuring primary and secondary fluidizing gas feeds), fuel feed sensors 650 and temperature sensors 20_i (i=1, 2, . . . , k), temperature sensor in FBHE and pressure sensor 116 in the return channel 102 (both only in a CFB boiler), and sensors 40 in the furnace 12.

Process data may be collected from the sensors by distributed control system (DCS) 201. The data collection may most conveniently be arranged over a field bus 290, for example. DCS 201 may have a display/monitor 202 for displaying operational status information to the operator. An EDGE server 203 may process measurement data from the obtained from sensors, such as, filter and smooth it. There may be a local storage 204 for storing data.

The DCS 201, display/monitor 202, EDGE server 203, local storage 204 may be in combustion boiler network 280 (local storage 204 preferably directly connected to the EDGE server). The combustion boiler network 280 is preferably separate from the field bus 290 that is used to communicate measurement results from the sensors to the DCS 201 and/or the EDGE server 203. Between the DCS 201 and EDGE server 203 there may be an open platform communications server 210 (cf. FIG. 3) to make the systems better interoperable.

Combustion boiler network 280 may be in connection with the internet 200, preferably, via a gateway 290. In this situation, measurement results may be transferred from the combustion boiler network 280 to a cloud service, such as process intelligence system 205 located in a computation cloud 206. The applicant currently operates a cloud service running an analysis platform. The cloud service may be operated on a virtualized server environment, such as on Microsoft® Azure®, which is a virtualized, easily scalable environment for distributed computing and cloud storage for data. Other cloud computing services may be suitable for running the analysis platform too. Further, instead of a cloud computing service, or in addition thereto, a local or a remote server can be used for running the analysis platform.

FIG. 2 illustrates a combustion boiler 10 that is a BFB boiler. A BFB boiler differs from a CFB boiler in that the fluidized bed is not a circulating bed but a bubbling bed. Thus, there is no need for the separator 17, loop seal 160, FBHE 100, and return channel 102.

There is normally at least one superheater 14 located in the furnace 12, preferably, on top of the furnace 12. Superheater 14 inlet 141 is preferably the steam drum or from another superheater and the outlet 142 is to high pressure turbine.

FIG. 4 illustrates the combustion boiler control method:

• • (a) the current load Q_h of combustion boiler 10 is monitored in step K1 (in the method illustrated in FIG. 4, also flue gas exit temperature T_{FG, exit} is monitored and heat duty Q_fluid,i for each heat transfer surface 21i in the flue gas flow channel (vertical pass 16);
  • (b) a numerical value Q_{h, candidate} is selected (step K3), after which heat duties at heat transfer surfaces 21_i are computed and flue gas temperatures in relation to Q_{h, candidate}. The numerical value Q_{h, candidate} is then used to compute (step K7) at least one flue gas factor df_i using currently monitored process data with a numerical model of the boiler fulfills an acceptance condition (which is tested in step K9), and selecting the numerical value; and Q_{h, candidate} as the current computational maximum boiler momentary load Q_{h, max} (step K11);
  • (c) the current computational maximum boiler momentary load Q_{h, max} is indicated to the operator (such as, by displaying on the monitor/screen 202) and/or, if the current load Q_h is (c1) less than the computational boiler maximum momentary load Q_h,max: (c1i) indicating the boiler operator that the boiler load Q_h may be increased, and/or (c1ii) automatically increasing the boiler load Q_h, and/or (c2) greater than the computational boiler maximum momentary load Q_h,max: c2i) indicating the boiler operator that the boiler load Q_h exceeds the boiler maximum momentary load, and/or (c2ii) automatically reducing the boiler load Q_h.

The step (b) is preferably carried out for the combustion boiler 10 heat transfer surfaces 21_i between furnace 12 and stack 19.

In the method, the currently monitored process data of the boiler may include (a) current flue gas exit temperature T_FG,exit in a flue gas flow channel and b) heat duty Q_fluid,i for each heat transfer surface 21_i in the flue gas flow channel (back pass 16).

Further, in the method, monitored process data from both (a) and (b) may be used in computation of the flue gas factor df_i and when finding the numerical value Q_{h, candidate} for the current computational maximum boiler momentary load Q_h,max.

