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

MIETTINEN JOUNI (FI)

*;*

**F23C10/28***;*

**F22B35/00***;*

**F22B35/18***;*

**F23N5/02**

**F23N5/24**Claims: 1. A combustion boiler control method, comprising the steps of: a) monitoring the current load (Q_{h}) of a combustion boiler; b) finding 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 fulfills an acceptance condition, and selecting the numerical value (Q_{h, candidate}) 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) smaller than the current computational maximum boiler 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) larger than the current computational maximum boiler momentary load (Q_{h,max}): 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}). 2. The method according to claim 1, wherein: i) the currently monitored process data of the boiler includes ia) current flue gas exit temperature (T_{flue gas,exit,current}) in a flue gas flow channel and ib) heat duty (Q_{fluid,i}) for each heat transfer surface (i) 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 (Q_{h, candidate}) for the current computational maximum boiler momentary load (Q_{h,max}). 3. The method according to claim 1 or 2, wherein: 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 fulfills an acceptance condition for the numerical value (Q_{h, candidate}) for the current computational maximum boiler momentary load (Q_{h,max}) fails to fulfill an acceptance condition, a next numerical value (Q_{h, candidate}) is automatically selected. 4. The method according to claim 3, wherein: the next numerical value (Q_{h, candidate}) is selected iteratively. 5. The method according to any one of claims 1 to 4, wherein: the finding is 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 in the flue gas flow channel 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 α_{j,I} (Q_{steam,max})^{j} - Σ α_{j,i} (Q_{steam,current})^{j}); - IV: using the computed heat duties (Q_{fluid, i, candidate}) for each heat transfer surface in the flue gas flow channel 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 in the upstream direction of flue gas flow, starting from the heat transfer surface 21_{k} that is closest to the flue gas exit using the estimate for the boiler flue gas exit temperature (T_{fluegas,out,k} = T_{FG, exit)}; - V: computing a flue gas factor (df_{i} , i = 1 , ..., k) for each heat transfer surface in the flue gas flow channel. 6. The method according to claim 5, wherein: the flue gas factor includes or is: where k_{i} is a non-zero parameter that may be chosen combustion-boiler specifically, preferably positive (non-zero) number, q_{m,fluegas} is 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 flue gas density at ith heat transfer surface and A is a cross section of flue gas channel at i^{th} heat transfer surface. 7. The method according to claim 6, wherein: n is 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. 8. The method according to claim 7, wherein: the value for n is changed over time. 9. The method according to claim 7 or 8, 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. 10. The method according to any one of claims 5 to 9, wherein: the computation in step I), the flue gas exit temperature is substantially estimated by equation T 22. The combustion boiler according to claim 19 or 20, wherein: the control system is configured to send data to a remote, preferably cloud- based, computing system which is configured to carry out the method step b) and return the current computational maximum boiler momentary 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) smaller 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) larger 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 its acceptance conditions suitable, 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 enabling increased power/heat production. Further, since the current computational maximum boiler momentary load may occasionally be smaller 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 as maximum allowable boiler load and/or 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 – 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 skilled person in the art and heat duty can be obtained, for instance, by using the following equation wherein is the fluid flow in ith heat transfer surface, is the enthalpy of fluid entering to the ith heat transfer surface and 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; - 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 hereinafter, “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 economizer) in the flue gas flow channel can be estimated numerically with the flue gas factor in the situation where 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 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 The fitting of the parameters (par

_{j,i }) can be done manually by human or automatically by 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. 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: where k

_{i }is a non-zero parameter that may be chosen combustion-boiler specifically, preferably positive (non-zero) number is a flue gas mass flow n is a model parameter that may be chosen combustion-boiler specifically, preferably positive (non-zero) number is the density of the flue gas at the i

^{th }heat transfer surface; and 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, it 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 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 flue gas factor represents a pressure loss, the comparison can be done between the sum of the flue gas factors df

_{i }and 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 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 – 4). In such case, the predetermined flue gas factor represents maximum ash loading value. It 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 df

_{max,i }– δ < df

_{i }≤ df

_{max,i }wherein δ>0 and depends on the numerical accuracy and/or method. When df

_{max,i }– δ < df

_{i }≤ df

_{max,i }, it 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 ^ (df

_{i }) = ^ (df

_{max,i }) but in practical circumstances the acceptance condition can be defined as utilizing the following sums Σ(df

_{max,i })– δ < Σ (df

_{i }) ≤ Σ (df

_{m }

_{ax,i }) wherein δ>0 and depends on the numeric accuracy and/or method. When Σ(df

_{max,i })– δ < Σ (df

_{i }) ≤ Σ (df

_{max,i }), it 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 larger 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 where 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, exit }= α

