Storage medium, hydrogenation catalyst full life cycle management method, device and equipment

Abstract

The invention discloses a storage medium, a hydrogen catalyst life cycle management method, device and equipment, and the method comprises the steps: before a process reaction, through a preset catalyst performance analysis model, respectively determining the optimal pore diameter, the optimal surface area and the optimal pore volume of each hydrogenation catalysis single agent, and the corresponding optimal use temperature; respectively constructing a catalyst activity simulation model corresponding to each single catalytic agent, and constructing a nonlinear catalyst filling optimization model of a maximized objective function; the method comprises the following steps: in a process reaction process, taking current data of a first preset working condition as input, and determining an optimal value of a preset controllable variable when a balance limit value of vulcanization time is reached through a preset catalyst pre-vulcanization simulation model; and determining a time point when the catalyst activity is lower than a preset threshold value through a preset catalyst reaction simulation model by taking the current data of the second preset working condition as input. According to the method, the technical operation can be guided, so that the heavy oil hydrogenation technical level is improved.

Description

Technical Field

[0001] The present invention relates to the field of hydrogenation catalysts, and in particular to a storage medium, a method, a device and equipment for managing the entire life cycle of a hydrogenation catalyst. Background Art

[0002] The modification technology of heavy oil to light oil is the key to heavy oil processing technology; among them, heavy oil hydrogenation technology is the first choice for many refineries to process heavy oil, that is to say, heavy oil hydrogenation technology is one of the main ways to lighten heavy oil.

[0003] The application of hydrogenation catalysts runs through the entire heavy oil hydrogenation technology, and the continuous optimization of the use process of hydrogenation catalysts has a positive role in promoting heavy oil hydrogenation technology.

[0004] In the prior art, at each stage of the hydrogenation catalyst process, the use of materials and the adjustment of operating conditions in the hydrogenation catalyst process are controlled by relying on experimental results or empirical judgment.

[0005] The inventors have found through research that the above-mentioned process optimization method of hydrogenation catalyst in the prior art has at least the following defects:

[0006] There are significant differences between the experimental environment in the laboratory and the actual working conditions of heavy oil hydrogenation (such as differences in the composition and dosage of the raw materials, and differences in the performance of the equipment, etc.), which will affect the correct analysis of the performance of the hydrogenation catalyst throughout its life cycle.

[0007] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0008] The purpose of the present invention is to improve the overall performance optimization effect of the hydrogenation catalyst by optimizing the management of the life cycle of the hydrogenation catalyst in the heavy oil hydrogenation process.

[0009] The present invention provides a hydrogen catalyst life cycle management method, comprising the steps of:

[0010] S11. Before the process reaction, the optimal pore size, optimal surface area and optimal pore volume of each hydrogenation catalyst and the corresponding optimal operating temperature are determined by using a preset catalyst performance analysis model;

[0011] S12, when the catalyst performance parameters and the total volume of the catalyst are known, the optimal loading amount of each catalyst single agent is determined by a preset catalyst loading optimization model; the construction of the catalyst loading optimization model includes: constructing a catalyst activity simulation model corresponding to each catalyst single agent, and constructing a nonlinear catalyst loading optimization model that maximizes the objective function based on multiple catalyst activity simulation models;

[0012] S13. In the process of the process reaction, the current data of the first preset working condition is used as input, and the optimal value of the preset controllable variable when the equilibrium limit value of the sulfidation time is reached is determined by a preset catalyst presulfidation simulation model; the preset controllable variable includes the catalyst metal amount; the first preset working condition includes the sulfidation temperature, the sulfide concentration in the circulating hydrogen, the bed temperature rise and the heating rate data;

[0013] S14, using the current data of the second preset operating condition as input, determining the time point when the catalyst activity is lower than a preset threshold value through a preset catalyst reaction simulation model.

[0014] Preferably, in the embodiment of the present invention, it also includes:

[0015] S15. Under heating conditions, the valuable metal oxides and sulfides are converted into water-soluble salts by reacting the molten leaching agent with the spent catalyst; after separating the filtrate and the residue, the metals in the filtrate are recovered by precipitation and / or extraction.

[0016] Preferably, in an embodiment of the present invention, the method for constructing the catalyst performance analysis model comprises:

[0017] Acquiring historical data of the target equipment, including temperature data, hydrogenation catalyst data, feedstock oil data and hydrogenation heavy oil data at different operating times; the hydrogenation catalyst data includes the pore size, surface area and pore volume of each catalyst single agent;

[0018] Calculating the impurity removal rate for characterizing the catalyst performance of each of the catalyst units, comprising: determining the impurity content according to the feedstock oil data and the hydrogenated heavy oil data, and calculating the impurity removal rate;

[0019] The pore size, surface area, pore volume and temperature data of the catalyst are used as modeling data to train a BP neural network to generate a catalyst performance analysis model with temperature, pore size, surface area and pore volume as input and impurity removal rate as output.

