METHOD OF OPERATING A HEAT RELEASING REACTOR, A HEAT RELEASING REACTOR, AND A COMPUTATION SYSTEM FOR A HEAT RELEASING REACTOR

Abstract

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

Technical Background

A commonly used technical field of heat releasing reactors is combustion boilers, such as grate boilers and fluidized bed boilers that are commonly utilized to generate steam that can be used for 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 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 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 pre-determined maximum flue gas velocity limit. The flue gas velocity is computed from a 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.

Additionally, it is known to there are also other processes, which produce product gas, a temperature of which needs to be controlled, meaning either heating or cooling the gas and/or the process.

Objective of the Invention

A heat releasing reactor is designed for a given capacity that is the respective maximum continuous rating (MCR) of the reactor. This is sometimes called the design load level.

It is a particular objective of the invention to improve performance, profitability, and flexibility of the heat releasing reactor, and also to improve control of the reactor's load. This objective can be achieved with the method of operating a heat releasing reactor and with the heat releasing reactor defined by the claims.

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

The dependent claims describe advantageous aspects of the method, of the reactor, and of the computation system.

Advantages of the Invention

The method of operating a heat releasing reactor producing product gas comprises the steps of

• • (a) monitoring the current load Q_h of the reactor;
  • (b) finding such a numerical value for a current computational maximum momentary load for which at least one product gas factor computed using currently monitored process data with a numerical model of the reactor fulfills an acceptance condition, and selecting the numerical value as the current computational maximum momentary load Q_{h, max}; and
  • (c) indicating the current computational maximum momentary load Q_h,max to the operator and/or, if the current load Q_h is:
    • (c1) less than the current computational maximum momentary load:
      • (c1i) indicating the operator that the load may be increased, and/or
      • (c1ii) automatically increasing the load, and/or:
    • (c2) greater than the current computational maximum momentary load:
      • (c2i) indicating the operator that the load Q_h exceeds the current computational maximum momentary load, and/or
      • (c2ii) automatically reducing the load Q_h.

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

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

Preferably, in the method:

• • (i) the currently monitored process data of the reactor includes:
    • (ia) current product gas exit temperature in a product gas flow channel and
    • (ib) heat duty for each heat transfer surface in the product gas flow channel and further:
  • (ii) monitored process data from both steps ia) and ib) is used in computation of the product gas factor and when finding the numerical value for the current computational maximum 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:

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

wherein q_m,fluid,i is the fluid flow in 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 product gas factor computed using currently monitored process data with a numerical model of the reactor 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 that solves roots of a function).

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

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

With this approach, the situation of each heat transfer surface (in this application, “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) in the product gas flow channel can be estimated numerically with the product gas factor in the situation when the thermal load of the reactor corresponds to the numerical value. So, we can now test whether a given numerical value that is a candidate for a current computational maximum momentary load would produce an acceptable situation at the heat transfer surface.

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

The fitting of the parameters (a_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.

Particularly, when the invention is applied to a boiler combusting fuel, 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 product gas factor includes or is:

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

where:

• • k_i is a non-zero parameter that may be chosen combustion-boiler specifically, preferably positive (non-zero) number;
  • q_{m,product gas} is a product gas mass flow;
  • n is a model parameter that may be chosen reactor-specifically, being preferably positive (non-zero) number;
  • p_productgas,i is the density of the product gas at the i^th heat transfer surface; and
  • A_cross,i is the cross-sectional area of the gas flow path at the i^th heat transfer surface.

This is particularly convenient since choosing this functional form for the product gas factor, it becomes very flexible and can be easily adapted to suit different needs of operation of the reactor, such as, based on the conditions in the current reactant used in the reactor.

• • (i) Particularly advantageously, the model parameter n may be selected to include at least one of the following: in the range of 0.9 to 1.1, preferably about 1.0, for using computed product gas velocity;
  • (ii) in the range of 2.9 to 3.5, preferably between 3.2 and 3.35, for using computed product gas caused erosion; or
  • (iii) in the range of 1.8 to 2.2, preferably about 2.0, for using pressure loss of the product gas flow.

