Control method, device and equipment of oxidation fan and storage medium

Abstract

The invention provides a control method and device of an oxidation fan, equipment and a storage medium. The method comprises the steps that sulfite to-be-removed amount data and unit operation data of slurry in a previous time period are obtained; based on the to-be-desulfurized sulfite amount data and the unit operation data, the unit operation load and the to-be-desulfurized total amount in the current time period are predicted; determining the working condition level of the current time period based on the unit operation load and the to-be-desulfurized total amount in the current time period; and based on the working condition level of the current time period, the blast volume of the oxidation fan is controlled. According to the method disclosed by the invention, the blast volume of the oxidation fan can be automatically adjusted according to the working condition level of the current time period, so that the power consumption of the oxidation fan can be effectively reduced while the over-discharge risk can be avoided under various working conditions.

Description

Technical Field

[0001] The present disclosure relates to the field of automation control technology, and in particular to a control method, device, equipment and storage medium for an oxidation blower. Background Art

[0002] With the continuous improvement of environmental protection requirements, most thermal power plants have built desulfurization facilities and gradually met the requirements of ultra-low emissions. As an important component of desulfurization facilities, the control of the air volume of the oxidation fan will directly affect the desulfurization effect and the power consumption of the thermal power unit. If the air volume is low, the slurry quality will deteriorate and an environmental over-emission accident will occur; if the air volume is high, the power consumption of the oxidation fan will increase. At present, most oxidation fans use fixed-frequency Roots blowers. In order to ensure that there are no environmental over-emission accidents, usually only a higher air volume is set. This leads to a large power consumption of the oxidation fan under low sulfur and low load. Therefore, how to effectively reduce the power consumption of the oxidation fan while reducing the risk of environmental over-emission is a technical problem that needs to be solved urgently by technical personnel in this field. Summary of the invention

[0003] In view of this, the present disclosure proposes a control method, device, equipment and storage medium for an oxidation blower, which can effectively reduce the power consumption of the oxidation blower while reducing the risk of environmental over-emission.

[0004] According to a first aspect of the present disclosure, a control method for an oxidation blower is provided, comprising:

[0005] Obtain the sulfite removal data of the slurry and the unit operation data in the previous time period;

[0006] Based on the data of the amount of sulfite to be removed and the unit operation data, predict the unit operation load and the total amount of sulfite to be removed in the current time period;

[0007] Determining the operating level of the current time period based on the unit operating load and the total amount to be desulfurized in the current time period;

[0008] Based on the operating level of the current time period, the air volume of the oxidation fan is controlled.

[0009] In a possible implementation, when predicting the unit operating load and the total amount to be desulfurized in the current time period based on the sulfite amount to be desulfurized data and the unit operating data, it includes:

[0010] Based on the unit operation data, predict the unit operation load within the current time period;

[0011] Based on the data on the amount of sulfite to be desulfurized and the unit operation data, the total amount of sulfite to be desulfurized in the current time period is predicted.

[0012] In a possible implementation, when predicting the unit operating load in the current time period based on the unit operating data, it includes:

[0013] Determining the unit operation load sequence for the previous time period based on the unit operation data;

[0014] Based on the unit operating load sequence in the previous time period, the LSTM model is used to predict the unit operating load in the current time period.

[0015] In a possible implementation, when predicting the total amount of sulfite to be desulfurized in the current time period based on the sulfite to be desulfurized amount data and the unit operation data, it includes:

[0016] dividing the unit operation data into first unit operation data and second unit operation data;

[0017] Based on the first unit operation data, the second unit operation data and the sulfite amount to be desulfurized data, the total amount to be desulfurized in the current time period is predicted.

[0018] In a possible implementation, when predicting the total amount to be desulfurized in the current time period based on the first unit operation data, the second unit operation data and the sulfite amount to be desulfurized data, a pre-built total amount to be desulfurized prediction model is used.

