Green efficient anthraquinone fluidized bed hydrogen peroxide production system

Abstract

This invention relates to the field of production control technology, and specifically discloses a green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system. The method includes: an acquisition module for acquiring scenario sets, multiple application requirement data, production formula data, and multiple historical production data; a first analysis module for determining multiple sets of requirement parameters and a set of requirement ranges; a second analysis module for determining multiple sets of production parameters and a set of production ranges for each formula's environmental equipment set; a determination module for determining candidate formula data for each application scenario; a calculation module for acquiring real-time production data, calculating real-time formula data and the real-time environmental equipment set; and a production module for determining the optimized operating value for each operating parameter, producing hydrogen peroxide in real-time, and achieving real-time control of the operating parameters. This system enables precise linkage between scenario requirements and production formulas, adapts to differences in requirements across multiple scenarios, ensures optimal operating parameters, reduces resource waste and product non-compliance, and balances production efficiency with environmental friendliness.

Description

Technical Field

[0001] This invention relates to the field of production control technology, and in particular to a green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system. Background Technology

[0002] The anthraquinone process is the mainstream technology for industrial hydrogen peroxide production. Historically, this technology has relied on fixed production formulas, making it difficult to adapt to the diverse needs of different applications such as paper bleaching and electronic cleaning. Traditional production control depends on manual experience. Although basic automation equipment has been gradually introduced to collect some production parameters with technological advancements, scenario requirements, formula data, and historical production data are still stored in a scattered manner. Furthermore, the interconnection, sharing, and intelligent analysis of multi-source data have not been achieved through industrial internet platforms, industrial cloud platforms, and edge-cloud collaboration. This lack of system integration and analysis capabilities prevents the automatic determination of the matching relationship between scenario requirements parameters and the formula production range. Current production still requires manual intervention and adjustment, lacking an intelligent linkage mechanism, resulting in poor adaptability to multiple scenarios, high energy consumption, and difficulty in balancing green production with high-efficiency output.

[0003] Therefore, this invention proposes a green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system. Summary of the Invention

[0004] This invention provides a green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system. By acquiring and analyzing a set of scenarios for anthraquinone-based hydrogen peroxide production, application requirement data for each scenario within the scenario set, production formula data, and historical production data for each sub-formula of the production formula, the system determines the set of required parameters, the set of required ranges, the set of production parameters for each sub-formula, multiple sets of environmental equipment for each formula, and the set of production ranges for each set of environmental equipment for each formula. It also determines candidate formula data for each application scenario within the scenario set, calculates the real-time formula data and real-time environmental equipment set for current hydrogen peroxide production, determines the optimized operating value for each operating parameter in the set of operating parameters, and enables real-time production of hydrogen peroxide with real-time control of operating parameters. This system achieves precise linkage between scenario requirements and production formulas, adapts to differences in requirements across multiple scenarios, avoids the limitations of fixed formulas, ensures optimal operating parameters through real-time control, reduces resource waste and product non-compliance, balances production efficiency and environmental friendliness, and improves product quality consistency and production flexibility.

[0005] This invention provides a green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system, comprising: Acquisition Module: Acquires a set of scenarios for anthraquinone fluidized bed hydrogen peroxide production, application requirement data for each application scenario in the scenario set, production formula data, and historical production data for each type of production formula sub-data in the production formula data; The first analysis module analyzes the scenario set and the application requirement data of each application scenario in the scenario set, and determines the set of requirement parameters and the set of requirement scope for each application scenario in the scenario set. The second analysis module analyzes the production formula data, the historical production data of each production formula sub-data in the production formula data, and determines the set of production parameters, multiple sets of formula environment equipment, and the set of production ranges for each set of formula environment equipment in the production formula data. Determining Module: Based on the set of requirement parameters, the set of requirement ranges, the set of production parameters for each type of production formula sub-data, and the set of production ranges for each formula environment and equipment set in the scenario set, determine the candidate formula data for each application scenario in the scenario set; Calculation module: Obtains real-time production data of hydrogen peroxide currently being produced, and calculates real-time formula data of hydrogen peroxide currently being produced and real-time environmental equipment set based on real-time production data, candidate formula data of all application scenarios in the scenario set; Production module: Based on the candidate formula data, production formula data, real-time formula data for hydrogen peroxide production, and real-time environmental equipment set for each application scenario in the scenario set, the module determines the optimized operation value for each operation parameter in the operation parameter set, produces hydrogen peroxide in real time, and achieves real-time control of operation parameters.

[0006] Preferred, green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system, the acquisition module includes: The first acquisition unit: acquires the set of scenarios for anthraquinone fluidized bed hydrogen peroxide production and the application requirement data for each application scenario in the set of scenarios. The application requirement data includes multiple requirement parameters and the requirement range, scenario label, direction label, and direction value for each requirement parameter. The scenario label includes strict and lenient, and the direction label includes lower is better, higher is better, and optimal range. The second acquisition unit acquires the production formula data for hydrogen peroxide production via anthraquinone fluidized bed process. The production formula data includes multiple sub-formulas for hydrogen peroxide production via anthraquinone fluidized bed process. The sub-formulas include working fluid data, hydrogenation amount, and catalyst data. The working fluid data includes anthraquinone derivatives, solvent, catalyst, and working fluid volume ratio. The third acquisition unit: Based on the production formula data of hydrogen peroxide production in anthraquinone fluidized bed, acquire the historical production data of each production formula sub-data. The historical production data includes historical production sub-data from multiple historical productions. The historical production sub-data includes historical production costs, multiple environmental parameters, environmental parameter values ​​for each environmental parameter, multiple production parameters, production parameter values ​​for each production parameter, multiple equipment parameters, equipment parameter values ​​for each equipment parameter, multiple operating parameters, and operating parameter values ​​for each operating parameter.

[0007] The preferred green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system includes, in its first analytical module: Demand parameter set unit: Based on all the demand parameters in the application demand data of each application scenario in the scenario set, determine the demand parameter set for each application scenario in the scenario set; Demand Scope Set Unit: Based on the demand parameter set of each application scenario in the scenario set and the demand scope of all demand parameters in the application demand data, determine the demand scope set of each application scenario in the scenario set.