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 fouling, ash agglomeration or reactant or bed conditions, should the reactor be a fluidized bed reactor. Thus, the product gas factor may be shifted over time, to better reflect the actual process situation.

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

${\mathrm{dp}}_{\mathrm{tot}}=\sum {\mathrm{df}}_i$

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

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

${\mathrm{df}}_i=k_{\mathrm{ph}}C\left(d\right)q_{m\_\mathrm{fa}}{v}_p^n$

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

According to a specific embodiment of the invention, the reactor is a fluidized bed (FB) reactor, such as an FB boiler or an FB gasifier, the product gas factor represents an ash loading factor and can be written in the form:

${\mathrm{df}}_i=k_{\mathrm{ph}}C\left(d\right)q_{m\_\mathrm{fa}}{v}_p^n$

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 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:

${\mathrm{df}}_{\max ,i}-\delta <{\mathrm{df}}_i\le {\mathrm{df}}_{\max ,i}$

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 product gas factor computed using currently monitored process data with a numerical model of the reactor fulfills the acceptance condition and in such a case maximum allowable load has been found and so the numerical value Q_{h, candidate} is selected as the current computational maximum 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:

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

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), means that at least one product 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 load has been found and so the numerical value Q_h, candidate is selected as the current computational maximum 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 product gas channel.

It may be particularly useful if the value for n is determined from a group of reactors comprising at least two separate reactors using operational data monitored for each of the reactor. Using a greater number of reactors (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 product gas exit temperature may be substantially estimated by equation:

$T_{G,\mathrm{exit}}=a_0+{\mathrm{Sa}}_i{Q}_{h,\mathrm{candidate}}^i$

or, preferably, its first, second, or third (or higher) degree approximation. The coefficients (a) may be obtained by fitting after measuring product gas exit values for a number of discrete reactor 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 reactor.

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 product gas exit temperature may be substantially estimated by utilizing artificial intelligence tools.

According to another embodiment of the invention, in step (I), the product gas exit temperature may be substantially estimated by utilizing neural network.

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

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

wherein α₀, α₁ and α₂ can be predefined constants. Alternatively, or in addition, the fitting of the coefficients (a) can be done manually by 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, α₀ term may be solved based on the current state values:

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

wherein T_{G,exit,current} represents measured product gas exit temperature.

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

In step (II), the computation of product gas mass flow may include taking into account gas-specific mass flow of components of the product gas. In a case of a combustion process, the components comprise 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 reactant parameters. This enables reflecting changes in the reactant properties. For example, for reactants that tend to cause more erosion, the acceptance condition may be stricter, while leaner acceptance condition may be used for reactants that tend to cause less erosion.

The step (b) may be performed remotely to the reactor, 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 reactor.

Alternatively, the step (b) may be performed locally at the site of the reactor, preferably, by 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 a reaction chamber and associated passes defining a product gas flow path and having a number of heat transfer surfaces, measurement instrumentation to monitor current load of the heat releasing reactor, further measurement instrumentation to currently monitor process data, and a control system configured to carry out the method of operating the heat releasing reactor.

According to an embodiment, the heat releasing reactor comprises a reactor chamber and associated passes defining a gas flow path and having a number of heat transfer surfaces in the gas flow path.

Such a heat releasing reactor can improve the control aspect of the reactor. The advantages are same as the advantages of the method of operating the heat releasing reactor.

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 momentary load locally. This facilitates to have fast decision making at the reactor since less or no data may need to be transferred from the reactor.

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

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 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 sixty to ninety gigabytes of monitored process data each day.

A reactor computation system comprises a group of reactors, each reactor comprising a reactor 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, 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 reactor model for each of the reactors, and return computation results for each of the reactors.