[0019] In one possible implementation, when determining the operating condition level of the current time period based on the unit operating load and the total amount to be desulfurized within the current time period, it is implemented based on a pre-constructed operating condition classification table, wherein the operating condition level table includes multiple operating condition levels divided according to the unit operating load and the total amount to be desulfurized, and each operating condition level is provided with a corresponding unit operating load range and a range of the amount to be desulfurized.

[0020] In a possible implementation, based on the operating level of the current time period, controlling the air volume of the oxidation fan includes:

[0021] Obtaining a range of the amount of desulfurization to be performed corresponding to the operating level of the current time period;

[0022] Calculating the oxygen demand range for the current time period according to the range of the amount to be desulfurized;

[0023] The air volume of the oxidation fan is controlled according to the oxygen demand range of the current time period.

[0024] According to a second aspect of the present disclosure, a control device for an oxidation blower is provided, comprising:

[0025] A data acquisition module is used to acquire the sulfite removal data of the slurry and the unit operation data in the previous time period;

[0026] A data prediction module, used to predict the unit operating load and the total amount to be desulfurized in the current time period based on the sulfite to-be-desulfurized amount data and the unit operating data;

[0027] An operating level determination module, used to determine the operating level of the current time period based on the unit operating load and the total amount to be desulfurized in the current time period;

[0028] The oxidation fan control module is used to control the air volume of the oxidation fan based on the operating level of the current time period.

[0029] According to a third aspect of the present disclosure, a control device for an oxidation blower is provided, comprising: a processor; and a memory for storing processor executable instructions; wherein the processor is configured to execute the method described in the first aspect of the present disclosure.

[0030] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions, when executed by a processor, implement the method described in the first aspect of the present disclosure.

[0031] The present disclosure provides a control method, device, equipment and storage medium for an oxidation fan, the method comprising: obtaining the data of the amount of sulfite to be removed and the unit operation data of the slurry in the previous time period; based on the data of the amount of sulfite to be removed and the unit operation data, predicting the unit operation load and the total amount to be desulfurized in the current time period; based on the unit operation load and the total amount to be desulfurized in the current time period, determining the operating level of the current time period; based on the operating level of the current time period, controlling the air volume of the oxidation fan. The method disclosed in the present disclosure can automatically adjust the air volume of the oxidation fan according to the operating level of the current time period, thereby ensuring that the power consumption of the oxidation fan can be effectively reduced while avoiding the risk of over-discharge under various working conditions.

[0032] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0034] Figure 1 A flow chart showing a method for controlling an oxidation blower according to an embodiment of the present disclosure;

[0035] Figure 2 A schematic block diagram of an online sulfite monitoring device according to an embodiment of the present disclosure is shown;

[0036] Figure 3 A schematic block diagram showing a total amount of desulfurization prediction model according to an embodiment of the present disclosure;

[0037] Figure 4 A flowchart showing an example of a method for controlling an oxidation blower according to an embodiment of the present disclosure;

[0038] Figure 5 A schematic block diagram showing a control device for an oxidation blower according to an embodiment of the present disclosure;

[0039] Figure 6 A schematic block diagram of a control device for an oxidation blower according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0040] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0041] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0042] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0043] <Method Example>

[0044] Figure 1 A flow chart showing a method for controlling an oxidation blower according to an embodiment of the present disclosure is shown. Figure 1 As shown, the method includes steps S1100-S1400.

[0045] S1100, obtaining the sulfite removal data of the slurry and the unit operation data in the previous time period.

[0046] It should be noted here that during the wet flue gas desulfurization process, the sulfite removal value in the slurry and the unit operation parameters will be collected at the same time according to the preset frequency. In this way, after each set time period, multiple sulfite removal values ​​can be collected. The time series formed by arranging the multiple sulfite removal values ​​in the order of collection time is the sulfite removal data of the slurry in the set time period. For example, k sulfite removal values ​​are collected in sequence in the set time period t and are recorded as m. 1t 、m 2t ,…,m kt , then the sulfite removal amount data m within the set time period t ={m 1t 、m 2t ,…,m kt Similarly, in a set time period, multiple sets of unit operating parameters can also be collected at the same time. The time series formed by arranging the multiple sets of unit operating parameters in the order of collection time is the unit operating data in the set time period. For example, in the set time period t, k sets of unit operating parameters are collected in sequence and recorded as f 1t 、f 2t ,…,f kt , then set the unit operation data f within time period t t ={f 1t 、f 2t ,…,f kt Each group of unit operating parameters includes at least one parameter of the unit operating load, FGD inlet SO2 concentration, flue gas volume, FGD outlet SO2 concentration and slurry pH.

