Chemical plant tail gas oxygen content calculation method, device, storage medium and processor

Abstract

The present invention provides a method, device, storage medium and processor for calculating the oxygen content of tail gas of a chemical plant, and belongs to the field of chemical technology. The calculation method comprises: detecting the current data of other sites other than the oxygen content of the tail gas of the chemical plant; calculating the oxygen content of the tail gas of the chemical plant based on the current data of the other sites and the oxygen content prediction model, wherein the oxygen content prediction model is a model obtained by neural network training based on the fluctuation history data of the tail gas oxygen content and the historical data of other sites related to the fluctuation history data of the tail gas oxygen content. The present invention does not rely on manpower and ensures the safe operation of the chemical plant.

Description

Technical Field

[0001] The present invention relates to the field of chemical engineering technology, and in particular to a method, a device, a storage medium and a processor for calculating the oxygen content of tail gas of a chemical plant. Background Art

[0002] Oxygen, as a reactant or reaction product in many chemical plants, often appears in the tail gas discharged at different reaction stages (for example, oxygen is discharged in both the hydrogenation stage and the oxidation stage of the anthraquinone method hydrogen peroxide plant). A high concentration of oxygen can easily become the fuse for dangerous accidents such as explosions and fires, especially when oxygen with a content exceeding the threshold comes into contact with other flammable and explosive materials (for example, the tail gas of the oxidation tower of the hydrogen peroxide plant will return to the hydrogenation tower to come into contact with hydrogen), which is more likely to cause safety accidents. Therefore, real-time monitoring of the oxygen content (i.e., oxygen content) in the tail gas of a chemical plant is one of the key points to ensure the safe operation of the plant.

[0003] Existing chemical plants lack online measurement equipment for tail gas oxygen content, and use manual sampling, which has a long sampling interval and high cost. The developed tail gas oxygen content automatic sampling tester has high cost, large measurement error, and the emissions cannot meet environmental protection standards, which does not meet the actual needs of my country's chemical companies. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device, storage medium and processor for calculating the oxygen content in tail gas of a chemical plant. The method, device, storage medium and processor for calculating the oxygen content in tail gas of a chemical plant do not rely on manpower and ensure the safe operation of the chemical plant.

[0005] In order to achieve the above-mentioned purpose, the present invention provides a method for calculating the oxygen content of exhaust gas from a chemical plant, the calculation method comprising: detecting current data of other locations other than the exhaust oxygen content of the chemical plant; calculating the exhaust oxygen content of the chemical plant based on the current data of the other locations and an oxygen content prediction model, wherein the oxygen content prediction model is a model obtained by training a neural network using the fluctuation historical data of the exhaust oxygen content and the historical data of other locations related to the fluctuation historical data of the exhaust oxygen content.

[0006] Preferably, the fluctuation historical data is historical data in which the duration of abnormal operation data within a preset time period is greater than a preset percentage of the preset time period.

[0007] Preferably, the oxygen content prediction model is obtained in the following manner: obtaining the fluctuation historical data of the exhaust gas oxygen content; calculating the grayscale correlation coefficient between the fluctuation historical data and the historical data of each site at other sites at the same operating time; extracting the fluctuation historical data and the historical data of other sites whose grayscale correlation coefficient is greater than a correlation coefficient threshold, wherein the correlation coefficient threshold is related to whether the exhaust gas continues to participate in the combustion and explosion reaction; and obtaining the oxygen content prediction model by training based on the extracted data and a neural network.

[0008] Preferably, the grayscale correlation coefficient is calculated by the following formula:

[0009] Among them, x0 is the historical data of fluctuation, x i is the historical data of the i-th site among other sites at the same operating time, ρ is the preset coefficient, and k is the sequence number of the historical data.

[0010] Preferably, when the tail gas continues to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.6; when the tail gas does not continue to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.8.

[0011] Preferably, the neural network training uses MATLAB neural network.

