Method, system and device for operation optimization of a desulfurization system

Abstract

The application discloses a kind of desulfurization system operation optimization method, system and device, comprising, S1, to the original desulfurization system operating condition data is carried out data cleaning and obtains cleaning desulfurization system operating condition data;S2, the cleaning desulfurization system operating condition data is judged stable, and obtains stable desulfurization system operating condition data;S3, stable desulfurization system operating condition data is divided into controllable parameter and uncontrollable parameter, uncontrollable parameter corresponds to operating condition condition, controllable parameter corresponds to operation guidance, and the controllable parameter and uncontrollable parameter are divided into region and obtain desulfurization system operating condition database;S4, desulfurization system operating condition database is statistically optimized and obtains desulfurization optimization guidance table;S5, according to desulfurization optimization guidance table, selects optimal controllable parameter operation guidance.The application can realize desulfurization system operation optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of desulfurization system operation optimization, in particular to a desulfurization system operation optimization method, system and device. BACKGROUND

[0002] Energy saving and emission reduction is an important strategy for environmental protection and sustainable development, and thermal power plants are major consumers of primary energy, emitting a large amount of SO2, NOx and dust into the atmosphere during coal combustion. x In order to reduce SO2 emissions, coal-fired units are equipped with desulfurization systems, which increase energy consumption and material consumption during operation.

[0003] The most efficient and widely used desulfurization technology is the limestone-gypsum wet desulfurization technology, which has the following desulfurization process: boiler flue gas enters the absorption tower through the flue, and the flue gas reacts with the circulating slurry sprayed from the spray layer from top to bottom, and the SO2, SO3, HCl, HF and other pollutants in the flue gas are removed. The circulating slurry is pressurized by a pump and rises to the spray layer of the absorption tower, and is sprayed through a nozzle to make the flue gas react with the limestone slurry. The byproduct of the reaction reacts with the oxygen brought in by the fan to produce gypsum (CaSO4·2H2O). When the gypsum reaches a certain saturation, part of the gypsum slurry is pumped out, dehydrated and finally produced into gypsum, while fresh limestone slurry is added to the reaction tank to replenish the liquid level. Finally, the purified flue gas meets the emission standards and is discharged through the chimney.

[0004] There are two main ways to optimize desulfurization: one is to study the relationship between input variables and output variables through mechanism modeling, and to give the best working range of controllable input variables. This method mainly uses neural network, least squares support vector regression and other algorithms for modeling, and there are many assumptions in the modeling process, which cannot accurately reflect the real situation in the desulfurization tower. The other is to use historical data for big data mining, and the main methods used are association rule data mining, clustering and other methods. These methods cannot well balance the operability of the desulfurization system, require constant speed pumps not to be frequently started and stopped, and the results of the mining may be lost due to the setting of support and confidence. SUMMARY

[0005] The purpose of the present application is to provide a desulfurization system operation optimization method, system and device, which aims to solve the problem of desulfurization system operation optimization.

[0006] The present application provides a desulfurization system operation optimization method, comprising:

[0007] S1, data cleaning is performed on original desulfurization system operating condition data to obtain cleaned desulfurization system operating condition data;

[0008] S2, judge the stable desulfurization system working condition data from the cleaned desulfurization system working condition data, and obtain the stable desulfurization system working condition data;

[0009] S3, divide the stable desulfurization system working condition data into controllable parameters and uncontrollable parameters, the uncontrollable parameters correspond to working condition conditions, the controllable parameters correspond to operation guidance, and the desulfurization system working condition database is obtained by partitioning the controllable parameters and the uncontrollable parameters;

[0010] S4, obtain the desulfurization optimization guidance table by statistically optimizing the desulfurization system working condition database;

[0011] S5, select the optimal controllable parameter operation guidance according to the desulfurization optimization guidance table.

