Method of operating a heat dissipation reactor, heat dissipation reactor and heat dissipation reactor calculation system

Abstract

To improve the control of a heat dissipation reactor, a method including the following steps is proposed: a) A step of monitoring the current load (Qh) of the heat dissipation reactor; b) finding a numerical value (Qh, candidate) for a current calculated maximum instantaneous load (Qh,max) for which at least one product gas coefficient (dfi) calculated using the numerical model of the reactor and currently monitored process data satisfies the acceptance condition, and selecting the numerical value (Qh, candidate) as the current calculated maximum instantaneous load (Qh,max); c) Indicate to the operator the current calculated maximum instantaneous load (Qh, max), and / or the current load (Qh) is c1) If it is smaller than the current calculated maximum instantaneous load (Qh, max): c1i) A step indicating to the operator that the load (Qh) may be increased, and / or c1ii) Step for automatically increasing the load (Qh), and / or c2) If it is greater than the current calculated maximum instantaneous load (Qh, max): c2i) Indicate to the operator that the load (Qh) currently exceeds the calculated maximum instantaneous load, and / or c2ii) Step to automatically reduce the load (Qh).

Description

Technology Field

[0001] The present invention relates to operating a heat dissipation reactor. Background Technology

[0002] The technical field where heat-dissipating reactors are typically used is combustion boilers, and combustion boilers such as grate boilers and fluidized bed boilers are generally used to generate steam that can be used for various purposes, such as electricity and heat production.

[0003] In a fluidized bed boiler, fuel and solid particle bed material are introduced into the furnace. Fluidizing gas is introduced at the bottom of the furnace to fluidize the bed material and fuel. Combustion of the fuel occurs within the furnace. In BFB combustion, the fluidizing gas passes through the bed to form bubbles in the bed. In BFB, the fluidized bed can be conveniently controlled by controlling the supply of fluidizing gas and fuel. In addition to fuel, specific additives such as aluminum silicate (e.g., unhydrated clay), alkaline earth metal carbonates, and mixtures thereof (e.g., limestone or calcium carbonate) can be added to the combustion to improve the adsorption of possible heavy metals and sulfur, and also improve alkali adsorption.

[0004] In CFB combustion, fluidized gas passes through the bed material. Most bed particles are carried along with the fluidized gas and flue gas. The particles are separated from the flue gas at at least one particle separator, circulated, and returned to the furnace. It is common practice to arrange a fluidized bed heat exchanger downstream of the particle separator(s) to recover heat from the particles before they are returned to the furnace.

[0005] In all boilers, regardless of the combustion technology, combustion conditions such as the mixing of air and fuel may not be ideal.

[0006] An international application published under WO 2016 / 202640 A1 of Improbed AB discloses a method for controlling the heat load of a combustion boiler. In this method, the heat load of the combustion boiler is reduced when the flue gas velocity monitored at at least one location of the boiler exceeds a predetermined maximum flue gas velocity limit. The flue gas velocity is calculated from the volumetric flow of flue gas divided by the cross-sectional area of ​​the flue gas duct located immediately downstream of the cyclone using a group of equations.

[0007] In addition, it is known that there are other processes that generate product gases requiring temperature control, which means heating or cooling the gas and / or process. The problem to be solved

[0008] The thermal reactor is designed to match the given capacity, which is the reactor's corresponding Maximum Continuous Rating (MCR). This is also referred to as the design load level.

[0009] A specific objective of the present invention is to improve the performance, profitability, and flexibility of a heat dissipation reactor and to improve the control of the load of the heat dissipation reactor. This objective can be achieved by the method of operating a heat dissipation reactor according to claim 1 and by the heat dissipation reactor according to claim 19.

[0010] Another objective of the present invention is to reduce the complexity of the control system of a combustion boiler. This objective can be satisfied by the reactor calculation system according to claim 24.

[0011] The dependent term describes an advantageous mode of the method, reactor, and computational system. means of solving the problem

[0012] A method for operating a heat-dissipating reactor to generate product gas includes the following steps:

[0013] a) Current load Q of the reactor h Step of monitoring;

[0014] b) Using the currently monitored process data along with the numerical model of the reactor, find a numerical value for the current calculated maximum instantaneous load for which at least one product gas coefficient calculated satisfies the allowable condition, and the numerical value is the current calculated maximum instantaneous load Q h, max Step of selecting as;

[0015] c) A step indicating to the operator the current calculated maximum instantaneous load Qh, max, and / or the current load Q h go

[0016] c1) If it is smaller than the current calculated maximum instantaneous load:

[0017] c1i) A step indicating to the operator that the load may increase, and / or

[0018] c1ii) Step for automatically increasing the load,

[0019] and / or

[0020] c2) If it is greater than the current calculated maximum instantaneous load:

[0021] c2i) Load Q to the operator h Indicates that the current calculated maximum instantaneous load is exceeded, and / or

[0022] c2ii) Load Q h A step that automatically reduces.

[0023] By using this method, which involves calculating the product gas coefficient and selecting appropriate allowable conditions instead of having a fixed maximum load, it is possible to safely operate the reactor at or near the current calculated maximum instantaneous load, which may sometimes be higher than the fixed maximum load. The current calculated maximum instantaneous load can be higher than the design load level. Consequently, the overall performance of the reactor can be improved, allowing for an increase in power / heat production. Furthermore, since the current calculated maximum instantaneous load may sometimes be lower than the design load level, wear on the reactor caused by exceeding the current calculated maximum instantaneous load can be reduced more effectively. In other words, the current calculated maximum instantaneous load can be considered the maximum allowable load and / or recommended load.

[0024] The applicant was able to obtain a power output of a combustion boiler that, on average, exceeded the maximum load of a fixed boiler in tests performed on the boiler. Through the tests, the applicant identified a potential for improvement of 120 MW for the combustion boiler. th In the case of a combustion boiler, for example, 3 to 6 MW th It was possible to prove that it could be between 2.5 and 5% corresponding to.

[0025] Preferably, in that method:

[0026] i) The currently monitored process data of the reactor is,

[0027] ia) Current product gas outlet temperature of the product gas flow channel and

[0028] ib) including a heat load for each heat transfer surface of the product gas flow channel.

[0029] And additionally:

[0030] ii) Monitored process data from both steps ia) and ib) is used when calculating the product gas coefficient and when finding the numerical value for the current calculated maximum instantaneous load Qh, max.

[0031] The calculation of the heat load of a heat exchanger is known to those skilled in the art, and, for example, the heat load can be obtained using the following equation, and

[0032]

[0033] Here, q m,fluid,i is the fluid flow at the i-th heat transfer surface, and h fluid,in is the enthalpy of the fluid flowing into the i-th heat transfer surface, and h fluid,out is the enthalpy of the fluid coming out of the i-th heat transfer surface.

[0034] If at least one product gas coefficient calculated using the currently monitored process data along with the numerical model of the reactor does not meet the acceptance conditions, a discovery can be performed so that the next numerical value is automatically selected. It is desirable to iteratively select the next numerical value. This can enable the use of computational library functions, in particular iterative solvers (e.g., the Python FSOLVE function for finding the roots of a function).

