Method, device, storage medium and processor for optimization of a cracking furnace operation

Abstract

The embodiment of the present application provides a kind of optimization method, device, storage medium and processor of cracking furnace operation, belong to chemical automatic control technical field.The optimization method of the cracking furnace operation includes: obtaining the cracking depth of cracking furnace in at least preset length of time;The reaction condition parameters of the cracking furnace are continuously obtained, and the reaction condition parameters are input into the cracking depth prediction model of depth learning, to obtain the cracking depth prediction value of cracking depth after the preset length of time;In the case where the change trend of the cracking depth prediction value does not comply with the change trend of the cracking depth, correct cracking furnace operation.Through the method, the real-time operation of cracking furnace can be quickly and effectively optimized, and the operation of cracking furnace complies with the optimization direction of cracking furnace reaction condition.

Description

Technical Field

[0001] This invention relates to the field of chemical automatic control technology, and specifically to an optimization method, apparatus, storage medium, and processor for the operation of a pyrolysis furnace. Background Technology

[0002] In the pyrolysis production process, the operating conditions of the pyrolysis furnace significantly impact its yield. In actual production, there are two main ways to adjust the operating conditions of the pyrolysis furnace: one is based on reference values ​​provided by the pyrolysis furnace manufacturer and human experience. However, in actual production, the source of the pyrolysis oil is usually not fixed, and different oils are switched as needed during production. Therefore, current human experience cannot optimize and adjust the pyrolysis furnace to its optimal operating condition. The other method is to use an online component analyzer to measure the relative content of key components in the pyrolysis gas and adjust the production operating conditions accordingly. However, online analyzers require a certain amount of time to analyze a sample, exhibiting significant lag and randomness, which is detrimental to the stable operation of the pyrolysis furnace. Furthermore, if the online analyzer malfunctions, this method will fail, potentially leading to an emergency shutdown of the pyrolysis furnace and severe economic losses. Therefore, to achieve rapid optimization and adjustment of the pyrolysis furnace operating conditions, a fast and effective real-time operation optimization method for pyrolysis furnaces is urgently needed. Summary of the Invention

[0003] The purpose of this invention is to provide an optimization method for pyrolysis furnace operation. This method can quickly and effectively optimize the real-time operation of the pyrolysis furnace and ensure that the operation of the pyrolysis furnace conforms to the optimization direction of the pyrolysis furnace reaction conditions.

[0004] To achieve the above objectives, embodiments of the present invention provide an optimization method for the operation of a pyrolysis furnace, the method comprising:

[0005] Obtain the pyrolysis depth of the pyrolysis furnace within at least a preset time period;

[0006] The reaction condition parameters of the cracking furnace are continuously acquired and input into a deep learning-based cracking depth prediction model to obtain the predicted cracking depth value after a preset time length; and

[0007] If the trend of the predicted cracking depth does not match the trend of the cracking depth, the cracking furnace operation should be corrected.

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

[0009] The reaction condition parameters are all operating parameters obtained through DCS monitoring.

[0010] Preferably, the reaction condition parameters are the feed ratio, pyrolysis gas and intermediate product content, and monitored values ​​of temperature and pressure conditions of the pyrolysis reaction determined according to the reaction mechanism of the pyrolysis feedstock. The reaction condition parameters include one or more of the following: pyrolysis gas pressure, bottom fuel gas pressure, temperature deviation and COT deviation of each furnace tube group, feed cross section temperature of each furnace tube group, pyrolysis gas outlet temperature of the waste heat boiler, furnace temperature, outlet temperature of each group of feed furnaces, and outlet temperature of the first stage of feed mixing and preheating of each group.

[0011] Furthermore, the cracking depth prediction model is a trained long short-term memory network model. The training dataset used includes a set of training samples, which are historical reaction condition data of the current cracking furnace cracking the current raw material. The training samples include sample values ​​of reaction condition parameters and corresponding cracking depth sample values.

