System and method for controlling operation of a recovery boiler to reduce fouling

Abstract

In some aspects, a computer-implemented method of reducing a fouling rate in a recovery boiler system is provided. A computing device receives boiler operating information for a period of time. The boiler operating information includes boiler operating parameters and a fouling rate for the period of time. The boiler operating parameters include one or more boiler input parameters. The computing device performs a regression analysis to determine at least one correlation between the boiler operating parameters and the fouling rate. The computing device causes at least one boiler input parameter to be adjusted in accordance with the at least one correlation to minimize the fouling rate. In some aspects, a system configured to perform such a method is provided. In some aspects, a computer-readable medium having instructions stored thereon for causing a computing device to perform such a method is provided.

Description

TECHNICAL FIELD

[0001] The present invention relates to a computer-implemented method of reducing a scale rate in a recovery boiler system, and a system configured to perform such a method. SUMMARY

[0002] In some aspects, a system is provided that includes a boiler, a scale sensor, a boiler controller device, and an analytics computing device. The scale sensor is associated with a component of the boiler. The analytics computing device includes a computer-readable medium and at least one processor. The computer-readable medium has stored thereon computer-executable instructions that, in response to execution by the at least one processor, cause the analytics computing device to perform actions including receiving boiler operational information for a period of time, wherein the boiler operational information includes boiler operational parameters and a scale rate for the period of time, performing a regression analysis to determine at least one correlation between the boiler operational parameters and the scale rate, adjusting at least one boiler input parameter to minimize the scale rate based on the at least one correlation, and communicating the adjusted at least one boiler input parameter to the boiler controller device for implementation.

[0003] In some aspects, a computer-implemented method of reducing a scale rate in a recovery boiler system is provided. A computing device receives boiler operational information for a period of time. The boiler operational information includes boiler operational parameters and a scale rate for the period of time. The boiler operational parameters include one or more boiler input parameters. The computing device performs a regression analysis to determine at least one correlation between the boiler operational parameters and the scale rate. The computing device causes adjustment of at least one boiler input parameter to minimize the scale rate based on the at least one correlation.

[0004] In some aspects, a non-transitory computer-readable medium is provided. The medium has stored thereon computer-executable instructions that, in response to execution by one or more processors of a computing device, cause the computing device to perform actions including receiving, by the computing device, boiler operational information for a period of time, wherein the boiler operational information includes boiler operational parameters and a scale rate for the period of time, wherein the boiler operational parameters include one or more boiler input parameters, performing, by the computing device, a regression analysis to determine at least one correlation between the boiler operational parameters and the scale rate, and adjusting, by the computing device, at least one boiler input parameter to minimize the scale rate based on the at least one correlation. BRIEF DESCRIPTION OF DRAWINGS

[0005] For ease of identification of the discussion of any particular element or action, the most significant digit or digits in the reference number refer to the figure number in which that element is first introduced.

[0006] Figure 1 The illustration shows components of a kraft paper black liquor recovery boiler system, according to different aspects of this disclosure, in a non-limiting example.

[0007] Figure 2 This illustration shows how, according to different aspects of this disclosure, the recycling boiler is installed in a steel beam support structure.

[0008] Figure 3 This illustration shows, according to different aspects of this disclosure, some components of the superheater system that are independently suspended within the boiler.

[0009] Figure 4 This is a block diagram illustrating a non-limiting example of a computing device component in a boiler system, according to different aspects of this disclosure.

[0010] Figure 5 This is a flowchart illustrating a non-limiting example of a method for minimizing scaling in a recycling boiler system, according to different aspects of this disclosure.

[0011] Figure 6 This is a block diagram illustrating a non-limiting example of a computing device suitable for use as a computing device having the features of this disclosure. Detailed Implementation

[0012] In the papermaking process, chemical pulping produces black liquor as a byproduct. Black liquor contains almost all the inorganic cooking chemicals, as well as lignin and other organic matter separated from the wood during pulping in the digester. This black liquor is burned in a recovery boiler. The two main functions of the recovery boiler are to recover the inorganic cooking chemicals used in the pulping process and to utilize the chemical energy in the organic portion of the black liquor to generate steam for the paper mill. The dual objectives of recovering both chemicals and energy make the design and operation of recovery boilers highly complex.

[0013] In a kraft paper recycling boiler, a superheater is placed in the upper furnace to extract heat from the furnace gas through radiation and convection. Saturated steam enters the superheater section and is discharged at a controlled temperature. The superheater consists of a series of tube sheets. The superheater surface continuously scales due to ash carried out of the furnace. The amount of black liquor that can be burned in a kraft paper recycling boiler is generally limited by the rate and extent of scaling on the superheater surface. This scaling reduces the heat absorbed from liquid combustion, resulting in low superheater outlet steam temperatures and high inlet gas temperatures. When the outlet steam temperature is too low to be used in downstream equipment, or when the inlet temperature exceeds the melting temperature of the deposits, causing blockage on the gas side of the boiler, the boiler is shut down for cleaning. Kraft paper recycling boilers are particularly prone to superheater scaling problems due to the high ash content in the fuel (typically exceeding 35%) and the low ash melting temperature.

[0014] There are three traditional methods for removing deposits from the superheaters of kraft paper recycling boilers, listed in ascending order of required shutdown time and descending order of frequency: 1) soot blowing; 2) cold blowing; and 3) water washing.