_{0 }+ Σ α

_{i }Q

^{i }

_{h,candidate }or preferably its first, second, or third (or higher) degree approximation. The coefficients α 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 (α) can be done manually by human or automatically by 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 neural network. According to an embodiment of the invention, in step I), the flue gas exit temperature may be estimated by equation T

_{boiler, exit }= α

_{0 }+

_{1 }* Q

_{h,canditate }+α

_{2 }*Q

_{h },

_{candidate }

^{2 }wherein α

_{0 }, α

_{1 }and α

_{2 }can be predefined constants. Alternatively or in addition, the fitting of the coefficients (α ) can be done manually by human or automatically by 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, α

_{0 }term may be solved based on the current state values α

_{0 }= T

_{boiler,exit,current }- α

_{1 }* Q

_{h,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 CO

_{2 }, H

_{2 }O, N

_{2 }, SO

_{2 }, O

_{2 }. 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 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 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. Such a combustion boiler, the boiler control can be improved. The advantages are same as the advantages of the method. The control system may comprise an edge server which 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 60 to 90 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 less 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 cloud-based, computing system which 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 amount of measurement data that is passed to the remote computing system. In this manner, a smaller bandwidth for transferring data may suffice. In certain installations this may be particularly useful especially in view of the fact that there may be 60 to 90 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 which 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 send the processed real-time measurement results to a remote computing system; - a remote computing system which 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 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 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 calibrate the numerical model for boiler control. The boiler computation system may be configured to adapt or calibrate a numerical model for a boiler using processed measurement data collected also from other boilers. This enables using a larger collection of data to adjust the numerical model for boiler control. List of 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 9, 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 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. 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 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. Flue gas channel may be provided with 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 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 CaCO

_{3 }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 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 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 21

_{1 }, 21

_{2 }, 21

_{3 }, ..., 21

_{k-1 }, 21

_{k }are illustrated. Heat transfer surface 21

_{k }depicts air preheater. Heat transfer surfaces 21

_{k-1 }, 21

_{2 }depict superheaters and heat transfer surfaces 21

_{1 }, 21

_{3 }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 in 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 100 million measurement results / day, which needs 25 GB of storage space. FIG 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 remote server can be used for running the analysis platform. FIG 2 illustrates a combustion boiler 10 that is a BFB boiler. BFB boiler differs from 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 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) smaller 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) larger 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 }+ Σα

_{j,i }(Q

_{h },

_{canditate })

^{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 }; - 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 human or automatically by 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, 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 dfi includes or is: df

_{i }= k

_{i }(q

_{m,fluegas }/(ρ

_{fluegas,I }A

_{cross,i }))

^{n }where ki is a predetermined non-zero parameter that may be chosen combustion-boiler specifically, preferably positive (non-zero) number, qm,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), ρfluegas,i is flue gas density obtainable from flue gas temperature TFG, in, i at i

^{th }heat transfer surface 21i 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

_{FG, exit }= α

_{0 }+ Σ α

_{j }(Q

_{h, candidate })

^{j }or preferably its first, second, third or higher degree approximation. The coefficients α

_{0 }, α

_{1 }, 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 = CO

_{2 }, H

_{2 }O, N

_{2 }, SO

_{2 }, O

_{2 }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,H20 }, 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 }which 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 }= Σ q

_{m },

_{fluegas,i }i.e., for example, the sums of the following flue gas mass flow components CO2, H2O, N2, SO2 and O2: 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 which 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 send 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 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 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 20

_{i }: 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 Qh,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 10 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 10 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 smaller 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 which 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.

List of reference numbers used: T _{FG, in, i } flue gas temperature at inlet of heat exchanger 21 _{i } (i = 1, 2, ... k) T _{FG, exit } flue gas temperature at outlet of heat exchanger 21 _{k } Sensors: 20 temperature sensor (FBHE) 20 _{i } temperature sensor (i = 1, 2, ... k) 30 gas volume flow sensor 40 sensor in the furnace 116 pressure sensor 165 pressure sensor (loop seal) 650 fuel feed sensor 10 combustion boiler 12 furnace 13 tube wall 14 superheater 15 horizontal pass 16 back pass 17 particle separator 18 flue gas conduit 19 flute 21 _{i } heat transfer surface (i = 1, 2, ... k) 22 fuel feed 100 fluidized bed heat exchanger (FBHE) 101 FBHE outlet 102 return channel 103 vortex finder 141 superheater inlet 142 superheater outlet 151 primary fludization gas feed 152 secondary fludization gas feed 153 fludization gas supply 161 reheater output 200 internet 201 distributed control system 202 display / monitor 203 EDGE server 204 local storage 205 process intelligence system 206 computation cloud 210 open platform communications server 280 combustion boiler network 290 field bus

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