The finding is performed such that, if the at least one flue gas factor df_i computed using currently monitored process data with a numerical model of the boiler that fails to fulfill an acceptance condition, a next numerical value Q_{h, candidate} is automatically selected. The automatic selection is preferably done iteratively.

As a specific example, the finding may be carried out with performing the computational steps of:

• • I: computing an estimate for boiler flue gas exit temperature T_{boiler, exit} that results in a computational boiler model when the thermal load of the boiler corresponds to the numerical value Q_{h, candidate};
  • II: computing flue gas mass flow q_m,fluegas;
  • III: computing a heat duty Q_{fluid, i, candidate} for each heat transfer surface 21_i in the flue gas flow channel (back pass 16) with its current heat duty Q_{fluid, i, current} that is corrected by using a numerical boiler model Q_{fluid, i, candidate}=Q_{fluid,i,current}+Σ αa_j,i (Q_h,candidate)^j−Σ par_j,i (Q_h,current)^j
  • IV: using the computed heat duties Q_{fluid, i, candidate} for each heat transfer surface 21_i in the flue gas flow channel (back pass 16) to compute flue gas temperatures at each heat transfer surface (T_fluegas,in,i, T_{fluegas,out,i}; i=1, . . . , k) in the flue gas flow channel (back pass 16) in the upstream direction of flue gas flow, starting from the heat transfer surface 21_k that is closest to the flue gas exit i.e. using the estimate for the boiler flue gas exit temperature T_{fluegas,out,m}=T_{FG, exit}; and V: computing a flue gas factor df_i, i=1, . . . , k for each heat transfer surface 21_i in the flue gas flow channel (back pass 16).

The fitting of the parameters (par_j,i) can be done manually by a human or automatically by a computer utilizing historical data. Automatic update of the parameters may be done, e.g., once per month. AI and neural network based algorithms can be utilized in automatic update.

Step (II) may include computing flue gas mass flow q_m,fluegas,m for selected flue gas components.

The flue gas temperatures at each heat transfer surface can be computed, for instance,

$T_{\mathrm{fluegas},in,i}=T_{\mathrm{fluegas},\mathrm{out},i}+\frac{Q_{\mathrm{fluid},i}}{q_{m,\mathrm{fluegas}}*c_p}$

wherein T_fluegas,in,i is the flue gas temperature at the inlet of ith heat transfer surface, c_p is specific heat capacity, and T_{fluegas,out,i} is the flue gas temperature at the outlet of ith heat transfer surface. The flue gas temperatures could be determined with artificial intelligence tools. The flue gas temperatures could be determined with neural network.

Preferably, the flue gas factor df_i includes or is:

$d{f}_i={k_i\left(q_{m,\mathrm{fluegas}}/\left({\rho }_{\mathrm{fluegas},I}{A}_{\mathrm{cross},i}\right)\right)}^n$

where k_i is a predetermined non-zero parameter that may be chosen combustion-boiler specifically, preferably, positive (non-zero) number, q_m,fluegas is a flue gas mass flow, n is a positive number (which may be selected as a natural number, rational number, real number, or even as complex number), ρ_fluegas,i is flue gas density obtainable from flue gas temperature T_{FG, in, i} at i^th heat transfer surface 21_i and A is a cross section of flue gas channel at i^th heat transfer surface 21_i.

Advantageously, n may be selected to include at least one of the following:

• • (i) in the range of 0.9 to 1.1, preferably, equivalent or about 1.0, for using computed flue gas velocity;
  • (ii) in the range of 2.9 to 3.5, preferably, between 3.2 and 3.35, for using computed flue gas caused erosion; or
  • (iii) in the range of 1.8 to 2.2, preferably equivalent or about 2.0, for using pressure loss.

The value for n may be changed over time. In particular, the value for n may be determined from a group of combustion boilers, the group comprising at least two separate combustion boilers 10, such that using operational data monitored for each of the combustion boilers 10 is used in the determination.