[0047] In a possible implementation, the amount of sulfite to be removed can be measured by an online sulfite monitoring device. The online sulfite monitoring device is as follows: Figure 2The multi-grid sulfite online monitoring box 13 is shown, in which a plurality of analysis units 14 are arranged, and the plurality of analysis units 14 are evenly distributed in a circular shape in the multi-grid sulfite online monitoring box 13. Each analysis unit 14 is connected to different depth positions inside the slurry pool 2 through a connecting pipe, and a slurry suction probe 15 is arranged at one end of each connecting pipe extending into the slurry pool 2, so that after receiving the acquisition signal, the slurry at different depth positions can be simultaneously sucked by each probe 15 and transported to each analysis unit 14 for detection. Each analysis unit 14 will detect the sulfite ion concentration value for the sucked slurry and upload the detected sulfite ion concentration value to the DCS system through the PLC. After receiving the sulfite ion concentration value uploaded by each analysis 14, the DCS system will calculate the average of the plurality of sulfite ion concentration values, and calculate the sulfite to be removed value collected this time based on the calculated average. After each collection of the sulfite to-be-removed value is completed, the slurry detected in each analysis unit 14 will be discharged into the absorption tower 1 through the discharge device 16 to prepare for the next collection of the sulfite to-be-removed value. Figure 2 As shown, each connecting pipeline is provided with a first valve, a filter screen and a second valve. The first valve is provided at one end close to the slurry pool, the second valve is provided at one end close to the entrance of the analysis unit, and the filter screen is provided between the first valve and the second valve and close to the first valve. The slurry absorption process can be controlled by the first valve and the second valve, and the slurry can be filtered by the filter screen, thereby increasing the amount of sulfite to be removed.

[0048] Each time the sulfite online device collects slurry, it will measure a sulfite to-be-removed value of the slurry. Multiple sulfite to-be-removed values ​​will be measured within a set time period. The time series formed by arranging multiple sulfite to-be-removed values ​​in sequence is the sulfite to-be-removed data of the slurry within the set time period. The sulfite online device can realize online monitoring of the sulfite to-be-removed data, thereby improving the timeliness and accuracy of the sulfite to-be-removed data acquisition.

[0049] It should be further explained that, after each set time period, a sulfite amount to be removed data and a unit operation data will be generated, so that the unit operation load and the total amount to be desulfurized in the next set time period can be predicted based on the acquired sulfite amount to be removed data and unit operation data, and then the oxidation fan control in the next time period can be performed based on the unit operation load and the total amount to be desulfurized in the next set time period. That is to say, in this embodiment, it is actually based on the sulfite amount to be removed data m obtained in the previous time period t-1. t-1 and unit operation data f t-1The historical data composed is used to predict the unit operating load Lt and the total amount to be desulfurized Mt in the current time period t, and then the oxidation fan in the current time period t is controlled.

[0050] After obtaining the sulfite removal amount data m t-1 and unit operation data f t-1 After that, step S1200 can be performed, based on the sulfite removal amount data m t-1 and unit operation data f t-1 , predict the unit operating load Lt and the total amount to be desulfurized Mt in the current time period t. Specifically, based on the unit operating data f t-1 , predict the unit operating load Lt in the current time period t; then based on the sulfite removal amount data m t-1 and unit operation data f t-1 , predict the total amount Mt to be desulfurized in the current time period t.