[0035] Discovery can be performed by carrying out the following computational steps:

[0036] - I: A step of calculating an estimate for the boiler product gas outlet temperature that results in a calculated reactor model when the reactor heat load corresponds to a numerical value;

[0037] - II: Step to calculate product gas mass flow rate

[0038] - III: A step of calculating the heat load for each heat transfer surface of the flue gas flow channel using the current heat load corrected using a numerical boiler model;

[0039] - IV: A step of calculating the product gas temperature at each heat transfer surface of the product gas flow channel in the upstream direction of the gas flow, starting from the heat transfer surface closest to the product gas outlet, using the heat load calculated for each heat transfer surface of the product gas flow channel and the estimate for the flue gas outlet temperature;

[0040] - V: Step for calculating the product gas coefficient for each heat transfer surface of the flue gas flow channel.

[0041] Using this approach, the conditions in the product gas flow channels of each heat transfer surface (in this application, "heat transfer surface" means a heat exchanger, heat exchanger tube, heat exchanger tube bundle, heat exchanger package, and / or a group of components of a heat exchanger) can be numerically estimated using product gas coefficients in a situation where the heat load of the reactor corresponds to that numerical value. Thus, we can now test whether a given numerical value, which is a candidate for the currently calculated maximum instantaneous load, creates an acceptable situation at the heat transfer surface.

[0042] According to an embodiment of the present invention, in step III), the numerical reactor model is Q fluid, i, candidate = Q fluid, i, current + Σα j,i (Q h, candidate ) j - Σα j,i (Q h, current ) j It is a form.

[0043] Parameters (α j,i The fitting of ) can be performed manually by a human or automatically by a computer using historical data. Automatic updating of parameters can be performed, for example, once a month. AI and neural network-based algorithms can be utilized for automatic updates.

[0044] In particular, when the present invention is applied to boiler combustion fuel, it makes it possible to predict the maximum computationally permissible current boiler instantaneous load without reaching the limit of the current boiler load, unlike the method disclosed in WO 2016 / 202640 A1, and on the other hand, and much more importantly, to reach the limit without exceeding the maximum computationally permissible current boiler instantaneous load.

[0045] Preferably, the product gas coefficient includes or is as follows:

[0046] df i =k i (q m,productgas / (ρ productgas,i *A cross,i )) n

[0047] Here,

[0048] k i is a positive non-zero parameter that can be selected depending on the type of reactor.

[0049] q m,productgas is a product gas mass flow.

[0050] n is a model parameter that can be selected depending on the type of reactor and is preferably a positive (non-zero) number.

[0051] ρ productgas, i is the density of the product gas at the i-th heat transfer surface; and

[0052] A cross,i is the cross-sectional area of ​​the gas flow path at the i-th heat transfer surface.

[0053] Selecting this functional form for the product gas coefficient is particularly convenient because it becomes very flexible and can be easily adapted to different operational requirements of the reactor, for example, based on the conditions of the current reactants used in the reactor.

[0054] i) Particularly advantageously, the model parameter n may be selected to include at least one of the following: a range of 0.9 to 1.1, preferably about 1.0, for using the calculated product gas rate;

[0055] ii) for use with calculated product gas-induced erosion in the range of 2.9 to 3.5, preferably in the range of 3.2 to 3.35; or

[0056] iii) For use, the pressure loss of the product gas flow is in the range of 1.8 to 2.2, preferably about 2.0.

[0057] The value of n can change over time. This is advantageous because flue gas flow conditions at the heat transfer surface can vary over time due to factors such as fouling, re-aggregation, or reactant or bed conditions, for example, if the reactor is a fluidized bed reactor. Therefore, the product gas coefficient can change over time to better reflect actual process conditions.

[0058] According to an embodiment of the present invention, when n=2 and the product gas coefficient represents pressure loss, the product gas coefficient df i and the predetermined maximum value df for the product gas coefficient max, i A comparison between them can be performed for each heat transfer surface. According to an embodiment, the acceptable condition is substantially df i = df max, i am.

[0059] According to an embodiment of the present invention, when n=2 and the product gas coefficient represents pressure loss, flue gas coefficients df i sum of

[0060]

[0061] and predetermined product gas coefficients df max, iA comparison between the sums can be made, or simply, a predetermined product gas coefficient represents the total pressure drop, and thus the comparison represents a comparison of the total pressure drop between the reactor and the chimney. According to the embodiments, the permissible condition is substantially dp tot = dp max, tot am.

[0062] According to an embodiment of the present invention, the product gas coefficient represents a particle loading factor and can be written in the following form:

[0063] df i =k ph C(d)q m_fa V p n

[0064] Here k ph is the particle hardness coefficient, C(d) is a function of particle diameter, and q m_fa is the particle mass flow rate, and v p is the particle velocity, and n is the exponent (0.3 - 4). In such cases, the predetermined product gas coefficient represents the maximum particle loading value. It may also be adjusted according to particle characteristics (softness, etc.).

[0065] According to a specific embodiment of the present invention, the reactor is a fluidized bed (FB) reactor such as an FB boiler or an FB vaporizer, and the product gas coefficient represents a reloading coefficient and can be written in the following form,

[0066] df i =k ph C(d)q m_fa V p n

[0067] Here k ph is the particle hardness coefficient, C(d) is a function of particle diameter, and q m_fa is the fly ash mass flow rate, and v pε is the particle velocity, and n is the exponent (0.3 - 4). In such cases, the predetermined flue gas coefficient represents the maximum reloading value. It may also be adjusted according to re-characteristics (softness, etc.).

[0068] According to one embodiment of the present invention, the acceptance condition is substantially df i = df max, i However, in actual situations, the acceptance condition can be defined as follows:

[0069] df max, i - δ < df i ≤ df max, i

[0070] Here, δ > 0 and depends on numerical accuracy and / or method. df max, i - δ < df i ≤ df max, i In this case, it means that at least one product gas coefficient calculated using currently monitored process data along with the numerical model of the reactor satisfies the allowable conditions, and since the maximum allowable load has been found, the numerical value Q h, candidate is the currently calculated maximum instantaneous load Q h, max It is selected as.

[0071] According to one embodiment of the present invention, the acceptance condition is substantially Σ (df i ) = Σ (df max, i However, in actual situations, the acceptance condition can be defined by using the following sums, and

[0072] Σ (df max, i ) - δ < Σ (df i ) ≤ Σ (df max, i )

[0073] Here, δ > 0 and depends on numerical accuracy and / or the method. Σ (df max, i ) - δ < Σ (df i ) ≤ Σ (df max, iIn the case where ), this means that at least one product gas coefficient calculated using currently monitored process data along with the boiler's numerical model satisfies the allowable conditions, and since the maximum allowable load has been found in this case, the numerical value Q h, candidate is the currently calculated maximum instantaneous load Q h, max It is selected as. According to one embodiment, the sum index i spans across all heat transfer surfaces. According to another aspect of the invention, the sum index i spans only some of the heat transfer surfaces, preferably across the product gas channels.

[0074] This can be particularly useful when the value of n is determined from a group of reactors containing at least two individual reactors using operational data monitored for each reactor. Using a larger number of reactors (2, 3, 4, ...) provides a larger data set. Consequently, more operational data is monitored. This can produce better results, which can be particularly beneficial in situations where the determination uses interpolation and / or extrapolation of experimental data.