[0008] The preferred green and efficient anthraquinone fluidized bed hydrogen peroxide production system includes a second analytical module comprising: Clustering Unit: Based on all environmental parameters, environmental parameter values ​​for each environmental parameter, all equipment parameters, and equipment parameter values ​​for each equipment parameter in the historical production data of all historical production sub-data of each production formula sub-data in the production formula data, cluster analysis is performed on the historical production sub-data of all historical production sub-data of each production formula sub-data in the production formula data to determine multiple formula environment equipment sets for each production formula sub-data in the production formula data. The formula environment equipment set includes historical production sub-data of multiple historical productions, fitted environmental parameter value vectors, and fitted equipment parameter value vectors. Production parameter set unit: Based on all production parameters in the historical production data of all historical production sub-data of each production formula sub-data in the production formula data, determine the production parameter set of each production formula sub-data in the production formula data; Production Scope Unit: Based on the production parameter values ​​of each production parameter in the historical production sub-data of all historical productions in each formula environment equipment set of each production formula sub-data in the production formula data, the production scope of each production parameter in each formula environment equipment set of each production formula sub-data in the production formula data is determined; Production Scope Set Unit: Based on the production parameter set of each formula environment equipment set for each formula sub-data in the production formula data and the production scope of each production parameter, determine the production scope set of each formula environment equipment set for each formula sub-data in the production formula data.

[0009] The preferred green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system comprises the following modules: The first calculation unit: Based on the demand parameter set and demand range set of each application scenario in the scenario set, and the production parameter set, all formula environment equipment sets, historical production sub-data of all past productions of each formula environment equipment set, and production range set of each production formula sub-data in the production formula data, it calculates each formula environment equipment set of each production formula sub-data in the production formula data, based on the first sub-candidate value of each application scenario in the scenario set, and calculates the candidate value of each production formula sub-data in the production formula data based on each application scenario in the scenario set; Candidate formula data unit: All production formula sub-data in the production formula data are sorted from largest to smallest based on the candidate values ​​of each application scenario in the scenario set. Based on the production formula sub-data corresponding to the first specified number of candidate values ​​after sorting, the candidate formula data for each application scenario in the scenario set is determined. The candidate formula data includes multiple production formula sub-data.

[0010] The preferred green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system includes a calculation module comprising: Real-time production data unit: acquires real-time production data of hydrogen peroxide production, including real-time scene, real-time environmental value vector and real-time equipment value vector; Scene tag unit: Based on the scene tags of the real-time scene and all application scenes in the scene set, determine the real-time scene and scene tag of the current hydrogen peroxide production; Real-time candidate data unit: Based on the real-time scenario and the candidate formula data of all application scenarios in the scenario set, determine the real-time candidate data for the current production of hydrogen peroxide; Second calculation unit: If the scene label of the real-time scene is severe, based on the real-time environment value vector, the real-time device value vector, the first sub-candidate value of each formula environment device set of all production formula sub-data in the production formula data, and the fitted environment parameter value vector and fitted device parameter value vector in all production formula sub-data and all formula environment device sets of each production formula sub-data in the current real-time candidate data of hydrogen peroxide production, calculate the real-time formula data and real-time environment device set of the current hydrogen peroxide production. Selection Unit: If the scene label of the real-time scene is loose, select the production formula sub-data with the largest candidate value in the current real-time candidate data for producing hydrogen peroxide as the current real-time formula data for producing hydrogen peroxide. Select the first sub-candidate value of the formula environment equipment set with the largest candidate value in the current real-time candidate data for producing hydrogen peroxide as the current real-time environment equipment set for producing hydrogen peroxide.

[0011] The preferred green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system includes the following production modules: Operation parameter set unit: Based on all operation parameters in the historical production data of all production formula sub-data in the candidate formula data of each application scenario in the scenario set, determine the operation parameter set of the candidate formula data for each application scenario in the scenario set; Operation parameter value set unit: Based on each operation parameter in the operation parameter set of each production formula sub-data in the candidate formula data of each application scenario in the scenario set, and the operation parameter value of each operation parameter in the historical production sub-data of all historical productions in each formula environment equipment set, determine the operation parameter value set of each operation parameter in the operation parameter set of each formula environment equipment set in the candidate formula data of each application scenario in the scenario set. The optimal operation value unit is determined by inputting the operation parameter value set of each operation parameter in the operation parameter set of each production formula sub-data of each application scenario in the scenario set, the production parameter value of all production parameters in the historical production sub-data of the second historical production in the formula environment and equipment set, and the direction label and direction value of all demand parameters of each application scenario into the Bayesian optimization model. Based on the output of the Bayesian optimization model, the optimal operation value of each operation parameter in the operation parameter set of each production formula sub-data of each application scenario in the scenario set is determined. Optimized Operation Value Unit: Based on the optimal operation value of each operation parameter in the operation parameter set of each production formula sub-data of each application scenario in the scenario set, and the real-time scenario, real-time formula data, and real-time environment equipment set of the current hydrogen peroxide production, determine the optimized operation value of each operation parameter in the operation parameter set.

[0012] The preferred green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system, in its production module, further includes: Production unit: Based on the real-time formula data, real-time environmental equipment set, and fluidized bed, hydrogen peroxide is produced in real time; Control unit: Real-time monitoring of operation data during the production process, including the monitored operation value of each operation parameter in the operation parameter set, and the control unit adjusts each operation parameter in the operation parameter set based on the optimized operation value and the monitored operation value.

[0013] The beneficial effects of this invention compared to existing technologies are as follows: By acquiring and analyzing a set of scenarios for hydrogen peroxide production via anthraquinone fluidized bed process, application requirement data for each application scenario within the scenario set, production formula data, and historical production data for each production formula sub-data within the production formula data, the invention determines the set of requirement parameters, the set of requirement ranges, the set of production parameters for each production formula sub-data, multiple sets of formula environment equipment, and the set of production ranges for each formula environment equipment set for each application scenario. It also determines candidate formula data for each application scenario within the scenario set, calculates the real-time formula data and real-time environment equipment set for current hydrogen peroxide production, determines the optimized operating value for each operating parameter in the operating parameter set, and enables real-time hydrogen peroxide production and real-time control of operating parameters. This allows for precise linkage between scenario requirements and production formulas, adapting to differences in requirements across multiple scenarios, avoiding the limitations of fixed formulas. Furthermore, through industrial internet platforms, industrial cloud platforms, and edge-cloud collaboration, it achieves cloud integration and dynamic coordination of scenario requirements, historical production data, and real-time parameters, further ensuring the timeliness and accuracy of optimal control of operating parameters, reducing resource waste and product non-compliance, balancing production efficiency and environmental friendliness, and improving product quality consistency and production flexibility.