[0012] The application also provides a desulfurization system operation optimization system, comprising:

[0013] The cleaning module is used for cleaning the original desulfurization system working condition data to obtain the cleaned desulfurization system working condition data;

[0014] The judging module is used for judging the stable desulfurization system working condition data from the cleaned desulfurization system working condition data, and obtaining the stable desulfurization system working condition data;

[0015] The working condition database module is used for dividing the stable desulfurization system working condition data into controllable parameters and uncontrollable parameters, the uncontrollable parameters correspond to working condition conditions, the controllable parameters correspond to operation guidance, and the desulfurization system working condition database is obtained by partitioning the controllable parameters and the uncontrollable parameters;

[0016] The statistical optimization module is used for obtaining the desulfurization optimization guidance table by statistically optimizing the desulfurization system working condition database;

[0017] The selecting module is used for selecting the optimal controllable parameter operation guidance according to the desulfurization optimization guidance table.

[0018] The application also provides a desulfurization system operation optimization device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, and the computer program is executed by the processor to realize the steps of the above method.

[0019] The application also provides a computer readable storage medium, and the computer readable storage medium stores an information transmission implementation program, and the program is executed by the processor to realize the steps of the above method.

[0020] The application can realize the desulfurization system operation optimization.

[0021] The above description is only a summary of the technical solutions of the present application, in order to more clearly understand the technical means of the present application, and to be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0023] Figure 1 is a flow chart of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0024] Figure 2 is a schematic diagram of the overall framework of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0025] Figure 3 is a partitioning schematic diagram of uncontrollable parameters of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0026] Figure 4 is a partitioning schematic diagram of controllable parameters of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0027] Figure 5 is a statistical optimization schematic diagram of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0028] Figure 6 is a schematic diagram of the relationship between load concentration and power consumption of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0029] Figure 7 is a specific combination working area division schematic diagram of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0030] Figure 8 is a working condition division schematic diagram of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0031] Figure 9 is a general position missing number schematic diagram of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0032] Figure 10 is a transverse position continuous missing number schematic diagram of the method for optimizing the operation of the desulfurization system of the embodiment of the present application;

[0033] Figure 11is a longitudinal position continuous missing number schematic diagram of the method for optimizing operation of a desulfurization system according to an embodiment of the present application;

[0034] Figure 12 is a carbon dioxide concentration limit position missing number schematic diagram of the method for optimizing operation of a desulfurization system according to an embodiment of the present application;

[0035] Figure 13 is a limit position longitudinal continuous missing number schematic diagram of the method for optimizing operation of a desulfurization system according to an embodiment of the present application;

[0036] Figure 14 is a load limit position missing number schematic diagram of the method for optimizing operation of a desulfurization system according to an embodiment of the present application;

[0037] Figure 15 is a limit position transverse continuous missing number schematic diagram of the method for optimizing operation of a desulfurization system according to an embodiment of the present application;

[0038] Figure 16 is an actual data guidance schematic diagram of the method for optimizing operation of a desulfurization system according to an embodiment of the present application;

[0039] Figure 17 is a schematic diagram of a system for optimizing operation of a desulfurization system according to an embodiment of the present application;

[0040] Figure 18 is a schematic diagram of an apparatus for optimizing operation of a desulfurization system according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] The technical solutions of the present application will be described below in conjunction with embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0042] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0043] In addition, the terms "first", "second", "third", etc. are used only for descriptive purposes and do not connote or imply any relative importance or a quantity of the indicated technical features. Thus, features with "first", "second", "third" designations can include one or more of the features implicitly or explicitly. In the description of the present application, the meaning of "a plurality" is two or more, unless otherwise expressly specified. In addition, the terms "mounting", "connecting", "connection" should be interpreted broadly, for example, can be fixed connection, can also be detachable connection, or integral connection; can be mechanical connection, can also be electrical connection; can be directly connected, can also be indirectly connected through an intermediate medium, can be internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0044] Method embodiment

[0045] According to the embodiment of the present application, a method for optimizing the operation of a desulfurization system is provided, Figure 1 The flow chart of the method for optimizing the operation of a desulfurization system is shown in Figure 1 Specifically, the method comprises the following steps:

[0046] S1, data cleaning is performed on original desulfurization system working condition data to obtain cleaned desulfurization system working condition data;