[0020] Preferably, in an embodiment of the present invention, the method for constructing the catalyst activity simulation model includes:

[0021] For each reactor, the porosity, volume, proportionality coefficient and catalyst performance parameters of the single catalyst at different sampling times are used as modeling data to train the BP neural network and establish the corresponding activity simulation model; the activity simulation model uses the porosity, proportionality coefficient and catalyst volume as input and the impurity removal rate as output.

[0022] Preferably, in an embodiment of the present invention, the nonlinear catalyst loading optimization model for maximizing the objective function is constructed based on multiple catalyst activity simulation models, including:

[0023] The objective function includes:

[0024] F max =max(f s )+max(f m ) +max(f c ) (Formula 4);

[0025] Where: F max represents the maximum impurity removal rate of all catalysts, f s 、f m 、f c Represents the activity simulation model of desulfurization catalyst, demetallization catalyst and residual carbon removal catalyst.

[0026] Preferably, in an embodiment of the present invention, the method for constructing the catalyst presulfurization simulation model includes:

[0027] The desulfurization rate, demetallization rate and residual carbon removal rate at different sampling times are calculated based on the device operation data, and then the data are normalized to fit the parameters representing the catalyst activity;

[0028] The BP neural network method is used to train the catalyst presulfurization simulation model with the catalyst metal content, sulfidation temperature, sulfide concentration in circulating hydrogen, bed temperature rise and heating rate data in multi-cycle device operation data as input, and the impurity removal rate and sulfidation time as output are obtained.

[0029] Preferably, in an embodiment of the present invention, the method for constructing the catalyst reaction simulation model includes:

[0030] The correlation between each parameter in the device operation data and the total pressure drop of the bed and the hot spot temperature is calculated based on the Pearson product-moment correlation coefficient, and the correlation is sorted from large to small, and the top five key parameters with the strongest correlation are selected;

[0031] The Pearson product-moment correlation coefficient calculation method includes the formula:

[0032]

[0033] Where: xo Represents the operation data of a device at different sampling times, y o Represents the total bed pressure drop at different sampling times, o=1,...,N, where N is the number of parameter samples.

[0034] Preferably, in the embodiment of the present invention, the formula for calculating the impurity removal rate includes:

[0035]

[0036] Where: a i Represents various impurities, where i=s, c, m, respectively, are sulfur, carbon residue, and metal. is the impurity removal capacity at different sampling time points, j = 1, ..., n, is the impurity content in the crude oil, It is the impurity content in heavy oil after hydrotreatment.

[0037] Preferably, in the embodiment of the present invention, the formula for calculating the porosity of the desulfurization catalyst, the demetallization catalyst and the residual carbon removal catalyst includes:

[0038]

[0039] Where: D i represents the porosity of different catalysts; where i = s, c or m, representing sulfur, carbon residue and metal, respectively; V i V is the volume of the catalyst loaded in the reactor; io is the volume of a single catalyst, V ik is the pore volume corresponding to a single catalyst;

[0040] The formula for calculating the proportional coefficient of each catalyst single agent includes:

[0041]

[0042] Where: K i The coefficient representing the proportion of each catalyst single agent to all catalysts.

[0043] Preferably, in the embodiment of the present invention, the desulfurization rate, demetallization rate and residual carbon removal rate at different sampling times are calculated according to the device operation data, and then the data are normalized to fit the parameters representing the catalyst activity, including:

[0044] The formula for normalizing the impurity removal rate data to eliminate the dimensional influence between the indicators includes:

[0045]

[0046] Where: is the normalized impurity removal rate, a i Represents various impurities, where i=s, c, m, respectively, are sulfur, carbon residue, and metal. is the impurity removal capacity at different sampling time points, j = 1, ..., n;

[0047] For the data at the same sampling time, the formulas used to fit the catalyst activity parameters include:

[0048]

[0049] Where: represents the catalyst activity parameter at time j, is the desulfurization rate at time j after normalization, is the normalized residual carbon removal rate at time j, is the normalized demetallization rate at time j.

[0050] In another aspect of the present invention, a hydrogen catalyst life cycle management device is also provided, comprising:

[0051] The performance optimization unit is used to determine the optimal pore size, optimal surface area and optimal pore volume of each hydrogenation catalyst and the corresponding optimal operating temperature through a preset catalyst performance analysis model before the process reaction;

[0052] A loading amount determination unit is used to determine the optimal loading amount of each catalyst single agent through a preset catalyst loading optimization model when the catalyst performance parameters and the total volume of the catalyst are known; the construction of the catalyst loading optimization model includes: constructing a catalyst activity simulation model corresponding to each catalyst single agent, and constructing a nonlinear catalyst loading optimization model that maximizes the objective function based on multiple catalyst activity simulation models;

[0053] A metal quantity optimization unit is used to determine the optimal value of a preset controllable variable when the equilibrium limit value of the sulfidation time is reached by using a preset catalyst presulfidation simulation model, taking the current data of the first preset working condition as input during the process reaction; the preset controllable variable includes the catalyst metal quantity; the first preset working condition includes the sulfidation temperature, the sulfide concentration in the circulating hydrogen, the bed temperature rise and the heating rate data;

[0054] The activity life prediction unit is used to take the current data of the second preset operating condition as input and determine the time point when the catalyst activity is lower than a preset threshold through a preset catalyst reaction simulation model.

