Cracking furnace operation optimization method and device, storage medium and processor

Abstract

The embodiment of the invention provides a cracking furnace operation optimization method and device, a storage medium and a processor, and belongs to the technical field of chemical engineering automatic control. The method for optimizing the operation of the cracking furnace comprises the following steps: acquiring the cracking depth of the cracking furnace within at least a preset time duration; continuously acquiring operation parameters of the cracking furnace, and inputting the operation parameters into the cracking depth prediction model of deep learning to obtain a cracking depth prediction value of the cracking depth after a preset time length; and correcting the operation of the cracking furnace under the condition that the variation trend of the predicted value of the cracking depth does not conform to the variation trend of the cracking depth. Through the method, the real-time operation of the cracking furnace can be quickly and effectively optimized.

Description

Technical Field

[0001] The invention relates to the technical field of chemical automatic control, and in particular to a method, device, storage medium and processor for optimizing the operation of a cracking furnace. Background Art

[0002] In the cracking production process, the operating conditions of the cracking furnace will have a significant impact on its yield. In the actual production process, there are two main ways to adjust the operating conditions of the cracking furnace: one is based on the reference value and manual experience provided by the cracking furnace manufacturer, but in the actual production process, the source of cracking oil products is usually not fixed, and different oil products will be switched as needed during the production process. Therefore, the current manual experience cannot achieve the optimization and adjustment of the optimal operating conditions of the cracking furnace; the other way is to use an online component analyzer to measure the relative content of key components in the cracking gas, and use this as a reference to adjust the production operating conditions. However, it takes a certain amount of time for the online analyzer to analyze a sample, which has a large hysteresis and randomness, which is not conducive to the stable operation of the cracking furnace. At the same time, if the online analyzer fails, this operation mode will fail, and in severe cases, it will even cause the emergency shutdown of the cracking furnace, resulting in serious economic losses. Therefore, in order to achieve rapid optimization and adjustment of the operating conditions of the cracking furnace, a fast and effective real-time operation optimization method for the cracking furnace is urgently needed. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a method for optimizing the operation of a cracking furnace, by which the real-time operation of the cracking furnace can be optimized quickly and effectively.

[0004] In order to achieve the above object, the present invention provides a method for optimizing the operation of a cracking furnace, the method comprising:

[0005] Obtaining a cracking depth of the cracking furnace for at least a preset time length;

[0006] Continuously obtaining operating parameters of the cracking furnace, and inputting the operating parameters into a deep learning cracking depth prediction model to obtain a predicted value of the cracking depth after a preset time length; and

[0007] When the variation trend of the predicted cracking depth value does not conform to the variation trend of the cracking depth, the cracking furnace operation is corrected.

[0008] Preferably, the cracking depth is determined based on the cracking feedstock and the cracking target product, including the conversion rate of the feedstock, the methane yield, the propylene / ethylene yield ratio, the methane / propylene yield ratio, the hydrogen content of the cracked liquid product, the outlet temperature, the kinetic depth, or the yield of C3 and lighter components; the operating parameters are all the operating parameters obtained by DCS monitoring.

[0009] Optionally, the operating parameters are the cracking furnace operation control conditions determined according to the cracking furnace tube type and cracking furnace model, and the operating parameters include one or more of the following: total steam feed ratio, total feed amount, first COT, target outlet temperature, feed amount of each furnace tube group, feed water flow rate, bottom fuel gas flow rate, side wall fuel flow rate, drum liquid level, radiation section furnace pressure, and feed cross section pressure.

[0010] Furthermore, the cracking depth prediction model is a trained long short-term memory network model, and the adopted training data set includes a group of training samples. The training samples are the historical operating data of the current cracking furnace in cracking the current raw material. The training samples include the operating parameter sample values ​​and the corresponding cracking depth sample values.

[0011] Furthermore, the training process of the cracking depth prediction model includes:

[0012] Obtaining an initialized long short-term memory network model, the input of the long short-term memory network model is the sample value of the operation parameter, and the output is the predicted value of the cracking depth;

[0013] The sample values ​​of the operating parameters are input into the cracking depth prediction model, and the deviation between the cracking depth prediction value and the cracking depth sample value is calculated by the error loss function;

[0014] When the deviation is less than a preset threshold or the training reaches a preset number of times, the training is terminated; otherwise, the long short-term memory network model is corrected and the next round of training is continued; and

[0015] The one with the smallest deviation is selected as the final cracking depth prediction model.