[0047] S2, the cleaned desulfurization system working condition data is judged to be stable to obtain stable desulfurization system working condition data;

[0048] S2 specifically comprises: data is judged to be stable by using the following formula:

[0049]

[0050] Wherein, A max and A min are the maximum value and the minimum value of the working condition data of a certain desulfurization system in a period of time, is the average value of the parameter in a period of time, and δ is a stability threshold value, which is 0.05 A If δ

[0051] S3, the stable desulfurization system working condition data is divided into controllable parameters and uncontrollable parameters, the uncontrollable parameters correspond to working condition, the controllable parameters correspond to operation guidance, and the desulfurization system working condition database is obtained by partitioning the controllable parameters and the uncontrollable parameters;

[0052] S3 specifically comprises: dividing the stable desulfurization system working condition data into controllable parameters and uncontrollable parameters, wherein the uncontrollable parameters comprise: load, flue gas inlet sulfur dioxide concentration, outlet sulfur dioxide concentration and liquid level, the controllable parameters are divided into slurry pH, fixed speed pump operation combination and frequency conversion pump frequency, the uncontrollable parameters are divided into zones, and the uncontrollable parameters are respectively marked as A, B, C and D, the number of load partition is m, the number of inlet sulfur dioxide partition is n, the number of liquid level partition is p, and the number of outlet sulfur dioxide partition is q, then according to the working condition, it can be divided into m*n*p*q kinds of cases, the controllable parameters comprise: slurry pH, fixed speed pump operation combination and frequency conversion pump frequency, the controllable parameters are not divided into zones, the uncontrollable parameters correspond to the working condition, and the controllable parameters correspond to the operation guidance, and the controllable parameters and the uncontrollable parameters are divided into zones to obtain the desulfurization system working condition database.

[0053] S4, the desulfurization system working condition database is statistically optimized to obtain a desulfurization optimization guidance table.

[0054] S5, according to the desulfurization optimization guidance table, the optimal controllable parameter operation guidance is selected.

[0055] Combined with the desulfurization system working condition database, the desulfurization optimization guidance table is obtained by statistical optimization according to the circulating slurry pump power consumption.

[0056] The main steps of the application are data cleaning-data stability determination-uncontrollable parameter partition-controllable parameter partition-statistics-decision-application, Figure 2 The overall framework diagram of the desulfurization system operation optimization method of the embodiment of the application is as shown in Figure 2

[0057] Step 1: data cleaning

[0058] The running condition in the desulfurization tower is relatively complex, sometimes the variable measurement point is cleaned and the measurement point has large fluctuation, and the running optimization working condition library software needs reliable and real running condition data samples. Therefore, the original data needs to be cleaned.

[0059] Step 2: data stability determination

[0060] The running optimization working condition library needs to reflect the actual running state of the desulfurization unit, so the stability of the desulfurization system needs to be analyzed.

[0061] (1) The fluctuation range of flue gas flow and inlet SO2 concentration within a certain time is less than a threshold value.

[0062] (2) The number of slurry circulating pumps does not change within a certain time.

[0063] According to the collected real-time data, the following formula is calculated: ​

[0064]

[0065] wherein, A max and A min are the maximum and minimum values of the working condition data of a certain desulfurization system in a period of time, is the average value of the parameter in a period of time, and δ is a stability threshold value, which is 0.05 A If δ

[0066] Step 3: Incremental data mining

[0067] The present application proposes an incremental data mining method for optimizing and guiding the desulfurization system, which can adjust the working condition library that has changed in time and generate an optimal working condition table. First, the historical data is processed for working condition partitioning.

[0068] According to the analysis of the influencing factors of the desulfurization island operation, the parameters required for building the working condition library are determined: load, flue gas inlet sulfur dioxide concentration, liquid level, outlet sulfur dioxide concentration, slurry pH value, constant speed pump operation combination, and frequency of variable frequency pump.