[0015] Soot blowing is a process that uses a strong stream of steam from nozzles called "soot blowers" to remove ash deposits from the superheater. Soot blowing occurs almost continuously during normal boiler operation, using different soot blowers activated at different times. Soot blowing reduces boiler efficiency because typically 5-10% of the boiler steam is used for soot blowing. Each soot blowing operation removes a portion of the nearby ash deposits, but ash continues to accumulate over time. As deposits grow, soot blowing gradually becomes less effective, leading to impaired heat transfer.

[0016] When ash buildup reaches a certain threshold, boiler efficiency significantly decreases, and soot blowing becomes ineffective, the deposits are removed through a second cleaning process known as "cold blowing" (also called "dry cleaning" because it doesn't use water). This requires a partial or complete shutdown of fuel combustion in the boiler, typically 4 to 12 hours, but not a complete boiler shutdown. During this time, the soot blowers continue to run to strip the deposits from the superheater section and allow them to fall to the bottom of the boiler. This process can be performed monthly, but the frequency can be reduced if soot blowing is performed optimally (with the best schedule and sequence). Like soot blowing, the cold blowing process reduces some of the nearby ash buildup, but the ash continues to grow over time. As the deposits accumulate, the effectiveness of the cold blowing process gradually decreases, thus requiring more frequent runs.

[0017] The third cleaning process – water washing – requires a complete boiler shutdown, typically for two days, resulting in a significant loss of the plant's pulping capacity. In heavily scaled recovery boilers, this may need to be done every four months, but if the cold-blowing process is timed properly (i.e., before a large amount of deposits form in parts of the boiler group), shutdowns and water washing can be avoided for up to a year or longer.

[0018] Since each of these cleaning processes reduces boiler efficiency or requires boiler shutdown, it is clearly desirable to minimize the time spent during cleaning. What is desired is an efficient technology for regulating boiler operation. This can be achieved by minimizing boiler scaling, thereby reducing the amount of time spent or the parasitic energy used in performing one or more of these cleaning processes.

[0019] Figure 1The diagram schematically shows the components of a typical kraft paper black liquor recovery boiler system 100. Black liquor is a byproduct of chemical pulping in the papermaking process. The initial concentration of the “weak black liquor” is approximately 15%. It is concentrated to combustion conditions (65% to 85% dry solids content) in evaporator 118 and then burned in recovery boiler 106.

[0020] Boiler 106 has a furnace section, or "furnace 122," for burning black liquor, and a convective heat transfer section 104, with a bullnose section 128 between them. Combustion converts the organic matter of the black liquor into gaseous products in a series of processes involving drying, devolatilization (pyrolysis, molecular pyrolysis), and char / gasification. Some of the organic matter is converted into solid carbon particles called "char." Char combustion occurs primarily on a char bed 108 covering the bottom of furnace 122, although some char burns in the air. As the carbon in the char is gasified or burned off, inorganic compounds in the char are released and form a molten salt mixture called a "melt," which flows to the lower part of the char bed 108 and is continuously discharged from furnace 122 through melt nozzle 110. The exhaust gas flow is passed through an induced draft fan 138, filtered by an electrostatic precipitator 136, and then discharged through chimney 102.

[0021] The vertical wall 124 of the furnace is lined with vertically aligned wall tubes 126, through which water evaporates using the heat from the furnace 122. The furnace 122 has a primary air port 112, a secondary air port 114, and a tertiary air port 120 for introducing air at three different height levels for combustion. Black liquor is sprayed into the furnace 122 from a black liquor gun 116.

[0022] The convective heat transfer section 104 includes the following three tube bundles (heat traps) that continuously heat feedwater into superheated steam in stages: 1) an economizer 134 in which feedwater is heated to just below its boiling point; 2) a boiler group 132 (or "steam generating group") in which water is evaporated into steam simultaneously using wall tubes 126; and 3) a superheater system 130 in which a series of parallel flow elements with intermediate headers are used to raise the steam temperature from the saturation temperature to the final superheated temperature.

[0023] Figure 2The diagram schematically illustrates how the recycled boiler 106 is installed within the steel beam support structure 208, showing only the boiler's outline and the components of current interest. The entire recycled boiler 106 is suspended in the middle of the steel beam support structure 208 by boiler hangers 202. Boiler hangers 202 connect the top 206 of the boiler 106 to the overhead beam 210 of the steel beam support structure 208. Another set of hangers, hereinafter referred to as "superheater hangers" or simply "hangers 212," suspends only the superheater system 130. That is, the superheater system 130 is suspended independently of the rest of the boiler 106. The open area between the boiler top 206 and the overhead beam 210 is referred to as the top open space 204.

[0024] Figure 3 Some components of the superheater system 130, which is independently suspended within the boiler 106, are illustrated schematically. The superheater system 130 has three superheater plates 310, 312, and 314 in this respect. Although three superheaters are shown, the addition of more superheaters as needed is also within the scope of this invention. For clarity, the following discussion describes the construction of superheater plate 310 or focuses on superheater plate 310; it should be understood that superheater plate 312 and superheater plate 314 have the same construction.