[0051] In a possible implementation, based on the unit operation data f t-1 , when predicting the unit operating load Lt in the current time period t, the following steps may be included:

[0052] First, based on the unit operation data t-1 , determine the unit operation load sequence of the previous time period t-1. Specifically, the unit operation data f t-1 It includes multiple sets of unit operating parameters, each set of operating parameters includes the unit operating load. Therefore, after obtaining the unit operating data f t-1 After that, the operating data of the components can be traversed in order. t-1 For each group of unit operating parameters, for the traversed current unit operating parameters, extract the unit operating load in the current unit operating parameters, and after the traversal, extract the multiple unit operating loads monitored in the previous time period t-1, and arrange the multiple unit operating loads monitored in the previous time period t-1 in the order of collection time to obtain the unit operating load sequence of the previous time period.

[0053] Second, based on the unit operating load sequence of the previous time period, the pre-trained LSTM model can be used to predict the unit operating load Lt in the current time period. Specifically, the unit operating load sequence of the previous time period is input into the pre-trained LSTM model, and the LSTM model can automatically predict the unit operating load Lt in the current time period.

[0054] In a possible implementation, based on the sulfite to-be-removed amount data m t-1 and unit operation data f t-1 , when predicting the total amount Mt to be desulfurized in the current time period t, the following steps may be included:

[0055] First, the unit operation data f t-1 The first unit operation data D1 and the second unit operation data D2 can be divided into the first unit operation data D1 and the second unit operation data D2. t-1 The first unit operation data D1 can be obtained by extracting the unit operation parameter group sorted as the cardinality and combining them in order. t-1 The second unit operation data D2 can be obtained by extracting the unit operation parameter groups with even order and combining them in order. For example, in the previous time period, the unit operation data f t-1 ={f 1(t-1) 、f 2(t-1) 、f 3(t-1) 、f 4(t-1) 、f 5(t-1) 、f (t-1)}, extract the base unit operating parameter group f in sequence 1(t-1) 、f 3(t-1) 、f 5(t-1) And the first unit operation data D1 can be obtained by combining them in sequence. The first unit operation data D1 = {f 1(t-1) 、f 3(t-1) 、f 5(t-1)}, at the same time, the operating parameter groups f of even-numbered units are sequentially extracted 2(t-1) 、f 4(t-1) 、f 6(t-1) And the second unit operation data D2 can be obtained by combining them in sequence. The second unit operation data D2 = {f 2(t-1) 、f 4(t-1) 、f 6(t-1)}.

[0056] Second, based on the first unit operation data D1, the second unit operation data D2 and the sulfite removal amount data m t-1 , predict the total amount Mt to be desulfurized in the current time period t.

[0057] In a possible implementation, based on the first unit operation data D1, the second unit operation data D2 and the sulfite to-be-desulfurized amount data m t-1 , when predicting the total amount of desulfurization Mt in the current time period t, the pre-built total amount of desulfurization prediction model is used. Figure 3As shown, the total amount to be desulfurized prediction model includes a first input layer, a second input layer, a first prediction module, a second prediction module and an output module. The first prediction module is connected between the first input layer and the output module, and is used to receive the first unit operation data D1 and the second unit operation data D2 input by the first input layer, and predict the first total amount to be desulfurized Ma based on the first unit operation data D1 and the second unit operation data D2, and input the first total amount to be desulfurized Ma to the output module. The second prediction module is connected between the second input layer and the output module, and is used to receive the sulfite amount to be desulfurized data m input by the second input layer. t-1 , based on the amount of sulfite to be desulfurized m t-1 The second total amount to be desulfurized Mb is predicted and input into the output module. The output module is used to sum the first total amount to be desulfurized Ma and the second total amount to be desulfurized Mb, and output the total amount to be desulfurized Mt in the current time period t after activation by the Sigmoid function.

[0058] In a possible implementation, the first prediction module includes a convolutional layer CNN, a time series prediction model Prophet, a 3-layer hidden layer dense, and a fully connected layer. After receiving the first unit operation data D1 and the second unit operation data D2, the first prediction module first uses the convolutional layer CNN to extract features from the first unit operation data D1 and the second unit operation data D2 respectively, and obtains the first unit operation features and the second unit operation features of the previous time period t-1; then, based on the first unit operation features and the second unit operation features of the previous time period, the time series prediction model Prophet is used to predict the first unit operation features and the second unit operation features of the current time period t respectively; then, the 3-layer hidden layer dense is used to splice the first unit operation features and the second unit operation features of the current time period and input them into the fully connected layer, and finally the first total amount Ma to be desulfurized is output through the fully connected layer. Among them, the function of the first prediction module is realized based on the training of multiple groups of first unit operation data and second unit operation data in historical time periods.