[0069] The parameters are divided into uncontrollable parameters and controllable parameters. The uncontrollable parameters correspond to the working condition, and the controllable parameters correspond to the operation guidance. The uncontrollable parameters include load, flue gas inlet sulfur dioxide concentration, outlet sulfur dioxide concentration, and liquid level. According to the actual situation, they are partitioned and labeled respectively, and are labeled as A, B, C, and D. If the number of load partitions is m, the number of inlet sulfur dioxide partitions is n, the number of liquid level partitions is p, and the number of outlet sulfur dioxide partitions is q, then according to the working condition, it can be divided into m*n*p*q kinds of cases. For example, A01B01C01D01 corresponds to one of the working conditions. In the actual application of the working condition library, the actual working condition can be abbreviated in this form and searched in the working condition library. Figure 3 is a partitioning diagram of the uncontrollable parameters of the desulfurization system operation optimization method of the embodiment of the present application, as shown in Figure 3 .

[0070] The uncontrollable parameter partitioning is only for generating the working condition. It is also necessary to partition the controllable parameters based on the working condition to proceed to the next step of statistical guidance.

[0071] Figure 4 is a partitioning diagram of the controllable parameters of the desulfurization system operation optimization method of the embodiment of the present application, as shown in Figure 4 .

[0072] Controllable parameters are divided into slurry pH, fixed speed pump operation combination, frequency of variable frequency pump, that is, after the optimal working condition library is generated, the optimal controllable parameters are needed to guide. The controllable parameters are respectively marked as K, L, M, N and O. When the fixed speed pump combination is divided, since the start-stop state of the fixed speed pump can be represented by 0 and 1, in order to facilitate the division, the fixed speed pump combination can be represented by a binary number converted into a decimal number.

[0073] Step 4: optimization guidance

[0074] The above work is only to form a rudiment of the working condition library, to statistically analyze and determine the optimal working condition library. The goal of the desulfurization tower operation optimization is to reduce energy consumption, and the statistical optimization of the working condition library focuses on energy consumption statistics.

[0075] Figure 5 Fig. 1 is a statistical optimization schematic diagram of the method for the desulfurization system operation optimization according to an embodiment of the present application, as shown in the figure: Figure 5

[0076] The energy consumption of the desulfurization tower mainly comes from the slurry pump power consumption and the booster fan (or induced draft fan) power consumption. Since the more the sprayed slurry is, the greater the flue gas resistance is, the greater the output of the booster fan (or induced draft fan) is, and the higher the power consumption is, the slurry pump power consumption and the booster fan power consumption are positively correlated. When performing statistical optimization, the slurry pump power consumption is temporarily used to perform statistical optimization. The two parameters of support degree and energy consumption are mainly counted. The support degree represents the frequency of the combination.

[0077] Figure 6 Fig. 2 is a relationship diagram of load concentration and power consumption of the method for the desulfurization system operation optimization according to an embodiment of the present application, as shown in the figure: Figure 6

[0078] Figure 7 Fig. 3 is a specific combination working area division schematic diagram of the method for the desulfurization system operation optimization according to an embodiment of the present application, as shown in the figure: Figure 7

[0079] From the analysis of the desulfurization influencing factors, the greater the liquid-gas ratio is, the higher the desulfurization efficiency is. The load is positively correlated with the flue gas flow, so the greater the load is, the higher the power consumption is. The greater the inlet sulfur dioxide concentration is, the more sulfur dioxide needs to be removed, and the more slurry is needed. Therefore, the higher the power consumption is. Under the premise of limiting the outlet sulfur dioxide concentration and the slurry pH, the product of the load and the inlet sulfur dioxide concentration can reflect the desulfurization capacity of a certain fixed speed pump combination in a certain sense. Therefore, the overall trend of the power consumption is shown in the following figure:

[0080] Since the fixed speed pump combination basically determines the size of the power consumption, the fixed speed pump combination under each working condition is first determined, and then the slurry pH and the variable frequency pump frequency are determined. The following determines the full working condition operation guidance diagram according to the fixed speed pump working area division diagram. First, the generation rule of the fixed speed pump working area division diagram is explained: ​​​

[0081] Figure 8 is a working condition division schematic diagram of the method for operation optimization of the desulfurization system of the embodiment of the present application,