[0025] The superheater plate 310 typically has 20-50 plates 306. Steam enters the plates 306 via a manifold called inlet manifold 308, is superheated within the plates, and then exits as superheated steam via another manifold called outlet manifold 304. The plates 306 are suspended from the inlet manifold 308 and outlet manifold 304, which themselves are suspended from the overhead beam 210 by hangers 212. Figure 2 Generally, 10-20 hangers 212 are evenly spaced along the length of each inlet manifold 308 and outlet manifold 304, and are fixed to the lower manifold and the upper overhead beam 210 by conventional methods such as welding, as described below. The superheater system 130 typically has 20 hangers 212—10 hangers for the inlet manifold 308 and 10 hangers for the outlet manifold 304. Each hanger has a threaded upper portion, around which a tension nut is rotated to adjust the tension of the hanger. The tension of each hanger is generally adjusted after every 1-3 water washes to maintain uniform (balanced) tension among all the hangers 212 of a single superheater plate 310.

[0026] During cleaning (right after a thorough water wash), each superheater plate 310 typically weighs 5000 kg, and each superheater hanger typically bears a load of 5000 kg. Subsequently, just before the next water wash, deposits (scale) add an additional weight of approximately 2000 kg to each superheater plate 310, resulting in an additional load of approximately 2000 kg on each hanger, and a load of approximately 5.0 × 10⁻⁶ kg on each hanger. -5 The additional strain is measured in cm / cm, which can be measured using common methods such as the use of strain gauge 302.

[0027] The sum of the strain on all the hangers 212 suspending the superheater plate 310 (after zeroing the strain just read after the previous water wash) is proportional to the weight of the deposits on the superheater. Each additional 1 kg of deposit produces a generally 2.0 × 10⁻⁶ ohmmeter. -8 An additional strain of cm / cm, which can be measured by a strain sensor such as strain gauge 302. Therefore, the weight of the deposits on each superheater plate 310 can be directly determined by measuring the strain on its corresponding hanger 212.

[0028] A typical system for determining the weight of deposits on a single superheater plate 310 may include twenty (20) strain gauges, each fixed to one of twenty (20) hangers 212 on the superheater, a computer (not shown) with data acquisition capabilities connected to the 60 strain gauges, and a computer program. Under the control of the program, the computer periodically (typically every minute) records strain readings from the 20 strain gauges (from each superheater plate 310, 312, 314), calculates the sum of the strain readings, subtracts the sum of the strain readings acquired just after the last flush, and then multiplies the result by a calibration factor to obtain the current deposit weight.

[0029] Based on the form of an equation, the formula is:

[0030] Sediment weight = (sum of current strain gauge readings - sum of strain gauge readings immediately after the previous wash) × calibration factor;

[0031] Alternatively, the equivalent expression is:

[0032] Sediment weight = (ΣSt - ΣSo) × C

[0033] in,

[0034] ΣSt = the sum of strain gauge readings at any time t.

[0035] ΣSo = the sum of strain gauge readings immediately after the last water wash, considered as at time zero, C = the calibration constant that converts strain into weight.

[0036] While strain gauge 302 allows for the determination of the weight of superheater plate 310, and this weight can be converted into the amount of scale buildup on superheater plate 310, the desired outcome is to minimize the scale buildup rate in order to extend the interval between dry cleaning and / or water cleaning. The relationship between different boiler operating parameters and scale buildup rates is complex, therefore simply manually adjusting the boiler to minimize scale buildup is ineffective. What is needed is a technique for determining the complex relationship between boiler operating parameters and scale buildup rates in order to identify the boiler input parameters that will minimize the scale buildup rate.

[0037] Figure 4 This is a block diagram illustrating a non-limiting example of the computing device components of a recovery boiler system according to different aspects of this disclosure. As shown, the recovery boiler system may include a boiler controller device 402 and an analysis and calculation device 404. The boiler controller device 402 and the analysis and calculation device 404 can be used to determine boiler input parameters that minimize scaling and implement these input parameters during operation of the recovery boiler system 100.

[0038] In some aspects, boiler controller device 402 is a computing device that electronically controls one or more components of the recovery boiler system 100. In some aspects, boiler controller device 402 may include an ASIC, FPGA, or other custom computing device for controlling the components of the recovery boiler system 100. In some aspects, boiler controller device 402 may include computing devices such as desktop computing devices, laptop computing devices, server computing devices, mobile computing devices, or any other type of computing device. In some aspects, more than one computing device may be used to collectively provide the functionality described as part of boiler controller device 402.

[0039] As shown in the figure, the boiler controller device 402 includes at least one processor 406, a network interface 410, a boiler component interface 414, and a computer-readable medium 416. In some aspects, the network interface 410 may include any suitable communication technology for communicating with the analysis and computing device 404, including but not limited to wired communication technologies (including but not limited to Ethernet, USB, and FireWire), wireless communication technologies (including but not limited to 2G, 3G, 4G, 5G, LTE, Bluetooth, ZigBee, Wi-Fi, and WiMAX), or combinations thereof. In some aspects, the boiler component interface 414 communicatively couples the boiler controller device 402 to one or more adjustable components of the recovery boiler system 100, including but not limited to the black liquor gun 116, the evaporator 118, the primary air port 112, the secondary air port 114, and the tertiary air port 120.

[0040] As shown in the figure, the computer-readable medium 416 includes logic responsive to the execution of the at least one processor 406, causing the boiler controller device 402 to provide an information reporting engine 426 and an input control engine 428. In some aspects, the information reporting engine 426 receives information from one or more components of the recovery boiler system 100 and transmits that information to the analysis and computing device 404. In some aspects, the input control engine 428 receives commands from the analysis and computing device 404 and adjusts adjustable components of the recovery boiler system 100 according to those commands.