[0075] For the calculation of Step I), the product gas outlet temperature is substantially given by the following equation,

[0076] T G, exit = α0 + Σ α i Q i h, candidate

[0077] Alternatively, it may preferably be estimated by its first, second, or third (or higher-order) approximation. The coefficients α can be obtained through fitting after measuring product gas emission values ​​for a number of individual reactor load values. This data can be collected over time and refreshed from time to time, such as periodically. Alternatively, or additionally, it may be collected from one or more calibration runs of the reactor.

[0078] The fitting of coefficients (α) can be performed manually by a human or automatically by a computer using historical data. Automatic updating of coefficients can be performed, for example, once a month. AI and neural network-based algorithms can be used for automatic updates.

[0079] According to an embodiment of the present invention, in step I), the product gas outlet temperature can be substantially estimated using an artificial intelligence tool. According to another embodiment of the present invention, in step I), the product gas outlet temperature may be estimated substantially by using a neural network.

[0080] According to one embodiment of the present invention, in step I), the product gas outlet temperature may be estimated by the following equation, and

[0081]

[0082] Here, α0, α1 and α2 can be a predefined constant. Alternatively, or additionally, the fitting of the coefficients (α) can be performed manually by a human or automatically by a computer using historical data. Automatic updating of the coefficients can be performed, for example, once a month. AI and neural network-based algorithms can be used for automatic updating.

[0083] According to one embodiment of the present invention, term α0 can be solved based on the current state value, and

[0084]

[0085] Here, T G,exit,current represents the measured product gas outlet temperature.

[0086] According to an embodiment of the present invention, in step II), the product gas mass flow is calculated using the reactor mass and energy balance equation.

[0087] In Step II), the calculation of the product gas mass flow may include considering the gas-specific mass flow of the product gas components. In the case of a combustion process, the components are CO2, H2O , It includes N2, SO2, and O2. The concentrations of these components can be reliably measured using simple equipment.

[0088] In Step II), component values ​​may include reactant parameters. This makes it possible to reflect changes in reactant characteristics. For example, for reactants that tend to cause more erosion, the acceptance conditions may be stricter, whereas for reactants that tend to cause less erosion, leaner acceptance conditions may be used.

[0089] Step b) can be performed remotely to the reactor, preferably via a cloud-based computing service. This helps simplify the maintenance of the combustion boiler, for example, by allowing remote computing equipment configured to run cloud-based computing services to be maintained separately from the combustion boiler. For instance, computing software updates can therefore be performed centrally at one or a few locations instead of updating the software at each reactor.

[0090] Alternatively, step b) can be performed locally at the reactor location, preferably by an edge server. Since there is no need to transmit data to a remote computing location, the computing speed can be increased.

[0091] Any currently monitored process data and / or current load can be obtained through real-time measurements. Alternatively, or additionally, the currently monitored process data and / or current load may be processed by filtering, averaging, calculation trends, or a combination thereof. This helps avoid noise or outlier measurements that affect calculation results, and thus helps increase the stability of the current computational maximum instantaneous load.

[0092] Acceptance conditions may include hysteresis conditions that require a predefined minimum change before changing the currently calculated maximum boiler instantaneous load. This can increase the stability of the currently calculated maximum instantaneous load and, preferably, helps avoid excessive fluctuations in the currently calculated maximum instantaneous load.

[0093] The applicant finds that it is useful not only when the combustion boiler is a circulating fluidized bed (CFB) or bubble fluidized bed (BFB) boiler, but more generally when it is a CFB and BFB reactor, and when step b) is performed on the heat transfer surface of the fluidized bed reactor.

[0094] According to an embodiment, step b) is performed on the heat transfer surface between the reactor and the chimney.

[0095] The heat dissipation reactor includes the following:

[0096] - Reaction chambers and associated passages having multiple heat transfer surfaces and defining product gas flow paths;

[0097] - Measuring equipment for monitoring the current load of the heat dissipation reactor;

[0098] - Additional measurement equipment for monitoring current process data; and

[0099] - A control system configured to perform the method of operating a heat dissipation reactor.

[0100] According to an embodiment, the heat dissipation reactor defines a reactor chamber and a gas flow path and includes related passages having a plurality of heat transfer surfaces in the gas flow path.

[0101] Such a heat dissipation reactor can improve the control mode of the reactor. The advantages are the same as the advantages of the method of operating the heat dissipation reactor.

[0102] The control system may include an edge server that can be configured to process real-time measurements of currently monitored process data and / or current load, i.e., through filtering, averaging, and / or trend calculation. The edge server helps reduce the amount of currently monitored process data. This can be particularly useful in certain installations where the daily monitored process data can reach 60 to 90 GB.

[0103] The control system can be configured to perform method step b) to locally determine the currently computationally maximum instantaneous load. This facilitates rapid decision-making in the reactor because there is little or no data to be transmitted from the reactor.

[0104] Alternatively or additionally, the control system may be configured to transmit data to a remote, preferably cloud-based, computing system configured to perform method step b), and to return the current computationally maximum boiler instantaneous load to the control system. This simplifies the reactor and makes updating the computing system easier. In this scenario, updates can be performed centrally rather than at all reactors.

[0105] Edge servers can be configured to reduce the amount of measurement data transmitted to remote computing systems. In this way, smaller bandwidth for data transmission may be sufficient. This can be particularly useful in certain installations where the process data monitored daily can reach 60 to 90 GB.

[0106] The reactor calculation system includes the following:

[0107] - A group of reactors, each reactor comprising a reactor control system including an edge server system configured to process currently monitored process data and / or real-time measurement results regarding the current load by filtering, averaging, and / or trend calculation, and to send the processed real-time measurement results to a remote computing system;

[0108] - A remote computing system, preferably a cloud-based computing system, configured to receive data processed from real-time measurement results, calculate data using a numerical reactor model for each reactor, and return the calculation results for each reactor.

[0109] In addition, in the reactor calculation system, the control system is configured to adjust its functions based on the calculation results.

[0110] The advantage of this arrangement is that it reduces the need for computing devices in the reactor while still enabling effective and fast computation results in remote computing systems.

[0111] The computing system can be configured to use currently monitored process data along with a numerical model of the reactor to discover a numerical value for the current computationally maximum instantaneous load at which at least one product gas coefficient calculated satisfies the allowable condition, and to select that numerical value as the current computationally maximum instantaneous load. This essentially enables the use of the method of the present invention even in a distributed environment.

[0112] The reactor calculation system can be configured to calibrate numerical models, such as product gas coefficient numerical models for the reactor, using processed measurement data for the reactor. This makes it easier to remotely adapt or calibrate numerical models for reactor control.

[0113] The reactor calculation system can be configured to adapt or calibrate a numerical model for the reactor using processed measurement data collected from other reactors as well. This allows the numerical model for reactor control to be adjusted using more collected data.

[0114] The present invention is applicable to use in various processes and reactors involving heat generation and recovery.

[0115] The present invention is applicable to use in relation to thermochemical reactors to control the load, that is, the rate of energy charging or discharging from the reactor.

[0116] The present invention is further applicable to use in relation to a waste gasifier for controlling the heat generated during the thermochemical destruction of waste while generating gas. This gas can be further synthesized into fuel and chemicals through the following synthesis process.

