Method for determining leaks in heat transfer fluid channels of a heat transfer reactor system, and heat transfer reactor - Patents.com

Abstract

To improve leak detection in the heat transfer fluid channels of a heat transfer reactor system. In order to improve tube leak detection, a tube leak determination method is used in a heat transfer fluid channel of a combustion heat transfer reactor system (10), the method comprising: determining a main heat transfer fluid flow rate (q MS,M ) and measuring the flow rate of the primary heat transfer fluid (q MS,C ) flow rate in the heat transfer fluid channels during operation by utilizing process data in a numerical model of the combustion heat transfer reactor system (10). MS,C ) and modeling the error measure (Δ MS comparing the measured heat transfer fluid flow rate and the modeled heat transfer fluid flow rate to obtain a heat transfer fluid circuit leak probability (FPR) and monitoring a set of error measures and a characteristic of a set of error measures that exceed a predetermined threshold for a predetermined period of time during operation to determine the presence of a heat transfer fluid circuit leak.

Description

[Technical Field]

[0001] The present invention relates to the detection and assessment of leaks in heat transfer fluid channels of heat transfer reactors, and in particular to fluidized bed reactors such as circulating fluidized bed (CFB) reactors or bubbling fluidized bed (BFB) reactors. [Background technology]

[0002] Combustion boilers, such as grate boilers and fluidized bed boilers, are commonly used to generate steam that can be used for a variety of purposes, such as to generate electricity and heat.

[0003] In a fluidized bed boiler or gasifier, a hot bed of fuel and solid particle fluidizing material is introduced into a furnace, and fluidizing gas is introduced into the bottom of the furnace to fluidize the fluidizing material and fuel. Combustion of the fuel takes place in the fluidized bed. In a bubbling fluidized bed reactor, the fluidizing gas passes through the bed so that most of the solid material remains within the bed.

[0004] In a circulating fluidized bed reactor (CFB), a fluidizing gas passes through a fluidizing medium. Most of the fluidizing particles become entrained in the fluidizing gas and are carried away with the flue gas. The particles are separated from the flue gas in at least one particle separator and circulated back into the reactor chamber. A fluidized bed heat exchanger is often located downstream of the particle separator to recover heat from the particles before they are returned to the furnace.

[0005] Generally, in heat transfer reactors, a flow channel leak can result in the heat transfer fluid leaking out of the heat transfer fluid circuit, which can then enter the reactor location in an uncontrolled manner. In the worst case, the leak can cause the need for extensive repairs of the reactor. In the majority of leak situations, the consequences are not too severe, at least if the leak is detected reasonably quickly.

[0006] A leak in a flow channel typically requires shutting down the reactor, locating the leak, and repairing or replacing the pipe (or flow channel in general) where the leak occurs. From the standpoint of plant operators, this can be an expensive procedure. Not only because of the expense incurred in locating the leak and then repairing or replacing the pipe, shutting down the reactor also stops the production of heat transfer fluid (which could be used to produce commercial goods), generally resulting in a loss of income for the operators during the shutdown. In view of the resulting costs and loss of heat transfer fluid production capacity, it is important to avoid unnecessary shutdowns. Leak detection should be performed with high reliability.

[0007] As an example of leak detection, the applicant's CFB boiler leak detection system is disclosed in Modern Power Systems' (www.modernpowersystems.com) December 2018 article, "Boiler Technology - SmartBoiler™: how the Internet of Things can improve boiler operating performance." The boiler leak detection module closely monitors furnace walls and other boiler heat exchange surfaces and predicts future problems based on regression models and self-learning algorithms using real-world process data. This allows for proactive maintenance planning and minimized restoration time. Summary of the Invention [Problem to be solved by the invention]

[0008] The objective is to improve leak detection in the heat transfer fluid channels of a heat transfer reactor system.

[0009] This object can be achieved by a method according to independent claim 1 and by a heat transfer reactor according to independent claim 13.

[0010] The dependent claims describe advantageous aspects of the method. [Means for solving the problem]

[0011] A method for determining leaks in heat transfer fluid channels of a heat transfer reactor system includes: The main heat transfer fluid flow rate Q prevailing in the heat transfer fluid circuit of the heat transfer reactor system during operation MS,M and measuring Heat transfer fluid q of a heat transfer reactor system under substantially leak-free conditions MS,C The main heat transfer fluid flow rate, q, in the heat transfer fluid channel during operation is determined by utilizing process data in a numerical model of the heat transfer reactor system that provides the flow rate. MS,C and modeling the comparing the measured heat transfer fluid flow rate and the modeled heat transfer fluid flow rate to obtain an error measure ΔMS for the heat transfer fluid flow rate included in an error measure set; monitoring the set of error measures and characteristics of the set of error measures for exceeding a predetermined threshold during a predetermined time period during operation to determine the presence of a heat transfer fluid circuit leak; Equipped with.

[0012] The method allows for improved leak detection in the heat transfer fluid circuit of a heat transfer reactor system. Even though there may be large variations in the heat transfer fluid flow rate between successive measurements, with an appropriate numerical model of the heat transfer reactor system, the main heat transfer fluid flow rate can be calculated numerically under substantially leak-free conditions quickly enough that the error measure ΔMS indicates the presence of a tube leak with sufficient probability. In a heat transfer reactor, the heat transfer devices and the channels connecting them may be generally referred to as a fluid circuit.

[0013] Furthermore, properly arranging the characteristic monitors allows for the predetermined threshold to be selected such that i) a sufficiently large error measure ΔMS (e.g., exceeding a predetermined threshold) will result in a determination of a heat transfer fluid circuit leak sooner than a smaller error measure ΔMS, and ii) a smaller error measure ΔMS, if persisted for a predetermined time (or number of measurements), will also result in a determination of a heat transfer fluid circuit leak. The selection of this characteristic monitor, and more particularly the selected "boosting factor" technique developed by the inventors and used in monitoring the error measure set and characteristics of the error measure set, contributes significantly to the functionality of the method.