[0082] According to the working condition division rule, any working condition A except the limit working condition m B n The working condition division around it is as shown in Figure 8

[0083] Since the constant speed pump operation embodied by the original data has certain randomness, the following situations may exist, part of the working conditions is missing, according to the principle that the power consumption is distributed with the working condition, the condition for judging whether a constant speed pump combination reaches the limit position is obtained: when the pump combination no longer appears in the increasing direction of the sulfur dioxide concentration and the load increasing direction under a certain working condition, the current position is the limit position of the pump combination. Based on this, the solution to the missing position is obtained, since a pump combination can handle the working condition with high sulfur dioxide concentration or the working condition with high load, the combination is also suitable for the working condition with low sulfur dioxide concentration and the working condition with low load.

[0084] Figure 9 is a general position missing schematic diagram of the method for operation optimization of the desulfurization system of the embodiment of the present application, as shown in Figure 9

[0085] According to the principle that the high working condition corresponding constant speed pump combination can solve the low working condition problem, if there is a missing working condition, the combination of the high working condition can be used to fill in the missing position as a candidate.

[0086] For general position missing, the following filling method is adopted:

[0087] A m B n+1 →A m B n

[0088] A m+1 B n →A m B n

[0089] Figure 12 is a carbon dioxide concentration limit position missing schematic diagram of the method for operation optimization of the desulfurization system of the embodiment of the present application, as shown in Figure 12

[0090] For sulfur dioxide concentration limit position missing, the following filling method is adopted:

[0091] A m+1 B n+1 →A m B n+1 ​​​

[0092] Figure 14 is a schematic diagram of missing load limit position for the method of desulfurization system operation optimization of the embodiment of the present application, as shown in Figure 14

[0093] For the missing load limit position, the following filling method is adopted:

[0094] A m+1 B n+1 →A m+1 B n

[0095] Figure 10 is a schematic diagram of missing transverse position for the method of desulfurization system operation optimization of the embodiment of the present application, as shown in Figure 10

[0096] For the missing transverse position, the following filling method is adopted, assuming that A m B n+k position has a number:

[0097] A m B n+k →A m B n+k-1 →L→A m B n+1 →A m B n

[0098] A m+1 B n →A m B n

[0099] A m+1 B n+1 →A m B n+1

[0100] Figure 11 is a schematic diagram of missing longitudinal position for the method of desulfurization system operation optimization of the embodiment of the present application, as shown in Figure 11

[0101] For the missing longitudinal position, the following filling method is adopted, assuming that A m+k B n position has a number:

[0102] A m+k B n →A m+k-1 B n →L→A m+1 B n →A m B n ​​​

[0103] A m B n+1 →A m B n

[0104] A m+1 B n+1 →A m+1 B n

[0105] Figure 15 is the limit position transverse continuous missing number schematic diagram of the desulfurization system operation optimization method of the embodiment of the present application, as shown in Figure 15

[0106] For the limit position transverse continuous missing number, the following filling method is adopted, assuming that A m B n+k position has number:

[0107] A m B n+k →A m B n+k-1 →L→A m B n+1 →A m B n

[0108] Figure 13 is the limit position longitudinal continuous missing number schematic diagram of the desulfurization system operation optimization method of the embodiment of the present application, as shown in Figure 13

[0109] For the limit position longitudinal continuous missing number, the following filling method is adopted, assuming that A m+k B n position has number:

[0110] A m+k B n →A m+k-1 B n →L→A m+1 B n →A m B n

[0111] After filling the missing number position, the desulfurization optimization guidance table of the full working condition is obtained.

[0112] Taking one month of operation data of a certain 660MW actual unit as an example, the load is divided into 38 intervals from 220 to 600MW, the sulfur dioxide concentration is divided into 18 intervals from 600 to 4200, the outlet concentration is limited to [0, 32], and the liquid level is divided into 3 intervals from 8 to 14, according to the above rules, the constant speed pump guidance table and the desulfurization optimization guidance table of the full working condition are obtained: ​​

[0113] Step 5: Decision application

[0114] In actual operation, operability, power consumption and support should be considered. The pump should not be frequently started and stopped when the working condition changes, and the pH value of the slurry has a delay. Therefore, the operation decision should be made based on the optimized guide table.