[0012] Preferably, 70% of the historical data on fluctuations in the exhaust gas oxygen content and historical data of other locations related to the historical data on fluctuations in the exhaust gas oxygen content are used to train the oxygen content prediction model, 15% of the data are used to test the oxygen content prediction model, and 15% of the data are used to verify the oxygen content prediction model.

[0013] The present invention also provides a device for calculating the oxygen content in the exhaust gas of a chemical plant, the calculation device comprising: a detection unit and a processing unit, wherein the detection unit is used to detect current data of other locations other than the exhaust oxygen content of the chemical plant; the processing unit is used to calculate the exhaust oxygen content of the chemical plant based on the current data of the other locations and an oxygen content prediction model, wherein the oxygen content prediction model is a model obtained by training a neural network using the fluctuation historical data of the exhaust oxygen content and the historical data of other locations related to the fluctuation historical data of the exhaust oxygen content.

[0014] Preferably, the fluctuation historical data is historical data in which the duration of abnormal operation data within a preset time period is greater than a preset percentage of the preset time period.

[0015] Preferably, the oxygen content prediction model is obtained in the following manner: obtaining the fluctuation historical data of the exhaust gas oxygen content; calculating the grayscale correlation coefficient between the fluctuation historical data and the historical data of each site at other sites at the same operating time; extracting the fluctuation historical data and the historical data of other sites whose grayscale correlation coefficient is greater than a correlation coefficient threshold, wherein the correlation coefficient threshold is related to whether the exhaust gas continues to participate in the combustion and explosion reaction; and obtaining the oxygen content prediction model by training based on the extracted data and a neural network.

[0016] Preferably, the grayscale correlation coefficient is calculated by the following formula:

[0017] Among them, x0 is the historical data of fluctuation, x i is the historical data of the i-th site among other sites at the same operating time, ρ is the preset coefficient, and k is the sequence number of the historical data.

[0018] Preferably, when the tail gas continues to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.6; when the tail gas does not continue to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.8.

[0019] Preferably, the neural network training uses MATLAB neural network.

[0020] Preferably, 70% of the historical data on fluctuations in the exhaust gas oxygen content and historical data of other locations related to the historical data on fluctuations in the exhaust gas oxygen content are used to train the oxygen content prediction model, 15% of the data are used to test the oxygen content prediction model, and 15% of the data are used to verify the oxygen content prediction model.

[0021] The present invention also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the above-mentioned method for calculating the oxygen content of tail gas from a chemical plant.

[0022] The present invention also provides a processor, characterized in that it is used to run a program, wherein the program is used to execute the above-mentioned method for calculating the oxygen content of tail gas from a chemical plant when it is run.

[0023] Through the above technical scheme, the method, device, storage medium and processor for calculating the oxygen content of tail gas of a chemical plant provided by the present invention are used to detect the current data of other sites other than the oxygen content of tail gas of the chemical plant; according to the current data of the other sites, based on the oxygen content prediction model, the oxygen content of tail gas of the chemical plant is calculated, wherein the oxygen content prediction model is a model obtained by neural network training based on the fluctuation history data of the tail gas oxygen content and the historical data of other sites related to the fluctuation history data of the tail gas oxygen content. The present invention obtains the oxygen content according to the model after constructing the model, does not rely on manpower, and ensures the safe operation of the chemical plant.

[0024] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0026] Figure 1 It is a flow chart of a method for calculating the oxygen content of tail gas from a chemical plant provided by one embodiment of the present invention;

[0027] Figure 2 is a flow chart of a method for establishing an oxygen content prediction model provided by one embodiment of the present invention;

[0028] Figure 3 It is a structural block diagram of a device for calculating the oxygen content in tail gas of a chemical plant provided by one embodiment of the present invention.