[0059] In a possible implementation, the second prediction module is an autoregressive model AR, which converts the sulfite to-be-desulfurized amount data m in the previous time period into t-1 The second total amount of sulfite to be desulfurized Mb can be predicted by the autoregressive model AR, wherein the autoregressive model AR is trained based on multiple sets of historical sulfite amount data to be desulfurized.

[0060] After predicting the total amount to be desulfurized Mt and the unit operating load Lt in the current time period t, step S1300 can be executed to determine the operating level of the current time period t based on the unit operating load Lt and the total amount to be desulfurized Mt in the current time period t.

[0061] In one possible implementation, when determining the operating condition level of the current time period t based on the unit operating load Lt and the total amount to be desulfurized Mt within the current time period t, it is implemented based on a pre-constructed operating condition classification table, wherein the operating condition level table includes multiple operating condition levels divided according to the unit operating load and the total amount to be desulfurized, and each operating condition level is provided with a corresponding unit operating load range and a range of the amount to be desulfurized.

[0062] When determining the operating condition level of the current time period based on a pre-constructed operating condition classification table, the following steps may be included: obtaining the unit operating load Lt and the total amount to be desulfurized Mt in the current time period t, determining within which operating condition level of the unit operating load range and the amount to be desulfurized in the operating condition classification table the unit operating load Lt and the total amount to be desulfurized Mt fall, and taking the operating condition level that falls within as the operating condition level of the current time period t.

[0063] In a possible implementation, the operating condition classification table is obtained by performing K-means clustering on a set number of historical operating data. Each piece of historical operating data includes the unit operating load and the total amount to be desulfurized within a set time period. The calculation method of the unit operating load and the total amount to be desulfurized within a set time period in each piece of historical operating data is as described above and will not be repeated here.

[0064] When performing K-means clustering on a set number of historical operating data to form an operating condition classification table, first cluster the unit operating load in each piece of historical operating data to obtain multiple unit operating load clustering clusters. Then, traverse each unit operating load clustering cluster, set the current load level corresponding to the current unit operating load clustering cluster for the traversed current unit operating load clustering cluster, and determine the unit operating load range of the current load classification based on the maximum and minimum values ​​of the unit operating load in the current unit operating load clustering cluster. Furthermore, cluster each piece of historical data in the current unit operating load clustering cluster according to the total amount to be desulfurized to obtain multiple total amount to be desulfurized clustering clusters, and set the corresponding desulfurization amount level and desulfurization amount range for each total amount to be desulfurized. In this way, the current load level can be further divided into multiple desulfurization amount levels. After the traversal is completed, multiple load levels can be obtained, and each load level is further divided into multiple desulfurization levels. The load level and a desulfurization level under the load level determine an operating condition level. For each operating condition level, a mapping relationship between the operating condition level, load level, unit operating load range, desulfurization level, and range of desulfurization to be desulfurized is constructed and stored in the mapping relationship table to obtain the operating condition classification table.

[0065] After determining the operating level of the current time period, step S1400 may be performed to control the air volume of the oxidation fan based on the operating level of the current time period. Specifically, the following steps may be included:

[0066] First, obtain the range of the amount of desulfurization to be performed corresponding to the operating level of the current time period, which can be obtained by querying the operating level table.

[0067] Second, according to the range of the amount to be desulfurized, calculate the range of oxygen demand in the current time period. Specifically, the calculation formula of oxygen demand is as follows:

[0068] Vo2=(2-η)*M

[0069] Wherein, M is the upper or lower limit of the amount to be desulfurized, η is the desulfurization efficiency factor, and Vo2 is the oxygen demand corresponding to the total amount to be desulfurized.