In the computation in step (I), the computational value for flue gas exit temperature T_{FG, exit} under any chosen numerical value Q_{h, candidate} for boiler load can be estimated by equation:

$T_{\mathrm{FG},\mathrm{exit}}={\alpha }_0+\sum {{\alpha }_j\left(Q_{h,\mathrm{candidate}}\right)}^i$

or, preferably, its first, second, third, or higher degree approximation. The coefficients a₀, a₁, a₂, . . . have been obtained beforehand by fitting after measuring flue gas exit temperature T_{FG, exit} values for a number of discrete boiler load Q_steam values.

In step (II), the computation of the components q_m,fluegas,m preferably includes at least some, most preferably, all of the following: m=CO₂, H₂O, N₂, SO₂, O₂ so as to determine flue gas mass flow. In other words, in step (IV) of the computation, as q_m,fluegas,m values some or all of q_{m,fluegas,CO2}, q_{m,fluegas,H2O}, q_m,fluegas,N2, q_{m,fluegas,SO2}, q_m,fluegas,O2 may be used. They are preferably measured in flue gas conduit 18 or in flute 19, for which reason suitable sensors are installed in the flue gas passage. In step (II), the component values may further include fuel parameters.

Flue gas mass flow may be based on computation of sums of flue gas component mass flows q_m,fluegas,m that are calculated based on fuel analysis (proximate and ultimate analysis of fuel), combustion air flow and/or recirculation gas flow according to boiler mass and energy balance calculation.

Preferably, the flue gas mass flow may be computed:

q _m,fluegas =Σq _m,fluegas,i

• • i.e., for example, the sums of the following flue gas mass flow components CO₂, H₂O, N₂, SO₂ and O₂:

$q_{m,\mathrm{fluegas},CO2}=x_{C,\mathrm{fuel}}*\frac{M_{\mathrm{CO}2}}{M_C}*q_{m,\mathrm{fuel}}$ $q_{m,fluegas,H2O}=0.5*x_{H,\mathrm{fuel}}*\frac{M_{H2O}}{M_H}*q_{m,\mathrm{fuel}}+x_{H2O,\mathrm{fuel}}*q_{m,\mathrm{fuel}}+x_{moist,\mathrm{air}}*q_{m,\mathrm{air}}$ $q_{m,\mathrm{fluegas},N2}=0.5*x_{N,fuel}*q_{m,\mathrm{fuel}}+x_{N2,\mathrm{air}}*q_{m,\mathrm{air}}$ $q_{m,\mathrm{fluegas},\mathrm{SO}2}=x_{S,fuel}*\frac{M_{\mathrm{SO}2}}{M_S}*q_{m,fuel}$ $q_{m,flue\mathrm{gas},O2}=x_{O2,\mathrm{air}}*q_{m,\mathrm{air}}-q_{m,fluegas,CO2}*\frac{M_{O2}}{M_{CO2}}-0.25*x_{H,fuel}*\frac{M_{H2O}}{M_H}*q_{m,fuel}-q_{m,flue\mathrm{gas},SO2}*\frac{M_{O2}}{M_{\mathrm{SO}2}}$

where, for instance, x_C,fuel represents carbon in fuel i.e. first subscript denotes component and second subscript is either fuel or combustion air referred, q_m,fuel is a fuel flow, q_m,air is combustion air flow and M_x denotes molar mass. Advantageously, fuel properties as utilized in flue gas mass flow components and combustion air properties. Fuel moisture may be measured or calculated.

The step (b) may be performed remotely to the combustion boiler, such as, in the process intelligence system 205. Alternatively, the step (b) may be performed locally at the combustion boiler, preferably, at the EDGE server 203.

Any of the currently monitored process data and/or current load may be obtained from real-time measurements, treated by filtering, treated by averaging, computing trends, or any combination of these.

The acceptance condition may include a hysteresis condition, requiring a predefined minimum change before changing the current computational maximum boiler momentary load Q_h,max.

The acceptance condition preferably includes comparing the computed at least one flue gas factor df_i against a respective maximum value df_max,i. The maximum value df_max,i is a preset value and preferably boiler specific. The numerical value Q_{h, candidate} is rejected if the maximum value df_max,i is exceeded.