[0029] Description of Reference Numerals

[0030] 1 detection unit 2 processing unit DETAILED DESCRIPTION

[0031] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0032] Embodiment 1

[0033] Figure 1 1 is a flow chart of a method for calculating the oxygen content of tail gas from a chemical plant provided by an embodiment of the present invention. Figure 1 As shown, the calculation method includes:

[0034] Step S11, detecting current data of other locations other than the oxygen content of tail gas of the chemical device;

[0035] Specifically, the sites may be different measurement objects of different chemical plants, such as the oxygen content of hydrogenation tower tail gas, the oxygen content of oxidation tower tail gas, or the concentration of hydrogen peroxide in raffinate, etc., or different measurement objects in the same chemical plant. For example, for a hydrogenation tower, the sites may be the oxygen content of hydrogenation tower tail gas, the hydrogen content, or other contents, etc. In the embodiment of the present invention, if the oxygen content of hydrogenation tower tail gas is to be detected, the current data of other sites other than the oxygen content of hydrogenation tower tail gas may be detected first.

[0036] Step S12, according to the current data of the other locations, based on the oxygen content prediction model, calculate the exhaust oxygen content of the chemical plant, wherein the oxygen content prediction model is a model obtained by neural network training of the fluctuation historical data of the exhaust oxygen content and the historical data of other locations related to the fluctuation historical data of the exhaust oxygen content, and the fluctuation historical data is historical data of abnormal operation within a preset time length, and the duration of the data is greater than a preset percentage of the preset time length.

[0037] Specifically, the embodiment of the present invention is based on the oxygen content prediction model, substitutes the current data of other detected sites, and calculates the tail gas oxygen content of the chemical device. The oxygen content prediction model is obtained by training the neural network through the fluctuation historical data of the tail gas oxygen content and the historical data of other sites related to the fluctuation historical data of the tail gas oxygen content. The fluctuation historical data refers to the historical data of abnormal operation data within the preset time length, and the preset percentage can be 30%, but is not limited to this. For example, assuming that the site has a total of 3 months of historical data, and the preset time length is 1 month, then all historical data can be divided into three parts. In the historical data of the first month, 10 days of historical data are abnormal operation data (abnormal operation can be marked on the data), then 10 days account for about 33% of 1 month, which is greater than the preset percentage, then the historical data of the first month is the fluctuation historical data. In the historical data of the second and third months, 5 days and 4 days of historical data are abnormal operation data, accounting for less than 30% of 1 month, then the historical data of the second and third months are not fluctuation historical data. The historical data of other locations related to the fluctuation historical data may be directly acquired, or may be acquired according to the acquisition method provided in the following embodiments of the present invention.

[0038] Embodiment 2

[0039] In this embodiment, what is different from the first embodiment is that a method for establishing an oxygen content prediction model is mainly provided, especially a more detailed method for obtaining historical data of other locations related to the fluctuation historical data. Other calculation methods are similar to those in the first embodiment and will not be repeated here.

[0040] Specifically, the oxygen content prediction model is obtained by:

[0041] Step S21, obtaining the fluctuation history data of the exhaust gas oxygen content;

[0042] Specifically, the data acquired by the embodiment of the present invention may come from the operation data stored in the distributed control system (DCS) and the laboratory information management system (LIMS) record data, but not all the historical data of the site are acquired, but only the fluctuation historical data is acquired. The meaning of the fluctuation historical data is as described above.

[0043] Step S22, calculating the grayscale correlation coefficient between the fluctuation historical data and the historical data of each of the other sites at the same operating time;

[0044] Specifically, since the historical data of other sites may also have a certain impact on the historical data of oxygen content, the embodiment of the present invention also needs to determine the correlation between the above-mentioned fluctuation historical data and the historical data of other sites at the same operating time. The same operating time means that if the fluctuation historical data is data from January 2020 (even specific to a few days, hours, minutes and seconds), then the historical data of other sites for calculating the grayscale correlation coefficient should also use the data from January 2020. Since there may be many other sites, the historical data corresponding to the operating time of each site can calculate the grayscale correlation coefficient with the fluctuation historical data.