[0115] The basic idea of operation decision is as follows: considering the rising and falling trend of the pH value of the slurry, when the pH value of the slurry rises, the optimal working condition library corresponding to the current working condition of the pH value of the slurry should not be applied, but the optimal pH value direction should be optimized, and when the pH value of the slurry falls, the working condition library corresponding to the lower pH value of the slurry should be applied. After determining the pH value, the least pump start-stop operation should be considered, and the most suitable constant speed pump combination and frequency conversion pump frequency should be selected. The specific operation is as follows:

[0116] According to the statistical optimization, the constant speed pump combination optimization table and the full working condition desulfurization optimization guide table are obtained. Most of the working conditions in the constant speed pump combination optimization table have multiple combination conditions. According to the lower power consumption of the constant speed pump combination, the constant speed pump combination optimization table with the lowest power consumption is finally obtained, and the working condition division method is adopted. According to the table, the operation has the strongest operability, which can avoid the frequent start and stop of the constant speed pump. Since the pH value has a certain delay, the decision application is made according to the idea of first determining the constant speed pump combination and then adjusting the frequency conversion pump frequency according to the current pH value.

[0117] When making a decision, it is necessary to consider whether the working condition library is available and whether it meets the current pH value change direction. There are mainly the following situations:

[0118] First, the current pH value is pH pre , the lowest pH value of the corresponding working condition in the working condition library is pH min , and the highest pH value of the corresponding working condition in the working condition library is pH max .

[0119] When the current pH value is in an upward trend:

[0120] If pH pre <pH min and pH min -pH pre <δ:

[0121]

[0122] If pH pre <pH min and pH min -pH pre >δ:

[0123]

[0124] or

[0125] If pH pre pH max

[0126]

[0127] When the current pH value is on a downward trend:

[0128] If pH pre <pH min

[0129]

[0130] or

[0131] If pH pre pH max

[0132]

[0133] Figure 16 This is a schematic diagram illustrating actual data guidance for the desulfurization system operation optimization method according to an embodiment of the present invention, such as... Figure 16 As shown:

[0134] The following example illustrates how to provide guidance for a specific set of data:

[0135] If the current operating condition is A02B04C01D03, the pH value is M06 and is on an upward trend, since K06L03M06 and K06L03M07 appear 7 and 9 times respectively in the operating condition library, the support is high. Therefore, the recommended guidance scheme is K06L03, that is, the constant speed pump combination mode is 0110, and the frequency of the variable frequency pump corresponds to the L03 range.

[0136] This invention proposes a solution that can ensure flue gas emissions from the desulfurization island meet standards while minimizing the energy consumption of the slurry circulation pump. This solution satisfies the desulfurization mechanism and can change the operating condition database as data increments, offering high flexibility. It also features high support, low power consumption, and strong operability. It avoids frequent start-stop of the constant-speed pump when operating conditions change and can find a suitable combination of constant-speed pumps, pH value, and variable frequency pump frequency based on the operating conditions.

[0137] System Implementation Examples

[0138] According to embodiments of the present invention, a system for optimizing the operation of a desulfurization system is provided. Figure 17 This is a schematic diagram of the desulfurization system operation optimization according to an embodiment of the present invention, such as... Figure 17 As shown, the desulfurization system operation optimization system according to an embodiment of the present invention specifically includes:

[0139] a cleaning module configured to clean the original desulfurization system working condition data to obtain cleaned desulfurization system working condition data;

[0140] a judging module configured to judge the cleaned desulfurization system working condition data to be stable to obtain stable desulfurization system working condition data;

[0141] The judging module is specifically configured to judge the data to be stable by using the following formula:

[0142]

[0143] wherein, A max and A min are the maximum value and the minimum value of the working condition data of a certain desulfurization system in a period of time, is the average value of the parameter in a period of time, and δ is a stability threshold value, which is 0.05 A If δ