[0012] Furthermore, the training process for the rift depth prediction model includes:

[0013] Obtain the initialized Long Short-Term Memory (LSTM) network model. The input of the LSM network model is the sample values ​​of the reaction condition parameters, and the output is the predicted value of the cleavage depth.

[0014] The sample values ​​of reaction condition parameters are input into the long short-term memory network model, and the deviation between the predicted value of pyrolysis depth and the sample value of pyrolysis depth is calculated by the error loss function.

[0015] Training ends when the deviation is less than a preset threshold or the preset number of training iterations is reached; otherwise, the Long Short-Term Memory (LSTM) network model is corrected, and the next round of training continues.

[0016] The model with the smallest deviation was selected as the final prediction model for fracture depth.

[0017] Preferably, the training dataset includes historical operating data of the pyrolysis furnace over the most recent 0.1–10 years.

[0018] Preferably, the preset time length is 1-30 minutes.

[0019] On the other hand, the present invention provides an optimized apparatus for pyrolysis furnace operation, comprising:

[0020] Run the monitoring module to obtain the pyrolysis depth of the pyrolysis furnace within at least a preset time period;

[0021] The prediction module continuously acquires the reaction condition parameters of the cracking furnace and inputs these parameters into a deep learning-based cracking depth prediction model to obtain the predicted cracking depth value after a preset time period; and

[0022] The optimization module is run to correct the operation of the pyrolysis furnace when the trend of the predicted pyrolysis depth does not match the trend of the pyrolysis depth.

[0023] On the other hand, the present invention provides a machine-readable storage medium storing instructions for causing a machine to perform an optimized method for operating a pyrolysis furnace according to the present application.

[0024] On the other hand, the present invention provides a processor for running a program, wherein the program is run to perform an optimized method for pyrolysis furnace operation according to the present application.

[0025] Through the above technical solution, on the one hand, the pyrolysis depth of the pyrolysis furnace is continuously acquired within a preset time period to determine the changing trend of the pyrolysis furnace's operating effect during that period. On the other hand, the continuously acquired pyrolysis furnace reaction condition parameters are input into a deep learning pyrolysis depth prediction model to determine the changing trend of the predicted value of the pyrolysis furnace's operating effect during that period. Finally, when there is a large deviation between the changing trends in the two aspects, the operation of the pyrolysis furnace is corrected, which can optimize the operation of the pyrolysis furnace in real time and make the operation of the pyrolysis furnace conform to the optimization direction of the pyrolysis furnace reaction conditions.

[0026] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0027] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0028] Figure 1 This is a flowchart of an embodiment of the optimized operation method of the pyrolysis furnace according to this application;

[0029] Figure 2 yes Figure 1 A schematic diagram of the training process for the rift depth prediction model in the embodiment; and

[0030] Figure 3 This is a structural diagram of an embodiment of the optimization device for pyrolysis furnace operation according to this application. Detailed Implementation

[0031] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0032] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0033] Additionally, it should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0034] This invention provides a method for optimizing the operation of a pyrolysis furnace, which can quickly and effectively optimize the real-time operation of the pyrolysis furnace. The flow of one embodiment of the pyrolysis furnace operation optimization method of this application is as follows: Figure 1 As shown, it includes:

[0035] Step 1: Obtain the pyrolysis depth of the pyrolysis furnace within at least a preset time period;

[0036] Step 2: Continuously acquire the reaction condition parameters of the cracking furnace, input the reaction condition parameters into the deep learning cracking depth prediction model, and obtain the cracking depth prediction value after a preset time length.

[0037] Step 3: If the trend of the predicted cracking depth does not match the trend of the cracking depth, adjust the operation of the cracking furnace.

[0038] In actual production, pyrolysis furnace operators can refer to the vector composed of various operating variables of the pyrolysis furnace and its corresponding pyrolysis depth, the first and second predicted values ​​of the pyrolysis depth, and select the optimal operating conditions of the pyrolysis furnace according to the actual production needs on site, and adjust and optimize the operating status of the pyrolysis furnace.