Further, in the reactor computation system, the 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 reactor 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 momentary load for which at least one product gas factor computed using currently monitored process data with a numerical model of the reactor that fulfills an acceptance condition and selecting the numerical value as the current computational maximum momentary load. This basically enables using the method of the invention also in a distributed environment.

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

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

The invention is applicable to be used in various processes and reactors, where heat generation and its recovery is involved.

The invention is applicable to be used in connection with thermochemical reactor for controlling its load, that is, a rate of charging or discharging energy from the reactor.

The invention is further applicable to be used in connection with a waste gasifier for controlling the heat produced during the thermochemical destruction of waste while producing gas. Such gas can be further synthesized into fuels and chemicals in a following synthesis process.

The invention is further applicable to be used in connection with a so-called carbon capture process so as to control the load of a looping carbon capture reactor (e.g. a calcium looping reactor) for additional heat production while capturing CO₂ from a gas flow.

BRIEF DESCRIPTION OF THE DRAWINGS

The reactor 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; and

FIG. 9 illustrates another heat releasing reactor according to an embodiment of the invention.

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

DETAILED DESCRIPTION

FIG. 1 shows a combustion boiler 10 operating as a heat releasing reactor producing heat in a form of a circulating fluidized bed reactor CFB. The CFB reactor may be used as a combustor/steam generator, a carbon capture reactor using (calciner and/or carbonator), as well as a waste material gasifier. In the following description, the CFB reactor is referred particularly as a circulating fluidized bed (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 an 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 CaCO₃ and/or clay for example. In addition, or alternatively, NOx reduction agents, such as ammonia 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 with 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 a 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 in flue gas exit temperature T_{G, 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_G,in,i, T_G,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_{G, exit}. The temperatures before and after each preceding heat transfer surface 21₁ (T_G,in,i, T_G,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 to 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 the 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), a 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 heat releasing reactor which is a combustion boiler 10 that is a bubbling fluidized bed (BFB) boiler. The BFB boiler differs from the CFB boiler in that the fluidized bed is not a circulating bed but a bubbling bed. Thus, there may not be a 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 method of operating a heat releasing reactor producing product gas:

• • (a) the current load Q_h of reactor 10 (such a combustion boiler, gasification reactor, liquid air energy storage, hydration reactor) is monitored in step K1 (in the method illustrated in FIG. 4, also product gas exit temperature T_{G, exit} is monitored and heat duty Q_fluid,i to heat transfer fluid for each heat transfer surface 21i in the gas flow channel 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 gas temperatures in relation to Q_{h, candidate}. The numerical value Q_{h, candidate} is then used to compute (step K7) at least one product gas factor df_i (may be referred to as flue gas factor in connection with combustion process)using currently monitored process data with a numerical model of the reactor fulfills an acceptance condition (which is tested in step K9), and selecting the numerical value Q_{h, candidate} as the current computational maximum momentary load Q_{h, max} of the reactor 10(step K11); and
  • (c) the current computational maximum 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 maximum momentary load Q_h,max:
    • (c1i) indicating the operator that the boiler load Q_h may be increased; and/or
    • (c1ii) automatically increasing the load Q_h of the reactor, and/or
    • (c2) greater than the computational maximum momentary load Q_h,max;
    • (c2i) indicating the operator that the load Q_h exceeds the maximum momentary load; and/or
    • (c2ii) automatically reducing the reactor load Q_h.

In the method, the currently monitored process data of the reactor may include (a) current product gas exit temperature T_G,exit in a gas flow channel and (b) heat duty Q_fluid,i for each heat transfer surface 21₁ in the gas flow channel 16.

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

The finding is performed such that, if the at least one product gas factor df_i computed using currently monitored process data with a numerical model of the reactor 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 product gas exit temperature T_{G, exit} that results in a computational model when the thermal load of the reactor corresponds to the numerical value Q_{h, candidate};
  • II: computing gas mass flow q_m,fluegas;
  • III: computing a heat duty Q_{fluid, i, candidate} for each heat transfer surface 21₁ in the flue gas flow channel (back pass 16) using 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,candidate)^j−Σα_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 gas flow channel 16 to compute gas temperatures at each heat transfer surface (T_G,in,i, T_G,out,i; i=1, . . . , k) in the gas flow channel 16 in the upstream direction of 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 gas exit temperature T_G,out,m=T_{G, exit}; and
  • V: computing a product 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 (a_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 product gas mass flow q_m,G,m for selected flue gas components.