[0144] a working condition database module configured to divide the stable desulfurization system working condition data into controllable parameters and uncontrollable parameters, mark the uncontrollable parameters corresponding to the working condition, mark the controllable parameters corresponding to the operation guidance, and divide the controllable parameters and the uncontrollable parameters to obtain a desulfurization system working condition database;

[0145] The working condition database module is specifically configured to divide the stable desulfurization system working condition data into controllable parameters and uncontrollable parameters, wherein the uncontrollable parameters include load, flue gas inlet sulfur dioxide concentration, outlet sulfur dioxide concentration and liquid level, the controllable parameters include slurry pH value, fixed-speed pump operation combination and frequency of variable frequency pump, the uncontrollable parameters are divided into zones, and the uncontrollable parameters are marked respectively as A, B, C and D, the load is divided into m zones, the inlet sulfur dioxide is divided into n zones, the liquid level is divided into p zones, and the outlet sulfur dioxide is divided into q zones, so that m*n*p*q conditions can be divided according to the working condition, the controllable parameters include slurry pH value, fixed-speed pump operation combination and frequency of variable frequency pump, the controllable parameters are not divided into zones, the uncontrollable parameters correspond to the working condition, the controllable parameters correspond to the operation guidance, and the controllable parameters and the uncontrollable parameters are divided to obtain the desulfurization system working condition database.

[0146] a statistical optimization module configured to statistically optimize the desulfurization system working condition database to obtain a desulfurization optimization guidance table;

[0147] a selection module configured to select optimal controllable parameter operation guidance according to the desulfurization optimization guidance table.

[0148] The statistical optimization module is specifically configured to: in combination with a desulfurization system working condition database, statistically optimizing the desulfurization system according to the circulating slurry pump power consumption to obtain a desulfurization optimization guide table.

[0149] The embodiment of the present application is a system embodiment corresponding to the above-mentioned method embodiment, and the specific operation of each module can be understood with reference to the description of the method embodiment, which will not be repeated here.

[0150] Device embodiment one

[0151] The embodiment of the present application provides a device for optimizing the operation of a desulfurization system, as shown in the figure, comprising: a memory 180, a processor 182, and a computer program stored on the memory 180 and executable on the processor 182, the computer program being executed by the processor to implement the steps in the above-mentioned method embodiment. Figure 18

[0152] Device embodiment two

[0153] The embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores an information transmission implementation program, and the program is executed by the processor 182 to implement the steps in the above-mentioned method embodiment.

[0154] The computer readable storage medium described in the embodiment includes but is not limited to ROM, RAM, magnetic disk or optical disk, etc.

[0155] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present application can be realized by a general computing device, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and optionally, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described can be executed in different order, or they can be manufactured into individual integrated circuit modules, or multiple modules or steps can be manufactured into a single integrated circuit module. Thus, the present application is not limited to any specific hardware and software combination.

[0156] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the above-mentioned embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements of the technical solutions of the embodiments of the present application do not make the essence of the corresponding technical solutions deviate from the scope of the present application.​

Claims

1. A method for optimizing the operation of a desulfurization system, characterized in that, include, S1. Clean the original desulfurization system operating data to obtain the cleaned desulfurization system operating data. S2. Stabilize the operating data of the cleaning and desulfurization system to obtain stable operating data of the desulfurization system. S3. The stable desulfurization system operating data is divided into controllable parameters and uncontrollable parameters. Uncontrollable parameters include: load, flue gas inlet sulfur dioxide concentration, outlet sulfur dioxide concentration, and liquid level. Controllable parameters are divided into slurry pH value, constant speed pump operation combination, and variable frequency pump frequency. Uncontrollable parameters are partitioned and labeled as A, B, C, and D respectively. There are m load partitions, n inlet sulfur dioxide partitions, p liquid level partitions, and q outlet sulfur dioxide partitions. According to the operating conditions, there are m*n*p*q ​​cases. Controllable parameters include: slurry pH value, constant speed pump operation combination, and variable frequency pump frequency. Controllable parameters are not partitioned. Uncontrollable parameters correspond to operating conditions, and controllable parameters correspond to operating guidelines. By partitioning the controllable and uncontrollable parameters, a desulfurization system operating condition database is obtained. S4. Statistically optimize the desulfurization system operating condition database to obtain a desulfurization optimization guidance table; S5. Based on the desulfurization optimization guide, select the optimal controllable parameters for operation guidance; among them, based on statistical optimization, the constant speed pump combination optimization table and the full-condition desulfurization optimization guide table are obtained. The operation is carried out according to the table by dividing the operating conditions into zones. Then, the constant speed pump combination is determined first, and the frequency of the variable frequency pump is adjusted according to the current pH value.