[0039] In this embodiment, the cracking furnace can be of type 1, 1-1, 2-1, 4-1, 4-1-1-1, 2-1-1-1, 1-1-1-1, or 8-4-2-1. The cracking feedstock of the cracking furnace is ethane, propane, LPG, naphtha, diesel, aviation kerosene, or hydrotreated tail oil, etc.

[0040] It should be noted that steps 1 and 2 above both include continuous monitoring of the relevant parameters of the pyrolysis furnace. In practice, these parameters can be monitored periodically, at regular intervals, or simultaneously or alternately as needed, and are not limited to the order of the steps described above.

[0041] In step 1, the cracking depth can be determined based on the cracking feedstock and the cracking reaction mechanism. For example, if the cracking feedstock is naphtha, the cracking depth can be the propylene-ethylene content ratio in the cracked gas, i.e., the cracking reaction depth. In other words, in this application, the cracking depth is a parameter reflecting the operating 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 cracked gas at the current moment can be measured using an online analyzer such as a gas chromatograph to obtain the propylene-ethylene content ratio in the cracked gas. Depending on the cracking feedstock and the target cracking product, the cracking depth can also be the feedstock conversion rate, methane yield, methane / propylene yield ratio, hydrogen content of the cracked liquid product, outlet temperature, kinetic depth, or the yield of C3 and lighter components, etc.

[0042] Taking naphtha as the cracking feedstock as an example, the composition of its cracking products is constrained by various factors, such as feedstock quality, residence time, cracking furnace tube configuration, hydrocarbon partial pressure, and reaction temperature. In step 2, the reaction condition parameters of the cracking furnace can be selected from the DCS system's detection parameters based on experience, or all parameters corresponding to the cracking furnace reaction conditions can be selected from the DCS system's detection parameters, or important parameters can be identified first by an artificial intelligence algorithm. In this embodiment, the reaction condition parameters include, but are not limited to, cracking feedstock flow rate, cracking feedstock temperature and pressure, dilution steam flow rate, dilution steam 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, and burner fuel flow rate.

[0043] In step 2, the cracking depth prediction model is a trained long short-term memory network model. The training samples are the historical reaction conditions data of the current cracking furnace in cracking the current raw material. That is, the prediction model is based on the reaction mechanism. By analyzing the historical reaction conditions data of the cracking furnace, the prediction model of cracking depth is established, which reflects the correspondence between the reaction conditions and cracking depth in the cracking furnace.

[0044] It should also be noted that this application can not only use the cracking depth of the cracking furnace as the object of monitoring and prediction, but also, depending on different cracking production purposes, any one of the following can be used as the object of monitoring and prediction: the maximum value of ethylene, propylene, ethylene and propylene in the cracking products, the maximum yield of triene, or the maximum yield of cracking product value. That is, the "cracking depth" in this embodiment can be replaced with any one of the above monitoring objects so that the cracking reaction proceeds in the desired direction.

[0045] Compared with the prior art, the technical advantages of this embodiment include:

[0046] (1) Gas chromatograph and other online analyzers measure the ethylene and propylene content in the cracked gas at the current moment, thereby obtaining the ratio of propylene to ethylene content in the cracked gas, which is the monitoring value of cracking depth that reflects the changing trend of the cracking furnace's operating effect during this period.

[0047] (2) A cracking depth prediction model is established based on the cracking reaction mechanism. It can be cross-referenced and verified with the monitoring value of cracking depth, and the deviation of cracking depth can be detected in time. This improves the real-time performance of the cracking furnace operation optimization method and makes the operation of the cracking furnace conform to the optimization direction of the cracking furnace reaction conditions.

[0048] (3) Based on the above monitoring and prediction values, a multi-objective real-time operation optimization model for cracking furnaces can be established, providing a set of multiple control schemes for cracking furnace operators. These schemes can be flexibly selected according to on-site production needs, effectively improving the overall production efficiency and economic benefits of the ethylene plant.

[0049] The following describes how the training dataset for the rift depth prediction model is obtained in some implementations.