[0014] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a green and efficient anthraquinone fluidized bed hydrogen peroxide production system in an embodiment of the present invention. Detailed Implementation

[0017] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. Example 1:

[0018] This invention provides a green and efficient anthraquinone-based fluidized bed hydrogen peroxide production system, referenced in [reference]. Figure 1 ,include: Acquisition Module: Acquires a set of scenarios for anthraquinone fluidized bed hydrogen peroxide production, application requirement data for each application scenario in the scenario set, production formula data, and historical production data for each type of production formula sub-data in the production formula data; The first analysis module analyzes the scenario set and the application requirement data of each application scenario in the scenario set, and determines the set of requirement parameters and the set of requirement scope for each application scenario in the scenario set. The second analysis module analyzes the production formula data, the historical production data of each production formula sub-data in the production formula data, and determines the set of production parameters, multiple sets of formula environment equipment, and the set of production ranges for each set of formula environment equipment in the production formula data. Determining Module: Based on the set of requirement parameters, the set of requirement ranges, the set of production parameters for each type of production formula sub-data, and the set of production ranges for each formula environment and equipment set in the scenario set, determine the candidate formula data for each application scenario in the scenario set; Calculation module: Obtains real-time production data of hydrogen peroxide currently being produced, and calculates real-time formula data of hydrogen peroxide currently being produced and real-time environmental equipment set based on real-time production data, candidate formula data of all application scenarios in the scenario set; Production module: Based on the candidate formula data, production formula data, real-time formula data for hydrogen peroxide production, and real-time environmental equipment set for each application scenario in the scenario set, the module determines the optimized operation value for each operation parameter in the operation parameter set, produces hydrogen peroxide in real time, and achieves real-time control of operation parameters.

[0019] In this embodiment, comprehensive basic data on the anthraquinone process for hydrogen peroxide production is collected. Specifically, this involves acquiring a set of scenarios comprising all possible application scenarios, application requirement data reflecting the needs of each scenario, production formula data used for production, and historical production data accumulated from past production for each production formula.

[0020] In this embodiment, the acquired set of scenarios and the application requirement data of each application scenario are analyzed in depth. The core parameters that need to be focused on in each scenario are identified from the requirement data to form a set of requirement parameters. At the same time, the reasonable fluctuation range of each requirement parameter is defined to form a set of requirement ranges, making the requirements of each scenario specific and quantifiable.

[0021] In this embodiment, the historical production data of the production formula data and each production formula sub-data are analyzed. First, the production parameters that need to be monitored during the production process of each formula are determined to form a set of production parameters. Then, based on the correlation between environmental and equipment parameters in the historical production data, multiple sets of formula environment and equipment are divided. Finally, the reasonable range of each production parameter under each set of formula environment and equipment is determined to form a set of production ranges.

[0022] In this embodiment, based on the set of required parameters and the set of required ranges for each application scenario, the production parameter set of each production formula sub-data and the production range set of each formula environment and equipment set are compared to screen out the production formulas that can meet the needs of the scenario, and finally determine the candidate formula data for each application scenario.

[0023] In this embodiment, real-time production data of hydrogen peroxide is first obtained, and then candidate formula data of all application scenarios are referenced. Through data comparison and adaptation calculation, the real-time formula data applicable to the current production and the set of real-time environmental equipment matching the formula are determined to ensure that the production configuration meets the current actual production conditions.

[0024] In this embodiment, candidate formula data, original production formula data, and calculated real-time formula data and real-time environmental equipment set for each application scenario are integrated. First, the optimized operation value of each operation parameter in the operation parameter set is determined. Then, based on the optimized value, the real-time production of hydrogen peroxide is started. At the same time, the operation parameters are adjusted in real time during the production process to ensure that the production is always in the optimal state.

[0025] The beneficial effects of the above technology are as follows: By acquiring and analyzing the set of scenarios for anthraquinone fluidized bed hydrogen peroxide production, the application requirement data and production formula data for each application scenario in the scenario set, and the historical production data for each production formula sub-data in the production formula data, the technology determines the set of requirement parameters, the set of requirement ranges, the set of production parameters for each production formula sub-data, multiple formula environment equipment sets, and the set of production ranges for each formula environment equipment set for each application scenario. It also determines the candidate formula data for each application scenario in the scenario set, calculates the real-time formula data and real-time environment equipment set for current hydrogen peroxide production, determines the optimized operating value for each operating parameter in the operating parameter set, and enables real-time hydrogen peroxide production and real-time control of operating parameters. This allows for precise linkage between scenario requirements and production formulas, adapting to the differences in requirements across multiple scenarios, avoiding the limitations of fixed formulas, ensuring optimal operating parameters through real-time control, reducing resource waste and product non-compliance, balancing production efficiency and environmental friendliness, and improving product quality consistency and production flexibility. Example 2:

[0026] Based on Example 1, the green and efficient anthraquinone fluidized bed hydrogen peroxide production system includes an acquisition module comprising: The first acquisition unit: acquires the set of scenarios for anthraquinone fluidized bed hydrogen peroxide production and the application requirement data for each application scenario in the set of scenarios. The application requirement data includes multiple requirement parameters and the requirement range, scenario label, direction label, and direction value for each requirement parameter. The scenario label includes strict and lenient, and the direction label includes lower is better, higher is better, and optimal range. The second acquisition unit acquires the production formula data for hydrogen peroxide production via anthraquinone fluidized bed process. The production formula data includes various sub-formulas for hydrogen peroxide production via anthraquinone fluidized bed process. The sub-formulas include working fluid data, hydrogenation amount, and catalyst data. The working fluid data includes anthraquinone derivatives, solvent, and working fluid volume ratio. The third acquisition unit: Based on the production formula data of hydrogen peroxide production in anthraquinone fluidized bed, acquire the historical production data of each production formula sub-data. The historical production data includes historical production sub-data from multiple historical productions. The historical production sub-data includes historical production costs, multiple environmental parameters, environmental parameter values ​​for each environmental parameter, multiple production parameters, production parameter values ​​for each production parameter, multiple equipment parameters, equipment parameter values ​​for each equipment parameter, multiple operating parameters, and operating parameter values ​​for each operating parameter.