[0117] The present invention is further applicable to use in connection with a so-called carbon capture process 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 stream. Brief explanation of the drawing

[0118] The reactor and the method for controlling it are described in more detail below in the context of the embodiments illustrated in the attached drawings of FIGS. 1 to 9. FIG. 1 illustrates a CFB boiler; FIG. 2 illustrates a BFB boiler; Figure 3 illustrates the flow of measurement data from the sensor. Figure 4 shows the currently calculated maximum boiler instantaneous load Q h, max This is a flowchart illustrating the first method for finding . Figure 5 shows the currently calculated maximum boiler instantaneous load Q h, max This is a flowchart illustrating the second method for calculating. Figure 6 shows the currently calculated maximum boiler instantaneous load Q h, max It shows how it can be presented to the boiler operator. Figure 7 shows the boiler instantaneous load Q h and calculated current maximum boiler instantaneous load Q h, max In addition, the effects of using the method according to the present invention during the test period are illustrated. Figure 8 shows the boiler instantaneous load Q h and calculated current maximum boiler instantaneous load Q h, max The data in Fig. 7, which shows [the result], is observed in detail, and here the effect of using the method according to the present invention during a 10-day test period is more clearly seen. FIG. 9 illustrates another heat dissipation reactor according to one embodiment of the present invention. Identical drawing symbols indicate identical technical features in all drawings. Specific details for implementing the invention

[0119] FIG. 1 illustrates a combustion boiler (10) operating as a heat-generating reactor in the form of a circulating fluidized bed (CFB) reactor. The CFB reactor can be used as a waste gasifier as well as a carbon capture reactor using a combustor / steam generator (calcination furnace and / or carbonator). In the following description, the CFB reactor is specifically referred to as a circulating fluidized bed (CFB) boiler and comprises a furnace (12) having tube walls (13) connected to the steam circuit of the combustion boiler (10). Water from a water tank (not shown) is supplied to an economizer and from the economizer is supplied through a steam drum to an evaporative heat transfer surface such as the tube walls (13), then guided through the steam drum to a superheater and then supplied to a turbine. An economizer and / or superheater may be provided in the flue gas channel.

[0120] A fluidizing gas (e.g., air and / or oxygen-containing gas) is supplied from a fluidizing gas source (153) through a windbox (not shown) under a grate (a grate not shown in FIG. 1), and the primary fluidizing air enters the furnace through a nozzle (not shown) (to fluidize the layer) and through a secondary fluidizing gas feed (152) (to supply oxygen-containing gas to control combustion). The effect is that the layer material is fluidized and the oxygen required for combustion is supplied to the furnace (12). Additionally, fuel is supplied into the furnace (12) through a fuel feed (22). Combustion can be controlled by controlling the fuel feed (22) (e.g., by decreasing or increasing the fuel supply) and by controlling the fluidizing gas feed (e.g., by decreasing or increasing the amount of oxygen supplied into the furnace (12). The fuel may be supplied with additives, particularly additives that act as alkali adsorbents, such as CaCO3 and / or clay. Additionally or alternatively, a NOx reducing agent such as ammonium or urea may be supplied into or over the combustion zone of the furnace (12).

[0121] The layer material is also fed into the furnace, and the layer material may include sand, limestone and / or clay, and in particular may include kaolin. One effect of the layer and generally combustion is that in the water-steam circuit, water and steam are heated at the tube walls (13) and the water is converted into steam.

[0122] The ash falls to the bottom of the furnace (12) and can be removed through the ash chute (omitted in Fig. 1 for clarity), and some of the ash, called fly ash, will be carried along with the flue gas.

[0123] Combustion products such as flue gas, unburned fuel, and bed material are passed from the furnace (12) to a particle separator (17) which may include a vortex finder (103). The particle separator (17) separates the flue gas from the solid. In particular, in a large combustion boiler (10), there may preferably be two or more (two, three, ...) separators (17) arranged in parallel with each other.

[0124] The solid separated by the separator (17) preferably passes through a loop seal (160) located at the bottom of the separator (17). The solid is then transferred to a fluidized bed heat exchanger (FBHE) (100), which is also a heat transfer surface, so that the FBHE (100) collects heat from the solid 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 may include heat transfer tubes or other types of heat transfer surfaces. The FBHE (100) may be arranged as a reheater or a superheater. From the FBHE outlet (101), the steam is transferred to a high-pressure turbine (if the FBHE (100) is a superheater) or a medium-pressure turbine (if the FBHE (100) is a reheater). For clarity, the turbine is not shown in FIG. 1. Solids can be returned from the FBHE (100) to the furnace (12) through the return channel (102). In particular, in a large combustion boiler (10), two or more (two, three, ...) loop seals (160) and FBHEs (100) and return channels (102) are preferably arranged parallel to each other, so that for each separator (17), there will be a loop seal (160), FBHE (100), and return channel (102). In practice, some of the FBHEs (100) may be arranged as superheaters and others as reheaters.

[0125] The flue gas is transferred from the separator (17) to a horizontal path (15), from there to a reverse path (16) (preferably a vertical path), and from there to a chimney (19) via a flue gas conduit (18).

[0126] The reverse path (16) has a plurality of heat transfer surfaces (21 i Includes )(where i = 1, 2, 3, ..., k, where k is the number of heat transfer surfaces). FIG. 1 includes heat transfer surfaces (211, 212, 213, ..., 21 k-1 ) is exemplified. Heat transfer surface 21 k describes an air preheater. Heat transfer surfaces 21 k-1 , 212 illustrates a superheater, and heat transfer surfaces 211 and 213 depict a reheater. For example, the actual number of different heat transfer surfaces of each of these components may be selected differently for each combustion boiler according to actual needs. There may also be additional components including heat transfer surfaces (21).

[0127] Last heat transfer surface 21 k The flue gas discharged from is at the flue gas outlet temperature T G,　exit It will be at. This temperature is temperature sensor 20 k It is measured as.

[0128] According to one embodiment, each heat transfer surface 21 i Temperatures before and after (each T G,in,i , T G,in,i+1 ) are each temperature sensor 20 i (Here, i = 1, 2, 3, …, k-1, k) can be measured.

[0129] However, according to another embodiment, and preferably, it is not necessary to measure this temperature. Flue gas outlet temperature T G,　exit It is sufficient to know each previous heat transfer surface 21 i Temperatures before and after (TG,in,i , T G,in,i+1 ) can be calculated numerically. This will be explained further below.

[0130] The combustion boiler (10) is equipped with multiple sensors and computer devices. In fact, one medium-sized (100~150MW) boiler. th The combustion boiler (10) can generate 100 million measurement results per day, and for this, 25 GB of storage space is required. FIGS. 1, FIGS. 2 and FIGS. 3 illustrate some of the sensors and computer devices. Examples of sensors include a combustion gas (usually combustion air) volumetric flow sensor (30) (for measuring primary and secondary fluidization gas supply), a fuel supply sensor (650), and temperature sensors 20 i (i = 1, 2, ..., k), the temperature sensor of the FBHE and the pressure sensor (116) of the return channel (102) (all applicable only to the CFB boiler), and the sensor (40) of the furnace (12).

[0131] Process data can be collected from sensors by a distributed control system (DCS) (201). Data collection can be arranged most conveniently, for example, via a field bus (290). The DCS (201) may have a display / monitor (202) for displaying operating status information to an operator. An EDGE server (203) may perform processing such as filtering and smoothing the measurement data acquired from the sensors. There may be a local storage (204) for storing data.