[0050] First, offline operating data of the pyrolysis furnace over the past 0.1-10 years were collected to establish a pyrolysis furnace pyrolysis depth database;

[0051] Then, the operational data in the pyrolysis furnace pyrolysis depth database is classified, filtered, and cleaned to remove invalid or interfering data;

[0052] Finally, based on the current actual conditions of the cracking furnace and cracking feedstock, a set of training samples is extracted from the cracking furnace cracking depth database, where each training sample includes the parameter values ​​required to train the cracking depth prediction model.

[0053] It should be noted that different furnace tube configurations exist for cracking furnaces, such as single-pass furnace tubes, 1-1 type, 2-1 type and 4-1 type two-pass furnace tubes, and 1-1-1-1 type and 2-1-1-1 type four-pass furnace tubes. For example, ethylene cracking furnaces using naphtha as cracking feedstock often use 1-1 type two-pass furnace tubes or 2-1 type two-pass furnace tubes. Therefore, when obtaining historical operating condition data of cracking furnaces, in addition to collecting cracking furnace operating parameter data, it is also necessary to include the corresponding cracking furnace tube configuration parameters in the cracking furnace cracking depth database so that they can be used as screening conditions when extracting samples.

[0054] Data was collected from offline operating data of the cracking furnace over the past 0.1-10 years, following a time-series approach. The collected data may also include the properties of the cracking feedstock, the type of cracking furnace tubes, all relevant operating parameters monitored on the DCS, and the corresponding diene yields. A cracking depth database was established using this data. Then, based on experience, key parameters reflecting the cracking furnace's operating conditions were selected from the DCS system's monitoring parameters. These operating parameters, determined by the cracking furnace tube type and model, include one or more of the following: total steam feed ratio, total feed volume, first COT (Current Operating Temperature), target outlet temperature, feed rate for each tube group, feedwater flow rate, bottom fuel gas flow rate, sidewall fuel flow rate, steam drum level, radiant furnace pressure, and feed cross-section pressure.

[0055] Considering that the composition of cracking products is constrained by various factors, such as the quality of cracking feedstock, residence time, cracking furnace tube configuration, hydrocarbon partial pressure, and reaction temperature, operating parameters include cracking feedstock flow rate, cracking feedstock 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, and burner fuel flow rate.

[0056] Before training the fracture depth prediction model, the training dataset or fracture furnace fracture depth database is preprocessed by the following data classification and filtering:

[0057] Based on the properties of the oil feedstock, the cracking properties are classified, and the oils are divided into L categories with similar cracking performance. 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 oil.

[0058] Based on the classification, the relevant data in each oil product classification are corrected, and the collected data is cleaned to delete cracking furnace operating condition data and diene yield data containing obvious errors. Data cleaning refers to filtering the extracted historical operating condition data of the cracking furnace according to the range of relevant operating condition parameters and diene yield during the operation of the cracking furnace, and removing abnormal operating condition values ​​with obvious errors.

[0059] Furthermore, the corrected data can be integrated, with 50%-95% of the total data used as the training set and the remaining 50%-5% used as the test set.

[0060] The rift depth prediction model is a trained Long Short-Term Memory (LSTM) network model; its training process is described in [link to training documentation]. Figure 2As shown, the input is the sample value of the reaction condition parameters of the training sample n, where the training sample n is the nth training sample selected from a set of training samples in the above training dataset. The sample value of the reaction condition parameters should include the operating conditions of the pyrolysis furnace at time t, including all the selected operating parameters. The output is the predicted value of the pyrolysis depth. The predicted value of the pyrolysis depth is compared with the sample value of the pyrolysis depth in the training sample n by the error loss function, thereby determining the current prediction deviation of the prediction model.

[0061] It should be noted that the input reaction condition parameter sample value is the cracking furnace operating condition at time t. Correspondingly, the cracking depth sample value used for comparison with the predicted value should be the actual cracking depth at time t+n (n≥1), and the output is the predicted cracking depth at time t+n (n≥1). Here, n can be any time before the end of the current feedstock cracking production, such as 1 min, 5 min, 10 min, 13 min, 15 min, 24 min, 35 min, 42 min, 48 min, 54 min, 60 min, etc. Preferably, n is 1-30 minutes.