[0055] Preferably, in the embodiment of the present invention, it also includes:

[0056] The recovery optimization unit is used to convert valuable metal oxides and sulfides into water-soluble salts by reacting a molten leaching agent with a waste catalyst under heating conditions; after separating the filtrate and the residue, the metal in the filtrate is recovered by precipitation and / or extraction.

[0057] In another aspect of the present invention, a hydrogen catalyst life cycle management device is also provided, comprising:

[0058] Memory for storing computer programs;

[0059] A processor is used to call and execute the computer program to implement each step of the hydrogen catalyst life cycle management method as described in any one of the above items.

[0060] On the other hand, a storage medium is provided on which a computer program is stored. When the computer program is executed by a processor, each step of the hydrogen catalyst life cycle management method as described in any one of the above items is implemented.

[0061] The hydrogen catalyst life cycle management device includes a computer program stored on a medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the methods described in the above aspects and achieves the same technical effects.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] In the present invention, based on various production data of the hydrogenation device, simulation technology and deep learning technology are used to construct data models for each stage in the data-driven hydrogenation catalyst process; the present invention achieves overall and comprehensive optimization management of the life cycle of the hydrogenation catalyst in the heavy oil hydrogenation process by measuring the process parameters of each stage and predicting the development trend; thereby effectively improving the overall performance optimization effect of the hydrogenation catalyst.

[0064] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, and to make the above and other purposes, technical features and advantages of the present invention easier to understand, one or more preferred embodiments are listed below and described in detail with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 is a step diagram of the hydrogen catalyst life cycle management method described in the present invention;

[0067] Figure 2 It is a structural schematic diagram of the hydrogen catalyst life cycle management device described in the present invention;

[0068] Figure 3 It is a structural schematic diagram of the hydrogen catalyst life cycle management device described in the present invention. DETAILED DESCRIPTION

[0069] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation modes.

[0070] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0071] In this document, the terms "first", "second", etc. are used to distinguish two different elements or parts, and are not used to limit a specific position or relative relationship. In other words, in some embodiments, the terms "first", "second", etc. can also be interchangeable.

[0072] Embodiment 1

[0073] In order to optimize the life cycle of the hydrogenation catalyst in the heavy oil hydrogenation process and improve the overall performance optimization effect of the hydrogenation catalyst, such as Figure 1 As shown, in an embodiment of the present invention, a hydrogen catalyst life cycle management method is provided, comprising the steps of:

[0074] S11. Before the process reaction, the optimal pore size, optimal surface area and optimal pore volume of each hydrogenation catalyst and the corresponding optimal operating temperature are determined by using a preset catalyst performance analysis model;

[0075] In practical applications, the heavy oil hydrogenation process generally uses three catalysts with different characteristics, namely desulfurization catalyst, demetallization catalyst, and residual carbon removal catalyst; these three catalysts are loaded into each reactor of the hydrogenation unit step by step as needed.

[0076] The operating data of the hydrogenation unit includes catalyst data, feedstock oil data and hydrogenated heavy oil data; among them, the sampling frequency of catalyst data is generally every 120 hours, and the sampling frequency of feedstock oil data and hydrogenated heavy oil data is generally once a day. The data sampling frequency of catalyst, feedstock oil and hydrogenated heavy oil is different. The missing parts of catalyst characterization data (such as macroscopic structure data such as catalyst pore size, surface area, pore volume, and microscopic structure data such as grain size, dispersion, and chemical composition) can be supplemented by least squares fitting.

[0077] Specifically, the method for constructing a catalyst performance analysis model in an embodiment of the present invention may include:

[0078] Acquiring historical data of the target equipment, including temperature data, hydrogenation catalyst data, feedstock oil data and hydrogenation heavy oil data at different operating times; the hydrogenation catalyst data includes the pore size, surface area and pore volume of each catalyst single agent;

[0079] Calculating the impurity removal rate for characterizing the catalyst performance of each of the catalyst units, comprising: determining the impurity content according to the feedstock oil data and the hydrogenated heavy oil data, and calculating the impurity removal rate;

[0080] The pore size, surface area, pore volume and temperature data of the catalyst are used as modeling data to train a BP neural network to generate a catalyst performance analysis model with temperature, pore size, surface area and pore volume as input and impurity removal rate (i.e., catalyst performance parameters) as output; in practical applications, time series weighting can be used in the training data to improve the accuracy of the model.