[0132] The DCS (201), display / monitor (202), EDGE server (203), and local storage (204) may be located within the combustion boiler network (280) (the local storage (204) is preferably directly connected to the EDGE server). The combustion boiler network (280) is preferably separated from the field bus (290) used to communicate measurement results from the sensor to the DCS (201) and / or the EDGE server (203). Between the DCS (201) and the EDGE server (203), there may be an open platform communication server (210) (see FIG. 3) to improve the interoperability of the system.

[0133] The combustion boiler network (280) can preferably be connected to the Internet (200) via a gateway (290). In this situation, measurement results can be transmitted from the combustion boiler network (280) to a cloud service, such as a processing intelligence system (205) located in a computing cloud (206). The applicant currently operates a cloud service that runs the analysis platform. The cloud service can be operated in a virtualized server environment, such as Microsoft® Azure®, which is a virtualized and easily scalable environment for distributed computing and cloud storage for data. Other cloud computing services may also be suitable for running the analysis platform. Additionally, local or remote servers may be used instead of, or in addition to, a cloud computing service to run the analysis platform.

[0134] FIG. 2 illustrates a heat-dissipating reactor that is a combustion boiler (10) which is a bubble fluidized bed (BFB) boiler. A BFB boiler differs from a CFB boiler in that the fluidized bed is a bubble bed rather than a circulation bed. Therefore, a separator (17), a loop seal (160), an FBHE (100), and a return channel (102) may not be required.

[0135] Generally, at least one superheater (14) is located within the furnace (12), preferably in the upper part of the furnace (12). The inlet (141) of the superheater (14) is preferably from a steam drum or another superheater, and the outlet (142) is to a high-pressure turbine.

[0136] Figure 4 illustrates a method of operating a heat-dissipating reactor that generates product gas:

[0137] a) Current load Q of the reactor (10) (such as a combustion boiler, gasification reactor, liquid air energy storage, hydration reactor) h is monitored in step K1 (in the method shown in Fig. 4, also the product gas outlet temperature T). G, exit Each heat transfer surface 21 of the gas flow channel (16) is monitored. i Heat load Q for the heat transfer fluid fluid, i It is monitored.

[0138] b) Numerical value Q h, candidate is selected (step K3), and then heat transfer surface 21 i The heat load at is calculated and Q h, candidate The gas temperature is calculated in relation to. Numerical value Q h, candidate Then, using currently monitored process data along with a numerical model of a reactor meeting acceptance conditions (tested in step K9), at least one product gas coefficient df (which may also be referred to as the flue gas coefficient in relation to the combustion process) i Used to calculate (step K7), and numeric value Q h, candidate The current calculated maximum instantaneous load Q of the reactor (10) h, max Select as (step K11);

[0139] c) Current calculated maximum instantaneous load Q h, max a is displayed to the operator (e.g., by displaying on a monitor / screen (202)), and / or the current load Q h go

[0140] c1) Calculated maximum instantaneous load Q h, max If smaller:

[0141] c1i) Indicate to the operator that the boiler load Qh may increase, and / or

[0142] c1ii) Reactor load Q h A step of automatically increasing;

[0143] and / or

[0144] c2) Calculated maximum instantaneous load Q h, max If it is larger:

[0145] c2i) Load Q to the operator h Indicates that the maximum instantaneous load is exceeded, and / or

[0146] c2ii) Reactor Load Q h Automatically reduces.

[0147] In this method, the currently monitored process data of the reactor is a) the current product gas outlet temperature T in the gas flow channel. G, exit and b) each heat transfer surface 21 within the gas flow channel (16) i Heat load Q for fluid, i It may include.

[0148] In addition, in that method, the monitored process data from both a) and b) is the product gas coefficient df i At the time of calculation and the currently calculated maximum instantaneous load Q h, max Numerical value Q for h, candidate It can be used when discovering.

[0149] At least one product gas coefficient df calculated using currently monitored process data along with the numerical model of the reactor i If A does not meet the allowable conditions, the following numerical value Q h, candidate Discovery is performed so that this is automatically selected. Automatic selection is preferably performed repeatedly.

[0150] As a specific example, discovery can be performed by carrying out the following computational steps:

[0151] - I: Numerical value Q of the reactor's heat load h, candidate The product gas outlet temperature T that results in the calculation model when corresponding to G, exit Step of calculating an estimate for;

[0152] - II: Gas mass flow rate q m,fluegas Step of calculating;

[0153] - III: 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 Current heat load Q corrected using fluid, i, current Each heat transfer surface 21 in the flue gas flow channel (reverse passage (16)) using i Heat load Q for fluid, i, candidate Step to calculate

[0154] - IV: Each heat transfer surface of the gas flow channel (16) 21 i Calculated heat load Q for fluid, i, candidate Using, the estimate T for the gas outlet temperature G,out,m = T G, exit Using , the heat transfer surface 21 closest to the flue gas outlet k Starting from the gas flow upstream direction, the gas temperature (T) at each heat transfer surface of the gas flow channel (16) lG,in,i , T G,out,i ; Step to calculate i = 1, ..., k);

[0155] - V: Each heat transfer surface 21 of the flue gas flow channel (reverse passage (16)) i Product gas coefficient df for iStep to calculate , i = 1 , ..., k.

[0156] Parameters (α j,i The fitting of ) can be performed manually by a human or automatically by a computer using historical data. Automatic updating of parameters can be performed, for example, once a month. AI and neural network-based algorithms can be utilized for automatic updates.

[0157] Step II) is the product gas mass flow q for the selected flue gas components. m,G,m It may include a step of calculating.

[0158] The gas temperature of each heat transfer surface can be calculated, for example, as follows, and

[0159]

[0160] Here, T G,in,i is the flue gas temperature at the inlet of the i-th heat transfer surface, and c p is the specific heat capacity, and T G,out,i is the flue gas temperature at the outlet of the i-th heat transfer surface. The flue gas temperature can be determined using artificial intelligence tools. The flue gas temperature can be determined through a neural network.

[0161] Preferably, product gas coefficient df i is or includes the following:

[0162] df i = k i (q m,G / (ρ G,I A cross,i )) n

[0163] Among the foods, k i is a positive non-zero parameter that can be selected depending on the type of reactor, and

[0164] q m,G is the flue gas mass flow, and

[0165] n is a positive number (can be chosen as a natural number, rational number, real number, or complex number), and

[0166] ρ G,i is the i-th heat transfer surface 21 i Flue gas temperature T G,　in,　i It is the flue gas density that can be obtained from

[0167] A is the i-th heat transfer surface 21 i This is a cross-section of the flue gas channel.

[0168] i) Advantageously, n may be selected to include at least one of the following: a range of 0.9 to 1.1, preferably equivalent or about 1.0, for using the calculated gas rate;

[0169] ii) for use with calculated gas-induced erosion in the range of 2.9 to 3.5, preferably in the range of 3.2 to 3.35; or

[0170] iii) For pressure loss to be used, range from 1.8 to 2.2, preferably equivalent or about 2.0.

[0171] The value of n may change over time. In particular, the value for n may be determined from a group of reactors that includes at least two separate reactors (10), and thus operational data monitored for each reactor (10) is used in the determination.

[0172] In the calculation of Step I), an arbitrary selected numerical value Q for the boiler load h, candidate Gas outlet temperature T under G, exit The calculated value for is the following equation

[0173] T G, exit = α0 + Σα j (Q h, candidate ) j

[0174] Or, preferably, it can be estimated by its first, second, third, or higher-order approximation. The coefficients α0, α1, α2, ... are a plurality of individual reactor loads Q h Year gas outlet temperature T for the values G, exit The values ​​were measured and fitted to be calculated in advance.