[0062] The training process of the rift depth prediction model is as follows: First, an initialized Long Short-Term Memory (LSTM) network model is obtained. The input of the LSM network model is the sample value of the reaction condition parameter, and the output is the predicted rift depth value. Then, multiple training sessions are conducted according to a preset number of training sessions and a preset deviation threshold. Each training session includes inputting the sample value of the operation parameter into the LSM network model, calculating the deviation between the predicted rift depth value and the sample rift depth value through the error loss function, and ending the training when the deviation is less than the preset threshold or the preset number of training sessions is reached. Otherwise, the LSM network model is corrected, and the next round of training continues. Finally, the model with the smallest deviation is selected as the final rift depth prediction model.

[0063] In some implementations, the root mean square error loss is used to calculate the deviation between the two, and the models saved after multiple training sessions are selected, with the one with the smallest root mean square error being the final prediction model.

[0064] In some embodiments, the reaction condition parameters are the feed ratio of the pyrolysis reaction, the content of pyrolysis gas and intermediate products, and the monitored values ​​of temperature and pressure conditions of the pyrolysis reaction, determined according to the reaction mechanism of the pyrolysis feedstock. The reaction condition parameters include one or more of the following: pyrolysis gas pressure, bottom fuel gas pressure, temperature deviation and COT deviation of each furnace tube group, feed cross section temperature of each furnace tube group, pyrolysis gas outlet temperature of the waste heat boiler, furnace temperature, outlet temperature of each group of feed furnaces, and outlet temperature of the first stage of feed mixing and preheating of each group.

[0065] In some implementations, a virtual component model is established based on the main performance parameters of the pyrolysis feedstock. The pyrolysis depth prediction model is then established as a pyrolysis reaction mechanism model (hereinafter referred to as the pyrolysis depth prediction model). The pyrolysis furnace operating conditions at time t are used as input variables to calculate the pyrolysis depth prediction value corresponding to the operating conditions. This prediction value is compared with the pyrolysis depth operating value obtained by the online pyrolysis gas analyzer to ensure that the relative error between the two is less than a given threshold of 2%. Otherwise, the pyrolysis reaction mechanism model is corrected, and finally, an optimal pyrolysis reaction mechanism model is established.

[0066] In some implementations, naphtha is used as the cracking feedstock. Since naphtha is a mixture of different hydrocarbons, its specific hydrocarbon composition is difficult to obtain through measurement. In actual industrial operations, its cracking performance is mainly characterized based on its key performance indicators. However, when establishing a specific cracking reaction mechanism model, it is necessary to construct its detailed hydrocarbon composition. For example, for naphtha as a special cracking feedstock, a virtual component model corresponding to its key performance indicators is established, mainly including C5-C12 alkanes and their isomers. Based on this, a cracking reaction mechanism model corresponding to this hydrocarbon composition is established. During the cracking process, the calculation results of the mechanism model are compared in real time with the measurement data from the online cracking gas analyzer, and the operation of the cracking furnace is optimized accordingly.

[0067] In some implementations, the goal is to maximize ethylene and propylene yields. The maximum and minimum allowable values ​​of each operating variable of the cracking furnace are given as constraints to establish a multi-objective real-time operation optimization model for the cracking furnace. Finally, the optimized setpoints of each operating variable of the cracking furnace are obtained, forming the optimal solution geometry of each control variable and its corresponding ethylene and propylene yields. This is provided to the on-site operators so that they can select the corresponding optimal operating conditions to optimize and adjust the operation of the cracking furnace in real time according to the current actual production needs.