[0041] In some aspects, the analytical computing device 404 may include one or more computing devices such as a desktop computing device, a laptop computing device, a mobile computing device, a server computing device, a cloud computing system, or any other type of computing device. In some aspects, more than one computing device may be used to collectively provide the functionality described as part of the analytical computing device 404.

[0042] As shown in the figure, the analysis computing device 404 includes at least one processor 408, a network interface 412, and a computer-readable medium 418. In some aspects, the network interface 412 may include any suitable communication technology for communicating with the network interface 410 of the boiler controller device 402.

[0043] As shown in the figure, the computer-readable medium 418 includes logic responsive to the execution of the at least one processor 408, causing the analysis computing device 404 to provide an information collection engine 420, an analysis engine 422, and an input adjustment engine 424. In some aspects, the information collection engine 420 receives information from at least the information reporting engine 426 of the boiler controller device 402. In some aspects, the analysis engine 422 analyzes the information collected by the information reporting engine 426 to determine the correlation between different boiler operating parameters and scaling rates. In some aspects, the input adjustment engine 424 uses the correlations determined by the analysis engine 422 to determine adjustments to one or more boiler input parameters and transmits these adjustments to the boiler controller device 402 for implementation. Further details of the actions performed by each of these components are provided below.

[0044] "Computer-readable media" means a removable or non-removable device that implements any technology capable of storing information read by a processor of a computing device in a volatile or non-volatile manner, including but not limited to: hard disk drives; flash memory; solid-state drives; random access memory (RAM); read-only memory (ROM); CD-ROM, DVD or other disc storage; magnetic tape cassettes; magnetic tape; and disk storage.

[0045] An "engine" refers to logic embodied in hardware or software instructions, which can be written in programming languages ​​such as C, C++, COBOL, JAVA™, PHP, Perl, HTML, CSS, JavaScript, VBScript, ASPX, Microsoft .NET™, Go, Python, and so on. Engines can be compiled into executable programs or written in interpreted programming languages. Software engines can be invoked from other engines or from themselves. Typically, the engine described here refers to a logical module that can be combined with other engines or can be divided into sub-engines. An engine can be implemented by logic stored in any type of computer-readable medium or computer storage device, and can be stored on and executed by one or more general-purpose computers, thereby creating a special-purpose computer configured to provide that engine or its functionality. Engines can be implemented by logic programmed into application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other hardware devices.

[0046] Figure 5 This is a flowchart illustrating a non-limiting example of a method for minimizing scaling in a reclaimed boiler system according to different aspects of this disclosure. In method 500, at least one correlation between boiler operating parameters and scaling rate is determined such that boiler operation can be automatically adjusted to minimize scaling rate.

[0047] Starting from the start block, method 500 proceeds to block 502, where the recovery boiler system 100 is operated by boiler controller device 402 according to one or more boiler input parameters. In some aspects, boiler input parameters may include any controllable aspect of operating the recovery boiler system 100. In some aspects, the chemical composition of the black liquor may be one example of a boiler input parameter. For example, the chloride content of the black liquor may affect the scaling rate. Therefore, the chloride level can be reduced by decreasing the ash recovered from the electrostatic precipitator 136, or by utilizing various techniques to selectively remove chloride from the ash and then recycle the clean ash back into the weak black liquor. In some aspects, the type of constituent chemicals can be changed to reduce the amount of chloride in the black liquor. In some aspects, the technique used for spraying the black liquor may be another example of a boiler input parameter. For example, the black liquor gun 116 may be adjustable via a liquid gun setting to spray the black liquor into the boiler 106 at different flow rates and / or at different droplet sizes.

[0048] In some respects, the technology used to introduce air into the boiler can be another example of boiler input parameters. For example, settings can be adjusted to change the amount of air allowed to enter by at least one of the primary air port 112, the secondary air port 114, and / or the tertiary air port 120, and / or the primary air port 112, the secondary air port 114, and / or the tertiary air port 120 can be used to change the air pressure at one or more locations within the boiler 106.

[0049] In block 504, the cleaning cycle of the recovery boiler system 100 is initiated and completed. In some respects, the cleaning cycle in block 504 is performed during the operation of the recovery boiler system 100. As described above, the cleaning method that can be used during the operation of the boiler 106 is soot blowing. Soot blowing can be performed by multiple soot blowers, and these soot blowers may not all be activated at once. Therefore, the "cleaning cycle" of soot blowing will include sufficient time for all soot blowers to be activated at least once, so that the entire boiler 106 is cleaned at least once. By allowing a complete cleaning cycle to be completed, sufficient information will be collected to compensate for any short-term anomalies in the detected scaling rate due to differences in the efficiency of individual soot blowers. In some respects, more than one cleaning cycle of the recovery boiler system 100 can be completed in block 504 while the recovery boiler system 100 is operating.

[0050] In block 506, during the operation and cleaning of the recovery boiler system 100, the information reporting engine 426 of the boiler controller unit 402 transmits boiler operating parameters to the information collection engine 420 of the analysis and calculation unit 404. The time period for transmitting boiler operating parameters includes at least the cleaning cycle described in block 504. In some aspects, this time period may include several weeks or months.