[0081] The hydrogenation catalyst data, feedstock oil data and hydrogenated heavy oil data at different operating times are obtained; then, the hydrogenation catalyst (catalytic single agent) at different sampling times is characterized, and the pore size, surface area and pore volume data of each catalyst single agent are obtained respectively; then, the impurity content in the feedstock oil and hydrogenated heavy oil is determined, and the impurity removal rate is calculated; the impurity removal rate is used to characterize the catalyst performance of each catalyst single agent.

[0082] The BP neural network is trained with the multi-cycle operation data of the hydrogenation unit, including temperature, catalyst pore size, surface area, pore volume and impurity removal rate data, to obtain a catalyst performance analysis model with impurity removal rate as output and temperature, pore size, surface area and pore volume as input. In this way, the catalyst performance analysis model can be used to determine the influence of pore size, surface area and pore volume on catalyst performance, thereby providing guidance for the subsequent hydrogenation catalyst preparation process.

[0083] A catalyst performance analysis model was established based on the analysis of previous hydrogenation unit operation data. The catalyst impurity removal rate is mainly used to calculate the removal conversion rate of impurity sulfur, metal (Ni+V), and residual carbon. The impurity removal rate represents the performance of the catalyst, that is, the catalyst performance parameters are calculated by (Formula 1).

[0084]

[0085] Where: a i Represents various impurities, where i=s, c, m, respectively, are sulfur, carbon residue, and metal. is the impurity removal capacity at each sampling time point, j = 1, ..., n, is the impurity content in the crude oil, It is the impurity content in heavy oil after hydrotreatment.

[0086] S12, when the catalyst performance parameters and the total volume of the catalyst are known, the optimal loading amount of each catalyst single agent is determined by a preset catalyst loading optimization model; the construction of the catalyst loading optimization model includes: constructing a catalyst activity simulation model corresponding to each catalyst single agent, and constructing a nonlinear catalyst loading optimization model that maximizes the objective function based on multiple catalyst activity simulation models;

[0087] Specifically, for each catalyst in each reactor, the porosity, volume, proportionality coefficient, and catalyst performance parameters of the catalyst at different sampling times are used as modeling data to train the BP neural network, and a corresponding activity simulation model is established; the activity simulation model uses the porosity, proportionality coefficient, and catalyst volume as inputs, and the impurity removal rate as output. Each catalyst in each reactor has a corresponding activity simulation model.

[0088] In practical applications, the formula for calculating the porosity of the desulfurization catalyst, the demetallization catalyst and the residual carbon removal catalyst in the embodiment of the present invention may specifically include:

[0089]

[0090] Where: D i represents the porosity of different catalysts; where i = s, c or m, representing sulfur, carbon residue and metal, respectively; V i V is the volume of the catalyst loaded in the reactor; io is the volume of a single catalyst, V ik is the pore volume corresponding to a single catalyst;

[0091] The formula used to calculate the proportional coefficient of each catalyst single agent includes:

[0092]

[0093] Where: K i The coefficient representing the proportion of each catalyst single agent to all catalysts.

[0094] For a single catalyst in a reactor, the BP neural network is trained with the porosity, volume, proportionality coefficient, and catalyst performance parameter data at different sampling times to obtain a catalyst activity simulation model with the impurity removal rate corresponding to the single catalyst as output and the porosity, proportionality coefficient, and catalyst volume as input. This method can be used to establish the activity simulation model corresponding to each single catalyst.

[0095] The maximization objective function in the embodiment of the present invention is to maximize the impurity removal rate after all catalysts are included; when constructing a nonlinear catalyst loading optimization model that maximizes the objective function, the objective function may specifically include:

[0096] F max =max(f s )+max(f m ) +max(f c ) (Formula 4);

[0097] Where: F max represents the maximum impurity removal rate of all catalysts, f s 、f m 、f c Represents the activity simulation model of desulfurization catalyst, demetallization catalyst and residual carbon removal catalyst.

[0098] In this way, when the catalyst parameters and the total volume of the catalyst are known, the catalyst loading optimization model can be used to obtain the optimal loading amount of each catalyst single agent.

[0099] S13. In the process of the process reaction, the current data of the first preset working condition is used as input, and the optimal value of the preset controllable variable when the equilibrium limit value of the sulfidation time is reached is determined by a preset catalyst presulfidation simulation model; the preset controllable variable includes the catalyst metal amount; the first preset working condition includes the sulfidation temperature, the sulfide concentration in the circulating hydrogen, the bed temperature rise and the heating rate data;

[0100] The specific implementation of this step can be as follows:

[0101] Firstly, the desulfurization rate, demetallization rate and residual carbon removal rate at different sampling times were calculated based on the device operation data, and then the data were normalized to fit the parameters representing the catalyst activity.