[0014] The "boosting factor" approach reflects the inventor's observation that leaks in the heat transfer fluid circuit of a heat transfer reactor system can develop gradually, i.e., start as small leaks. Small leaks, if unnoticed, can become large leaks within some time. Reliably detecting small leaks in terms of large fluctuations or differences in heat transfer fluid measurements has not previously been possible without the use of specific markers in the heat transfer fluid channels. Thus, leaks have historically tended to be reliably detected only after the leak has become sufficiently severe. However, this tends to increase the effort required to repair the heat transfer reactor system. The present invention can improve leak detection reliability, thereby enabling leaks to be detected quickly while helping to avoid false alarms (which can lead to unnecessary shutdowns and costly unused time for the heat transfer reactor system).

[0015] The heat transfer fluid flow rate is preferably measured after the last or final heat exchanger in the heat transfer fluid channel, which represents the final temperature of the heat transfer fluid.

[0016] The error measure ΔMS for the main heat transfer fluid flow rate is the error measure ΔMS for the measured heat transfer fluid flow rate (q MS,MESURED ) and the calculated heat transfer fluid flow rate (q MS,COMPUTED ) and the difference (ΔMS=q MS,MESURED -q MS,COMPUTED) is preferred.

[0017] Alternatively, the error measure ΔMS for the main heat transfer fluid flow rate may be calculated by multiplying the measured heat transfer fluid flow rate (q MS,MESURED ) and the calculated heat transfer fluid flow rate (q MS,COMPUTED ) may be a ratio.

[0018] These aspects are such that the error measure ΔMS for the main heat transfer fluid flow rate is: The measured heat transfer fluid flow rate (q MS,MESURED ) and the calculated heat transfer fluid flow rate (q MS,COMPUTED ) and the difference (ΔMS=q MS,MESURED -q MS,COMPUTED ) and / or The measured heat transfer fluid flow rate (q MS,MESURED ) and the calculated heat transfer fluid flow rate (q MS,COMPUTED ) may be combined.

[0019] When using the method in a heat transfer fluid channel or a circuit of a heat transfer reactor, the method comprises: measuring at least one process parameter across at least one location in a reaction chamber of the reactor system; modeling at least one of the corresponding process parameters during operation of the reactor system by utilizing the process data in a numerical model that provides the corresponding process parameter of the reactor system under substantially leak-free conditions; comparing the at least one measured process parameter and the corresponding at least one modeled process parameter to each other to obtain an error measure for at least one process parameter that is also included in the error measure set; may further comprise:

[0020] Using this approach, measurements can be placed in the reaction chamber to improve the accuracy of the method and / or also include detection of leaking reactor system components. Most conveniently, the process parameters comprise or consist of at least one of temperature and / or pressure.

[0021] The error measure for the at least one process reaction chamber parameter may be the difference between the measured process parameter and the modeled process parameter.

[0022] Alternatively, the error measure for the at least one process reaction chamber parameter may be a ratio of the measured process parameter to the modeled process parameter.

[0023] These may also be combined, so that the error measure for at least one process reaction chamber parameter may be the difference between the measured process parameter and the modeled process parameter and / or the ratio of the measured process parameter to the modeled process parameter.

[0024] According to one embodiment of the present invention, the characteristics of the error measure set may comprise a number of occurrences exceeding a predetermined threshold during a predetermined time period while driving.

[0025] One embodiment of the present invention is a circulating fluidized bed (CFB) reactor system, although the present invention can be implemented in other types of systems as well.

[0026] In the case of a CFB reactor system, the process parameter measured at at least one internal location preferably comprises or consists of the pressure in a return section, or in other words a loop seal, located downstream of the particle separator in the return channel, which is arranged to return the separated particles into the reaction chamber.

[0027] In this situation, the method preferably comprises monitoring the number of occurrences of an error measure for the main heat transfer fluid flow rate exceeding a predetermined threshold, the number of occurrences being included in the characteristic of the error measure, and the method further comprises monitoring the number of occurrences of an error measure for pressure at the loop seal exceeding a predetermined threshold, the number of occurrences being included in the characteristic of the error measure. A heat transfer fluid circuit leak may then be determined to be in the loop seal if i) the number of occurrences of the error measure for the heat transfer fluid flow rate and the error measure for the main heat transfer fluid flow rate exceed a predetermined threshold, and further ii) the number of occurrences of the error measure for pressure at the loop seal and the loop seal parameter for pressure at the loop seal exceed a predetermined threshold.

[0028] In the case of a CFB reactor system, the process parameter measured at at least one location within the reactor preferably includes or consists of the product gas temperature at the outlet of the particle separator.

[0029] In this situation, a leak is preferably determined to be in the particle separator when i) both the error measure for the main heat transfer fluid flow rate and the number of occurrences of the error measure for the main heat transfer fluid flow rate exceed predetermined thresholds for the corresponding error measures, respectively, and further, ii) both the error measure for the product gas temperature at the outlet of the particle separator and the number of occurrences of the product gas temperature at the outlet of the particle separator exceed predetermined thresholds for the product gas temperature error measure, respectively.

[0030] For a CFB reactor system, the process parameter measured at at least one location within the reactor preferably includes or consists of the bed temperature in a fluidized bed heat exchanger comprising the heat exchanger.

[0031] Common to all aspects and embodiments of the method is that the characteristic of the error measure may include or consist of the respective number of occurrences that exceed a predetermined threshold.

[0032] The heat transfer reactor system includes a local control system and / or is connected to a remote control system, the control system being configured to implement the leak determination method, and the heat transfer reactor system further includes an indication means, such as a display / monitor, for indicating to an operator the presence of a tube leak detected using the method.