[0051] In some aspects, boiler operating parameters may include boiler input parameters. In some aspects, boiler operating parameters may also include other information regarding the operation of the recovery boiler system 100, including but not limited to the temperature of the boiler 106 at different locations, the amount of black liquor processed by the recovery boiler system 100, the pressure drop across the heat transfer surfaces, and / or the operating load on the induced draft fan 138. In some aspects, boiler operating parameters may include weight information generated by at least one strain gauge 302. In some aspects, boiler operating parameters may be provided as one or more time series of boiler operating parameter values.

[0052] In block 508, during the operation and cleaning of the recovery boiler system 100, the information collection engine 420 collects time series of scale quantity values. In some aspects, the information collection engine 420 can extract weight information received within the boiler operating parameters and can determine the time series of scale quantity values ​​by subtracting the tare weight of the element suspended by at least one strain gauge 302 from each weight value. In block 510, the analysis engine 422 of the analysis calculation device 404 determines the scale rate based on the time series of scale quantity values. In some aspects, the scale rate can be determined for each step in the time series, thereby allowing determination of the change in scale rate over time.

[0053] In box 512, the analysis engine 422 performs regression analysis on the boiler input parameters and the scaling rate. In some aspects, the regression analysis can be configured to detect the correlation between changes in boiler input parameters and changes in the scaling rate. In some aspects, the regression analysis can detect the correlation between a single boiler input parameter and changes in the scaling rate. In some aspects, the regression analysis can detect the correlation between a combination of two or more boiler input parameters and changes in the scaling rate. In some aspects, the regression analysis can also detect the correlation between one or more boiler operating parameters other than the boiler input parameters and changes in the scaling rate, and / or can determine additional correlations between these boiler operating parameters and the boiler input parameters. For example, the regression analysis can detect the correlation between boiler operating temperature and the scaling rate, and the additional correlation between liquid gun settings and boiler operating temperature.

[0054] Any suitable regression analysis can be used, including but not limited to Classification and Regression Tree (CART) analysis. In some aspects, CART analysis recursively divides observations in a matched dataset into progressively smaller groups, the matched dataset comprising a categorical (for a classification tree) or continuous (for a regression tree) dependent (response) variable and one or more independent (explanatory) variables. Each partition can be a binary split. During each recursion, the split for each explanatory variable is examined, and the split that maximizes the homogeneity of the resulting two groups relative to the dependent variable is selected. When examining boiler input parameters and scaling rates, a non-limiting example approach is to divide boiler behavior into several moments of “low scaling” and “high scaling”, and develop a CART classification tree using the boiler input parameters to create homogeneous groups separating low-scaling and high-scaling conditions. The range of boiler input parameters that promotes low-scaling conditions can then be selected as the control range.

[0055] In block 514, the input adjustment engine 424 of the analysis and calculation device 404 determines the adjusted boiler input parameters based on the results of regression analysis. For example, the input adjustment engine 424 can use the correlation between the liquid gun setting and the scaling rate determined by regression analysis to determine the adjustment of the liquid gun setting. As another example, the input adjustment engine 424 can use the correlation between the setting of one or more air ports and the scaling rate to determine the adjustment of one or more air ports. As yet another example, the input adjustment engine 424 can use the correlation between the black liquor chemical composition and the scaling rate to determine the adjustment of the chemical composition. As yet another example, the input adjustment engine 424 can use the correlation between combined boiler input parameters and the scaling rate to determine the combined optimal setting, or determine the combined optimal setting while keeping one boiler input parameter (e.g., chemical substance) constant, and determine the adjusted boiler input parameters based on the combined optimal setting.

[0056] In box 516, input adjustment engine 424 causes adjusted boiler input parameters to be used by the recovery boiler system 100 to minimize scaling. In some aspects, input adjustment engine 424 can cause the adjusted boiler input parameters to be automatically implemented by the recovery boiler system 100. For example, input adjustment engine 424 can transmit the adjusted boiler input parameters to input control engine 428 of boiler controller device 402, which can automatically adjust the boiler input parameters to minimize scaling. In some aspects, such adjustment of boiler input parameters may include transmitting commands to actuators of black liquor gun 116 or one or more air ports to change the settings of black liquor gun 116 or one or more air ports. In some aspects, such adjustment of boiler input parameters may include transmitting commands to valve actuators to reduce chloride levels; valves are used to control the amount of dust removed or sent to the recovery boiler soot removal system. In some respects, the input adjustment engine 424 does not automatically implement the adjusted boiler input parameters, but rather presents the adjusted boiler input parameters to the operator, who can then create commands to change the settings of the components of the recovery boiler system 100 to adjust the boiler input parameters as presented.

[0057] Method 500 then proceeds to the end box and terminates.

[0058] Figure 6 This is a block diagram illustrating several aspects of an exemplary computing device 600 suitable for use as a computing device of this disclosure. While various different types of computing devices have been discussed above, the various elements described in the exemplary computing device 600 are common to many different types of computing devices. Figure 6This description is made with reference to a computing device implemented as a device on a network, but the following description applies to servers, personal computers, mobile phones, smartphones, tablets, embedded computing devices, and other devices that can be used to implement certain aspects of this disclosure. Some aspects of the computing device may be implemented in or may include application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other custom devices. Furthermore, those skilled in the art and others will recognize that computing device 600 can be any of any number of currently available or undeveloped devices.