[0045] Step S23, extracting the fluctuation historical data and the historical data of other sites where the grayscale correlation coefficient is greater than a correlation coefficient threshold, wherein the correlation coefficient threshold is related to whether the exhaust gas continues to participate in the combustion and explosion reaction;

[0046] Specifically, if the grayscale correlation coefficient calculated between the historical data of the H site and the fluctuation historical data is greater than the correlation coefficient threshold, it means that the historical data of the H site is highly correlated with the fluctuation historical data and needs to be extracted, that is, on the basis of extracting the fluctuation historical data, the historical data of the H site also needs to be extracted. Considering safety, the correlation coefficient threshold depends on whether the exhaust gas continues to participate in the explosion reaction. If the exhaust gas continues to participate in the explosion reaction, the correlation coefficient threshold will be smaller than if the exhaust gas does not continue to participate in the explosion reaction.

[0047] Step S24, training is performed based on the extracted data and the neural network to obtain the oxygen content prediction model.

[0048] Specifically, for example, using the MATLAB neural network toolbox, the number of nodes in the input layer of the neural network is the number of other sites, the middle layer is 7 layers, the number of nodes in each layer is 50, and the output layer is 1 node, corresponding to the output variable. 70% of the data samples are used to train the model, 15% of the data samples are used to test the model, and 15% of the data samples are used to verify the model. The operating environment is WINDOWS7, MATLAB2019a.

[0049] Embodiment 3

[0050] In this embodiment, what is different from Embodiments 1 and 2 is that a more detailed method for calculating the grayscale correlation coefficient between the fluctuation historical data and the historical data of one site among other sites at the same operating time is mainly provided. Other calculation methods are similar to Embodiments 1 and 2 and will not be repeated here.

[0051] Specifically, the grayscale correlation coefficient is calculated by the following formula:

[0052] Among them, x0 is the historical data of fluctuation, x i is the historical data of the ith site among other sites at the same operating time, ρ is the preset coefficient, and k is the serial number of the historical data. That is to say, assuming that the first site among other sites is taken as an example, the first site will have a lot of historical data, and these historical data correspond to the operating time of the fluctuation historical data. For example, the fluctuation historical data is a total of 60 data obtained every 12 hours within 30 days, then the first site also uses a total of 60 data obtained every 12 hours within these 30 days, and can be regarded as having serial numbers 1-60, and the sorting is based on time order.

[0053] Embodiment 4

[0054] In this embodiment, the difference from the first to third embodiments is that different correlation coefficient thresholds are mainly provided due to different preset time lengths, data intervals and subsequent exhaust gas processing. Other extraction methods are similar to those of the first to third embodiments and will not be described in detail here.

[0055] Specifically, with respect to the preset time length and data interval, for the detection of oxygen content, the preset time length may be 30 days, and the data interval may be 12 hours.

[0056] For correlation coefficient thresholds, see Table 1:

[0057] Table 1

[0058]

[0059] When the tail gas continues to participate in the combustion and explosion reaction, the correlation coefficient threshold may be 0.6; when the tail gas does not continue to participate in the combustion and explosion reaction, the correlation coefficient threshold may be 0.8.

[0060] Although specific numerical values ​​are provided in the above embodiment, such numerical values ​​are only preferred numerical values ​​in the embodiment of the present invention, and other numerical values ​​may also be used, and the embodiment of the present invention is not limited thereto.

[0061] Embodiment 5

[0062] Figure 3 1 is a block diagram of a device for calculating the oxygen content of tail gas from a chemical plant provided by an embodiment of the present invention. Figure 3 As shown, the calculation device includes: a detection unit 1 and a processing unit 2, wherein the detection unit 1 is used to detect current data of other locations other than the exhaust oxygen content of the chemical device; the processing unit 2 is used to calculate the exhaust oxygen content of the chemical device based on the current data of the other locations and an oxygen content prediction model, wherein the oxygen content prediction model is a model obtained by neural network training using the fluctuation historical data of the exhaust oxygen content and the historical data of other locations related to the fluctuation historical data of the exhaust oxygen content.