[0027] In this embodiment, the application scenarios in the scenario set can be medical disinfection, pulp / textile bleaching, food / beverage sterilization, electronic cleaning (e.g., semiconductors, photovoltaics), chemical synthesis (e.g., caprolactam, propylene oxide), military propulsion, advanced wastewater oxidation, etc.

[0028] In this embodiment, application requirement data is collected for each application scenario. This data includes multiple aspects, including various requirement parameters, each with a defined range. For example, the required concentration of hydrogen peroxide in a certain application scenario might range from 50% to 70%. The data also includes scenario tags, which are of two types: stringent and lenient. A stringent tag indicates extremely high requirements for all parameters, allowing no significant deviations, while a lenient tag indicates a higher tolerance for parameter variations. Directional tags clarify the optimization direction of the requirement parameters, specifically categorized as lower is better, higher is better, and optimal within a given range. For example, the directional tag for product purity might be higher is better. The directional values ​​are specific reference values ​​associated with the directional tags.

[0029] In this embodiment, the direction label of the demand parameter is "the lower the better," and the direction value of the demand parameter is the lower limit of the demand range; the direction label of the demand parameter is "the higher the better," and the direction value of the demand parameter is the upper limit of the demand range; the direction label of the demand parameter is "range optimal," and the demand parameter is U-shaped or inverted U-shaped. The lower the demand parameter, the slower the response; the higher the demand parameter, the higher the side reactions, energy consumption, or cost increase. The direction value is in the middle of the demand range, and the middle endpoint of the U-shape or inverted U-shape is taken as the direction value.

[0030] In this embodiment, the required parameters can be product concentration, organic carbon, metal ions, stability, hydrogen efficiency, degree of hydrogenation, palladium content of catalyst, degradation rate of working fluid, etc. The required range of the same required parameter varies in the application requirement data of different application scenarios.

[0031] In this embodiment, the production formulation data includes several different sub-data sets. Each sub-data set consists of three core components: working fluid data, hydrogenation amount, and catalyst data. The working fluid data is more detailed, encompassing key elements such as the anthraquinone derivative, solvent, and working fluid volume ratio. The anthraquinone derivative is the core active ingredient in the working fluid; different types of anthraquinone derivatives affect reaction efficiency and product quality. The solvent dissolves the anthraquinone derivative, allowing it to mix thoroughly with other substances for reaction; different solvents have varying solubility and chemical stability. The working fluid volume ratio specifies the exact proportions of the anthraquinone derivative and solvent in the working fluid, directly affecting the overall performance of the working fluid and subsequent reaction effects. The hydrogenation amount refers to the amount of hydrogen gas introduced during the reaction; the amount of hydrogen gas affects the degree of anthraquinone hydrogenation reaction, thus influencing the amount of hydrogen peroxide produced.

[0032] In this embodiment, the catalyst data includes catalyst name, catalyst content, catalyst particle size, catalyst support, etc.

[0033] In this embodiment, for each production formula sub-data in the production formula data, historical production data generated during past actual production processes are collected. Historical production data includes historical production sub-data from multiple historical production runs. Each historical production sub-data entry is comprehensive, firstly including historical production costs, which reflect the total cost of various resources consumed in that production process and are an important basis for evaluating production economics. Secondly, it includes multiple environmental parameters and their corresponding values. Environmental parameters typically refer to the external environmental conditions during the production process, which have a certain impact on the production process and product quality; the corresponding environmental parameter values ​​are the specific values ​​of these conditions during that production run. Then, it includes multiple production parameters and their values. Production parameters are parameters directly related to the reaction during the production process; the values ​​of these parameters directly determine the progress and effect of the reaction; the production parameter values ​​are the actual monitored values ​​of these parameters during that production run. It also includes multiple equipment parameters and their values. Equipment parameters refer to the operating parameters of the equipment used in production; the equipment parameter values ​​reflect the operating status of the equipment during that production process. Stable equipment operation is crucial for ensuring smooth production. Finally, it includes multiple operation parameters and the operation parameter value for each operation parameter. The operation parameters refer to the parameters related to the operation performed by the operator during the production process, and the operation parameter value is the specific execution status of these operations in this production.

[0034] In this embodiment, environmental parameters may include cooling water inlet temperature, anthraquinone purity, ambient temperature, and utility hydrogen mains pressure, while production parameters may include hydrogen efficiency, degradation rate, product concentration, and catalyst lifetime.

[0035] In this embodiment, the equipment parameters include fluidized bed pressure drop, axial temperature rise, distributor pressure drop, hydrogen partial pressure, inner coil wall temperature, heat exchanger fouling thermal resistance, catalyst wear rate, etc., and the operating parameters include hydrogenation temperature setpoint, hydrogen partial pressure setpoint, liquid / gas phase space velocity setpoint, cooling water valve opening, online replenishment rate, etc.

[0036] The beneficial effects of the above technologies are: obtaining a set of scenarios for anthraquinone fluidized bed hydrogen peroxide production, application requirement data for each application scenario in the scenario set, production formula data, and historical production data for each production formula sub-data in the production formula data, which can provide data support for subsequent analysis. Example 3:

[0037] Based on Example 1, the green and efficient anthraquinone fluidized bed hydrogen peroxide production system, in its first analytical module, includes: Demand parameter set unit: Based on all the demand parameters in the application demand data of each application scenario in the scenario set, determine the demand parameter set for each application scenario in the scenario set; Demand Scope Set Unit: Based on the demand parameter set of each application scenario in the scenario set and the demand scope of all demand parameters in the application demand data, determine the demand scope set of each application scenario in the scenario set.

[0038] In this embodiment, a clear list of requirement parameters is compiled for each application scenario in the scenario set. Specifically, the application requirement data corresponding to each application scenario is focused on, and all requirement parameters related to that scenario are extracted from this data. These requirement parameters may cover multiple dimensions such as product purity, production efficiency, and energy consumption indicators. Then, the unit organizes and summarizes these extracted requirement parameters, removing duplicates or irrelevant content to form a complete set. This set is the requirement parameter set for that application scenario.