[0068] Current actual production needs mainly include the following situations:

[0069] Based on the current market prices of ethylene and propylene, determine the specific values ​​of the output variables to maximize profits;

[0070] • Based on the different requirements of downstream processes of the cracking furnace for ethylene and propylene, select the one with the highest demand and determine the specific value of the output variable;

[0071] • Based on the range of the ratio of ethylene to propylene yield on site, we aim to obtain the highest overall yield of ethylene and propylene, and thus determine the specific value of the output variable;

[0072] When the production process does not have specific requirements for the yield of other products, and only the ethylene yield is of concern,

[0073] Choose the option with the highest ethylene yield and determine the specific values ​​for the output variables.

[0074] This invention provides an optimized device for the operation of a pyrolysis furnace, the structure of which is as follows: Figure 3 As shown, it includes an operation monitoring module, a prediction module, and an operation optimization module. The operation monitoring module acquires the pyrolysis depth of the pyrolysis furnace within at least a preset time period. The prediction module continuously acquires the reaction condition parameters of the pyrolysis furnace and inputs the reaction condition parameters into a deep learning pyrolysis depth prediction model to obtain the predicted pyrolysis depth value after the preset time period. The operation optimization module corrects the operation of the pyrolysis furnace when the trend of the predicted pyrolysis depth value does not conform to the trend of the pyrolysis depth.

[0075] In this embodiment, the operation monitoring module is used to monitor changes in the operation effect of the cracking furnace, specifically the changes in the monitored value of the cracking depth (or the calculated value obtained from the monitored value). The cracking depth can be the ethylene content in the cracked gas or the propylene-ethylene ratio in the cracked gas. The prediction module obtains the predicted value of the cracking depth based on the reaction condition parameters of the cracking furnace. When the 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. The module provides the cracking furnace operator with a vector composed of various operation variables of the cracking furnace and their corresponding cracking depth and predicted value of cracking depth, 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 status of the cracking furnace.

[0076] In this embodiment, the training method and training dataset used for the pyrolysis depth prediction model can be referred to the implementation method of the optimization method for pyrolysis furnace operation, and will not be repeated here.

[0077] This invention also provides an optimization device for pyrolysis furnace operation, including a processor and a memory. The aforementioned operation monitoring module, prediction module, and operation optimization module are all stored as program units in the memory, and the processor executes the aforementioned program units stored in the memory to achieve the corresponding functions.

[0078] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured; adjusting kernel parameters determines if there are any deviations in the pyrolysis furnace and makes corresponding adjustments.

[0079] The memory may include non-permanent memory in computer-readable media, such as 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.

[0080] This invention provides a storage medium storing a program that, when executed by a processor, implements the optimization method for pyrolysis furnace operation as described in this application.

[0081] This invention provides a processor for running a program, wherein the program executes the optimization method for pyrolysis furnace operation of this application.

[0082] This invention provides an apparatus including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the optimized method for operating a pyrolysis furnace as described in this application. The apparatus described herein may be a server, PC, PAD, mobile phone, etc.

[0083] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform the program of the optimized method steps for initializing the operation of the pyrolysis furnace of this application.

[0084] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0086] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0089] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0090] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0091] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0092] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An optimization method for pyrolysis furnace operation, comprising: Obtain the pyrolysis depth of the pyrolysis furnace within at least a preset time period; The reaction condition parameters of the pyrolysis furnace are continuously acquired, and these parameters are input into a deep learning-based pyrolysis depth prediction model to obtain a predicted pyrolysis depth value after a preset time length. The rupture depth prediction model is a trained Long Short-Term Memory (LSTM) network model, which is trained using training samples from the training set data of the reaction condition parameters. The training samples include sample values ​​of the reaction condition parameters and corresponding rupture depth sample values. The reaction condition parameter sample values ​​are used as input to the rupture depth prediction model, causing the model to output the predicted rupture depth value. If the trend of the predicted pyrolysis depth does not conform to the trend of the pyrolysis depth, the operation of the pyrolysis furnace shall be corrected. The trend of the cracking depth is the trend of the monitored value of cracking depth, which includes the maximum value of ethylene, propylene, and ethylene and propylene in the cracking products, the maximum yield of triene, or the maximum yield of cracking product value. When calibrating the operation of the pyrolysis furnace: Based on the vector composed of each operating variable of the pyrolysis furnace and its corresponding pyrolysis depth, as well as the predicted value of the pyrolysis depth, the optimal operating conditions of the pyrolysis furnace are selected, and the operating state of the pyrolysis furnace is adjusted and optimized.