[0063] Preferably, the fluctuation historical data is historical data in which the duration of abnormal operation data within a preset time period is greater than a preset percentage of the preset time period.

[0064] Preferably, the oxygen content prediction model is obtained in the following manner: obtaining the fluctuation historical data of the exhaust gas oxygen content; calculating the grayscale correlation coefficient between the fluctuation historical data and the historical data of each site at other sites at the same operating time; extracting the fluctuation historical data and the historical data of other sites whose grayscale correlation coefficient is greater than a correlation coefficient threshold, wherein the correlation coefficient threshold is related to whether the exhaust gas continues to participate in the combustion and explosion reaction; and obtaining the oxygen content prediction model by training based on the extracted data and a neural network.

[0065] Preferably, the grayscale correlation coefficient is calculated by the following formula:

[0066] Among them, x0 is the historical data of fluctuation, x i is the historical data of the i-th site among other sites at the same operating time, ρ is the preset coefficient, and k is the sequence number of the historical data.

[0067] Preferably, when the tail gas continues to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.6; when the tail gas does not continue to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.8.

[0068] Preferably, the neural network training uses MATLAB neural network.

[0069] Preferably, 70% of the historical data on fluctuations in the exhaust gas oxygen content and historical data of other locations related to the historical data on fluctuations in the exhaust gas oxygen content are used to train the oxygen content prediction model, 15% of the data are used to test the oxygen content prediction model, and 15% of the data are used to verify the oxygen content prediction model.

[0070] This embodiment provides a device for calculating the oxygen content of tail gas from a chemical plant, which is similar to the above-mentioned embodiments one to four and will not be described in detail here.

[0071] Through the above technical scheme, the method, device, storage medium and processor for calculating the oxygen content of tail gas of a chemical plant provided by the present invention are used to detect the current data of other sites other than the oxygen content of tail gas of the chemical plant; according to the current data of the other sites, based on the oxygen content prediction model, the oxygen content of tail gas of the chemical plant is calculated, wherein the oxygen content prediction model is a model obtained by neural network training based on the fluctuation history data of the tail gas oxygen content and the historical data of other sites related to the fluctuation history data of the tail gas oxygen content. The present invention obtains the oxygen content according to the model after constructing the model, does not rely on manpower, and ensures the safe operation of the chemical plant.

[0072] The chemical plant tail gas oxygen content calculation device comprises a processor and a memory. The detection unit and the processing unit are stored in the memory as program units. The processor executes the program units stored in the memory to realize corresponding functions.

[0073] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be provided, and the oxygen content can be detected by adjusting kernel parameters (the purpose of the present invention).

[0074] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0075] An embodiment of the present invention provides a storage medium on which a program is stored. When the program is executed by a processor, the method for calculating the oxygen content of tail gas from a chemical plant is implemented.

[0076] An embodiment of the present invention provides a processor, which is used to run a program, wherein the method for calculating the oxygen content of tail gas from a chemical plant is executed when the program is run.

[0077] An embodiment of the present invention provides a device, the device including a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, the following steps are implemented:

[0078] Detect current data of other locations other than the exhaust oxygen content of the chemical plant; calculate the exhaust oxygen content of the chemical plant based on the current data of the other locations and an oxygen content prediction model, wherein the oxygen content prediction model is a model obtained by training a neural network using the fluctuation history data of the exhaust oxygen content and the historical data of other locations related to the fluctuation history data of the exhaust oxygen content.

[0079] The fluctuation historical data is historical data in which the duration of abnormally running data within a preset time period is greater than a preset percentage of the preset time period.