[0039] In this embodiment, the requirement scope set unit further clarifies the acceptable range of each requirement parameter based on the requirement parameter set. This unit first obtains the pre-determined requirement parameter set for each application scenario. Then, it finds the requirement scope information corresponding to these requirement parameters one-to-one in the application requirement data. These requirement ranges include upper and lower limits. Next, each requirement parameter is associated and matched with its corresponding requirement range to ensure that each parameter has a clear qualification standard, ultimately integrating them to form the requirement scope set for the application scenario.

[0040] The beneficial effects of the above technologies are: analyzing the set of scenarios and the application requirement data of each application scenario in the set of scenarios, determining the set of requirement parameters and the set of requirement ranges for each application scenario in the set of scenarios, realizing the structured expression of the requirements of each application scenario in the set of scenarios, and providing data support for determining the candidate formula data for each application scenario in the set of scenarios. Example 4:

[0041] Based on Example 2, the second analytical module of the green and efficient anthraquinone fluidized bed hydrogen peroxide production system includes: Clustering Unit: Based on all environmental parameters, environmental parameter values ​​for each environmental parameter, all equipment parameters, and equipment parameter values ​​for each equipment parameter in the historical production data of all historical production sub-data of each production formula sub-data in the production formula data, cluster analysis is performed on the historical production sub-data of all historical production sub-data of each production formula sub-data in the production formula data to determine multiple formula environment equipment sets for each production formula sub-data in the production formula data. The formula environment equipment set includes historical production sub-data of multiple historical productions, fitted environmental parameter value vectors, and fitted equipment parameter value vectors. Production parameter set unit: Based on all production parameters in the historical production data of all historical production sub-data of each production formula sub-data in the production formula data, determine the production parameter set of each production formula sub-data in the production formula data; Production Scope Unit: Based on the production parameter values ​​of each production parameter in the historical production sub-data of all historical productions in each formula environment equipment set of each production formula sub-data in the production formula data, the production scope of each production parameter in each formula environment equipment set of each production formula sub-data in the production formula data is determined; Production Scope Set Unit: Based on the production parameter set of each formula environment equipment set for each formula sub-data in the production formula data and the production scope of each production parameter, determine the production scope set of each formula environment equipment set for each formula sub-data in the production formula data.

[0042] In this embodiment, the clustering unit first comprehensively analyzes the historical production data corresponding to each production formula sub-data. Using the environmental parameters, environmental parameter values, equipment parameters, and equipment parameter values ​​from all historical production runs, similar production environments and equipment conditions are clustered to form multiple formula environment and equipment categories. Each formula environment and equipment category represents a typical production environment pattern, encompassing all historical production sub-data from multiple historical production runs under similar conditions. The formula environment and equipment set also includes the fitted environmental parameter value vector and fitted equipment parameter value vector for the corresponding formula environment and equipment category. The fitted vectors are formed by weighted averaging and distribution fitting of the feature parameters of historical samples, resulting in a parameter set that best represents the characteristics of the environment and equipment for that category. This fitting result not only preserves the physical meaning of the original data but also significantly reduces the impact of noise, enabling complex multidimensional production environments to be expressed in a low-dimensional but high-fidelity vector form.

[0043] In this embodiment, the production parameter unit extracts the set of production parameters from all historical production sub-data for each production formula sub-data based on cluster analysis. By summarizing and analyzing the production parameters of all historical production samples, the system can form a complete set of production parameter features.

[0044] In this embodiment, the production range unit analyzes the value distribution of each production parameter in all historical production sub-data for each formulation environment equipment category to determine the production range of each production parameter. The production range of a production parameter can be calculated based on the number of historical production sub-data in the corresponding formulation sub-data. If the number is greater than 30, it is obtained by calculating the average and standard deviation of the production parameter values ​​in all historical production sub-data of the corresponding formulation sub-data, and the production range is expressed as [average value - 2 × standard deviation, average value + 2 × standard deviation]. If the number is less than 30, it is obtained by calculating the 95% upper confidence limit and 95% lower confidence limit of the production parameter values ​​in all historical production sub-data of the corresponding formulation sub-data.

[0045] In this embodiment, the production range set unit integrates the production range of all production parameter sets of each formula environment equipment set for each formula sub-data in the production formula data, and determines the production range set for each formula environment equipment set for each formula sub-data.

[0046] The beneficial effects of the above technologies are as follows: by analyzing production formula data and historical production data of each production formula sub-data in the production formula data, the production parameter set of each production formula sub-data in the production formula data, multiple formula environment equipment sets, and the production range set of each formula environment equipment set, the structured expression of the production parameters of each production formula sub-data is realized, and data support is further provided for determining the candidate formula data of each application scenario in the scenario set. Example 5:

[0047] Based on Example 4, the green and efficient anthraquinone process fluidized bed hydrogen peroxide production system is defined by the following modules: The first calculation unit: Based on the demand parameter set and demand range set of each application scenario in the scenario set, and the production parameter set, all formula environment equipment sets, historical production sub-data of all past productions of each formula environment equipment set, and production range set of each production formula sub-data in the production formula data, it calculates each formula environment equipment set of each production formula sub-data in the production formula data, based on the first sub-candidate value of each application scenario in the scenario set, and calculates the candidate value of each production formula sub-data in the production formula data based on each application scenario in the scenario set; Candidate formula data unit: All production formula sub-data in the production formula data are sorted from largest to smallest based on the candidate values ​​of each application scenario in the scenario set. Based on the production formula sub-data corresponding to the first specified number of candidate values ​​after sorting, the candidate formula data for each application scenario in the scenario set is determined. The candidate formula data includes multiple production formula sub-data.