[0033] The method and reactor system will now be described in more detail with reference to exemplary embodiments disclosed in the accompanying drawings. [Brief explanation of the drawings]

[0034] [Figure 1] FIG. 1 illustrates a CFB reactor system. [Figure 2] FIG. 1 illustrates a BFB reactor system. [Figure 3] FIG. 1 illustrates a calibration method for a numerical model in a CFB reactor system. [Figure 4] FIG. 1 illustrates the training of mathematical models and the possibility of using data. [Figure 5] FIG. 10 illustrates the calculation of leakage risk. [Figure 6A] FIG. 1 shows selected data from a test in which the method was applied to real CFB boiler system data to verify the function of the method. [Figure 6B] FIG. 1 shows selected data from a test in which the method was applied to real CFB boiler system data to verify the function of the method. [Figure 6C] FIG. 1 shows selected data from a test in which the method was applied to real CFB boiler system data to verify the function of the method. [Figure 6D] FIG. 1 shows selected data from a test in which the method was applied to real CFB boiler system data to verify the function of the method. [Figure 6E]FIG. 1 shows selected data from a test in which the method was applied to real CFB boiler system data to verify the function of the method. [Figure 6F] FIG. 1 shows selected data from a test in which the method was applied to real CFB boiler system data to verify the function of the method. [Figure 6G] FIG. 1 shows selected data from a test in which the method was applied to real CFB boiler system data to verify the function of the method. [Figure 6H] FIG. 1 shows selected data from a test in which the method was applied to real CFB boiler system data to verify the function of the method. [Figure 6I] FIG. 1 shows selected data from a test in which the method was applied to real CFB boiler system data to verify the function of the method. DETAILED DESCRIPTION OF THE INVENTION

[0035] In all figures, the same reference numbers refer to the same technical features.

[0036] FIG. 1 illustrates a heat transfer reactor system 10. More specifically, FIG. 1 discloses a circulating fluidized bed (CFB) boiler in which heat is generated by the combustion of fuel and transferred to a heat transfer fluid (water-steam). The reactor 10 includes tube walls and various heat exchangers through which the heat transfer fluid (water-steam) flows to receive the heat obtained from the combustion of the fuel. Thus, a CFB boiler is an example of a heat transfer reactor. The reactor includes a reactor space 12, specifically a furnace 12 including tube walls 13 (typically having a front wall, a rear wall, and a side wall) connected to the heat transfer fluid (water-steam) circuit of the combustion boiler system 10. FIG. 1 illustrates the case of a once-through steam generator in which water is delivered from a water supply tank 50 to an evaporator (furnace wall) and then led to a turbine (not shown) via a superheater. An economizer and / or superheater may be provided in the flue gas channel.

[0037] A fluidizing gas (such as air and / or an oxygen-containing gas) is delivered to the reactor from fluidizing gas supply 153. This fluidizing gas is delivered to the reactor chamber 12 via primary fluidizing gas feed 151, typically through a nozzle in grid 250 so that the primary fluidizing gas enters the reactor space, to fluidize the fluidizing medium, and via secondary gas feed 152 to deliver gas to control the reaction in the reactor. The effect of this is to fluidize the fluidizing medium and also to provide gas into reactor 12 necessary for the reaction. Additionally, fuel or other reactants are delivered into reactor chamber 12 via feed inlet 22.

[0038] The reaction in the chamber can be adjusted by controlling the reactant feed 22 by decreasing or increasing the feed rate, and by controlling the fluidizing gas feed by decreasing or increasing the gas flow rate into the reactor chamber 12. Specifically, when the reactor is used for fuel combustion, the fuel can be fed with additives, particularly additives that act as alkaline sorbents, such as CaCO and / or clay. Additionally or alternatively, a NOx reducing agent, such as ammonium or urea, can be fed into or above the combustion zone of the furnace 12.

[0039] A fluidizing medium may also be fed into and removed from the reactor, and may comprise sand, limestone, and / or clay, particularly kaolin, and oxides of alkali metals such as CaO, depending on the practical application. One effect of fluidization, and combustion in general, is that the heat transfer fluid is heated more efficiently when a hot surface interacts with the fluidized bed.

[0040] The so-called bottom ash (or any particles that may not be fluidized) falls to the bottom of the reactor 12 and can be removed via a chute (omitted from FIG. 1 for clarity). Some solid media, particularly lighter particles, will be carried along with the product gas.

[0041] Reaction products, such as product gases and lighter particles, pass from reactor 12 to particle separator 17, which may include a vortex finder 103. Particle separator 17 separates solid particles from the product gases. The product gases may vary depending on the reaction taking place in reactor system 10.

[0042] When the reactor is a CFB boiler, the combustion products such as flue gases, unburned fuel, and fluidized medium pass from the furnace 12 to a particle separator 17, which may comprise a vortex finder 103. The particle separator 17 separates the flue gases from the solids. In particularly large combustion boilers 10, there may be more than one (two, three, ...) separators 17, which are preferably arranged in parallel.

[0043] The solids separated by separator 17 pass through a loop seal 200, which is preferably located at the bottom of separator 17. The solids may then pass to a fluidized bed heat exchanger (FBHE) 100, which also includes a heat transfer surface (e.g., comprising, but not limited to, tubes and / or heat transfer panels), such that the FBHE 100 receives heat from the solids to further heat the heat transfer fluid in the heat transfer fluid circuit.

[0044] The FBHE 100 may be fluidized, may include heat transfer tubes or other types of heat transfer surfaces, and may be configured as a reheater or superheater. The solids may exit the FBHE 100 and return into the reactor 12 via a return channel 102.

[0045] The gases produced by the combustion process, called flue gases, pass from the separator 17 to a crossover duct 15, from which they pass further to a back passage 16 (which may preferably be a vertical passage) and from there via a gas duct 18 to a chimney 19. If the gases are to be utilized in other ways, they are collected and directed to further processing.

[0046] The rear passage 16 comprises several heat transfer surfaces 21i (where i=1, 2, 3, ..., k, where k is the number of heat transfer surfaces). In FIG. 1, the heat transfer surfaces 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251 k is shown. For example, the actual number of different heat transfer surfaces on each of these components may be selected differently for each combustion boiler according to actual needs. There may also be additional components with heat transfer surfaces 21. The heat transfer devices and the channels connecting them are generally referred to as a fluid circuit.

[0047] The heat transfer reactor system 10 includes multiple sensors and a computer unit. FIGS. 1 and 2 show some of the sensors and computer unit. Examples of sensors include a heat transfer fluid flow rate sensor 260 that measures the heat transfer fluid temperature at the FBHE 100 outlet 101, a temperature sensor 280 that measures the bed temperature in the FBHE 100 chamber, a temperature sensor 270 that measures the product gas outlet temperature at the separator 17, a temperature sensor 290 that measures the temperature in the loop seal 200, and / or a pressure sensor 291 that measures the pressure in the loop seal 200. The FBHE may also be equipped with a pressure sensor to measure the pressure in the FBHE chamber. In the embodiment shown in FIG. 1 , the FBHE 100 is the final heat exchanger from which the heat transfer fluid is directed for further processing via the FBHE outlet 101. The final heat exchanger in the heat transfer fluid channel can also be located elsewhere in the reactor system 10, if desired.