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

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

wherein T_G,in,i is the flue gas temperature at the inlet of ith heat transfer surface, c_p is specific heat capacity, and T_G,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 product gas factor df_i includes or is:

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

• • where k_i is a predetermined non-zero parameter that may be chosen combustion-boiler specifically, preferably positive (non-zero) number,
  • q_m,G 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),
  • p_G,i is flue gas density obtainable from flue gas temperature T_{G, 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.
    • (i) Advantageously, n may be selected to include at least one of the following: in the range of 0.9 to 1.1, preferably equivalent or about 1.0, for using computed gas velocity;
    • (ii) in the range of 2.9 to 3.5, preferably between 3.2 and 3.35, for using computed 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 reactors, the group comprising at least two separate reactors 10, such that using operational data monitored for each of the reactors 10 is used in the determination.

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

$T_{G,\mathrm{exit}}={\alpha }_0+{{\mathrm{\Sigma \alpha }}_j\left(Q_{h,\mathrm{candidate}}\right)}^j$

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_{G, exit} values for a number of discrete reactor load Q_h values.

In step (II), the computation of the components q_m,G,m in case the process in the reactor is combustion of fuel preferably includes at least some, most preferably, all of the following: m=CO₂, H₂O, N₂, SO₂, O₂ so as to determine gas mass flow. In other words, in step (IV) of the computation, as q_m,G,m values some or all of q_m,G,CO2, q_m,G,H2O, q_m,G,N2,q_m,G,SO2,q_m,G,O2 may be used. They are preferably measured in gas conduit 18 or in stack 19, for which reason suitable sensors are installed in the gas passage. In step (II), the component values may further include reactant (such as fuel of alkali oxide, like CaO) parameters.

In case of the reactor is for combustion process the product gas, i.e., flue gas mass flow may be based on computation of sums of flue gas component mass flows q_m,G,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:

$q_{m,G}=\sum q_{m,G,i}$

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

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

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.

According to another embodiment of the invention, when the reactor is a thermochemical reactor, particularly, based on CaO/Ca(OH)₂ hydration/dehydration reaction, the flow of H₂O (steam) and air as fluidization gas are the mass flows in the step (II), the computation mass flows of which the components are computed making use of hydration and dehydration reactions.

According to another embodiment of the invention, when the reactor is a waste material gasifier the gas flow determination may be provided in a same manner as in the case of the combustion process, but the gas composition may be different including also CO and H₂ and some minor gasification products.

According to another embodiment of the invention, when the reactor is a so called carbon capture reactor, a fluidized be carbonator and calciner are connected with each other suitably. The reactor configured to reduce particularly CO₂ from a gas to be cleaned (carbonator)by reaction with CaO and producing substantially pure CO₂ (calciner) by calcination of CaCO₃. The gas flow determination may be provided in a same manner as in the case of the combustion process, considering that fuel is being burned in a calciner, including gas flow of oxygen and the gas to be cleaned.

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 21₁ 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 to 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 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 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 product gas factors df_i can be directly computed for all heat transfer surfaces 20_i: if the temperatures T_G,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 ten day test period, the 120 MW_th boiler power obtained in average a three to six 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 control method applied to a boiler, the current computational maximum boiler momentary load Q_h,max of the combustion boiler is estimated using a numerical model using determined boiler's 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.

Above the invention has been explained referring to a combustion process in CFB and BFB. It is clear that the invention is applicable to, for example, in CFB and BFB gasifiers as well, where, instead of flue gas, a cooled product gas is formed for use as desired. Naturally, in a gasification application the product gas is stored or delivered to a desired further processing instead of admitting to atmosphere through a stack.