In the combustion boiler 10, the furnace 12 and associated passes (horizontal pass 15 and back pass 16) define a flue gas flow path. The furnace 12 and the passes 15, 16 have a number of heat transfer surfaces 21_i in the flue gas flow path. The combustion boiler 10 also has measurement instrumentation to monitor current load Q_h of the combustion boiler, and further measurement instrumentation to currently monitor process data.

The control system (DCS 201, and EDGE server 203, or DCS 201 remote process intelligence system 205, possibly, under the participation of the EDGE server 203) is configured to carry out the boiler control method.

The EDGE server 203 may be configured to process the real-time measurement results for currently monitored process data and/or current load, namely, by filtering, averaging, and/or computing trends.

The control system may be configured to carry out the method step (b) to determine the current computational maximum boiler momentary load Q_h,max locally at the combustion boiler 10, and/or to send data to a remote, preferably cloud-based (such as, computation cloud 206), computing system (such as, process intelligence system 205) which is configured to carry out the method step (b) and return the current computational maximum boiler momentary load Q_h,max to the control system. The control system may then use the display/monitor to indicate the information, such as in method step (c), to the boiler operator, such as, by displaying the information.

The EDGE server 203 may be configured to reduce amount of measurement data that is passed to the remote computing system.

A combustion boiler computation system comprises a group of combustion boilers 10, each combustion boiler 10 comprising a boiler control system (CS) comprising an EDGE server (203) system that is configured to process the real-time measurement results for currently monitored process data and/or current load, namely, by filtering, averaging, and/or computing trends, and sending the processed real-time measurement results to a remote computing system. The remote computing system is preferably a cloud-based computing system, configured to receive data processed from real time measurement results and to compute data using a numerical boiler model for each of the combustion boilers 10, and to return computation results for each of the combustion boilers 10. The boiler control system may be configured to adapt its function based on the computation results.

The computing system is preferably configured to find such a numerical value Q_{h, candidate} for a current computational maximum boiler momentary load Q_h,max for which at least one flue gas factor df_i computed using currently monitored process data with a numerical model of the boiler that fulfills an acceptance condition, and selecting the numerical value Q_{h, candidate} as the current computational maximum boiler momentary load Q_h,max.

The boiler computation system may be configured to adapt or to calibrate a numerical model for a boiler using processed measurement data for the combustion boiler 10.

Alternatively, or in addition, the boiler computation system may be configured to adapt or to calibrate a numerical model for a combustion boiler 10 using processed measurement data collected also from other combustion boilers 10.

FIG. 5 shows a modification of the method shown in FIG. 4. Steps L1, L3, L7, L9 are the same as steps K1, K3, K9, K11, respectively, but, in step L5, the flue gas factors df_i can be directly computed for all heat transfer surfaces 201: if the temperatures T_FG,in,i are measured using the respective temperature sensors 21_i, the back-calculation will not be necessary and thus the step K7 can be omitted in the method illustrated in FIG. 5.

FIG. 6 shows in step N1 the use of possible inputs to the numerical boiler model. In step N3 the Q_h,max is computed numerically using the boiler model, and in step N5, the estimated maximum load Q_h,max is presented to boiler operator via a specific user interface (UI), preferably, via display/monitor 202.

FIG. 7 shows boiler momentary load Q_h and computed current computational maximum boiler momentary load Q_{h, max}, as well as the effect of using the method according to the invention during a test period. During the ten day test period, the 120 MW_th boiler power obtained in average a 3 to 6 MW_th higher load as outside the test period. FIG. 8 illustrates the ten day test period in more detail.

In other words, in the boiler control method, the current computational maximum boiler momentary load Q_h,max of the combustion boiler is estimated using a numerical model using determined fluidized bed combustion boiler operating parameters. The current boiler load Q_h is computed using steam circuit measurement data.

Then, if the boiler load Q_h is less than the current computational maximum boiler momentary load Q_h,max, it is (i) indicated to the boiler operator that the boiler load may be increased, and/or ii) the boiler load is automatically increased. Alternatively or in addition, if the boiler load Q_h is larger than the boiler maximum momentary load Q_h,max, it is (i) indicated to the boiler operator that the boiler load exceeds the boiler maximum momentary load, and/or (ii) the boiler load is automatically reduced.