[0048] Process data can be collected from sensors by a distributed control system (DCS) 301. Data collection may be most conveniently located via, for example, fieldbus 370. The DCS 301 may have a display / monitor 302 for displaying operational status information to operators. An EDGE server 303 can process the measurement data from the sensor acquisitions, for example, by filtering and smoothing. There may be local storage 304 for saving the data.

[0049] The DCS 301, display / monitor 302, EDGE server 303, and local storage 304 may be in a reactor network 380 (with local storage 304 preferably connected directly to the EDGE server 303). The reactor network 380 is preferably separate from the fieldbus 370 used to communicate measurements from sensors to the DCS 301 and / or EDGE server 303. To make the systems more interoperable, there may be an open platform communication server between the DCS 301 and the EDGE server 303.

[0050] The reactor network 380 may be connected to the Internet 306, preferably via a gateway 305. In this situation, measurement results can be transmitted from the reactor network 380 to a cloud service, such as a process intelligence system 308 located in a computation cloud 207. The applicant currently operates a cloud service that runs the analytics platform. The cloud service may be operated, for example, on a virtual server environment such as Microsoft® Azure®, which is a virtualized and easily scalable environment for distributed computing and cloud storage of data. Other cloud computing services may also be suitable for running the analytics platform. Furthermore, instead of or in addition to a cloud computing service, a local or remote server can be used to run the analytics platform.

[0051] Figure 2 shows a reactor system 10, which may be a bubbling fluidized bed BFB reactor. BFB reactors differ from CFB reactors in that the fluidization velocity is lower than that of a CFB. Therefore, there may be no need for a separator 17, loop seal 160, FBHE 100, and return channel 102.

[0052] Typically, there is at least one heat exchanger 14 within the reactor chamber 12, preferably located at the top of the chamber 12. A temperature sensor 240 measures the temperature at the heat exchanger outlet 144. Specifically, the heat transfer fluid flow rate sensor 240 measures the heat transfer fluid flow rate at the heat exchanger outlet 144, which is the last heat exchanger in the reactor system 10 from which the heat transfer fluid is directed for further processing.

[0053] A method for determining leaks in a heat transfer fluid circuit of a heat transfer flow reactor system 10 comprises: The heat transfer fluid flow rate q prevailing in the heat transfer fluid circuit of the reactor system 10 during operation MS,M and measuring The heat transfer fluid flow rate q of the reactor system 10 under substantially leak-free conditions MS,C The heat transfer fluid flow rate q in the heat transfer fluid circuit during operation can be calculated by utilizing process data in a numerical model of the reactor system 10 that gives MS,C and modeling the The error measure Δ for the heat transfer fluid flow rate included in the error measure set MS comparing the measured and modeled heat transfer fluid flow rates to each other to obtain monitoring the set of error measures and characteristics of the set of error measures for exceeding a predetermined threshold during a predetermined time period during operation to determine the presence of a heat transfer fluid circuit tube leak; Equipped with.

[0054] The method is: measuring at least one process parameter across at least one location within the reactor system; modeling at least one of the corresponding process parameters during operation of the heat transfer reactor system 10 by utilizing the process data in a numerical model that provides the corresponding process parameter of the reactor system 10 under substantially leak-free conditions; comparing the at least one measured process parameter and the corresponding at least one modeled process parameter to each other to obtain an error measure for at least one process parameter that is also included in the error measure set; may further comprise:

[0055] The process parameters may comprise or consist of at least one of temperature and / or pressure.

[0056] CFB reactor, loop seal 290: The process parameter may include or consist of the pressure at the loop seal 290, which is located downstream of the particle separator 17 in a return section configured to return separated particles into the reactor chamber 12. The method then preferably includes monitoring the number of occurrences of an error measure for the main heat transfer fluid flow rate exceeding a predetermined threshold. The number of occurrences of the exceedance is included in the characteristic of the error measure. The method further includes monitoring the number of occurrences of an error measure for the pressure at the loop seal exceeding a predetermined threshold, the number of occurrences of the exceedance being included in the characteristic of the error measure. A heat transfer fluid circuit leak is determined to be in the loop seal if the error measure for the main heat transfer fluid flow rate and the number of occurrences of the error measure for the main heat transfer fluid flow rate exceed a predetermined threshold, and further if the number of occurrences of the error measure for the pressure at the loop seal and the loop seal parameter for the pressure at the loop seal exceed a predetermined threshold.

[0057] CFB reactor, separator 17: The process parameter may include or consist of the product gas temperature at the outlet of the particle separator. A leak is then preferably determined to be in the particle separator when both the error measure for the heat transfer fluid flow rate and the number of occurrences of the error measure for the heat transfer fluid flow rate exceed predetermined thresholds for the corresponding error measures, and further when both the error measure for the product gas temperature at the outlet of the particle separator and the number of occurrences of the flue gas temperature at the outlet of the particle separator exceed predetermined thresholds for the product gas temperature error measure, respectively.

[0058] FBHE100: The process parameters may include or consist of bed temperature in a heat transfer fluidized bed heat exchanger comprising a heat exchange surface.

[0059] Steam Generation Process, Superheater 14: The process parameters may include or consist of the bed temperature of a BFB boiler system that is a fluidized bed heat exchanger with a superheater heat transfer surface.

[0060] A leak can be determined in a fluidized bed heat exchanger 100 operating as a steam reheater connected between turbine stages when both the error measure of the bed temperature and the number of occurrences of the error measure each exceed a predetermined threshold, preferably without requiring that the error measure for the main steam (heat transfer fluid) flow rate exceed a respective threshold, since the reheater is located after the heat transfer fluid circuit.

[0061] Common to all embodiments is that the characteristic of the error measure may include or consist of the respective number of occurrences that exceed a predetermined threshold.