The heat releasing reactor may also be, for example, a waste material gasifier reactor, or a carbon capture reactor.

The process requires controllable input flows (22, 23) arranged at suitable locations into the reactor 10, at least as follows.

Thermochemical reactor, CaO (generally alkali metal oxides) hydration:

• • i. Reactant input, such as CaO, hydration reactor,
  • ii. H₂O (steam) input,
  • iii. Ca(OH)₂ dehydration reactor, and
  • iv. Air input.Waste gasifier reactor:
  • i. Corresponding input flows as in combustion process, and
  • ii. CO and H₂ and some minor gasification products.

According to another embodiment of the invention, when the reactor is a so-called carbon capture reactor, including a fluidized bed carbonator and calciner connected with each other suitably, which may be arranged in a CFB reactor as shown in the FIG. 1. The reactor configured to reduce particularly CO₂ from a gas to be cleaned (carbonator)by reaction with CaO and producing substantially pure CO₂ (calciner) by calcination of CaCO₃. The gas flow determination may be provided in a same manner as in a case of the combustion process, considering that fuel is being burned in a calciner, including gas flow of oxygen and the gas to be cleaned.

FIG. 9 depicts a heat releasing reactor 10 according to an embodiment of the invention. The heat releasing reactor may be for example thermochemical reactor, such as CaO (generally alkali metal oxides) hydration reactor. The reactor 10 comprises a reactor chamber 12 that is enclosed by walls 13, which may be optionally cooled walls connected to fluid circuit for extracting heat from process practiced in the reactor chamber—depending on the practical application.

Reactant is fed into the reaction chamber 12 via a reactant inlet 22. The reaction can be adjusted by controlling the reactant feed inlet 22, and, generally, by controlling the process variables which relate the heat releasing process of the reactor 10.

Reaction products, which may generally be referred to as product gas, flows from the reactor chamber 12 to a gas flow channel 16. The reactor 10 may be also provided with an outlet 22′ for solid material which has at least partially reacted in the reactor chamber 12. It is described here as a vertical pass, but it may as well be differently designed, such as horizontally. From the product gas flow channel, the product gas is led to further processing 19, which may comprise simple storage, or a gas delivery pipework.

The product gas flow channel 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. 9, heat transfer surfaces 21₁, 21₂, 21₃, . . . , 21_k-1, 21_k are illustrated. The actual number of different heat transfer surfaces in each of these components, for example, may be selected for each reactor 10 differently according to actual needs.

Product gas exiting the last heat transfer surface 21k will be in exit temperature T_{G, 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_G,in,i, T_G,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_{G, exit}. The temperatures before and after each preceding heat transfer surface 21_i (T_G,in,i, T_FG,in,i+1) can be obtained numerically. This will be explained further below.

The reactor 10 is equipped with a plurality of sensors 40, and computer units, and only some of the sensors and computer units are presented for sake of clarity. For example, there may be sensors 40 for determining flow rate of a reactants into the reactor 10, sensor 40 indicating temperature at one or more locations in the reactor 10, sensors 40 for indicating pressure in the reactor, depending on the actual practical application of the reactor 10.

Process data may be collected from the sensors by distributed control system (DCS) 201. This is similar to that disclosed in connection with the FIG. 1, of course the combustion boiler network is instead a heat releasing network, operating in corresponding manner.

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

2. The method according to claim 1, wherein: (i) the currently monitored process data of the reactor includes: (ia) current product gas exit temperature (TG,exit,current) in a gas flow channel, and(ib) heat duty (Qfluid,i) for each heat transfer surface (i) in the product gas flow channel,and, further, wherein:(ii) monitored process data from both (ia) and (ib) is used in computation of the product gas factor and when finding the numerical value (Qh, candidate) for the current computational maximum momentary load (Qh,max).

3. The method according to claim 1, wherein the finding is performed such that, if the at least one product gas factor (dfi) computed using currently monitored process data with a numerical model of the reactor fails to fulfill an acceptance condition, a next numerical value (Qh, candidate) is automatically selected.