It is obvious to the skilled person that, along with the technical progress, the basic idea of the invention can be implemented in many ways. The invention and its embodiments are thus not limited to the examples and samples described above but they may vary within the contents of patent claims and their legal equivalents.

In addition, or instead of using above mentioned specific empirical equations, it is possible to utilize artificial intelligence tools and/or neural network in the numerical model computations.

In the claims that follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word “comprise” or variations such as “comprises” or “comprising” is used in an inclusive sense, i.e., to specify the presence of the stated feature, but not to preclude the presence or addition of further features in various embodiments of the invention.

Claims

1.-27. (canceled)

28. A combustion boiler control method comprising the steps of: (a) monitoring a current load (Qh) of a combustion boiler;(b) finding such a numerical value (Qh, candidate) for a current computational maximum boiler momentary load (Qh, max) for which at least one flue gas factor (dfi) computed using currently monitored process data with a numerical model of the boiler fulfills an acceptance condition, and selecting the numerical value (Qh, candidate) as the current computational maximum boiler momentary load (Qh,max);(c) indicating the current computational maximum boiler momentary load (Qh,max) to a boiler operator and/or, if the current load (Qh) is (c1) less than the current computational maximum boiler momentary load (Qh,max): (c1i) indicating to the boiler operator that the boiler load (Qh) may be increased; and/or(c1ii) automatically increasing the boiler load (Qh); and/or(c2) greater than the current computational maximum boiler momentary load (Qh,max): (c2i) indicating to the boiler operator that the boiler load (Qh) exceeds the current computational maximum boiler momentary load; and/or(c2ii) automatically reducing the boiler load (Qh).

29. The method according to claim 28, wherein: (i) the currently monitored process data of the boiler includes: (ia) current flue gas exit temperature (Tflue gas,exit,current) in a flue gas flow channel; and(ib) heat duty (Qfluid,i) for each heat transfer surface in the flue gas flow channel,and further wherein:(ii) monitored process data from both (ia) and (ib) is used in computation of the flue gas factor and when finding the numerical value (Qh, candidate) for the current computational maximum boiler momentary load (Qh,max).

30. The method according to claim 28, wherein the finding is performed such that, if the at least one flue gas factor (dfi) computed using currently monitored process data with a numerical model of the boiler that fulfills an acceptance condition for the numerical value (Qh, candidate) for the current computational maximum boiler momentary load (Qh,max) fails to fulfill an acceptance condition, a next numerical value (Qh, candidate) is automatically selected.

31. The method according to claim 30, wherein the next numerical value (Qh, candidate) is selected iteratively.

32. The method according to claim 28, wherein the finding is carried out by performing the computational steps of: (I) computing an estimate for boiler flue gas exit temperature (Tboiler, exit) that results in a computational boiler model when the thermal load of the boiler corresponds to the numerical value (Qh, candidate);(II) computing flue gas mass flow (qm,fluegas);(III) computing a heat duty (Qfluid, i, candidate) for each heat transfer surface in the flue gas flow channel with its current heat duty (Qfluid, i, current) that is corrected by using a numerical boiler model (Qfluid, i, candidate=Qfluid,i,current+S aj,I(Qsteam,max)j−S aj,i(Qsteam,current)j);(IV) using the computed heat duties (Qfluid, i, candidate) for each heat transfer surface in the flue gas flow channel to compute flue gas temperatures at each heat transfer surface (Tfluegas,in,i, Tfluegas,out,i; i=1, . . . , k) in the flue gas flow channel in the upstream direction of flue gas flow, starting from the heat transfer surface 21k that is closest to the flue gas exit using the estimate for the boiler flue gas exit temperature (Tfluegas,out,k=TFG, exit); and(V) computing a flue gas factor (dfi, i=1, . . . , k) for each heat transfer surface in the flue gas flow channel.

33. The method according to claim 32, wherein the flue gas factor includes or is:

34. The method according to claim 33, wherein n is selected to include at least one of the following: (i) in the range of 0.9 to 1.1, for using computed flue gas velocity;(ii) in the range of 2.9 to 3.5, for using computed flue gas caused erosion; or(iii) in the range of 1.8 to 2.2, for using pressure loss.