[0016] Furthermore, the training data set includes historical operating data of the cracking furnace in the last 0.1-10 years.

[0017] Preferably, the preset time length is 1-30 minutes.

[0018] In another aspect, the present invention provides an optimization device for cracking furnace operation, the device comprising:

[0019] Running a monitoring module to obtain a cracking depth of the cracking furnace within at least a preset time length;

[0020] A prediction module continuously obtains operating parameters of the cracking furnace and inputs the operating parameters into a deep learning cracking depth prediction model to obtain a predicted value of the cracking depth after a preset time length; and

[0021] The optimization module is run to correct the cracking furnace operation when the change trend of the cracking depth prediction value does not conform to the change trend of the cracking depth.

[0022] On the other hand, the present invention provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the method for optimizing the operation of a cracking furnace of the present application.

[0023] In another aspect, the present invention provides a processor for running a program, wherein the program, when being run, is used to execute the method for optimizing the operation of a cracking furnace of the present application.

[0024] Through the above technical scheme, on the one hand, the cracking depth of the cracking furnace within a preset time period is continuously obtained to determine the changing trend of the operating effect of the cracking furnace within the time period; on the other hand, the continuously obtained cracking furnace operating parameters are input into the deep learning cracking depth prediction model to determine the changing trend of the predicted value of the operating effect of the cracking furnace within the time period; finally, when there is a large deviation in the changing trends of the two aspects, the operation of the cracking furnace is corrected, so that the cracking furnace operation can be optimized in real time.

[0025] 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

[0026] 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:

[0027] Figure 1 It is a flow chart of an embodiment of a method for optimizing cracking furnace operation of the present application;

[0028] Figure 2 yes Figure 1 A schematic diagram of the training process of the cracking depth prediction model in the embodiment; and

[0029] Figure 3 It is a composition structure diagram of an embodiment of an optimization device for cracking furnace operation of the present application. DETAILED DESCRIPTION

[0030] 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.

[0031] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatus.

[0032] In addition, it should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] The embodiment of the present invention provides a method for optimizing the operation of a cracking furnace, by which the real-time operation of the cracking furnace can be optimized quickly and effectively. The process of the first embodiment of the method for optimizing the operation of the cracking furnace of the present application is as follows: Figure 1 As shown, including:

[0034] Step 1: obtaining a cracking depth of a cracking furnace within at least a preset time length;

[0035] Step 2: Continuously obtain the operating parameters of the cracking furnace, and input the operating parameters into the cracking depth prediction model of deep learning to obtain a predicted value of the cracking depth after a preset time length;

[0036] Step 3: When the variation trend of the cracking depth prediction value does not conform to the variation trend of the cracking depth, the cracking furnace operation is corrected.

[0037] In actual production, the cracking furnace operator can refer to the vector composed of the operating variables of the cracking furnace and its corresponding cracking depth, the first and second predicted values ​​of the cracking depth, and select the optimal operating conditions of the cracking furnace according to the actual production needs on site, and adjust and optimize the operating state of the cracking furnace.

[0038] In this embodiment, the cracking furnace can be 1, 1-1, 2-1, 4-1, 4-1-1-1, 2-1-1-1, 1-1-1-1, or 8-4-2-1 furnace type, and the cracking raw materials of the cracking furnace are ethane, propane, LPG, naphtha, diesel, aviation kerosene, or hydrogenated tail oil.

[0039] It should be noted that the above steps 1 and 2 both include continuous detection of relevant parameters of the cracking furnace. In practice, detection can be performed periodically, regularly or irregularly, simultaneously or alternately as needed, without being limited to the execution order of the above steps.

[0040] In step 1, the cracking depth can be determined according to the cracking raw material and the cracking furnace operation principle. For example, if the cracking raw material is naphtha, the cracking depth can be the propylene-ethylene content ratio in the cracking gas, i.e. the cracking reaction depth. That is to say, the cracking depth in the present application is a parameter that reflects the operation effect of the cracking furnace. In actual operation, it can also be the ethylene or propylene yield, and the ethylene and propylene content in the cracking gas at the current moment are measured by online analyzers such as gas chromatographs, thereby obtaining the propylene and ethylene content ratio in the cracking gas. According to the difference between the cracking raw material and the cracking target product, the cracking depth can also be the conversion rate of the raw material, the methane yield, the methane / propylene yield ratio, the hydrogen content of the cracked liquid product, the outlet temperature, the kinetic depth, or the yield of C3 and lighter components, etc.