[0102] Then, the BP neural network method was used to train the catalyst metal content, sulfidation temperature, sulfide concentration in circulating hydrogen, bed temperature rise and heating rate data in multi-cycle device operation data, and a catalyst pre-sulfurization simulation model with impurity removal rate and sulfidation time as output was obtained.

[0103] Among them, the desulfurization rate, demetallization rate and residual carbon removal rate at different sampling times are calculated according to the device operation data, and then the data are normalized to fit the parameters representing the catalyst activity, which may specifically include:

[0104] The formula for normalizing the impurity removal rate data to eliminate the dimensional influence between the indicators includes:

[0105]

[0106] Where: is the normalized impurity removal rate, a i Represents various impurities, where i=s, c, m, respectively, are sulfur, carbon residue, and metal. is the impurity removal capacity at different sampling time points, j=1,....,n.

[0107] For the data at the same sampling time, the formulas used to fit the catalyst activity parameters include:

[0108]

[0109] Where: represents the catalyst activity parameter at time j, is the desulfurization rate at time j after normalization, is the normalized residual carbon removal rate at time j, is the normalized demetallization rate at time j.

[0110] S14, using the current data of the second preset operating condition as input, determining the time point when the catalyst activity is lower than a preset threshold value through a preset catalyst reaction simulation model.

[0111] Preferably, in an embodiment of the present invention, the method for constructing the catalyst reaction simulation model includes:

[0112] The correlation between each parameter in the device operation data and the total pressure drop of the bed and the hot spot temperature is calculated based on the Pearson product-moment correlation coefficient, and the correlation is sorted from large to small, and the top five key parameters with the strongest correlation are taken respectively; based on these five key parameters with the strongest correlation, the parameter items included in the second preset working condition can be determined;

[0113] The Pearson product-moment correlation coefficient calculation method includes the formula:

[0114]

[0115] Where: x o Represents the operation data of a device at different sampling times, y o Represents the total bed pressure drop at different sampling times, o=1,...,N, where N is the number of parameter samples.

[0116] In this way, the BP neural network is trained to construct a catalyst reaction simulation model with five key parameters and impurity removal rate parameters as modeling data, and a catalyst performance analysis model is obtained with impurity removal rate parameters as output and temperature, pore size, surface area, pore volume and total bed pressure drop data as input; in practical applications, the accuracy of the model can be improved by using a time series weighting method in the training data. In this way, after predicting the time when the catalyst activity is too low, the loss can be reduced by stopping the work and unloading the catalyst in time.

[0117] Furthermore, in the embodiment of the present invention, the steps may also be included:

[0118] S15. Under heating conditions, the valuable metal oxides and sulfides are converted into water-soluble salts by reacting the molten leaching agent with the spent catalyst; after separating the filtrate and the residue, the metals in the filtrate are recovered by precipitation and / or extraction.

[0119] After the process reaction is completed, the reactor is cooled to the specified temperature and cleaned with VGO and diesel for several hours. After the cleaning is completed, the reactor is cooled and the oil pumps are stopped in sequence after reaching the specified temperature, and the reactor is vented to normal pressure, and the catalyst is removed. Under heating conditions (500-900°C), the molten leaching agent Na2CO3 is used to react with the waste catalyst to separate the converted water-soluble salt from the filter residue, and the metal in the filtrate is recovered by precipitation and extraction methods.

[0120] In summary, the embodiments of the present invention are based on various production data of the hydrogenation device, and utilize simulation technology and deep learning technology to construct data models for each stage in the data-driven hydrogenation catalyst process; the embodiments of the present invention achieve overall and comprehensive optimization management of the life cycle of the hydrogenation catalyst in the heavy oil hydrogenation process by measuring the process parameters of each stage and predicting the development trend; thereby effectively improving the overall performance optimization effect of the hydrogenation catalyst.

[0121] Embodiment 2

[0122] Corresponding to the method embodiment, in another aspect of the embodiment of the present invention, a hydrogen catalyst life cycle management device is also provided. Figure 2 The schematic diagram of the structure of the hydrogen catalyst life cycle management device provided by the embodiment of the present invention is shown. Figure 1The device corresponding to the hydrogen catalyst life cycle management method described in the corresponding embodiment, that is, implemented by means of a virtual device Figure 1 In the corresponding embodiment of the hydrogen catalyst life cycle management method, each virtual module constituting the hydrogen catalyst life cycle management device can be executed by an electronic device, such as a network device, a terminal device or a server. Specifically, the hydrogen catalyst life cycle management device in the embodiment of the present invention includes:

[0123] Performance optimization unit 01 is used to determine the optimal pore size, optimal surface area and optimal pore volume of each hydrogenation catalyst and the corresponding optimal operating temperature through a preset catalyst performance analysis model before the process reaction;

[0124] The loading amount determination unit 02 is used to determine the optimal loading amount of each catalyst single agent through a preset catalyst loading optimization model when the catalyst performance parameters and the total volume of the catalyst are known; the construction of the catalyst loading optimization model includes: constructing a catalyst activity simulation model corresponding to each catalyst single agent, and constructing a nonlinear catalyst loading optimization model that maximizes the objective function based on multiple catalyst activity simulation models;

[0125] The metal content optimization unit 03 is used to determine the optimal value of the preset controllable variables when the equilibrium limit value of the sulfidation time is reached by using the preset catalyst presulfidation simulation model during the process reaction, taking the current data of the first preset working condition as input; the preset controllable variables include the catalyst metal content; the first preset working condition includes the sulfidation temperature, the sulfide concentration in the circulating hydrogen, the bed temperature rise and the heating rate data;

[0126] The activity life prediction unit 04 is used to take the current data of the second preset operating condition as input and determine the time point when the catalyst activity is lower than a preset threshold value through a preset catalyst reaction simulation model.

[0127] Preferably, in the embodiment of the present invention, the following may also be included:

[0128] The recovery optimization unit (not shown in the figure) is used to convert valuable metal oxides and sulfides into water-soluble salts by reacting the spent catalyst with a molten leaching agent under heating conditions; after separating the filtrate and residue, the metals in the filtrate are recovered by precipitation and / or extraction.

[0129] It should be noted that the specific implementation method and technical effect of the hydrogen catalyst life cycle management device in the embodiment of the present invention can be referred to Figure 1 The corresponding hydrogen catalyst life cycle management method will not be elaborated here.

[0130] Embodiment 3

[0131] Corresponding to the method embodiment, in the embodiment of the present invention, a hydrogen catalyst life cycle management device, such as a terminal, a server, etc., is also provided. Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto.

[0132] An example of a hardware structure block diagram of a hydrogen catalyst life cycle management device provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, it may include:

[0133] Processor 1, communication interface 2, memory 3 and communication bus 4;

[0134] The processor 1, the communication interface 2, and the memory 3 communicate with each other via the communication bus 4;

[0135] Optionally, the communication interface 2 may be an interface of a communication module, such as an interface of a GSM module;

[0136] The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0137] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0138] The processor 1 is specifically used to execute the computer program stored in the memory 3 to perform the following steps:

[0139] S11. Before the process reaction, the optimal pore size, optimal surface area and optimal pore volume of each hydrogenation catalyst and the corresponding optimal operating temperature are determined by using a preset catalyst performance analysis model;

[0140] S12, when the catalyst performance parameters and the total volume of the catalyst are known, the optimal loading amount of each catalyst single agent is determined by a preset catalyst loading optimization model; the construction of the catalyst loading optimization model includes: constructing a catalyst activity simulation model corresponding to each catalyst single agent, and constructing a nonlinear catalyst loading optimization model that maximizes the objective function based on multiple catalyst activity simulation models;

[0141] S13. In the process of the process reaction, the current data of the first preset working condition is used as input, and the optimal value of the preset controllable variable when the equilibrium limit value of the sulfidation time is reached is determined by a preset catalyst presulfidation simulation model; the preset controllable variable includes the catalyst metal amount; the first preset working condition includes the sulfidation temperature, the sulfide concentration in the circulating hydrogen, the bed temperature rise and the heating rate data;

[0142] S14, using the current data of the second preset operating condition as input, determining the time point when the catalyst activity is lower than a preset threshold value through a preset catalyst reaction simulation model.

[0143] Preferably, in the embodiment of the present invention, it also includes:

[0144] S15. Under heating conditions, the valuable metal oxides and sulfides are converted into water-soluble salts by reacting the molten leaching agent with the spent catalyst; after separating the filtrate and the residue, the metals in the filtrate are recovered by precipitation and / or extraction.

[0145] The above product can execute the method provided in the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the hydrogen catalyst life cycle management method provided in the embodiment of the present invention.