35. The method according to claim 34, wherein the value for n is changed over time.

36. The method according to claim 34, wherein the value for n is determined from a group of boilers comprising at least two separate boilers using operational data monitored for each of the boilers.

37. The method according to claim 32, wherein, in the computation in step (I), the flue gas exit temperature is substantially estimated by an equation:

38. The method according to claim 32, wherein, in step (II), computation of flue gas mass flow utilizes mass flow (qm,fluegas,m) of flue gas components, wherein the components include CO2, H2O, N2, SO2, and O2.

39. The method according to claim 32, wherein, in step (II), the computation of flue gas mass flow includes fuel parameters.

40. The method according to claim 28, wherein the step (b) is performed remotely from the combustion boiler.

41. The method according to claim 28, wherein the step (b) is performed locally at the combustion boiler.

42. The method according to claim 28, wherein any of the currently monitored process data and/or current load is obtained from real-time measurements, treated by filtering, treated by averaging, computing trends, or any combination of these.

43. The method according to claim 28, wherein the acceptance condition includes a hysteresis condition, requiring a predefined minimum change before changing the current computational maximum boiler momentary load (Qh,max).

44. The method according to claim 28, wherein the acceptance condition includes comparing the computed at least one flue gas factor (dfi) against a respective design value, and wherein, in the method, the numerical value (Qh, candidate) is rejected if the design value is exceeded.

45. The method according to claim 28, wherein the combustion boiler is a circulating fluidized bed (CFB) or a bubbling fluidized bed (BFB) boiler, and the step (b) is carried out for the combustion boiler heat transfer surfaces, between the furnace and the stack, optionally, including the furnace.

46. A combustion boiler comprising: a furnace and associated passes defining a flue gas flow path and having a number of heat transfer surfaces that are located in the flue flow path;measurement instrumentation to monitor current load (Qh) of the combustion boiler;further, measurement instrumentation, such as sensors, to currently monitor process data; anda control system configured to carry out the combustion boiler control method according to claim 28.

47. The combustion boiler according to claim 46, wherein the control system comprises an edge server that is configured to process real-time measurement results for currently monitored process data and/or current load, namely, by filtering, averaging, and/or computing trends.

48. The combustion boiler according to claim 46, wherein the control system is configured to carry out the method step (b) to determine the current computational maximum boiler momentary load (Qh,max) locally.

49. The combustion boiler according to claim 46, wherein the control system is configured to send data to a remote computing system that is configured to carry out the method step (b) and to return the current computational maximum boiler momentary load (Qh,max) to the control system.

50. The combustion boiler according to claim 49, further comprising an edge server that is configured to reduce an amount of measurement data that is passed to the remote computing system.

51. A combustion boiler computation system comprising: a group of combustion boilers comprising at least two separate combustion boilers according to claim 46, each boiler comprising a boiler control system comprising an edge server system that is configured to process the real-time measurement results for currently monitored process data and/or current load, namely, by filtering, averaging, and/or computing trends, and to send the processed real-time measurement results to a remote computing system;a remote computing system configured to receive data processed from real-time measurement results and to compute data using a numerical boiler model for each of the combustion boilers, and to return computation results for each of the combustion boilers,wherein the control system is configured to adapt its function based on the computation results, and the computing system is configured to find such a numerical value (Qh, candidate) for a current computational maximum boiler momentary load (Qh,max) for which at least one flue gas factor (dfi) computed using currently monitored process data with a numerical model of the boiler that fulfills an acceptance condition, and selecting the numerical value (Qh, candidate) as the current computational maximum boiler momentary load (Qh,max).

52. The combustion boiler computation system according to claim 51, wherein the combustion boiler computation system is configured to adapt or to calibrate a numerical model for a combustion boiler using processed measurement data for the combustion boiler.

53. The boiler computation system according to claim 51, wherein the boiler computation system is configured to adapt or to calibrate a numerical model for a combustion boiler using processed measurement data also collected from other combustion boilers.