[0059] In its most basic configuration, computing device 600 includes at least one processor 602 and a system memory 604 connected via a communication bus 606. Depending on the exact configuration and type of the device, system memory 604 may be volatile or non-volatile memory, such as read-only memory (“ROM”), random access memory (“RAM”), EEPROM, flash memory, or similar memory technologies. Those skilled in the art and others will recognize that system memory 604 generally stores data and / or program modules that are readily accessible to and / or currently being operated by processor 602. In this regard, processor 602 can act as the computing center of computing device 600 by supporting instruction execution.

[0060] like Figure 6 As further shown, computing device 600 may include network interface 610, which includes one or more components for communicating with other devices via a network. Several aspects of this disclosure allow access to basic services that utilize network interface 610 to perform communication using common network protocols. Network interface 610 may also include a wireless network interface configured to communicate via one or more wireless communication protocols such as Wi-Fi, 2G, 3G, LTE, WiMAX, Bluetooth, and Bluetooth Low Energy. As those skilled in the art will understand, Figure 6 The network interface 610 shown may represent one or more wireless interfaces or physical communication interfaces described and illustrated above for a specific component of the computing device 600.

[0061] exist Figure 6 In the exemplary aspect depicted, computing device 600 also includes storage medium 608. However, a computing device can be used to access services, but this computing device does not include means for persistently storing data to local storage medium. Therefore, Figure 6The storage medium 608 depicted is indicated by a dashed line to show that the storage medium 608 is optional. In any case, the storage medium 608 can be volatile or non-volatile, removable or non-removable, and can be implemented using any technology capable of storing information, such as, but not limited to, hard disk drives, solid-state drives, CD-ROMs, DVDs or other disc storage devices, tape cartridges, magnetic tapes, disk storage, and so on.

[0062] Suitable implementations of a computing device including processor 602, system memory 604, communication bus 606, storage medium 608, and network interface 610 are known and commercially available. For ease of explanation and because they are not essential for understanding the claimed subject matter, Figure 6 Some typical components of the various computing devices are not shown. In this regard, computing device 600 may include input devices such as a keyboard, keypad, mouse, microphone, touch input device, touchscreen, tablet computer, etc. Such input devices can be connected to computing device 600 via wired or wireless connections, including RF, infrared, serial, parallel, Bluetooth, Bluetooth Low Energy, USB, or other suitable connection protocols using wireless or physical connections. Similarly, computing device 600 may also include output devices such as a display, speaker, printer, etc. Since these devices are well known in the art, they are not illustrated or further described herein.

[0063] The foregoing description has set forth numerous specific details to provide a thorough understanding of the various aspects. However, those skilled in the art will recognize that the techniques described herein can be practiced without one or more specific details, or using other methods, components, materials, etc. In other instances, well-known structures, materials, or operations have not been shown or described in detail to avoid obscuring certain aspects.

[0064] The order in which some or all process blocks appear in each process should not be considered limiting. Rather, those skilled in the art who benefit from this disclosure will understand that some process blocks may be executed in various orders not shown, or even in parallel.

[0065] The foregoing description of several aspects of the invention, including those described in the abstract, is not intended to be exhaustive or to limit the invention to the precise forms disclosed. While specific aspects and examples of the invention have been described herein for illustrative purposes, those skilled in the art will recognize that various modifications can be made within the scope of the invention.

[0066] These modifications to the invention can be made with reference to the detailed description above. The terminology used in the following claims should not be construed as limiting the invention to the specific aspects disclosed in the specification. Rather, the scope of the invention should be fully defined by the following claims, which will be interpreted in accordance with established principles of claim interpretation.

[0067] Generally speaking, this invention describes:

[0068] A system includes: a boiler; a scaling sensor associated with a component of the boiler; a boiler controller; and an analysis and computing device, the analysis and computing device including a computer-readable medium storing computer-executable instructions thereon and at least one processor, the computer-executable instructions being responsive to execution by the at least one processor to cause the analysis and computing device to perform actions including: receiving boiler operating information over a period of time, wherein the boiler operating information includes boiler operating parameters and a scaling rate over a period of time, wherein the boiler operating parameters include one or more boiler input parameters; performing regression analysis to determine at least one correlation between the boiler operating parameters and the scaling rate; adjusting at least one boiler input parameter based on the at least one correlation to minimize the scaling rate; transmitting the adjusted at least one boiler input parameter to the boiler controller for implementation, and / or preferably, the boiler includes a heat exchange element, and wherein the scaling sensor is associated with the heat exchange element, and / or preferably, the scaling sensor is a weight sensor configured to generate a value indicating the weight of the heat exchange element, and / or preferably... Receiving scaling rate over a period of time includes: receiving a time series of scaling quantity values; determining the scaling rate based on the time series of scaling quantity values; and / or preferably, performing regression analysis to determine at least one correlation between boiler operating parameters and scaling rate includes: performing CART analysis on boiler operating information; and / or preferably, further includes one or more soot blowers configured to operate in cycles; and receiving boiler operating information over a period of time includes: receiving boiler operating information for a period of time including at least one complete cycle; and / or preferably, further includes: one or more valves configured to control the amount of dust removed or sent to the soot removal system to affect chloride levels; and one or more actuators configured to control one or more valves; wherein the at least one boiler input parameter includes valve settings; wherein transmitting the adjusted at least one boiler input parameter to a boiler controller device for implementation includes: transmitting the valve settings to the one or more actuators; and wherein the one or more actuators are configured to adjust the one or more valves according to the valve settings;And / or preferably, it further includes one or more liquid nozzles, wherein the at least one boiler input parameter includes liquid nozzle settings, wherein transmitting the adjusted at least one boiler input parameter to the boiler controller device for implementation includes: transmitting the liquid nozzle settings to the boiler controller device, and wherein the boiler controller device is configured to change the operation of the one or more liquid nozzles according to the liquid nozzle settings, and / or preferably, it further includes one or more air ports, wherein the at least one boiler input parameter includes settings for one or more air ports, wherein transmitting the adjusted at least one boiler input parameter to the boiler controller device for implementation includes: transmitting the adjusted settings for one or more air ports to the boiler controller device, and wherein the boiler controller device is configured to change the operation of the one or more air ports according to the adjusted settings for the one or more air ports.