[0062] Common to all embodiments is that the exceedance is tested within an evaluation time window, which may be a suitably chosen time interval such as the last 60 minutes.

[0063] As explained above, the heat transfer reactor system 10 includes local control systems 301, 303 and / or is connected to a remote control system 308. The control systems are configured to implement the leak determination method. The reactor system 10 includes an indication means, such as a display / monitor 302, for indicating to boiler operators the presence of a tube leak detected using the method.

[0064] FIG. 3 shows an example of a model building or calibration process.

[0065] After a start-up (step A1) of model building or calibration at the beginning, in step A3 a numerical model is constructed, for example by means of a regression model, of the heat transfer fluid balance in the reactor system 10. The model may differ depending on the type of reactor system 10, for example:

[0066] In a steam boiler, the equation for the water / steam balance, for a drum boiler, is: q ms,c =a0+a1q fw +a2Dt(q fw )+a3q cbd +a4q sbd +a5Dt(DL) In the above equation, q ms,c is the modeled main steam flow rate, q fw is the feedwater flow rate, which can be measured, for example, before the economizer; Dt(q fw ) is Dt (feedwater flow rate), which is the time derivative of the feedwater flow rate (how much the feedwater flow rate changes in a specific time), q cbd is the constant blowdown flow rate from the steam and is the water discharged from the drum, q sbd is the soot blowing steam flow rate, which may be steam from the superheater flow path before the final superheater; Dt(DL) is Dt(drum level), which is the time derivative of the drum level (how much the drum level changes in a specific time), a0, a1... a5 are calibration coefficients determined by linear regression.

[0067] Alternatively, the modeled main steam flow may be obtained using artificial intelligence tools and / or neural networks.

[0068] Equation for Water / Steam Balance, Once-Through Boiler: q ms,c =a0+a1q fw +a2DT(q fw )+a3p fw +a4Dt(p fw ) In the above equation, q ms,c is the modeled main steam flow rate, q fw is the feedwater flow rate, Dt(q fw ) is Dt (supply water flow rate), p fw is the water supply pressure, Dt(p fw ) is Dt (water supply pressure), a0, a1, ..., a4 are calibration coefficients determined by linear regression.

[0069] Alternatively, the modeled main steam flow may be obtained using artificial intelligence tools and / or neural networks.

[0070] In process A5, each FBHE100 i For example, by using a regression model, i A numerical model is constructed for the temperature calculation of

[0071] FBHE i Equations for bed temperature calculation T i,j,c =b0+b1T w,i +b2T se,i +b3qms,m +b4Dt(q ms,m ) In the above equation, T i,j FBHE100 i is the modeled bed temperature, (The number of temperature points is N, so j=1,…,N) T w,i Loop Seal 200 i is the temperature, T se,i Separator 17 i is the flue gas outlet temperature, q ms,m is the main steam flow rate, Dt(q ms,m ) is Dt (main steam flow rate), b0, b1...b4 are coefficients determined by linear regression.

[0072] Alternatively, the modeled bed temperature may be obtained using artificial intelligence tools and / or neural networks.

[0073] In step A7, each separator 17 i For example, by a regression model, i A numerical model is constructed for the temperature calculation of

[0074] separator i Equations for Temperature Calculation T separator exit,i,c =c0+c1T inlet,i +c2T msei In the above equation, T separator exit,i,c is the modeled separator 17 i is the flue gas outlet temperature, T msei (Separator i That is, all other separators 17 except j≠i j (calculated for other separators 17) j is the average of T separator inlet,i Separator 17 i is the inlet temperature, c0, c1...c2 are coefficients determined by linear regression.

[0075] Alternatively, the modeled separator flue gas outlet temperature may be obtained using artificial intelligence tools and / or neural networks.

[0076] In step A9, each loop seal 200 i For example, by using a regression model, i A numerical model for the pressure at is constructed.

[0077] Loop Seal 200 i Equation for pressure calculation: p ws,I,C =d0+d1p mwsi In the above equation, p wsi,C The modeled loop seal i It is pressure, p mwsj (Loop Seal 200 i That is, all other loop seals 200 except j≠i j is the average of the other loop seal pressures (calculated for d0 and d1 are factors determined by linear regression.

[0078] Alternatively, the modeled laminar loop seal pressure may be obtained using artificial intelligence tools and / or neural networks.

[0079] Typically, numerical models for the process parameters in the reactor system 10 are constructed, for example, by regression models. Depending on the type of reactor system 10, the models may be different, such as material balances for at least the main process parameters that characterize the process run. For example, the bed pressure value and its normal fluctuations in space and time are very different in a CFB bed inside a reactor or in a BFB bed, such as a BFB heat exchanger connected to a CFB reactor. Also, a separate BFB reactor bed behaves differently from a CFB reactor bed, and both have distinct characteristics.

[0080] 4 illustrates the operation of the leak detection system, with separate diagnostic (A) and training (i.e., model building or calibration) (B). In diagnostic block (A), the leak diagnostic method J1 according to the present invention is preferably executed at predetermined time intervals, such as once per minute, or periodically.

[0081] In the training block (B), there are at least two separate sets of training data used for training the model. The first training data set K1 comprises X2 days of process data (data obtained during a period of X2 days) starting from X1 days before the day the model training procedure is run. The second training data set K3 also comprises X2 days of process data starting from X1 days before the day the model training procedure is run. The start and / or end times of the training procedures using the first training data set K1 and the second training data set K3 are different (the difference is shown as X3 days). The training data sets K1, K3 may be partially overlapping or may be separated so as not to overlap.

[0082] Model training K5 (see FIG. 3) using the first data set K1 can be invoked at predetermined intervals or periodically, such as every X1 days. Similarly, second model training K7 (see FIG. 3) using the second data set K3 can be invoked after a predetermined interval (X3 days) has elapsed since running the first model training K5.

[0083] The purpose of this practice is that if there is a leak in the heat transfer fluid circuit of the reactor system 10, the leak will corrupt the calibration data. Intermittently running model training using different training data at different times allows possible leaks to be detected before the data is used for modeling, thus ignoring such corrupted data. This is believed to improve the reliability of the detection algorithm, as some leaks develop slowly.