[0175] In Step II), components q when the process in the reactor is the combustion of fuel m,G,m The calculation preferably includes at least some, most preferably all, of the following to determine the gas mass flow: m = CO2, H2O, N2, SO2, O 2. That is, in step IV of the calculation, q m,G,m As values, q m,G,CO2 , q m,G,H20 , q m,G,N2 , q m,G,SO2 ,q m,G,O2 Some or all of them may be used. It is preferable that they be measured in a gas conduit (18) or a chimney (19), and for this reason, a sensor suitable for the gas passage is installed. In step II), the component values ​​may further include parameters of the reactant (such as fuel of an alkali oxide, such as CaO).

[0176] If the reactor is for a combustion process, the product gas, i.e., flue gas mass flow, is the flue gas component mass flow q calculated based on fuel analysis (approximate and final analysis of fuel), combustion air flow and / or recirculation gas flow according to boiler mass, and energy balance calculations. m,G,m It can be based on the calculation of the sums.

[0177] Preferably, the flue gas mass flow can be calculated and:

[0178]

[0179] That is, for example, the sums of the gas mass flow components CO2, H2O, N2, SO2, and O2 for the following years:

[0180] Here, for example, x C,fuel represents carbon in the fuel, that is, the first subscript indicates the component and the second subscript indicates the fuel or combustion air, and q m,fuel is the fuel flow, and q m,air is the combustion air flow and Mx represents the molar mass. Fuel characteristics utilized for flue gas mass flow components and combustion air characteristics are advantageous. Fuel moisture can be measured or calculated.

[0181] According to another embodiment of the present invention, where the reactor is a thermochemical reactor particularly based on a CaO / Ca(OH)2 hydration / dehydration reaction, the flow of H2O (vapor) and air as fluidizing gases is a mass flow of step II), which is a calculated mass flow in which components are calculated using the hydration and dehydration reactions.

[0182] According to another embodiment of the present invention, when the reactor is a waste gasifier, the gas flow determination can be provided in the same manner as in the case of a combustion process, but the gas composition may differ, including CO and H2 and some minor gasification products.

[0183] According to another embodiment of the present invention, where the reactor is a so-called carbon capture reactor, the fluidization device comprises a carbonation device and a calcination device appropriately connected to each other. The reactor is configured to reduce, in particular, CO2 from the gas to be purified by reaction with CaO (carbonator) and to calcine CaCO3 to produce substantially pure CO2 (calcinerator). The gas flow determination can be provided in the same manner as in the case of a combustion process, taking into account the combustion of fuel in the calciner, including the gas flow of oxygen and the gas to be purified.

[0184] Step b) can be performed remotely to the combustion boiler, for example, in a process intelligence system (205). Alternatively, step b) can be performed locally at the combustion boiler, preferably at an EDGE server (203).

[0185] One of the currently monitored process data and / or current loads may be obtained from real-time measurements, processed by filtering, or processed through averaging, trend calculation, or a combination thereof.

[0186] The acceptance conditions include the currently calculated maximum boiler instantaneous load Q h,max A hysteresis condition requiring a predefined minimum change before changing may be included.

[0187] The allowable condition is preferably at least one calculated flue gas coefficient df i df for each maximum value max,i Includes comparing with respect to the maximum value df max,i is a preset value and is preferably boiler-specific. Numerical value Q h, candidate is at least one calculated year gas coefficient df i df is the maximum value max,i It is rejected if it exceeds.

[0188] In a combustion boiler (10), the furnace (12) and associated passages (horizontal passage (15) and reverse passage (16)) define a flue gas flow path. The furnace (12) and passages (15, 16) have a plurality of heat transfer surfaces 21 in the flue gas flow path. i The combustion boiler (10) also has the current load Q of the combustion boiler. h It has measuring equipment for monitoring and additional measuring equipment for monitoring current process data.

[0189] A control system (DCS (201) and EDGE server (203), or DCS (201) remote process intelligence system (205), possibly with the participation of EDGE server (203)) is configured to perform a boiler control method.

[0190] The EDGE server (203) can be configured to process real-time measurement results of currently monitored process data and / or current load, i.e., filtering, averaging and / or trend calculation.

[0191] The control system locally controls the current calculated maximum boiler instantaneous load Q at the combustion boiler (10). h,max To determine, perform method step b), and / or perform method step b) and the current calculated maximum boiler instantaneous load Q on the control system h,max It may be configured to transmit data to a remote, preferably cloud-based (e.g., computing cloud (206)), computing system (e.g., process intelligence system (205)) configured to return. The control system may then use a display / monitor to display information to the boiler operator, as in method step c), for example, by displaying information.

[0192] The EDGE server (203) can be configured to reduce the amount of measurement data transmitted to the remote computing system.

[0193] The combustion boiler calculation system comprises a group of combustion boilers (10), and each combustion boiler (10) comprises a boiler control system (CS) including an EDGE server (203) system configured to process real-time measurement results for currently monitored process data and / or current load, i.e., by filtering, averaging, and / or trend calculation, 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 the processed data from the real-time measurement results, calculate the data using a numerical boiler model for each combustion boiler (10), and return the calculation results for each combustion boiler (10). The boiler control system may be configured to adapt its functions based on the calculation results.

[0194] The computing system preferably uses currently monitored process data along with a numerical model of the boiler to calculate at least one flue gas coefficient df i The current calculated maximum boiler instantaneous load Q that satisfies the allowable conditions h,max Numerical value Q for h, candidate Discover, and the corresponding numerical value Q h, candidate The current calculated maximum boiler instantaneous load Q h, max It can be configured to be selected as.

[0195] The boiler calculation system may be configured to adapt or calibrate a numerical model for the boiler using processed measurement data for the combustion boiler (10). Alternatively or additionally, the boiler calculation system may be configured to adapt or calibrate a numerical model for the combustion boiler (10) using processed measurement data collected from other combustion boilers (10).

[0196] Figure 5 shows a modification of the method illustrated in Figure 4. Steps L1, L3, L7, and L9 are identical to steps K1, K3, K9, and K11, respectively, but in step L5, the product gas coefficients df i is all heat transfer surface 20 i It can be calculated directly for: temperatures T G,in,i Each of these 21 temperature sensors i When measured using, inverse calculation is not required, so step K7 can be omitted in the method shown in FIG. 5.

[0197] Figure 6 shows the use of possible inputs for the numerical boiler model in step N1. In step N3, Q h,max is numerically calculated using a boiler model, and the maximum load Q estimated in step N5 h,max It is presented to the boiler operator through a specific user interface (UI), preferably through a display / monitor (202).

[0198] Figure 7 shows the boiler instantaneous load Q h and calculated current maximum boiler instantaneous load Q h, max ...in addition, the effect of using the method according to the present invention during the test period is illustrated. 120MW during a 10-day test period th Boiler power averages 3 to 6 MW at loads outside the test period. th A higher load was obtained. Figure 8 illustrates a 10-day test period in more detail.

[0199] That is, regarding the control method applied to the boiler, the currently calculated maximum instantaneous boiler load Q of the combustion boiler h,max It is estimated using a numerical model that utilizes the determined boiler operating parameters. Current boiler load Q h It is calculated using steam circuit measurement data.