[0041] Taking naphtha as the cracking raw material, its cracking product composition is subject to various factors, such as cracking raw material quality, residence time, cracking furnace tube configuration, hydrocarbon partial pressure, reaction temperature, etc. In step 2, the operating parameters of the cracking furnace can be selected from the detection parameters of the DCS system according to experience to reflect the important parameters of the cracking furnace operating conditions, or all parameters corresponding to the cracking furnace operating conditions are selected from the detection parameters of the DCS system, or it can also be the important parameters first selected according to the artificial intelligence algorithm. In the present embodiment, the operating parameters include but are not limited to cracking raw material feed flow rate, cracking raw material feed temperature and pressure, dilution steam feed flow rate, dilution steam feed temperature, cracking furnace cross section temperature, cracking furnace cross section pressure, cracking furnace tube outlet temperature and pressure, waste heat boiler outlet temperature, cracking furnace furnace temperature, burner fuel flow, etc.

[0042] In step 2, the cracking depth prediction model is a trained long short-term memory network model, and the training samples are the historical operating condition data of the current cracking furnace in cracking the current raw material, that is, the prediction model based on the operating principle of the cracking furnace. By analyzing the historical operating condition data of the cracking furnace, a cracking depth prediction model is established to reflect the corresponding relationship between the operating parameters in the cracking furnace and the cracking depth.

[0043] It should also be noted that the present application can not only monitor and predict the cracking depth of the cracking furnace, but also monitor and predict any one of the following according to different cracking production purposes: the maximum values ​​of ethylene, propylene, ethylene and propylene in the cracking products, the maximum yield of trienes, or the maximum yield of the value of the cracking products, that is, replace the "cracking depth" in this embodiment with any one of the above monitoring objects to allow the cracking reaction to proceed in the desired direction.

[0044] Compared with the prior art, the technical advantages of this embodiment include:

[0045] (1) An online analyzer such as a gas chromatograph measures the ethylene and propylene contents in the cracked gas at the current moment, thereby obtaining a propylene to ethylene content ratio in the cracked gas, i.e., a monitoring value of the cracking depth reflecting a changing trend of the operating effect of the cracking furnace during the period;

[0046] (2) A cracking depth prediction model is established based on the cracking furnace operation principle, which can be cross-referenced with the cracking depth monitoring value to timely discover the deviation of the cracking depth, thereby improving the real-time performance of the cracking furnace operation optimization method;

[0047] (3) Based on the above monitoring values ​​and prediction values, a multi-objective real-time operation optimization model for the cracking furnace can be established to provide a variety of control schemes for the cracking furnace operators, which can be flexibly selected according to the on-site production needs, effectively improving the overall production efficiency and economic benefits of the ethylene plant.

[0048] The following describes how to obtain a training data set for a cracking depth prediction model in some embodiments.

[0049] First, collect the offline operation data of the cracking furnace in the past 0.1-10 years to establish a cracking depth database of the cracking furnace;

[0050] Then, the operation data in the cracking depth database of the cracking furnace is classified, screened and cleaned, and invalid or interfering data is deleted;

[0051] Finally, according to the actual conditions of the current cracking furnace and cracking raw materials, a group of training samples are extracted from the cracking furnace cracking depth database, wherein each training sample includes the parameter values ​​required for training the cracking depth prediction model.

[0052] It should be noted that for the different boiler tube configurations of cracking furnace, such as single-pass boiler tube, 1-1 type, 2-1 type and 4-1 type two-pass boiler tube and 1-1-1-1 type and 2-1-1-1 type four-pass boiler tube etc., for example, the ethylene cracking furnace taking naphtha as the cracking raw material adopts 1-1 type two-pass boiler tube or 2-1 type two-pass boiler tube more, therefore when obtaining the cracking furnace historical operation condition data, except gathering the cracking furnace operating parameter data, also need to include the corresponding cracking furnace boiler tube configuration parameters in the cracking furnace cracking depth database, in preparation for as screening condition when extracting samples.