[0048] In this embodiment, based on the demand parameter set and demand range set of each application scenario in the scenario set, and the production parameter set, all formula environment equipment sets, historical production sub-data of all past productions for each formula environment equipment set, and production range set of each production formula sub-data in the production formula data, each formula environment equipment set in the production formula data is calculated. Based on the first sub-candidate value of each application scenario in the scenario set, and the candidate value of each production formula sub-data in the production formula data based on each application scenario in the scenario set, the calculation formula can be expressed as: ; in, Let represent the set of production parameters for the j-th production formula sub-data in the production formula data, and let represent the first indicator function based on the set of demand parameters for the i-th application scenario in the scenario set. This represents the production range of the q-th formula environment equipment set of the j-th production formula sub-data in the production formula data, and the production range of the q-th formula environment equipment set of the m-th production parameter, based on the range coverage value of the k-th demand parameter in the demand range set of the i-th application scenario in the scenario set. This represents the m-th production parameter in the production range set of the q-th formula environment equipment set of the j-th production formula sub-data in the production formula data, and the directional proximity value of the k-th demand parameter in the demand parameter set of the i-th application scenario in the scenario set. This represents the production parameter value of the m-th production parameter based on the production parameter set in the n-th historical production sub-data of the q-th production formula sub-data of the j-th production formula sub-data, within the set of environment and equipment for the j-th production formula, and the second indicator function based on the k-th demand parameter in the demand parameter set of the i-th application scenario in the scenario set. This represents the nth historical production sub-data in the qth formulation environment and equipment set of the jth production formulation sub-data in the production formulation data, based on the third indicator function of the demand parameter set of the i-th application scenario in the scenario set. Let α1 represent the first sub-candidate value of the q-th formulation environment and equipment set of the j-th production formulation sub-data in the production formulation data, based on the i-th application scenario in the scenario set. α2 represents the first weight and α1 represents the second weight. This indicates that the j-th production formula sub-data in the production formula data is a candidate value based on the i-th application scenario in the scenario set. This represents the set of requirement parameters for the i-th application scenario in the scenario set. This represents the set of production parameters for the j-th sub-data of the production formula. This represents the upper limit of the requirement range of the k-th requirement parameter in the requirement range set of the i-th application scenario in the scenario set. This represents the lower limit of the requirement range of the k-th requirement parameter in the requirement range set of the i-th application scenario in the scenario set. This represents the upper limit of the production range of the m-th production parameter in the production range set of the q-th formula environment equipment set of the j-th production formula sub-data in the production formula data. This represents the lower limit of the production range of the m-th production parameter in the production range set of the q-th formula environment and equipment set of the j-th production formula sub-data in the production formula data. This represents the direction value of the k-th requirement parameter in the requirement parameter set of the i-th application scenario in the scenario set. This represents the k-th requirement parameter in the requirement parameter set of the i-th application scenario in the scenario set. This represents the production parameter value of the m-th production parameter based on the production parameter set in the n-th historical production sub-data within the n-th historical production sub-data within the q-th formulation environment and equipment set of the j-th production formulation sub-data. Let j represent the m-th production parameter in the production range set of the q-th formulation environment equipment set of the j-th production formulation sub-data in the production formulation data; jqN1 represents the number of historical production sub-data in the q-th formulation environment equipment set of the j-th production formulation sub-data in the production formulation data; jN3 represents the number of production parameters in the production range set of the j-th production formulation sub-data in the production formulation data; iN2 represents the number of demand parameters in the demand parameter set of the i-th application scenario in the scenario set; and jN4 represents the number of formulation environment equipment sets of the j-th production formulation sub-data in the production formulation data. This represents the historical production cost in the nth historical production sub-data within the qth formulation environment and equipment set of the jth production formulation sub-data.

[0049] In this embodiment, the first weight α1 is the importance of the product of the range coverage value and the direction proximity value to the first sub-candidate value, and the value is 0-1. The second weight α2 is the importance of the average historical cost of all historical production sub-data in the formula environment equipment set of the corresponding production formula sub-data to the first sub-candidate value, and the value is 0-1, and α1+α2=1.

[0050] In this embodiment, the candidate formula data unit sorts all production formula sub-data from largest to smallest based on the candidate values ​​of each application scenario in the scenario, selects the production formula sub-data corresponding to a specified number of candidate values ​​before sorting from the sorting results, and forms the candidate formula data for each application scenario. This set includes a specified number of preferred formulas with high candidate values.

[0051] The beneficial effects of the above technologies are as follows: Based on the set of demand parameters, the set of demand ranges, the set of production parameters for each type of production formula data, and the set of production ranges for each set of formula environment and equipment in the scenario set, the candidate formula data for each application scenario in the scenario set can be determined, thereby enabling formula screening for each application scenario and improving formula adaptability and production efficiency. Example 6:

[0052] Based on Example 5, the green and efficient anthraquinone fluidized bed hydrogen peroxide production system includes a calculation module comprising: Real-time production data unit: acquires real-time production data of hydrogen peroxide production, including real-time scene, real-time environmental value vector and real-time equipment value vector; Scene tag unit: Based on the scene tags of the real-time scene and all application scenes in the scene set, determine the real-time scene and scene tag of the current hydrogen peroxide production; Real-time candidate data unit: Based on the real-time scenario and the candidate formula data of all application scenarios in the scenario set, determine the real-time candidate data for the current production of hydrogen peroxide; Second calculation unit: If the scene label of the real-time scene is severe, based on the real-time environment value vector, the real-time device value vector, the first sub-candidate value of each formula environment device set of all production formula sub-data in the production formula data, and the fitted environment parameter value vector and fitted device parameter value vector in all production formula sub-data and all formula environment device sets of each production formula sub-data in the current real-time candidate data of hydrogen peroxide production, calculate the real-time formula data and real-time environment device set of the current hydrogen peroxide production. Selection Unit: If the scene label of the real-time scene is loose, select the production formula sub-data with the largest candidate value in the current real-time candidate data for producing hydrogen peroxide as the current real-time formula data for producing hydrogen peroxide. Select the first sub-candidate value of the formula environment equipment set with the largest candidate value in the current real-time candidate data for producing hydrogen peroxide as the current real-time environment equipment set for producing hydrogen peroxide.

[0053] In this embodiment, the real-time production data unit collects the real-time scene of hydrogen peroxide production, the real-time environmental value vector consisting of the environmental parameters currently in operation, and the real-time equipment value vector consisting of the operating status of all equipment. The real-time scene is the application scenario where hydrogen peroxide is currently produced. The real-time environmental value vector includes parameter values ​​such as cooling water inlet temperature, anthraquinone purity, ambient temperature, and the pressure of the utility hydrogen main pipe. The real-time equipment value vector includes parameter values ​​such as fluidized bed pressure drop, bed height, distributor pressure drop, inner coil wall temperature, heat exchanger fouling thermal resistance, and catalyst wear rate.