[0080] The oxygen content prediction model is obtained in the following manner: obtaining the fluctuation historical data of the exhaust gas oxygen content; calculating the grayscale correlation coefficient between the fluctuation historical data and the historical data of each site at other sites at the same operating time; extracting the fluctuation historical data and the historical data of other sites whose grayscale correlation coefficient is greater than a correlation coefficient threshold, wherein the correlation coefficient threshold is related to whether the exhaust gas continues to participate in the combustion and explosion reaction; and training based on the extracted data and a neural network to obtain the oxygen content prediction model.

[0081] The grayscale correlation coefficient is calculated by the following formula:

[0082] Among them, x0 is the historical data of fluctuation, x i is the historical data of the i-th site among other sites at the same operating time, ρ is the preset coefficient, and k is the sequence number of the historical data.

[0083] When the tail gas continues to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.6; when the tail gas does not continue to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.8.

[0084] The neural network training uses MATLAB neural network.

[0085] 70% of the historical data on the fluctuation of the exhaust gas oxygen content and the historical data of other locations related to the historical data on the fluctuation of the exhaust gas oxygen content are used to train the oxygen content prediction model, 15% of the data are used to test the oxygen content prediction model, and 15% of the data are used to verify the oxygen content prediction model.

[0086] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0087] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing the following method steps:

[0088] Detect current data of other locations other than the exhaust oxygen content of the chemical plant; calculate the exhaust oxygen content of the chemical plant based on the current data of the other locations and an oxygen content prediction model, wherein the oxygen content prediction model is a model obtained by training a neural network using the fluctuation history data of the exhaust oxygen content and the historical data of other locations related to the fluctuation history data of the exhaust oxygen content.

[0089] The fluctuation historical data is historical data in which the duration of abnormally running data within a preset time period is greater than a preset percentage of the preset time period.

[0090] The oxygen content prediction model is obtained in the following manner: obtaining the fluctuation historical data of the exhaust gas oxygen content; calculating the grayscale correlation coefficient between the fluctuation historical data and the historical data of each site at other sites at the same operating time; extracting the fluctuation historical data and the historical data of other sites whose grayscale correlation coefficient is greater than a correlation coefficient threshold, wherein the correlation coefficient threshold is related to whether the exhaust gas continues to participate in the combustion and explosion reaction; and training based on the extracted data and a neural network to obtain the oxygen content prediction model.

[0091] The grayscale correlation coefficient is calculated by the following formula:

[0092] Among them, x0 is the historical data of fluctuation, x i is the historical data of the i-th site among other sites at the same operating time, ρ is the preset coefficient, and k is the sequence number of the historical data.

[0093] When the tail gas continues to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.6; when the tail gas does not continue to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.8.

[0094] Preferably, the neural network training uses MATLAB neural network.

[0095] Preferably, 70% of the historical data on fluctuations in the exhaust gas oxygen content and historical data of other locations related to the historical data on fluctuations in the exhaust gas oxygen content are used to train the oxygen content prediction model, 15% of the data are used to test the oxygen content prediction model, and 15% of the data are used to verify the oxygen content prediction model.

[0096] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0097] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0101] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0102] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0103] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0104] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for calculating the oxygen content of tail gas from a chemical plant, characterized in that: The calculation method includes: Detecting current data of other locations other than the oxygen content of tail gas of the chemical plant; According to the current data of the other sites, based on the oxygen content prediction model, the oxygen content of the tail gas of the chemical device is calculated, wherein the oxygen content prediction model is a model obtained by neural network training based on the fluctuation history data of the tail gas oxygen content and the historical data of other sites related to the fluctuation history data of the tail gas oxygen content, The fluctuation historical data is historical data in which the duration of abnormal operation data within a preset time period is greater than a preset percentage of the preset time period; The oxygen content prediction model is obtained by the following method: Obtain historical data on fluctuations in exhaust gas oxygen content; Calculate the grayscale correlation coefficient between the fluctuation historical data and the historical data of each site at other sites at the same operating time; Extracting the fluctuation historical data and the historical data of other locations where the grayscale correlation coefficient is greater than a correlation coefficient threshold, wherein the correlation coefficient threshold is determined according to whether the tail gas continues to participate in the combustion and explosion reaction; Performing training based on the extracted data and the neural network to obtain the oxygen content prediction model; When the tail gas continues to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.6; when the tail gas does not continue to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.