4. The method according to claim 3, wherein the next numerical value (Qh, candidate) is selected iteratively.

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

7. The method according to claim 6, wherein n is selected to be at least one of the following: (i) in the range of 0.9 to 1.1, for using computed product gas velocity;(ii) in the range of 2.9 to 3.5 for using computed product gas caused erosion; or(iii) in the range 1.8 to 2.2, for using pressure loss of the product gas flow.

8. The method according to claim 7, wherein the value for n is changed over time.

9. The method according to claim 7, wherein the value for n is determined from a group of reactors comprising at least two separate reactors using operational data monitored for each of the reactors.

10. The method according to claim 5, wherein, in the computation in step (I), the flue gas exit temperature is substantially estimated by an equation:

11. The method according to claim 5, wherein, in step (II), computation of product gas mass flow utilizes mass flow (qm,G,m) of product gas components m.

12. The method according to claim 5, wherein, in step (II), the computation of product gas mass flow includes using reactant parameters.

13. The method according to claim 1, wherein the step (b) is performed remotely to the reactor.

14. The method according to claim 1, wherein the step (b) is performed locally at the reactor site.

15. The method according to claim 1, 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.

16. The method according to claim 1, wherein the acceptance condition includes a hysteresis condition, requiring a predefined minimum change before changing the current computational maximum momentary load (Qh,max).

17. The method according to claim 1, wherein the acceptance condition includes comparing the computed at least one product 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.

18. The method according to claim 1, wherein the reactor is a circulating fluidized bed (CFB) or a bubbling fluidized bed (BFB) reactor, and the step (b) is carried out for the heat transfer surfaces in at least one of the reactor, and the product gas channel.

19. A heat releasing reactor comprising: a reactor chamber and associated passes defining a product gas flow path and having a number of heat transfer surfaces;measurement instrumentation to monitor current load (Qh) of the heat releasing reactor;further measurement instrumentation, such as sensors, to currently monitor process data; anda control system configured to carry out the method of operating the heat releasing reactor according to claim 1.

20. The heat releasing reactor (10) according to claim 19, wherein the control system comprises (CS) an edge server (203) that is configured to process real-time measurement results for at least one of currently monitored process data and current load, by filtering, averaging, and/or computing trends.

21. The heat releasing reactor according to claim 19, wherein the control system is configured to carry out the method step (b) to determine the current computational maximum momentary load (Qh,max) locally.

22. The heat releasing reactor according to claim 19, 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 momentary load (Qh,max) to the control system.

23. The heat releasing reactor according to claim 22, wherein the edge server is configured to reduce an amount of measurement data that is passed to the remote computing system.

24. A reactor computation system comprising: a group of reactors, according to claim 19, each reactor comprising a control system (DCS), comprising an edge server system that is configured to process the real-time measurement results for at least one of currently monitored process data and current load, by performing at least one of filtering, averaging, and computing trends, and to send the processed real-time measurement results to a remote computing system;a remote computing system that is configured to receive data processed from real-time measurement results and to compute data using a numerical model for each of the reactors, and to return computation results for each of the reactors; and,further, wherein the control system is configured to adapt its function based on the computation results.

25. The reactor computation system according to claim 24, wherein the computing system is configured to find such a numerical value (Qh, candidate) for a current computational maximum momentary load (Qh,max) for which at least one product gas factor (dfi) computed using currently monitored process data with a numerical model of the reactor that fulfills an acceptance condition, and selecting the numerical value (Qh, candidate) as the current computational maximum momentary load (Qh,max).

26. The reactor computation system according to claim 24, wherein the reactor computation system is configured to calibrate a numerical model for a heat releasing reactor using processed measurement data for the heat releasing reactor.

27. The reactor computation system according to claim 24, wherein the reactor computation system is configured to calibrate a numerical model for a heat releasing reactor using processed measurement data also collected from other heat releasing reactors.