[0054] In this embodiment, based on the acquired real-time scenario, the scenario tag corresponding to the real-time scenario is found by comparing the scenario tags of all application scenarios in the scenario set, thereby clarifying whether the current production scenario is strict or lenient.

[0055] In this embodiment, the corresponding real-time candidate data is determined based on the current real-time scenario. From the candidate recipe data of all application scenarios in the scenario set, candidate recipe data for the application scenario that matches the real-time scenario is found and used as the real-time candidate data for current production.

[0056] In this embodiment, when the scene label of the real-time scene is severe, the real-time environment equipment set for the current hydrogen peroxide production is calculated based on the real-time environment value vector, the real-time equipment value vector, the first sub-candidate value of each application scene in the scene set based on all production formula sub-data in the current real-time candidate data for hydrogen peroxide production, the fitted environment parameter value vector in all formula environment equipment sets of each production formula sub-data, and the fitted equipment parameter value vector. The calculation formula is expressed as follows: ; ; in, This represents the vector of fitted environmental parameter values ​​in the set of environmental equipment for the qth formulation of the j-th production formulation sub-data. REn represents the fitted equipment parameter value vector in the environmental equipment set of the j-th production formula sub-data for the j-th production formula, REq represents the real-time environmental value vector for the current hydrogen peroxide production, and REq represents the real-time equipment value vector for the current hydrogen peroxide production. This represents the i-th application scenario in the scenario set, and RS represents the real-time scenario. The fourth indicator function represents the i-th application scenario in the scenario set based on the real-time scenario, and the j-th production formula sub-data in the production formula data based on the a-th production formula sub-data in the real-time candidate data. This represents the j-th production formula sub-data in the production formula data. This represents the a-th production formula sub-data in the real-time candidate data. This represents the selection value of the environmental equipment set for the qth formula in the a-th production formula sub-data from the real-time candidate data. Rc2 represents the set of environmental equipment for the qth formulation of the a-th production formulation sub-data in the real-time candidate data, Rc2 represents the real-time formulation data for the current production of hydrogen peroxide, RE represents the set of real-time environmental equipment for the current production of hydrogen peroxide, Nu represents a specified quantity, and aN5 represents the number of sets of environmental equipment for the a-th production formulation sub-data in the real-time candidate data.

[0057] In this embodiment, the selection unit is applicable when the scene label of the real-time scenario is lenient. In this case, it selects the production formula sub-data with the largest candidate value from the currently produced real-time candidate data and uses it as the real-time formula data for the current production of hydrogen peroxide. At the same time, it selects the first sub-candidate set of the environmental equipment set with the largest candidate value from the environmental equipment set of the production formula sub-data with the largest candidate value in the real-time candidate data and uses it as the real-time environmental equipment set for the current production of hydrogen peroxide.

[0058] The beneficial effects of the above technologies are as follows: obtaining real-time production data of hydrogen peroxide, and calculating real-time formula data of hydrogen peroxide production based on real-time production data, candidate formula data of all application scenarios in the scenario set, and real-time environmental equipment set can improve the flexibility and accuracy of production adaptation and provide structured support for efficient production in different scenarios. Example 7:

[0059] Based on Example 6, the green and efficient anthraquinone fluidized bed hydrogen peroxide production system includes the following production module: Operation parameter set unit: Based on all operation parameters in the historical production data of all production formula sub-data in the candidate formula data of each application scenario in the scenario set, determine the operation parameter set of the candidate formula data for each application scenario in the scenario set; Operation parameter value set unit: Based on each operation parameter in the operation parameter set of each production formula sub-data in the candidate formula data of each application scenario in the scenario set, and the operation parameter value of each operation parameter in the historical production sub-data of all historical productions in each formula environment equipment set, determine the operation parameter value set of each operation parameter in the operation parameter set of each formula environment equipment set in the candidate formula data of each application scenario in the scenario set. The optimal operation value unit is determined by inputting the operation parameter value set of each operation parameter in the operation parameter set of each production formula sub-data of each application scenario in the scenario set, the production parameter value of all production parameters in the historical production sub-data of the second historical production in the formula environment and equipment set, and the direction label and direction value of all demand parameters of each application scenario into the Bayesian optimization model. Based on the output of the Bayesian optimization model, the optimal operation value of each operation parameter in the operation parameter set of each production formula sub-data of each application scenario in the scenario set is determined. Optimized Operation Value Unit: Based on the optimal operation value of each operation parameter in the operation parameter set of each production formula sub-data of each application scenario in the scenario set, and the real-time scenario, real-time formula data, and real-time environment equipment set of the current hydrogen peroxide production, determine the optimized operation value of each operation parameter in the operation parameter set.

[0060] In this embodiment, for the candidate formula data of each application scenario in the scenario set, the historical production data of all production formula sub-data is first found. Then, all relevant operation parameters are extracted from the historical production sub-data of all previous historical productions contained in these historical production data. After summarizing and deduplicating these operation parameters, the final set of operation parameters for the candidate formula data of the application scenario is determined. This set completely covers all types of operation parameters that need to be controlled during the production process of the candidate formula data.

[0061] In this embodiment, the set of operational parameters for each production formula sub-data in the candidate formula data for each application scenario is clearly defined, along with each operational parameter within it. Then, the operational parameter values ​​for the corresponding operational parameters in the historical production sub-data of all historical production cycles under each formula environment and equipment set for that production formula sub-data are found. All these values ​​are collected and organized into a set, forming the operational parameter value set for each operational parameter. This set can intuitively reflect the actual numerical changes of the operational parameters in past production under a specific formula and environment / equipment combination.

[0062] In this embodiment, a Bayesian optimization model is used to calculate the ideal control target of the operating parameters. It inputs multiple types of key data into the Bayesian optimization model. This data includes the set of operating parameter values ​​for each set of equipment under each production formula sub-data in each application scenario's candidate formula data, the production parameter values ​​for all production parameters in the historical production sub-data corresponding to that formula environment equipment set, and the direction labels and direction values ​​for all required parameters in each application scenario. The Bayesian optimization model first receives the input set of operating parameter values, production parameter values, direction labels, and direction values. It uses the operating parameters as independent variables and, combined with the direction labels, transforms the production parameter values ​​into performance indicators. For example, lower energy consumption and purity that better meet the range requirements result in higher performance indicators. Next, a surrogate model is constructed using Gaussian processes to fit the correlation between operating parameters and performance indicators, while simultaneously calculating the prediction uncertainty for each parameter point. Then, through a data collection function, such as the desired improvement function balance, the next parameter point to be verified is selected by utilizing known high-quality parameters and exploring uncertain regions. Finally, the surrogate model is updated using the performance data corresponding to that point in the historical data. This process is iterated repeatedly until the optimal operating value for each operating parameter that matches the scenario requirements is found.