[0053] The data collected from the off-line operating data of the cracking furnace in the past 0.1-10 years according to the time series, the collected data types can also include the properties of the cracking raw oil products, the type of cracking furnace tubes, all relevant operating parameters monitored on the DCS and the corresponding diene yield, and the above data are used to establish a cracking furnace cracking depth database, and then, according to experience, the important parameters that can reflect the operating conditions of the cracking furnace are selected from the detection parameters of the DCS system. Wherein, the operating parameters are the cracking furnace operation control conditions determined according to the cracking furnace tube type and the cracking furnace model, and the operating parameters include one or more of the following: total steam feed ratio, total feed amount, first COT, target outlet temperature, feed amount of each furnace tube group, feed water flow, bottom fuel gas flow, side wall fuel flow, drum liquid level, radiation section furnace pressure, and feed cross section pressure.

[0054] Considering that the composition of cracking products is restricted by many factors, such as cracking raw material quality, residence time, cracking furnace tube configuration, hydrocarbon partial pressure, reaction temperature, etc. The operating parameters include cracking raw material feed flow rate, cracking raw material feed temperature and pressure, dilution steam feed flow rate, dilution steam feed temperature, cracking furnace cross section temperature, cracking furnace cross section pressure, cracking furnace tube outlet temperature and pressure, waste heat boiler outlet temperature, cracking furnace furnace temperature, burner fuel flow rate.

[0055] Before training the cracking depth prediction model, the training data set or cracking furnace cracking depth database is subjected to the following data classification and screening preprocessing:

[0056] According to the properties of the cracking raw materials, their cracking properties are classified and the oils are divided into L categories with similar cracking performances. The classification is based on their main performance indicators, namely the relative density, distillation range, group composition (PONA value), viscosity, hydrogen content and average molecular weight of the oils.

[0057] On the basis of classification, the relevant data in each oil product classification is corrected, the collected data is cleaned, and the cracking furnace operating condition data and diene yield data containing obvious errors are deleted; data cleaning means that according to the range of relevant operating condition parameters and diene yield range during the operation of the cracking furnace, the extracted historical operating condition data of the cracking furnace is screened to remove abnormal operating condition values ​​with obvious errors;

[0058] Furthermore, the corrected data may be integrated, with 50%-95% of the total data used as training set data and the remaining 50%-5% used as test set data.

[0059] The cracking depth prediction model is a trained long short-term memory network model. The training process is shown in Figure 2As shown, the input is the operating parameter sample value of training sample n, wherein training sample n is the nth training sample selected from a group of training samples in the above training data set, and the operating parameter sample value should include all the selected operating parameters and the cracking furnace operating conditions at time t; the output is the cracking depth prediction value, and the cracking depth prediction value is compared with the cracking depth sample value in training sample n through the error loss function to determine the current prediction deviation of the prediction model.

[0060] It should be noted that the input operating parameter sample value is the cracking furnace operating condition at time t, and accordingly, the cracking depth sample value used for comparison with the predicted value should be the actual value of the cracking depth at time t+n (n≥1), and the output is the predicted value of the cracking depth at time t+n (n≥1). Wherein, the value of n can be any time before the cracking production of the current raw material ends, such as 1min, 5min, 10min, 13min, 15min, 24min, 35min, 42min, 48min, 54min, 60min, etc. Preferably, n is 1-30 minutes.

[0061] The training process of the cracking depth prediction model is as follows: first, an initialized long short-term memory network model is obtained, the input of the long short-term memory network model is the operation parameter sample value, and the output is the cracking depth prediction value; then multiple trainings are performed according to a preset number of training times and a preset deviation threshold, wherein each training includes inputting the operation parameter sample value into the long short-term memory network model, and calculating the deviation between the cracking depth prediction value and the cracking depth sample value through an error loss function; when the deviation is less than the preset threshold or the training reaches a preset number of times, the training is terminated, otherwise the long short-term memory network model is corrected and the next round of training continues; finally, the one with the smallest deviation is selected as the final cracking depth prediction model.

[0062] In some embodiments, the root mean square error loss is used to calculate the deviation between the two, and the models saved after multiple trainings are screened, and the one with the smallest root mean square error is selected as the final prediction model.