[0200] After that, boiler load Q hQ is the currently calculated maximum boiler instantaneous load h,max If it is smaller, i) the boiler operator is indicated that the boiler load may be increased, and / or ii) the boiler load is automatically increased. Alternatively or additionally, the boiler load Q h If the boiler's maximum instantaneous load Qh,max is greater than, i) the boiler operator is notified that the boiler load exceeds the boiler's maximum instantaneous load, and / or ii) the boiler load is automatically reduced.

[0201] The present invention has been described above with reference to the combustion processes of CFB and BFB. It is evident that the invention is likewise applicable to CFB and BFB gasifiers configured, for example, so that cooled product gases are used as desired instead of flue gases. Naturally, in gasification applications, the product gases are stored or transferred to desired additional processing steps instead of being released into the atmosphere through a chimney.

[0202] The heat dissipation reactor may be, for example, a waste gasification reactor or a carbon capture reactor.

[0203] The process requires at least a controllable input flow (22, 23) arranged in a suitable location within the reactor (10) as follows.

[0204] Thermochemical reactor, CaO (generally alkali metal oxide) hydration

[0205] i. Input of reactants such as CaO and hydration reactors

[0206] ii. Input of H2O (steam)

[0207] iii. Ca(OH)2 dehydration reactor

[0208] iv. Air injection

[0209] Waste gasifier reactor

[0210] i. Corresponding input streams as in the combustion process,

[0211] ii. CO and H2 and some minor gasification products.

[0212] According to another embodiment of the present invention, where the reactor is a so-called carbon capture reactor, a fluidized bed carbonator and a calciner are appropriately connected to each other, and this can be placed within the CFB reactor as shown in FIG. 1. The reactor is configured to reduce, in particular, CO2 from the gas to be purified by reaction with CaO (carbonator) and to produce substantially pure CO2 by calcining CaCO3 (calcinerator). The gas flow determination can be provided in the same manner as in the case of a combustion process, taking into account the combustion of fuel in the calciner, including the gas flow of oxygen and the gas to be purified.

[0213] FIG. 9 illustrates a heat dissipation reactor (10) according to one embodiment of the present invention. The heat dissipation reactor may be a thermochemical reactor, such as, for example, a CaO (generally an alkali metal oxide) hydration reactor. The reactor (10) comprises a reactor chamber (12) enclosed by walls (13), which may be optionally cooled walls connected to a fluid circuit to extract heat from a process running in the reactor chamber, depending on the actual application.

[0214] The reactants are supplied into the reaction chamber (12) through the reactant inlet (22). The reaction can be controlled by controlling the reactant supply inlet (22) and controlling process variables generally related to the heat release process of the reactor (10).

[0215] The reaction product, which may generally be referred to as product gas, flows from the reactor chamber (12) into the gas flow channel (16). The reactor (10) may also be provided with an outlet (22') for solid material that has been at least partially reacted in the reactor chamber (12). Although it is described here as a vertical passage, it may be designed differently, such as horizontally. From the product gas flow channel, the product gas is directed to further processing (19), which may include simple storage or gas transfer piping.

[0216] The product gas flow channel (16) is a plurality of heat transfer surfaces 21 i Includes (where i = 1, 2, 3, ..., k, where k is the number of heat transfer surfaces). FIG. 9 includes heat transfer surfaces 211, 212, 213, ..., 21 k-1 , 21 k This is exemplified. For example, the actual number of different heat transfer surfaces of each of these components can be selected differently for each reactor (10) according to actual needs.

[0217] Last heat transfer surface 21 k The product gas discharged from is at the outlet temperature T G,　exit It will be at. This temperature is temperature sensor 20 k It is measured as.

[0218] According to one embodiment, each heat transfer surface 21 i Temperature before and after (each T G,in,i , T G,in,i+1 ) are each temperature sensor 20 i (Here, i = 1, 2, 3, …, k-1, k) can be measured.

[0219] However, according to another embodiment, and preferably, it is not necessary to measure this temperature. Flue gas outlet temperature T G,　exit It is sufficient to know each previous heat transfer surface 21 iTemperatures before and after (T G,in,i , T FG,in,i+1 ) can be calculated numerically. This will be explained further below.

[0220] The reactor (10) is equipped with a plurality of sensors (40) and computer devices, and for clarity, only some of the sensors and computer devices are shown. For example, depending on the actual practical application of the reactor (10), there may be a sensor (40) for determining the flow rate of the reactants into the reactor (10), a sensor (40) indicating the temperature at one or more locations of the reactor (10), and a sensor (40) indicating the pressure of the reactor.

[0221] Process data can be collected from sensors by a distributed control system (DCS) (201). This is similar to that disclosed in relation to FIG. 1, and of course, the combustion boiler network is a heat release network that operates in a corresponding manner instead.

[0222] It is obvious to those skilled in the art that the basic concept of the present invention can be implemented in various ways depending on technological advancements. The present invention and its embodiments are not limited to the examples and samples described above, and various modifications are possible according to the claims and their legal equivalents.

[0223] Additionally, or instead of using the specific empirical formulas mentioned above, it is possible to utilize artificial intelligence tools and / or neural networks for numerical model calculations.

[0224] In the following claims and the foregoing description of the present invention, except where otherwise required by context due to expressive language or necessary connotations, variations such as the word "include," "comply," or "complying" are used in an inclusive sense. That is, they specify the presence of the mentioned features but do not exclude the presence or addition of additional features in various embodiments of the present invention.