2. The method according to claim 1, characterized in that, The pyrolysis depth is determined based on the pyrolysis feedstock and target pyrolysis product, including feedstock conversion rate, methane yield, propylene / ethylene yield ratio, methane / propylene yield ratio, hydrogen content of the pyrolyte liquid product, outlet temperature, kinetic depth, or yield of C3 and lighter components; and The reaction condition parameters are all the operating parameters obtained through DCS monitoring.

3. The method according to claim 1, characterized in that, The reaction condition parameters are the feed ratio, pyrolysis gas and intermediate product content, and temperature and pressure conditions of the pyrolysis reaction determined according to the reaction mechanism of the pyrolysis feedstock. The reaction condition parameters include one or more of the following: pyrolysis gas pressure, bottom fuel gas pressure, temperature deviation and COT deviation of each furnace tube group, feed cross section temperature of each furnace tube group, pyrolysis gas outlet temperature of the waste heat boiler, furnace temperature, outlet temperature of each group of feed furnaces, and outlet temperature of the first stage of feed mixing and preheating of each group.

4. The optimized operation method of the pyrolysis furnace according to claim 3, characterized in that, The training dataset used by the Long Short-Term Memory Network model includes a set of training samples, which are historical reaction conditions of the current pyrolysis furnace in pyrolyzing the current raw material.

5. The optimized operation method of the pyrolysis furnace according to claim 4, characterized in that, The training process of the rift depth prediction model includes: Obtain the initialized long short-term memory network model; The sample values ​​of the reaction condition parameters are input into the long short-term memory network model, and the deviation between the predicted rupture depth and the sample rupture depth is calculated using the error loss function. Training ends when the deviation is less than a preset threshold or the training reaches a preset number of iterations; otherwise, the long short-term memory network model is corrected, and the next round of training continues. The model with the smallest deviation was selected as the final prediction model for fracture depth.

6. The method according to claim 5, characterized in that, The training dataset includes historical operating data of the pyrolysis furnace over the most recent 0.1–10 years.

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

8. An optimized apparatus for pyrolysis furnace operation, characterized in that, include: Run the monitoring module to obtain the pyrolysis depth of the pyrolysis furnace within at least a preset time period; The prediction module continuously acquires the reaction condition parameters of the pyrolysis furnace, inputs these parameters into a deep learning-based pyrolysis depth prediction model, and obtains the predicted pyrolysis depth value after the preset time length. The rupture depth prediction model is a trained Long Short-Term Memory (LSTM) network model, which is trained using training samples from the training set data of the reaction condition parameters. The training samples include sample values ​​of the reaction condition parameters and corresponding rupture depth sample values. The reaction condition parameter sample values ​​are used as input to the rupture depth prediction model, causing the model to output the predicted rupture depth value. The optimization module is run to correct the operation of the pyrolysis furnace if the trend of the predicted pyrolysis depth does not conform to the trend of the pyrolysis depth. The trend of the cracking depth is the trend of the monitored value of cracking depth, which includes the maximum value of ethylene, propylene, and ethylene and propylene in the cracking products, the maximum yield of triene, or the maximum yield of cracking product value. When calibrating the operation of the pyrolysis furnace: Based on the vector composed of each operating variable of the pyrolysis furnace and its corresponding pyrolysis depth, as well as the predicted value of the pyrolysis depth, the optimal operating conditions of the pyrolysis furnace are selected, and the operating state of the pyrolysis furnace is adjusted and optimized.

9. A machine-readable storage medium storing instructions for causing a machine to perform: an optimized method for operating a pyrolysis furnace as described in any one of claims 1-7.

10. A processor, characterized in that, Used to run a program, wherein the program is run to perform: an optimized method for operating a pyrolysis furnace as described in any one of claims 1-7.

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