[0063] In some embodiments, the operating parameters are monitoring values ​​of the feed ratio of the cracking reaction, the content of cracking gas and intermediate products, and the temperature and pressure conditions of the cracking reaction determined according to the cracking model and the operating principle. The operating parameters include one or more of the following: total steam feed ratio, total feed amount, first COT, target outlet temperature, feed amount of each furnace tube group, feed water flow, bottom fuel gas flow, side wall fuel flow, drum liquid level, radiation section furnace pressure, and feed cross section pressure.

[0064] In some embodiments, a virtual component model of the cracking raw material is established with the main performance index parameters as a guide. The cracking depth prediction model established on this basis is a prediction model based on the cracking furnace operation principle. The cracking furnace operating conditions at time t are used as input variables, and the cracking depth prediction value corresponding to the operating conditions is calculated. This prediction value is compared with the cracking depth operating value obtained by the cracking gas online analyzer to ensure that the relative error of the two is less than a given threshold of 2%. Otherwise, the prediction model is corrected to finally establish a preferred cracking depth prediction model.

[0065] In some embodiments, the cracking raw material is naphtha. Since naphtha is a mixture of different hydrocarbons, its specific hydrocarbon composition is difficult to obtain by measurement. In actual industrial operation, its cracking performance is mainly characterized according to its main performance indicators. However, when establishing a specific cracking depth prediction model, its detailed hydrocarbon composition needs to be constructed. For example, for naphtha, a special cracking raw material, a virtual component model corresponding to it is established according to its main performance indicators, mainly including C5-C12 alkanes and their isomers. Based on this, a cracking depth prediction model of the cracking furnace operation principle corresponding to this hydrocarbon composition is established. During the cracking process, the calculation results of the cracking depth prediction model are compared in real time with the measurement data of the cracking gas online analyzer, and the operation of the cracking furnace is optimized accordingly.

[0066] In some embodiments, the maximum and minimum allowable values ​​of each operating variable of the cracking furnace are given as constraints with the goal of maximizing the yield of ethylene and propylene, and a multi-objective real-time operation optimization model for the cracking furnace is established. Ultimately, the optimized set values ​​of each operating variable of the cracking furnace are obtained, and the optimal solution geometry of each control variable and its corresponding ethylene and propylene yield are provided to the on-site operator. According to the current actual production needs, the corresponding optimal operating conditions are selected to optimize and adjust the operating conditions of the cracking furnace in real time.

[0067] The current actual production needs mainly include the following situations:

[0068] ·Determine the specific value of the output variable based on the current market prices of ethylene and propylene and the maximum profit;

[0069] According to the different demands for ethylene and propylene in the downstream process of the cracking furnace, the one with the highest demand is selected to determine the specific value of the output variable;

[0070] Based on the range of the ratio of ethylene and propylene yields on site, we hope to obtain the highest comprehensive yield of ethylene and propylene, thereby determining the specific value of the output variable;

[0071] When there is no specific requirement for the yield of other products in the production process and only the yield of ethylene is concerned, the highest yield of ethylene is selected to determine the specific value of the output variable.

[0072] The embodiment of the present invention provides a device for optimizing the operation of a cracking furnace, the composition structure of which is as follows: Figure 3 As shown, it includes an operation monitoring module, a prediction module and an operation optimization module, wherein the operation monitoring module obtains the cracking depth of the cracking furnace within at least a preset time length; the prediction module continuously obtains the operating parameters of the cracking furnace, and inputs the operating parameters into a deep learning cracking depth prediction model to obtain a cracking depth prediction value after a preset time length; the operation optimization module corrects the cracking furnace operation when the change trend of the cracking depth prediction value does not conform to the change trend of the cracking depth.

[0073] In this embodiment, the operation monitoring module is used to monitor the changes in the operation effect of the cracking furnace, which is specifically reflected in the changes in the monitored value of the cracking depth (or the calculated value obtained based on the monitored value), and the cracking depth can be the ethylene content in the cracking gas, or the propylene-ethylene content ratio in the cracking gas; the prediction module obtains the predicted value of the cracking depth based on the operating parameters of the cracking furnace. When the change trends of the monitored value and the predicted value of the cracking depth are inconsistent, it is considered that the current operation of the cracking furnace needs to be optimized, and the vector composed of the operating variables of the cracking furnace and its corresponding cracking depth and the predicted value of the cracking depth are provided to the cracking furnace operator, so that the operator can select the optimal operating conditions of the cracking furnace according to the actual production needs on site, and adjust and optimize the operating state of the cracking furnace.