[0069] A computer-executed method for reducing scaling rate in a recovery boiler system, the method comprising: receiving boiler operating information for a period of time by a computing device, wherein the boiler operating information includes boiler operating parameters and scaling rate for the period of time, and wherein the boiler operating parameters include one or more boiler input parameters; performing regression analysis by the computing device to determine at least one correlation between the boiler operating parameters and the scaling rate; and adjusting at least one boiler input parameter by the computing device according to the at least one correlation to minimize the scaling rate, and / or preferably, wherein receiving the scaling rate for a period of time includes: receiving a time series of scaling quantity values ​​by the computing device; and determining the scaling rate by the computing device based on the time series of scaling quantity values; and / or preferably, wherein receiving the time series of scaling quantity values ​​includes: from a configured The time series of scaling quantity values ​​received by the weight sensor for weighing the heat exchange elements, and / or preferably, wherein performing regression analysis to determine at least one correlation between boiler operating parameters and scaling rate includes: performing CART analysis on boiler operating information, and / or preferably, wherein the recovery boiler system includes one or more soot blowers configured to operate in cycles, and wherein receiving boiler operating information for a period of time includes: receiving boiler operating information for a period of time including at least one complete cycle, and / or preferably, wherein adjusting the at least one boiler input parameter according to the at least one correlation to minimize scaling rate includes at least one of the following adjustments: adjusting the chemical composition of the boiler input, adjusting the liquid gun settings, and adjusting the settings of one or more air ports.

[0070] A non-transitory computer-readable medium storing computer-executable instructions, which, in response to execution by one or more processors of a computing device, cause the computing device to perform actions including: receiving boiler operating information for a period of time, wherein the boiler operating information includes boiler operating parameters and scaling rate for that period of time, and wherein the boiler operating parameters include one or more boiler input parameters; performing regression analysis by the computing device to determine at least one correlation between the boiler operating parameters and the scaling rate; and adjusting at least one boiler input parameter according to the at least one correlation to minimize the scaling rate, and / or preferably, wherein receiving the scaling rate for a period of time includes: receiving a time series of scaling quantity values ​​by the computing device; and adjusting the scaling quantity values ​​by the computing device... The scaling rate is determined based on a time series of scaling quantity values; and / or preferably, performing regression analysis to determine at least one correlation between boiler operating parameters and scaling rate includes performing CART analysis on boiler operating information; and / or preferably, the recovery boiler system includes one or more soot blowers configured to operate in cycles; and wherein receiving boiler operating information for a period of time includes receiving boiler operating information for a period of time including at least one complete cycle; and / or preferably, adjusting the at least one boiler input parameter according to the at least one correlation to minimize scaling rate includes at least one of the following adjustments: adjusting the chemical composition of the boiler input, adjusting the liquid gun settings, and adjusting the settings of one or more air ports.

Claims

1. A system comprising: boiler; Scaling sensors associated with boiler components; Boiler controller device; as well as An analytical computing device includes a computer-readable medium having computer-executable instructions stored thereon and at least one processor, the computer-executable instructions being responsive to execution by the at least one processor to cause the analytical computing device to perform actions to minimize the fouling rate and thus reduce the amount of time spent performing cleaning processes, said actions including: Receive boiler operation information for a period of time, wherein the boiler operation information includes multiple boiler operation parameters and scaling rate for a period of time, and the multiple boiler operation parameters include multiple boiler input parameters; Perform regression analysis to determine at least one correlation between the combination of the plurality of boiler input parameters and the scaling rate; Adjust at least one boiler input parameter based on the at least one correlation to minimize scaling rate; and At least one adjusted boiler input parameter is transmitted to the boiler controller device for implementation; The step of performing regression analysis to determine at least one correlation between the combination of the plurality of boiler input parameters and the scaling rate includes: performing CART analysis on boiler operating information, wherein performing the CART analysis includes: The boiler operation information is divided into low-scaling periods and high-scaling periods; and A CART classification tree was developed using boiler input parameters to separate low-scaling periods from high-scaling periods.

2. The system as claimed in claim 1, wherein, The boiler includes heat exchange elements, and the scaling sensor is associated with the heat exchange elements.

3. The system as described in claim 2, wherein, The scaling sensor is a weight sensor configured to generate a value indicating the weight of the heat exchange element.

4. The system as claimed in claim 1, wherein, The scaling rate over a period of time includes: Time series of received scale quantity values; and The scaling rate is determined based on the time series of scaling quantity values.

5. The system of claim 1, further comprising one or more soot blowers configured to operate in a cyclic manner, wherein, Receiving boiler operation information for a period of time includes receiving boiler operation information for a period of time that includes at least one complete cycle.