[0084] Example of how to use the model: The model outputs are modeled values ​​compared to measured values ​​such as:

[0085] Water / Steam Balance: ΔMS=q' ms -q ms q' ms is the modeled main steam flow rate, q ms is the measured main steam flow rate, ΔMS<ΔMS under normal process conditions limit and ΔMS limit is a process-dependent / model-dependent or reactor-dependent value.

[0086] separator 17 i (where i=1, 2, ... N, and N is the number of separators 17 in the combustion boiler system 10) i is the number of ): Δse i =T' se,i -T se,i T' se,i is the modeled separator 17 i is the flue gas outlet temperature, T se,i is the measured separator 17 i is the flue gas outlet temperature, Under normal process conditions of the separator, Δse i <Δse limit and Δse limit is a process / model / boiler dependent value.

[0087] Fluidized bed heat exchanger FBHE100: ΔT i1…n =T' i1…n -T i1…n T' i1…n FBHE100 i are the modeled bed temperatures 1...n, T i1…n FBHE100 i are the measured bed temperatures 1...n, Under normal process conditions of FBHE, ΔT i1…n <ΔT limit and ΔT limit is a process / model / boiler dependent value.

[0088] Loop Seal 200 i (where i=1, 2, ... N, and N is the loop seal 200 in the combustion boiler system 10) i is the number of ): Δp i =p' ws,i -p ws,i p' ws,i Modeled after the Loop Seal 200 i It is pressure, p Se,i is measured by LoopSeal 200 i It is pressure, Under normal process conditions of the separator, Δp i <Δp limit and Δp limit is a process / model / boiler dependent value.

[0089] Superheater 14: ΔT sh =T' SH -T SH T' SH is the modeled temperature of the superheater 14, TSH is the measured temperature of the superheater 14, Under normal process conditions in the superheater 14, ΔT SH <ΔT SH,limit and ΔT SH,limit is a process / model / boiler dependent value.

[0090] Generally, a reactor- and / or process-dependent process parameter X is selected that is affected by leakage in the fluid channel. The process parameter is modeled and the modeled value is compared to the measured value of the process parameter. The difference ΔX = X' (modeled value) - X (measured value) is used to assess the leakage condition, such that ΔX < ΔX limit If so, this is a normal condition.

[0091] ΔX limit is the limit on the allowable difference in parameter X.

[0092] FIG. 5 shows the leak diagnosis step (J1 in FIG. 4), and more specifically the calculation of the pipe leak risk.

[0093] In step J13, the delta (the difference between the modeled value and the measured value) is calculated.

[0094] First, in a CFB boiler system, ΔMS and, optionally, Δse i and / or ΔT i1…n and / or Δp i (and ΔMS and optionally ΔT for BFB boiler systems, respectively) sh , and similarly) can be calculated over a predetermined time interval, such as the last 60 minutes.

[0095] In the next step J15, the difference is compared with the respective warning limits. The warning limits are set as constants for each model, and when the difference is below the respective warning limit, the process is in a normal state. The diagnosis then calculates the warning limit exceeded in step J17.i In the case of multiple models such as i1…n If the component exceeds the respective process / model / boiler dependent value, such as when >x, the component is set as abnormal.

[0096] The pipe leak risk level can be calculated using the equation (internal value): n e *BF>t u If so, R=100+(n e *BF-t u ) / t r *100 Otherwise, R C =(n e *BF-t l ) / (t u -t l )*100 In the above equation, R C is the leakage risk level of the component (location) or water / steam balance, n e is the excess in the reference period, t r is the length of the reference period (minutes), t l is a lower bound, t u is an upper bound, BF is the boost factor.

[0097] BF = 1 + (E s / (WL*N)-1)*B In the above equation, BF is the boosting factor, B is the boosting gradient, WL is the warning limit for errors, N is the excess number, E s is sum(error) when error > warning limit.

[0098] The leakage index can be calculated using the equation: Rc <100 then I c =R c , R c >100 then I c =100 In the above equation, I c is the component leakage index (location) or water / steam balance index, R C is the leakage risk level component (location) or water / steam balance.

[0099] If the leak index is greater than or equal to 50 but less than 100, it is a "yellow" warning for the location or water / steam balance.

[0100] A leak index greater than 100 is a "red" warning for the location or water / steam balance.

[0101] Total Breach Indicators: I cm If <50, I=R ws / 2 I cm If ≧50, I=R ws / 2+I cm / 2 In the above equation, I is the total leakage index, R ws is the water / steam balance leakage risk level, I cm is the maximum component leakage index.

[0102] The inventors have validated the functionality of the method on archived real data collected from a CFB-fired boiler system. The data is disclosed in Figures 6A through 6I and shows in one exemplary manner (as can be seen simulating what is displayed on the DCS 301, the EDGE system, and the display / monitor 302 to the boiler operator, possibly with the participation of a remote process intelligence system 308) how the method can be used to indicate to a boiler operator the presence of a tube leak in the water-steam circuit of the fired boiler system.

[0103] Figure 6A shows the calculated total leakage index I for the test period, calculated as described above. As can be seen, the index reaches 100 in the right-most time period column. A boiler leak is present. In the actual situation in which the process data was collected, the boiler was shut down.

[0104] FIG. 6B shows the calculated water / steam balance difference, or ΔMS, for the same fired boiler system 10 process data. Somewhat larger fluctuations are visible, with a significant increase in the right-most time period column. FIG. 6C shows the leakage index, I, calculated for the water / steam balance only. MS This shows:

[0105] FIG. 6D shows the calculated difference, i.e., ΔT, for the same combustion boiler system 10 process data. 3 1-n The difference increases somewhat slowly. Figure 6E shows the leakage index I FBHE 3 , i.e., the leakage index calculated only for component FBHE1003.

[0106] FIG. 6F shows the calculated difference, Δse3, for the separator 173 of the same combustion boiler system 10 process data. SE,3 , i.e., the leakage index calculated for component separator 173 only.

[0107] FIG. 6H shows the calculated difference, Δws3, for the loop seal 2003 of the same combustion boiler system 10 process data. WS,3 , i.e., the leakage index calculated for component loop seal 2003 only.

[0108] With the total leak indicator I, the presence of a tube leak in the water-steam circuit of the combustion boiler system 10 can be detected reliably, and in some cases earlier than in previous implementations of the applicant's combustion boiler system.