[0070] Substituting the upper and lower limits of the desulfurization amount range into the formula, the upper and lower limits of the oxygen demand in the current time period can be calculated. The upper and lower limits of the oxygen demand in the current time period determine the oxygen demand range in the current time period.

[0071] Third, according to the oxygen demand range of the current time period, control the air volume of the oxidation fan. Specifically, based on the pre-configured selection strategy, an oxygen demand that can effectively avoid over-exhaustion and use the lowest power consumption of the oxidation fan is selected from the oxygen demand range as the target oxygen demand, and the pressure head and frequency of the oxidation fan are automatically adjusted based on the target oxygen demand, so that the oxidation fan can be controlled to blow air according to the target oxygen demand. Among them, it should be noted here that the oxidation fan in the present disclosure does not adopt the traditional Roots blower, but uses a magnetically levitated oxidation variable frequency blower that can be automatically adjusted. After calculating the pressure head and frequency, the pressure head and frequency of the magnetically levitated oxidation variable frequency blower are adjusted to achieve automatic adjustment of the air volume.

[0072] In order to clearly illustrate the inventive concept of the present disclosure, the following Figure 4 The method disclosed in the present invention is described again. Figure 4 As shown, the following steps may be included when controlling the oxidation blower:

[0073] First, historical data is obtained and processed such as data cleaning. The historical data here refers to the sulfite removal data of the slurry and the unit operation data in the previous time period.

[0074] Second, based on the historical data, the unit operating load Lt and the total amount to be desulfurized Mt in the current time period t are predicted. The specific steps are as mentioned above and will not be repeated here.

[0075] Third, based on the unit operating load Lt and the total amount to be desulfurized Mt in the current time period t, the operating condition classification table is obtained in combination with K-means clustering to determine the operating condition level of the current time period t.

[0076] Fourth, based on the operating level in the current time period, the magnetic levitation permanent magnet variable frequency fan is adjusted to achieve automatic control of the oxidation fan.

[0077] The present disclosure provides a control method for an oxidation fan, including: obtaining the data of the amount of sulfite to be removed and the unit operation data of the slurry in the previous time period; based on the data of the amount of sulfite to be removed and the unit operation data, predicting the unit operation load and the total amount to be desulfurized in the current time period; based on the unit operation load and the total amount to be desulfurized in the current time period, determining the operating level of the current time period; based on the operating level of the current time period, controlling the air volume of the oxidation fan. The method disclosed in the present disclosure can automatically adjust the air volume of the oxidation fan according to the operating level of the current time period, thereby ensuring that the power consumption of the oxidation fan can be effectively reduced while avoiding the risk of over-discharge under various working conditions.

[0078] <Device Example>

[0079] Figure 5 FIG. 1 is a schematic block diagram of a control device for an oxidation blower according to an embodiment of the present disclosure. Figure 5 As shown, the device 100 includes:

[0080] The data acquisition module 110 is used to acquire the sulfite removal data of the slurry and the unit operation data in the previous time period;

[0081] A data prediction module 120, for predicting the unit operating load and the total amount of sulfite to be desulfurized in the current time period based on the sulfite to be desulfurized amount data and the unit operating data;

[0082] The operating level determination module 130 is used to determine the operating level of the current time period based on the unit operating load and the total amount of desulfurization to be performed in the current time period;

[0083] The oxidation fan control module 140 is used to control the air volume of the oxidation fan based on the operating level of the current time period.

[0084] <Equipment Embodiment>

[0085] Figure 6 FIG. 1 is a schematic block diagram of a control device for an oxidation blower according to an embodiment of the present disclosure. Figure 6As shown, the control device 200 of the oxidation blower includes: a processor 210 and a memory 220 for storing executable instructions of the processor 210. The processor 210 is configured to implement any of the above-mentioned control methods of the oxidation blower when executing the executable instructions.