2. The method according to claim 1, characterized in that, Specifically, S2 includes: using the following formula to determine data stability: ， in, and These represent the maximum and minimum values ​​of operating data for a certain desulfurization system over a period of time. The average value of the parameter over a period of time. To stabilize the threshold, a value of 0.05 is chosen over a period of time n. If the data within this time period is considered to be in a steady state, then the window is considered to slide backward by a step size k to continue sampling, and the data in the new window is determined.

3. The method according to claim 1, characterized in that, S4 specifically includes: combining the desulfurization system operating condition database and statistically optimizing the power consumption of the circulating slurry pump to obtain a desulfurization optimization guidance table.

4. A system for optimizing the operation of a desulfurization system, characterized in that, include, Cleaning module: Used to clean the raw desulfurization system operating data to obtain cleaned desulfurization system operating data; Judgment module: used to determine the stability of the operating data of the cleaning and desulfurization system, and obtain stable operating data of the desulfurization system; The operating condition database module is used to divide stable desulfurization system operating condition data into controllable and uncontrollable parameters. Uncontrollable parameters include load, flue gas inlet sulfur dioxide concentration, outlet sulfur dioxide concentration, and liquid level. Controllable parameters are divided into slurry pH value, constant speed pump operation combination, and variable frequency pump frequency. Uncontrollable parameters are partitioned and labeled as A, B, C, and D respectively. There are m load partitions, n inlet sulfur dioxide partitions, p liquid level partitions, and q outlet sulfur dioxide partitions. Therefore, there are m*n*p*q ​​possible cases based on the operating conditions. Controllable parameters include slurry pH value, constant speed pump operation combination, and variable frequency pump frequency. Controllable parameters are not partitioned. Uncontrollable parameters correspond to operating conditions, and controllable parameters correspond to operating guidelines. The desulfurization system operating condition database is obtained by partitioning the controllable and uncontrollable parameters. Statistical optimization module: used to perform statistical optimization on the desulfurization system operating condition database to obtain a desulfurization optimization guidance table; Selection module: Used to select the optimal controllable parameters for operation based on the desulfurization optimization guide table; among them, the constant speed pump combination optimization table and the full-condition desulfurization optimization guide table are obtained based on statistical optimization. The operation is carried out according to the table by dividing the operating conditions into zones. Then, the constant speed pump combination is determined first and the frequency of the variable frequency pump is adjusted according to the current pH value.

5. The system according to claim 4, characterized in that, The judgment module is specifically used to determine data stability using the following formula: ， in, and These represent the maximum and minimum values ​​of operating data for a certain desulfurization system over a period of time. The average value of the parameter over a period of time. To stabilize the threshold, a value of 0.05 is chosen over a period of time n. If the data within this time period is considered to be in a steady state, then the window is considered to slide backward by a step size k to continue sampling, and the data in the new window is determined.

6. The system according to claim 4, characterized in that, The statistical optimization module is specifically used to: combine the desulfurization system operating condition database and perform statistical optimization based on the power consumption of the circulating slurry pump to obtain a desulfurization optimization guidance table.

7. A device for optimizing the operation of a desulfurization system, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for optimizing the operation of a desulfurization system as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an information transmission implementation program, which, when executed by a processor, implements the steps of the desulfurization system operation optimization method as described in any one of claims 1 to 3.

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