[0109] Component-specific leakage indicators are preferably calculated for all leak-prone components of the combustion boiler system 10 (in this example, for each FBHE 100) i , each separator 17 i , 200 each loop seal i By means of the leak indicator for , the location of the component where the pipe leak is present can be reliably detected.

[0110] In other words, in the leak detection method according to the first aspect of the present invention, the risk level is calculated using a time series of measures between model-based quantities estimated for the actual bed conditions using the determined fluidized bed combustion boiler operating parameters and respective quantities calculated from the measurements, so that the measures each contribute disproportionately to the risk level relative to their magnitude. The risk level can be indicated to the boiler operator. If the risk level exceeds a preset limit, the exceedance is indicated to the boiler operator, the boiler operator is alerted, and / or a boiler shutdown is automatically proposed or initiated.

[0111] In a leak detection method according to a second aspect of the present invention, a risk level is calculated using time series measures between model-based quantities estimated for actual bed conditions using the determined fluidized bed combustion boiler operating parameters and respective quantities calculated from measurements, where the measures are evaluated in at least two overlapping time windows of different lengths, where a narrower time window requires a proportionally greater number of measures exceeding thresholds than a wider time window. The risk level can be indicated to boiler operators. If the risk level exceeds a preset limit, the exceedance is indicated to the boiler operators, who are alerted and / or a boiler shutdown is automatically proposed or initiated.

[0112] In a leak detection method according to a third aspect of the present invention, a risk level is calculated using a time series of measurements between model-based quantities estimated for actual bed conditions using the determined fluidized bed combustion boiler operating parameters and respective quantities calculated from measurements, and the model-based quantities are then estimated using calibrated values, where the calibrated values ​​are obtained by analyzing historical data as training data from further back in time than the time series used in calculating the risk level. The risk level can be indicated to a boiler operator. If the risk level exceeds a preset limit, the exceedance is indicated to the boiler operator, the boiler operator is alerted, and / or a boiler shutdown is automatically proposed or initiated.

[0113] The model-based quantities estimated for the actual bed conditions using the determined fluidized bed combustion boiler operating parameters and the respective quantities calculated from the measured values ​​preferably include one or more of the water-steam balance, flue gas outlet temperature, bed temperature, and pressure, and thus the water-steam balance is advantageously used.

[0114] The risk level is preferably calculated as a weighted sum of any different measures, optionally requiring that for each measure, the exceedance of a specific threshold for that measure be included in the calculation. The risk level may further be calculated such that it is only displayed as 100% when the risk level exceeds 100%.

[0115] The differences between the model-based quantities and the respective quantities calculated from measurements can be rather large. These are due to the fact that the combustion conditions are under continuous change and there are certain fluctuations that occur in a fired boiler all the time. For a fired boiler that produces superheated steam at a rate of 400 kg / s, in reality the steam flow rate can fluctuate up and down by 5-10 kg / s.

[0116] The discovery behind the first aspect of the present invention is that given moderately large fluctuations in the model-based quantities and the respective quantities calculated from measurements, there is a high probability that smaller measures will be very frequent in the time series analysis, while larger measures are less likely to be present multiple times in the time series analysis without justification. Thus, if several threshold-crossing measures in a time window are disproportionately large relative to their magnitudes, occupying a risk level proportional to the sum of the measure magnitudes, a larger tube leak in a combustion boiler can be detected much faster than in the background art (Modern Power Systems, December 2018 article). As an example, we refer to the results of the Modern Power Systems article, Ill. 6, p. 38. Applicant's previous method was able to detect a leak in the furnace wall approximately 30 minutes (second arrow from the left) after the onset of the leak (first arrow from the left). Using this method, the inventors were able to reliably detect the same leak based on the same data in approximately 2-4 minutes.

[0117] The discovery behind the second aspect of the present invention is that, given moderately large fluctuations in the model-based quantities and the respective quantities calculated from measurements, there is a high probability that smaller measures will be very frequent in the time series analysis, while it is less certain that smaller measures will be present for longer periods of time without justification. Thus, if measures are evaluated in at least two overlapping time windows of different lengths, such that a narrower time window requires a proportionally greater number of small measures exceeding a threshold than a wider time window, smaller tube leaks in a combustion boiler can be detected significantly more reliably than in the background art (Modern Power Systems, December 2018 article). Using this method, the inventors were able to more frequently rule out suspected tube leaks as not leaks, even in situations where the background art method would have resulted in false leak alerts.

[0118] The discovery behind the third aspect is that relatively large fluctuations in the model-based quantities and the respective quantities calculated from measurements may have some time-shift characteristics in time-series analysis. When there is a time shift, calculation of estimated values ​​using a numerical model gives inaccurate results that may no longer be reliable. In this situation, since the model-based quantities are estimated using a mathematical model calibrated with coefficient values ​​obtained using numerical fitting, the effect of the time-shift characteristics can be suppressed or even eliminated if the calibrated values ​​are obtained by analyzing historical data from further back in time than the time series used in calculating the risk level when the numerical fitting is repeated on the training data. The historical data should preferably be from at least several days ago, and even better, from one or even two weeks ago. This method can detect slowly developing pipe leaks more reliably than the method described in the Background Art (Modern Power Systems, December 2018 article).

[0119] In a fourth aspect of the present invention, a risk level is calculated using a time series of measurements between model-based quantities estimated for actual bed conditions using the determined fluidized bed combustion boiler operating parameters and respective quantities calculated from measurements, including at least one, but preferably all, of at least one separator, at least one solids return chamber heat exchanger, and at least one loop seal. The risk level can be indicated to boiler operators. If the risk level exceeds a preset limit, the exceedance is indicated to the boiler operators, who are alerted and / or a boiler shutdown is automatically proposed or initiated.

[0120] The discovery behind the fourth aspect is that in a fluidized bed boiler, a tube leak can cause an effect generally equivalent to sandblasting, in which an abrasive fluidized media is pressed against boiler structures, such as other tubes, by high-pressure steam or water. Thus, CFB boiler leak detection performed on at least one separator, at least one solids return chamber heat exchanger (FBHE), and / or at least one loop seal can help reduce damage to those parts of the boiler.