6. The system of claim 1, further comprising: Electrostatic precipitator; One or more valves are configured to control the amount of dust from the electrostatic precipitator that is removed or sent to the dust removal system in order to affect the chloride level; as well as One or more actuators configured to control the one or more valves; The multiple boiler input parameters include valve settings; Specifically, transmitting the adjusted boiler input parameter to the boiler controller device for implementation includes: transmitting the valve settings to the one or more actuators; and The one or more actuators are configured to adjust the one or more valves according to the valve settings.

7. The system of claim 1, further comprising one or more liquid guns, wherein, The plurality of boiler input parameters include liquid gun settings, wherein transmitting at least one adjusted boiler input parameter to the boiler controller device for implementation includes: transmitting the liquid gun settings to the boiler controller device, wherein the boiler controller device is configured to change the operation of the one or more liquid guns according to the liquid gun settings.

8. The system of claim 1, further comprising one or more air ports, wherein, The plurality of boiler input parameters include settings for one or more air ports, wherein transmitting the adjusted at least one boiler input parameter to the boiler controller device for implementation includes: transmitting the adjusted settings for one or more air ports to the boiler controller device, wherein the boiler controller device is configured to change the operation of the one or more air ports according to the adjusted settings for the one or more air ports.

9. The system according to any one of claims 1 to 8, wherein, The multiple boiler input parameters are two boiler input parameters.

10. A computer-executed method for reducing the scaling rate in a boiler recovery system and thus reducing the amount of time spent performing cleaning processes, the method comprising: A computing device receives boiler operation information for a period of time, wherein the boiler operation information includes multiple boiler operation parameters and scaling rate for that period of time, and the multiple boiler operation parameters include multiple boiler input parameters. The computing device is used to perform regression analysis to determine at least one correlation between the combination of the plurality of boiler input parameters and the scaling rate. The computing device adjusts at least one boiler input parameter according to the at least one correlation to minimize the scaling rate; and The adjusted boiler input parameters are transmitted to the boiler control device via the computing device for implementation. The step of performing regression analysis to determine at least one correlation between the combination of the plurality of boiler input parameters and the scaling rate includes: performing CART analysis on boiler operating information, wherein performing the CART analysis includes: The boiler operation information is divided into low-scaling periods and high-scaling periods; and A CART classification tree was developed using boiler input parameters to separate low-scaling periods from high-scaling periods.

11. The computer execution method as claimed in claim 10, wherein, The scaling rate over a period of time includes: The computing device receives a time series of scaling quantity values; and The scaling rate is determined by the computing device based on the time series of scaling quantity values.

12. The computer execution method as claimed in claim 11, wherein, The time series for receiving the scale quantity value includes: a time series for receiving the scale quantity value from a weight sensor configured to weigh the heat exchange element.

13. The computer execution method as claimed in claim 10, wherein, The recovery boiler system includes one or more soot blowers configured to operate in a cycle, and receiving boiler operation information for a period of time includes receiving boiler operation information for a period of time comprising at least one complete cycle.

14. The computer execution method as claimed in claim 10, wherein, Adjusting at least one boiler input parameter according to the at least one correlation to minimize scaling includes at least one of the following adjustments: adjusting the chemical composition of the boiler input, adjusting the liquid gun settings, and adjusting the settings of one or more air ports.

15. The computer-executed method as described in any one of claims 10 to 14, wherein, The multiple boiler input parameters are two boiler input parameters.

16. A non-transitory computer-readable medium storing computer-executable instructions that, in response to execution by one or more processors of a computing device, cause the computing device to perform actions to minimize the scaling rate in a boiler recovery system and thereby reduce the amount of time required to perform a cleaning process, said actions comprising: The computing device receives boiler operation information over a period of time, wherein the boiler operation information includes multiple boiler operation parameters and scaling rate during that period, and the multiple boiler operation parameters include multiple boiler input parameters. The computing device is used to perform regression analysis to determine at least one correlation between the combination of the plurality of boiler input parameters and the scaling rate. The computing device adjusts at least one boiler input parameter according to the at least one correlation to minimize the scaling rate; and The adjusted boiler input parameters are transmitted to the boiler control device via the computing device for implementation. The step of performing regression analysis to determine at least one correlation between the combination of the plurality of boiler input parameters and the scaling rate includes: performing CART analysis on boiler operating information, wherein performing the CART analysis includes: The boiler operation information is divided into low-scaling periods and high-scaling periods; and A CART classification tree was developed using boiler input parameters to separate low-scaling periods from high-scaling periods.

17. The computer-readable medium of claim 16, wherein, The scaling rate over a period of time includes: The computing device receives a time series of scaling quantity values; and The scaling rate is determined by the computing device based on the time series of scaling quantity values.

18. The computer-readable medium of claim 16, wherein, The boiler recovery system includes one or more soot blowers configured to operate in a cyclic manner, and receiving boiler operation information for a period of time includes receiving boiler operation information for a period of time comprising at least one complete cycle.

19. The computer-readable medium of claim 16, wherein, Adjusting at least one boiler input parameter according to the at least one correlation to minimize scaling includes at least one of the following adjustments: adjusting the chemical composition of the boiler input, adjusting the liquid gun settings, and adjusting the settings of one or more air ports.

20. The computer-readable medium according to any one of claims 16 to 19, wherein, The multiple boiler input parameters are two boiler input parameters.

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