Claims

Claim 1 A method for operating a heat-dissipating reactor that generates product gas, comprising: a) the current load (Q) of the reactor h a) a step of monitoring ) and b) at least one product gas coefficient (df) calculated using currently monitored process data together with a numerical model of the reactor. i The current computationally maximum instantaneous load (Q) that satisfies the allowable conditions h, max Numerical value for ) (Q h, candidate ) discover the above numerical value (Q h, candidate ) The currently calculated maximum instantaneous load (Q h, max Step of selecting as ); c) the above-mentioned current calculated maximum instantaneous load (Q h, max Display ) to the operator, and / or the above current load (Q h ) is c1) the above currently calculated maximum instantaneous load (Q h, max If smaller than ) c1i) The load (Q) to the above operator h Indicates that ) may be increased, and / or c1ii) the load (Q h A step of automatically increasing ) and / or c2) the above current calculated maximum instantaneous load (Q h, max If greater than ) c2i) The load (Q) to the above operator h ) indicates that the above-mentioned current calculated maximum instantaneous load exceeds, and / or c2ii) the load (Q h A method of operating a heat-dissipating reactor that generates product gas, comprising a step of automatically reducing ). Claim 2 In claim 1, i) the currently monitored process data of the reactor, ia) the current product gas outlet temperature (T) of the product gas flow channel G,exit,current ) and ib) heat load (Q) for each heat transfer surface (i) of the product gas flow channel. fluid,i ) including, and additionally, ii) monitored process data from both ia) and ib) at the time of calculating the product gas coefficient and the current calculated maximum instantaneous load (Q h, max The above numerical value (Q) for ) h, candidate A method of operating a heat-dissipating reactor that generates product gas, used when discovering ). Claim 3 In Article 1, The at least one product gas coefficient (df) calculated using currently monitored process data along with the numerical model of the reactor above i If ) does not satisfy the allowable conditions, the following numerical value (Q h, candidate A method of operating a heat-dissipating reactor that generates product gas, wherein the above discovery is performed so that ) is automatically selected. Claim 4 A method of operating a heat-dissipating reactor that generates product gas, wherein the above discovery is performed repeatedly in claim 3. Claim 5 In claim 1, the above discovery is the following calculation steps: - I: the load of the reactor is the numerical value (Q h, candidate When corresponding to ), the product gas outlet temperature (T) from the calculation model G, exit Step for calculating estimates for );- II: Product gas mass flow rate (q) using combustion air flow, recirculation gas flow, and energy balance equations according to reactor mass m,productgas Step to calculate ) ;- III: Numerical reactor model (Q fluid, i, candidate = Q fluid,i,current + Σα j,I (Q h,candidate ) j - Σα j,i (Q h, current ) j Current heat load (Q) corrected using ) fluid, i, current Using ) the heat load (Q) for each heat transfer surface within the product gas flow channel fluid, i, candidate Step of calculating );- IV: The calculated heat loads (Q) for each heat transfer surface within the product gas flow channel. fluid, i, candidate Using ), the estimate (T) for the product gas outlet temperature G,out,k = T G, exit Using ) the heat transfer surface 21 closest to the product gas outlet k Starting from, each heat transfer surface (T) within the product gas flow channel in the upstream direction of the product gas flow G,in,i , T G,out,i ; Step of calculating the product gas temperature at i = 1, ..., k); - V: the product gas coefficient (df) for each heat transfer surface within the product gas flow channel. i This is performed by carrying out the step of calculating , i = 1 , ..., k), and in the calculation of step I), the product gas outlet temperature is given by equation T G, exit = α0 + Σα j (Q h, candidate ) j Or estimated by his first, second, third, or higher-order approximations, and each coefficient (α0, α1, α2, ...) is a plurality of individual reactor loads (Q h Product gas outlet temperature (T) for ) values G, exit A method of operating a heat-dissipating reactor that generates product gas, which is obtained in advance by measuring and fitting the values. Claim 6 In claim 5, the product gas coefficient comprises or is as follows: Among the foods, k i is a positive non-zero parameter that can be selected depending on the type of reactor, and q m,productgas is the product gas mass flow rate, n is a model parameter that can be selected depending on the type of reactor, is a positive non-zero number, and ρ productG,i is the product gas density at the i-th heat transfer surface and A cross, i A method for operating a heat dissipation reactor that generates product gas, wherein the cross-sectional area of ​​the product gas channel at the i-th heat transfer surface. Claim 7 A method of operating a heat-dissipating reactor that generates product gas, wherein n is selected to be at least one of the ranges of 0.9 to 1.1, 2.9 to 3.5, and 1.8 to 2.

2. Claim 8 In claim 7, a method of operating a heat-dissipating reactor that generates product gas, wherein the value for n changes over time. Claim 9 In Article 7, A method for operating a heat-dissipating reactor that generates product gas, wherein the value for n is determined from operational data monitored for each of the reactors in a group of reactors comprising at least two individual reactors. Claim 10 In claim 5, in step II), the calculation of the product gas mass flow rate is the mass flow rate (q) of the product gas components (m). m,G,m A method of operating a heat-dissipating reactor that generates product gas using ). Claim 11 A method for operating a heat-dissipating reactor that generates product gas, wherein, in step II) the calculation of the product gas mass flow rate includes the use of reactant parameters to reflect changes in reactant characteristics. Claim 12 A method of operating a heat-dissipating reactor that generates product gas, wherein, in claim 1, step b) is performed remotely with respect to the reactor. Claim 13 A method of operating a heat-dissipating reactor that generates product gas, wherein step b) is performed locally at the reactor location. Claim 14 A method of operating a heat-dissipating reactor that generates product gas, wherein any of the currently monitored process data and / or current load is obtained from real-time measurements, processed by filtering, or processed through averaging, trend calculation, or any combination thereof. Claim 15 In Article 1, The above allowable condition is the above currently calculated maximum instantaneous load (Q h,max A method for operating a heat-dissipating reactor that generates product gas, including a hysteresis condition requiring a predefined minimum change before changing ). Claim 16 In Article 1, The above allowable conditions are the above At least one calculated product gas coefficient (df i The method includes comparing ) with respect to each design value, wherein, in the method, the numeric value (Q h, candidate ) is at least one calculated product gas coefficient (df) above i A method for operating a heat-dissipating reactor that generates product gas, which is rejected if ) exceeds the above design value. Claim 17 A method of operating a heat-dissipating reactor that generates product gas, wherein the reactor is a circulating fluidized bed (CFB) or bubble fluidized bed (BFB) reactor, and step b) is performed on heat transfer surfaces in the reactor and / or in the product gas channels. Claim 18 As a heat-dissipating reactor, - defines product gas flow paths and multiple heat transfer surfaces (21 i A reactor chamber (12) having ) and associated passages (15, 16); - current load (Q) of the heat dissipation reactor (10). h Measuring equipment for monitoring ); - Sensors for monitoring current process data (20, 20 i Additional measuring equipment such as , 30, 40, 116, 165, 650); and a heat dissipation reactor comprising a control system (CS; 201, 203, 205) configured to perform a method of operating the heat dissipation reactor described in any one of claims 1 to 17. Claim 19 In Article 18, A heat dissipation reactor, wherein the control system (CS) comprises an edge server (203) configured to process currently monitored process data and / or real-time measurement results of the current load through filtering, averaging, and / or trend calculation. Claim 20 In Article 18, The above control system is the current calculated maximum instantaneous load (Q h,max A heat dissipation reactor configured to perform method step b) to determine ) locally. Claim 21 In Article 19, The control system transmits data to a remote computing system configured to perform method step b), and the current computationally maximum instantaneous load (Q h,max A heat dissipation reactor configured to return ) to the above control system. Claim 22 In claim 21, the edge server is a heat dissipation reactor configured to reduce the amount of measurement data transmitted to the remote computing system. Claim 23 A heat dissipation reactor calculation system comprising: - a group of reactors (10) described in claim 18, wherein each reactor comprises a control system comprising an edge server (203) system configured to process real-time measurement results for currently monitored process data and / or current load by filtering, averaging and / or trend calculation, and to transmit the processed real-time measurement results to a remote computing system (205); - a remote computing system (205) configured to receive data processed from real-time measurement results, calculate data using a numerical model for each of the reactors (10), and return calculation results for each of the heat dissipation reactors (10); and additionally, wherein the control system is configured to adapt the function of the control system based on the calculation results. Claim 24 In claim 23, the computing system calculates at least one product gas coefficient (df) using currently monitored process data together with a numerical model of the reactor. i The current computationally maximum instantaneous load (Q) that satisfies the allowable conditions h,max Numerical value for ) (Q h, candidate ) discover the above numerical value (Q h, candidate ) The currently calculated maximum instantaneous load (Q h, max A heat dissipation reactor calculation system configured to be selected as ). Claim 25 In Article 23, The above-described heat dissipation reactor calculation system is configured to calibrate a numerical model for the heat dissipation reactor (10) using processed measurement data for the heat dissipation reactor (10). Claim 26 In claim 23, the heat dissipation reactor calculation system is configured to correct a numerical model for the heat dissipation reactor (10) using processed measurement data also collected from other heat dissipation reactors (10). Claim 27 delete

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