[0121] While a tube leak does not necessarily have very negative consequences within the furnace if the furnace wall water tube leaks, the situation is significantly different in certain CFB boiler structures (separators, solids return chamber heat exchangers, loop seals) where heat exchanger tubes are relatively close to each other. For example, in a solids return chamber heat exchanger, adjacent heat exchanger tubes may be separated by only 10 cm. A tube leak in such a component, which also has a high bed media density, can rapidly worsen the leak due to the increased abrasive effect of the bed media caused by the leak. For example, in the lower parts of a CFB furnace, bed media densities may be in the range of several tens of kg / m3, while in a solids return chamber heat exchanger, bed media densities may be in the range of 1000–1500 kg / m3. Furthermore, leaks in the furnace tube walls generally do not damage adjacent tubes because they are not in the direction of the bed media blast caused by the leak.

[0122] Correspondingly, the present invention and its embodiments can be utilized to determine leaks in a variety of reactors and processes where a heat transfer fluid carries heat and a heat transfer surface receives or extracts heat between the process and the heat transfer fluid. Suitable processes and reactors include thermochemical reactors, gasifiers, autothermal reactors, which include heat generation and heat recovery, and are connected to CO2 capture devices, and in processes that convert waste to reusable products.

[0123] It is obvious to those skilled in the art that with technological advances, the basic idea of ​​the present invention can be implemented in many ways. Therefore, the present invention and its embodiments are not limited to the examples and samples described above, but may vary within the content of the claims and their legal equivalents.

[0124] In the claims that follow, and in the preceding description of the invention, except in contexts which dictate otherwise due to linguistic expression or necessary implication, the terms "comprise" or variations such as "comprises" or "comprising" are used in an inclusive sense, i.e., to specify the presence of stated features in various embodiments of the invention, but do not preclude the presence or addition of further features.

Claims

1. 1. A method for determining leaks in a heat transfer fluid channel of a heat transfer reactor system (10), comprising: The heat transfer fluid flow rate (q ＭＳ，Ｍ ) measuring the The heat transfer fluid (q ＭＳ，Ｃ ) flow rate in the heat transfer fluid channel during operation by utilizing process data other than the heat transfer fluid flow rate in a numerical model of the heat transfer reactor system (10) that provides a ＭＳ，Ｃ ) modeling the The error measure (Δ ＭＳ comparing the measured heat transfer fluid flow rate and the modeled heat transfer fluid flow rate in the heat transfer fluid channel to each other to obtain a monitoring said set of error measures and the number of occurrences in said set of error measures during operation; During a predetermined time period, the error measure (Δ ＭＳ determining the presence of a heat transfer fluid channel leak if the error measure set exceeds a predetermined threshold or if the number of occurrences in said error measure set exceeds a predetermined threshold; A method comprising:

2. measuring at least one process parameter across at least one location within a reaction chamber of said reactor system (10); modeling at least one of the corresponding process parameters during operation of the heat transfer reactor system (10) by utilizing process data in a numerical model that provides the corresponding process parameter of the heat transfer reactor system (10) under substantially leak-free conditions; comparing the at least one measured process parameter and the corresponding at least one modeled process parameter to each other to obtain an error measure for the at least one process parameter that is also included in the set of error measures; The method of claim 1 further comprising:

3. The method of claim 2 , wherein the process parameters comprise or consist of at least one of temperature and pressure.

4. 4. The method according to any one of claims 1 to 3, wherein the heat transfer reactor system (10) is a fluidized bed reactor system.

5. 5. The method of claim 4 in combination with claim 2 or 3, wherein the process parameters comprise or consist of a pressure at a loop seal (290) located downstream of the particle separator (17) in a return section (102) arranged to return separated particles into the reaction chamber (12).

6. the method comprising monitoring the number of occurrences of an error measure for the heat transfer fluid flow rate exceeding a predetermined threshold, the number of occurrences exceeding being included in a characteristic of the error measure; The method further comprises detecting a pressure (p ｗ，ｉ monitoring the number of occurrences of an error measure for the error measure, an excess of said occurrences being included in said characteristic of the error measure; Heat transfer fluid channel leakage if the error measure for the main heat transfer fluid flow rate and the number of occurrences of error measures for the main heat transfer fluid flow rate exceed the predetermined threshold; and If the error measure relating to the pressure at the loop seal (200) and the number of occurrences of the pressure at the loop seal (200) parameter at the loop seal exceed the predetermined threshold, The method of claim 5, wherein the loop seal (200) is determined to be in the loop seal.

7. The process parameter is the product gas temperature (T ｓｅ，ｉ ), or the generated gas temperature (T ｓｅ，ｉ 10. The method of claim 4 in combination with claim 2 or 3, or alternatively claim 6, comprising:

8. The leak, if both the error measure for the main heat transfer fluid flow rate and the number of occurrences of error measures for the main heat transfer fluid flow rate exceed the predetermined threshold for the corresponding error measure, respectively; and an error measure for the product gas temperature at the outlet of the particle separator and a number of occurrences of the product gas temperature at the outlet of the particle separator each exceed a predetermined threshold for the product gas temperature error measure; The method of claim 7 , wherein the particle separator is determined to be in the particle separator.

9. 9. The method of claim 4 in combination with claim 2 or 3, or alternatively any one of claims 6, 7, or 8, wherein the process parameters comprise or consist of a bed temperature in a heat transfer fluidized bed heat exchanger.

10. Heat transfer fluid channel leakage When both the error measure of the bed temperature of the heat transfer fluidized bed heat exchanger and the number of occurrences of the error measure exceed predetermined thresholds, respectively, The method of claim 9 determined in the heat transfer fluidized bed heat exchanger.

11. 11. The method of claim 1, wherein the characteristic of the error measure comprises or consists of the number of occurrences of each exceeding a predetermined threshold.

12. A heat transfer reactor system (10) comprising a control system (301, 303, 308) configured to implement the method according to any one of claims 1 to 11.

13. 13. The heat transfer reactor system (10) of claim 12, comprising an indication means, such as a display / monitor (302), for indicating to an operator the presence of a flow channel leak detected using said method.

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