8.

2. The method for calculating the oxygen content of tail gas from a chemical plant according to claim 1, characterized in that: The grayscale correlation coefficient is calculated by the following formula: Among them, x0 is the historical data of fluctuation, x i is the historical data of the i-th site among other sites at the same operating time, ρ is the preset coefficient, and k is the sequence number of the historical data.

3. The method for calculating the oxygen content of tail gas from a chemical plant according to claim 1, characterized in that: The neural network training uses MATLAB neural network.

4. The method for calculating the oxygen content of tail gas from a chemical plant according to claim 1, characterized in that: 70% of the historical data on the fluctuation of the exhaust gas oxygen content and the historical data of other locations related to the historical data on the fluctuation of the exhaust gas oxygen content are used to train the oxygen content prediction model, 15% of the data are used to test the oxygen content prediction model, and 15% of the data are used to verify the oxygen content prediction model.

5. A device for calculating the oxygen content of tail gas from a chemical plant, characterized in that: The computing device comprises: A detection unit and a processing unit, wherein: The detection unit is used to detect current data of other locations other than the oxygen content of the tail gas of the chemical plant; The processing unit is used to calculate the oxygen content of the tail gas of the chemical device according to the current data of the other sites and based on the oxygen content prediction model, wherein the oxygen content prediction model is a model obtained by neural network training based on the fluctuation history data of the tail gas oxygen content and the historical data of other sites related to the fluctuation history data of the tail gas oxygen content; The fluctuation historical data is historical data in which the duration of abnormal operation data within a preset time period is greater than a preset percentage of the preset time period; The oxygen content prediction model is obtained by the following method: Obtain historical data on fluctuations in exhaust gas oxygen content; Calculate the grayscale correlation coefficient between the fluctuation historical data and the historical data of each site at other sites at the same operating time; Extracting the fluctuation historical data and the historical data of other locations where the grayscale correlation coefficient is greater than a correlation coefficient threshold, wherein the correlation coefficient threshold is related to whether the exhaust gas continues to participate in the combustion and explosion reaction; Performing training based on the extracted data and the neural network to obtain the oxygen content prediction model; When the tail gas continues to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.6; when the tail gas does not continue to participate in the combustion and explosion reaction, the correlation coefficient threshold is 0.

8.

6. The device for calculating the oxygen content of tail gas from a chemical plant according to claim 5, characterized in that: The grayscale correlation coefficient is calculated by the following formula: Among them, x0 is the historical data of fluctuation, x i is the historical data of the i-th site among other sites at the same operating time, ρ is the preset coefficient, and k is the sequence number of the historical data.

7. The device for calculating the oxygen content of tail gas from a chemical plant according to claim 5, characterized in that: The neural network training uses MATLAB neural network.

8. The device for calculating the oxygen content of tail gas from a chemical plant according to claim 5, characterized in that: 70% of the historical data on the fluctuation of the exhaust gas oxygen content and the historical data of other locations related to the historical data on the fluctuation of the exhaust gas oxygen content are used to train the oxygen content prediction model, 15% of the data are used to test the oxygen content prediction model, and 15% of the data are used to verify the oxygen content prediction model.

9. A machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the method for calculating the oxygen content in tail gas from a chemical plant as described in any one of claims 1 to 4.

10. A processor, characterized in that: Used to run a program, wherein the program, when run, is used to execute the method for calculating the oxygen content in tail gas from a chemical plant as described in any one of claims 1 to 4.

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