[0086] Here, it should be noted that the number of processors 210 may be one or more. At the same time, the control device 200 of the oxidation blower in the embodiment of the present disclosure may also include an input device 230 and an output device 240. The processor 210, the memory 220, the input device 230 and the output device 240 may be connected via a bus or in other ways, which are not specifically limited here.

[0087] The memory 220 is a computer-readable storage medium that can be used to store software programs, computer executable programs, and various modules, such as the program or module corresponding to the control method of the oxidation blower in the embodiment of the present disclosure. The processor 210 executes various functional applications and data processing of the control device 200 of the oxidation blower by running the software program or module stored in the memory 220.

[0088] The input device 230 may be used to receive input numbers or signals. The signals may be key signals related to user settings and function control of the device / terminal / server. The output device 240 may include display devices such as display screens.

[0089] <Storage Medium Embodiment>

[0090] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is also provided, on which computer program instructions are stored. When the computer program instructions are executed by the processor 210, any of the above-mentioned control methods for the oxidation blower is implemented.

[0091] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A control method for an oxidation fan, characterized in that: include: Obtain the sulfite removal data of the slurry and the unit operation data in the previous time period; Based on the data of the amount of sulfite to be removed and the unit operation data, predict the unit operation load and the total amount of sulfite to be removed in the current time period; Determining the operating level of the current time period based on the unit operating load and the total amount to be desulfurized in the current time period; Based on the operating level of the current time period, the air volume of the oxidation fan is controlled.

2. The method according to claim 1, characterized in that When predicting the unit operating load and the total amount of sulfite to be desulfurized in the current time period based on the sulfite to be desulfurized amount data and the unit operating data, it includes: Based on the unit operation data, predict the unit operation load within the current time period; Based on the data on the amount of sulfite to be desulfurized and the unit operation data, the total amount of sulfite to be desulfurized in the current time period is predicted.

3. The method according to claim 2, characterized in that When predicting the unit operating load in the current time period based on the unit operating data, it includes: Determining the unit operation load sequence for the previous time period based on the unit operation data; Based on the unit operating load sequence in the previous time period, the LSTM model is used to predict the unit operating load in the current time period.

4. The method according to claim 2, characterized in that: When predicting the total amount of sulfite to be desulfurized in the current time period based on the sulfite to be desulfurized amount data and the unit operation data, it includes: dividing the unit operation data into first unit operation data and second unit operation data; Based on the first unit operation data, the second unit operation data and the sulfite amount to be desulfurized data, the total amount to be desulfurized in the current time period is predicted.

5. The method according to claim 4, characterized in that When predicting the total amount to be desulfurized in the current time period based on the first unit operation data, the second unit operation data and the sulfite amount to be desulfurized data, a pre-built total amount to be desulfurized prediction model is used.

6. The method according to claim 1, characterized in that When determining the operating condition level of the current time period based on the unit operating load and the total amount to be desulfurized within the current time period, it is implemented based on a pre-constructed operating condition classification table, wherein the operating condition level table includes multiple operating condition levels divided according to the unit operating load and the total amount to be desulfurized, and each operating condition level is provided with a corresponding unit operating load range and a range of the amount to be desulfurized.

7. The method according to claim 1, characterized in that Based on the operating level of the current time period, controlling the air volume of the oxidation fan includes: Obtaining a range of the amount of desulfurization to be performed corresponding to the operating level of the current time period; Calculating the oxygen demand range for the current time period according to the range of the amount to be desulfurized; The air volume of the oxidation fan is controlled according to the oxygen demand range of the current time period.

8. A control device for an oxidation fan, characterized in that: include: A data acquisition module is used to acquire the sulfite removal data of the slurry and the unit operation data in the previous time period; A data prediction module, used to predict the unit operating load and the total amount to be desulfurized in the current time period based on the sulfite to-be-desulfurized amount data and the unit operating data; An operating level determination module, used to determine the operating level of the current time period based on the unit operating load and the total amount to be desulfurized in the current time period; The oxidation fan control module is used to control the air volume of the oxidation fan based on the operating level of the current time period.

9. A control device for an oxidation fan, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the method described in any one of claims 1 to 7 when executing the executable instructions.

10. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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