[0146] Embodiment 4

[0147] In an embodiment of the present invention, a storage medium is further provided, which may store a program suitable for execution by a processor, wherein the program is used to:

[0148] S11. Before the process reaction, the optimal pore size, optimal surface area and optimal pore volume of each hydrogenation catalyst and the corresponding optimal operating temperature are determined by using a preset catalyst performance analysis model;

[0149] S12, when the catalyst performance parameters and the total volume of the catalyst are known, the optimal loading amount of each catalyst single agent is determined by a preset catalyst loading optimization model; the construction of the catalyst loading optimization model includes: constructing a catalyst activity simulation model corresponding to each catalyst single agent, and constructing a nonlinear catalyst loading optimization model that maximizes the objective function based on multiple catalyst activity simulation models;

[0150] S13. In the process of the process reaction, the current data of the first preset working condition is used as input, and the optimal value of the preset controllable variable when the equilibrium limit value of the sulfidation time is reached is determined by a preset catalyst presulfidation simulation model; the preset controllable variable includes the catalyst metal amount; the first preset working condition includes the sulfidation temperature, the sulfide concentration in the circulating hydrogen, the bed temperature rise and the heating rate data;

[0151] S14, using the current data of the second preset operating condition as input, determining the time point when the catalyst activity is lower than a preset threshold value through a preset catalyst reaction simulation model.

[0152] Preferably, in the embodiment of the present invention, it also includes:

[0153] S15. Under heating conditions, the valuable metal oxides and sulfides are converted into water-soluble salts by reacting the molten leaching agent with the spent catalyst; after separating the filtrate and the residue, the metals in the filtrate are recovered by precipitation and / or extraction.

[0154] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the methods provided by other embodiments of the present invention.

[0155] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0157] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0159] It should be understood that in the embodiments of the present application, the various embodiments and features can be combined with each other to solve the aforementioned technical problems.

[0160] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0161] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing the life cycle of a hydrogenation catalyst, characterized in that: Includes steps: S11. Before the process reaction, the optimal pore size, optimal surface area and optimal pore volume of each hydrogenation catalyst and the corresponding optimal operating temperature are determined by using a preset catalyst performance analysis model; S12, when the catalyst performance parameters and the total volume of the catalyst are known, the optimal loading amount of each catalyst single agent is determined by a preset catalyst loading optimization model; the construction of the catalyst loading optimization model includes: constructing a catalyst activity simulation model corresponding to each catalyst single agent, and constructing a nonlinear catalyst loading optimization model that maximizes the objective function based on multiple catalyst activity simulation models; S13. In the process of the process reaction, the current data of the first preset working condition is used as input, and the optimal value of the preset controllable variable when the equilibrium limit value of the sulfidation time is reached is determined by a preset catalyst presulfidation simulation model; the preset controllable variable includes the catalyst metal amount; the first preset working condition includes the sulfidation temperature, the sulfide concentration in the circulating hydrogen, the bed temperature rise and the heating rate data; S14, using the current data of the second preset operating condition as input, determining the time point when the catalyst activity is lower than a preset threshold value through a preset catalyst reaction simulation model.

2. The hydrogenation catalyst life cycle management method according to claim 1, characterized in that: Also includes: S15. Under heating conditions, the valuable metal oxides and sulfides are converted into water-soluble salts by reacting the molten leaching agent with the spent catalyst; after separating the filtrate and the residue, the metals in the filtrate are recovered by precipitation and / or extraction.

3. The hydrogenation catalyst life cycle management method according to claim 1, characterized in that: The method for constructing the catalyst performance analysis model comprises: Acquiring historical data of the target equipment, including temperature data, hydrogenation catalyst data, feedstock oil data and hydrogenation heavy oil data at different operating times; the hydrogenation catalyst data includes the pore size, surface area and pore volume of each catalyst single agent; Calculating the impurity removal rate for characterizing the catalyst performance of each of the catalyst units, comprising: determining the impurity content according to the feedstock oil data and the hydrogenated heavy oil data, and calculating the impurity removal rate; The pore size, surface area, pore volume and temperature data of the catalyst are used as modeling data to train a BP neural network to generate a catalyst performance analysis model with temperature, pore size, surface area and pore volume as input and impurity removal rate as output.

4. The hydrogenation catalyst life cycle management method according to claim 1, characterized in that: The method for constructing the catalyst activity simulation model comprises: For each reactor, the porosity, volume, proportionality coefficient and catalyst performance parameters of the single catalyst at different sampling times are used as modeling data to train the BP neural network and establish the corresponding activity simulation model; the activity simulation model uses the porosity, proportionality coefficient and catalyst volume as input and the impurity removal rate as output.

5. The method for managing the life cycle of a hydrogenation catalyst according to claim 4, characterized in that: The nonlinear catalyst loading optimization model that maximizes the objective function is constructed based on multiple catalyst activity simulation models, including: The objective function includes: F max =max(f s )+max(f m ) +max(f c ) (Formula 4); Where: F max represents the maximum impurity removal rate of all catalysts, f s 、f m 、f c Represents the activity simulation model of desulfurization catalyst, demetallization catalyst and residual carbon removal catalyst.