[0074] In this embodiment, the training method of the cracking depth prediction model and the training data set used can refer to the embodiment of the optimization method for cracking furnace operation, which will not be described in detail here.

[0075] An embodiment of the present invention also provides an optimization device for cracking furnace operation, including a processor and a memory. The above-mentioned operation monitoring module, prediction module and operation optimization module are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0076] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to determine whether there is a deviation in the cracking furnace and make corresponding adjustments.

[0077] 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.

[0078] 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 optimizing the operation of a cracking furnace of the present application is implemented.

[0079] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the method for optimizing the operation of a cracking furnace of the present application when it is run.

[0080] The embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the steps of the optimization method for cracking furnace operation of the present application when executing the program. The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0081] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initiating the steps of the optimization method for cracking furnace operation of the present application.

[0082] 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.

[0083] 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.

[0084] 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 including 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.

[0085] 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 A step that specifies a function in one or more boxes.

[0086] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 optimizing cracking furnace operation, comprising: Obtaining a cracking depth of the cracking furnace for at least a preset time length; Continuously acquiring the operating parameters of the cracking furnace, and inputting the operating parameters into a cracking depth prediction model of deep learning to obtain a cracking depth prediction value after the preset time length; as well as In the case where the variation trend of the predicted cracking depth value does not conform to the variation trend of the cracking depth, the cracking furnace operation is corrected.

2. The method according to claim 1, characterized in that Determining the cracking depth according to the cracking feedstock and the cracking target product includes the conversion rate of the feedstock, the methane yield, the propylene / ethylene yield ratio, the methane / propylene yield ratio, the hydrogen content of the cracked liquid product, the outlet temperature, the kinetic depth, or the yield of C3 and lighter components; and The operating parameters are all operating parameters obtained through DCS monitoring.

3. The method according to claim 1, characterized in that The operating parameters are the cracking furnace operation control conditions determined according to the cracking furnace tube type and cracking furnace model, and the operating parameters include one or more of the following: total steam feed ratio, total feed amount, first COT, target outlet temperature, feed amount of each furnace tube group, feed water flow, bottom fuel gas flow, side wall fuel flow, drum liquid level, radiation section furnace pressure, and feed cross section pressure.

4. The method for optimizing cracking furnace operation according to claim 3, characterized in that: The cracking depth prediction model is a trained long short-term memory network model. The training data set used includes a group of training samples. The training samples are historical operating data of the current cracking furnace in cracking the current raw material. The training samples include operating parameter sample values ​​and corresponding cracking depth sample values.

5. The method for optimizing cracking furnace operation according to claim 4, characterized in that: The training process of the cracking depth prediction model includes: Obtaining an initialized long short-term memory network model, wherein the input of the long short-term memory network model is the sample value of the operation parameter, and the output is the predicted value of the cracking depth; Inputting the sample value of the operating parameter into the cracking depth prediction model, and calculating the deviation between the cracking depth prediction value and the cracking depth sample value by using an error loss function; When the deviation is less than a preset threshold or the training reaches a preset number of times, the training is terminated; otherwise, the long short-term memory network model is corrected and the next round of training is continued; and The one with the smallest deviation is selected as the final cracking depth prediction model.

6. The method according to claim 5, characterized in that The training data set includes historical operating data of the cracking furnace within the last 0.1-10 years.

7. The method according to claim 1, characterized in that The preset time length is 1-30 minutes.

8. An optimization device for cracking furnace operation, characterized in that: include: Running a monitoring module to obtain a cracking depth of the cracking furnace within at least a preset time length; A prediction module, continuously acquiring the operating parameters of the cracking furnace, and inputting the operating parameters into a cracking depth prediction model of deep learning to obtain a predicted value of the cracking depth after the preset time length; as well as The optimization module is run to correct the cracking furnace operation when the variation trend of the cracking depth prediction value does not conform to the variation trend of the cracking depth.

9. A machine-readable storage medium having instructions stored thereon, the instructions being used to cause a machine to execute: the method for optimizing the operation of a cracking furnace as claimed in any one of claims 1 to 7.

10. A processor, characterized in that: Used to run a program, wherein the program, when run, is used to execute: the method for optimizing the operation of a cracking furnace as described in any one of claims 1-7.

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