6. The method for managing the life cycle of a hydrogenation catalyst according to claim 1, characterized in that: The method for constructing the catalyst presulfurization simulation model includes: The desulfurization rate, demetallization rate and residual carbon removal rate at different sampling times are calculated based on the device operation data, and then the data are normalized to fit the parameters representing the catalyst activity; The BP neural network method is used to train the catalyst presulfurization simulation model with the catalyst metal content, sulfidation temperature, sulfide concentration in circulating hydrogen, bed temperature rise and heating rate data in multi-cycle device operation data as input, and the impurity removal rate and sulfidation time as output are obtained.

7. The method for managing the life cycle of a hydrogenation catalyst according to claim 1, characterized in that: The method for constructing the catalyst reaction simulation model comprises: The correlation between each parameter in the device operation data and the total pressure drop of the bed and the hot spot temperature is calculated based on the Pearson product-moment correlation coefficient, and the correlation is sorted from large to small, and the top five key parameters with the strongest correlation are selected; The Pearson product-moment correlation coefficient calculation method includes the formula: Where: x o Represents the operation data of a device at different sampling times, y o Represents the total bed pressure drop at different sampling times, o=1,...,N, where N is the number of parameter samples.

8. The method for managing the life cycle of a hydrogenation catalyst according to claim 3, characterized in that: The formula for calculating the impurity removal rate includes: Where: a i Represents various impurities, where i=s, c, m, respectively, are sulfur, carbon residue, and metal. is the impurity removal capacity at different sampling time points, j = 1, ..., n, is the impurity content in the crude oil, It is the impurity content in heavy oil after hydrotreatment.

9. The method for managing the life cycle of a hydrogenation catalyst according to claim 4, characterized in that: The formulas used to calculate the porosity of desulfurization catalysts, demetallization catalysts, and carbon removal catalysts include: Where: D i represents the porosity of different catalysts; where i = s, c or m, representing sulfur, carbon residue and metal, respectively; V i V is the volume of the catalyst loaded in the reactor; io is the volume of a single catalyst, V ik is the pore volume corresponding to a single catalyst; The formula for calculating the proportional coefficient of each catalyst single agent includes: Where: K i The coefficient representing the proportion of each catalyst single agent to all catalysts.

10. The method for managing the life cycle of a hydrogenation catalyst according to claim 6, characterized in that: The desulfurization rate, demetallization rate and residual carbon removal rate at different sampling times are calculated based on the device operation data, and then the data are normalized to fit the parameters representing the catalyst activity, including: The formula for normalizing the impurity removal rate data to eliminate the dimensional influence between the indicators includes: Where: is the normalized impurity removal rate, a i Represents various impurities, where i=s, c, m, respectively, are sulfur, carbon residue, and metal. is the impurity removal capacity at different sampling time points, j = 1, ..., n; For the data at the same sampling time, the formulas used to fit the catalyst activity parameters include: Where: represents the catalyst activity parameter at time j, is the desulfurization rate at time j after normalization, is the normalized residual carbon removal rate at time j, is the normalized demetallization rate at time j.

11. A hydrogen catalyst life cycle management device, characterized in that: include: The performance optimization unit is used to determine the optimal pore size, optimal surface area and optimal pore volume of each hydrogenation catalyst and the corresponding optimal operating temperature through a preset catalyst performance analysis model before the process reaction; A loading amount determination unit is used to determine the optimal loading amount of each catalyst single agent through a preset catalyst loading optimization model when the catalyst performance parameters and the total volume of the catalyst are known; the construction of the catalyst loading optimization model includes: constructing a catalyst activity simulation model corresponding to each catalyst single agent, and constructing a nonlinear catalyst loading optimization model that maximizes the objective function based on multiple catalyst activity simulation models; A metal quantity optimization unit is used to determine the optimal value of a preset controllable variable when the equilibrium limit value of the sulfidation time is reached by using a preset catalyst presulfidation simulation model, taking the current data of the first preset working condition as input during the process reaction; the preset controllable variable includes the catalyst metal quantity; the first preset working condition includes the sulfidation temperature, the sulfide concentration in the circulating hydrogen, the bed temperature rise and the heating rate data; The activity life prediction unit is used to take the current data of the second preset operating condition as input and determine the time point when the catalyst activity is lower than a preset threshold through a preset catalyst reaction simulation model.

12. The hydrogenation catalyst life cycle management device according to claim 11, characterized in that: Also includes: a recovery optimization unit for converting valuable metal oxides and sulfides into water-soluble salts by reacting a molten leaching agent with spent catalyst under heating conditions; After separation of the filtrate and the residue, the metals in the filtrate are recovered by precipitation and / or extraction.

13. A hydrogen catalyst life cycle management device, characterized in that: include: Memory for storing computer programs; A processor is used to call and execute the computer program to implement the steps of the hydrogen catalyst life cycle management method as described in any one of claims 1 to 10.

14. A storage medium, characterized in that: The method comprises a software program, wherein the software program is suitable for executing the steps of the hydrogen catalyst life cycle management method according to any one of claims 1 to 10 by a processor.

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