[0063] In this embodiment, based on the optimal operating values ​​of the corresponding production formula sub-data and formula environment equipment set in the candidate formula data of each application scenario, and combined with the current real-time scenario of hydrogen peroxide production (i.e., the current application scenario), the real-time formula data (i.e., the currently used production formula), and the real-time environment equipment set (i.e., the currently used combination of environmental equipment), the optimal operating value of each operating parameter of the formula environment equipment set of the production formula sub-data of the application scenario that matches the real-time scenario, real-time formula data, and real-time environment equipment set is determined as the optimized operating value of each operating parameter of the current hydrogen peroxide production.

[0064] The beneficial effects of the above technologies are as follows: Based on the candidate formula data, production formula data, and real-time formula data and real-time environmental equipment set of each application scenario in the scenario set, the optimized operation value of each operation parameter in the operation parameter set can be determined, which can realize the customized and scientific control of operation parameters, provide accurate operation basis for different scenarios and formulas, and improve the efficiency and adaptability of production parameter control. Example 8:

[0065] Based on Example 7, the green and efficient anthraquinone fluidized bed hydrogen peroxide production system, in its production module, further includes: Production unit: Based on the real-time formula data, real-time environmental equipment set, and fluidized bed, hydrogen peroxide is produced in real time; Control unit: Real-time monitoring of operation data during the production process, including the monitored operation value of each operation parameter in the operation parameter set, and the control unit adjusts each operation parameter in the operation parameter set based on the optimized operation value and the monitored operation value.

[0066] In this embodiment, the real-time formula data includes key production information such as the component ratio of the working fluid, the amount of hydrogen added, and the type and amount of catalyst. The production unit will convert this information into actual production instructions, control the feeding system of the fluidized bed reactor to deliver the working fluid, hydrogen and catalyst according to the formula ratio, and at the same time adjust the reaction conditions inside the fluidized bed, such as temperature and pressure, according to the fitted environmental parameter value vector and fitted equipment parameter value vector in the real-time environmental equipment set, to ensure that the reaction proceeds according to the requirements set in the real-time formula, thereby continuously producing hydrogen peroxide.

[0067] In this embodiment, real-time operational data is collected during the production process. This data primarily includes the monitored operational values ​​of each operational parameter in the parameter set, such as the real-time actual values ​​of parameters like online replenishment rate and cooling water valve opening. The control unit then compares the monitored operational value of each parameter with the previously determined optimized operational value. If a deviation is detected, the control mechanism is immediately activated to adjust the actual value of the operational parameter back to the range of the optimized operational value, ensuring that the production process remains in an optimal state.

[0068] The beneficial effects of the above technologies are: real-time production of hydrogen peroxide and real-time control of operating parameters; production can be started precisely according to the real-time formula, ensuring the stability of the production process, improving the continuity of hydrogen peroxide production and the consistency of product quality, and reducing resource waste or substandard products caused by parameter deviations.

[0069] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A fluidized bed hydrogen peroxide production system by anthraquinone process, characterized in that, include: Acquisition Module: Acquires a set of scenarios for anthraquinone fluidized bed hydrogen peroxide production, application requirement data for each application scenario in the scenario set, production formula data, and historical production data for each type of production formula sub-data in the production formula data; The first analysis module analyzes the set of scenarios and the application requirement data of each application scenario in the set of scenarios, and determines the set of requirement parameters and the set of requirement scope for each application scenario in the set of scenarios; The second analysis module analyzes the production formula data, the historical production data of each production formula sub-data in the production formula data, and determines the set of production parameters, multiple sets of formula environment equipment, and the set of production ranges for each set of formula environment equipment in the production formula data. Determining Module: Based on the set of requirement parameters, the set of requirement ranges, the set of production parameters for each type of production formula sub-data, and the set of production ranges for each formula environment and equipment set in the scenario set, determine the candidate formula data for each application scenario in the scenario set; Calculation module: Obtains real-time production data of hydrogen peroxide currently being produced, and calculates real-time formula data of hydrogen peroxide currently being produced and real-time environmental equipment set based on real-time production data, candidate formula data of all application scenarios in the scenario set; Production module: Based on the candidate formula data, production formula data, real-time formula data for hydrogen peroxide production, and real-time environmental equipment set for each application scenario in the scenario set, determine the optimized operation value for each operation parameter in the operation parameter set, produce hydrogen peroxide in real time, and realize real-time control of operation parameters. The acquisition module includes: The first acquisition unit: acquires the set of scenarios for anthraquinone fluidized bed hydrogen peroxide production and the application requirement data for each application scenario in the set of scenarios. The application requirement data includes multiple requirement parameters and the requirement range, scenario label, direction label, and direction value for each requirement parameter. The scenario label includes strict and lenient, and the direction label includes lower is better, higher is better, and optimal range. The second acquisition unit acquires the production formula data for hydrogen peroxide production via anthraquinone fluidized bed process. The production formula data includes various sub-formulas for hydrogen peroxide production via anthraquinone fluidized bed process. The sub-formulas include working fluid data, hydrogenation amount, and catalyst data. The working fluid data includes anthraquinone derivatives, solvent, and working fluid volume ratio. The third acquisition unit: Based on the production formula data for hydrogen peroxide production using the fluidized bed anthraquinone process, acquire the historical production data for each production formula sub-data. The historical production data includes historical production sub-data from multiple historical production processes. The historical production sub-data includes historical production costs, multiple environmental parameters, environmental parameter values ​​for each environmental parameter, multiple production parameters, production parameter values ​​for each production parameter, multiple equipment parameters, equipment parameter values ​​for each equipment parameter, multiple operating parameters, and operating parameter values ​​for each operating parameter.

2. The anthraquinone fluidized bed hydrogen peroxide production system according to claim 1, characterized by, The first analysis module includes: Demand parameter set unit: Based on all the demand parameters in the application demand data of each application scenario in the scenario set, determine the demand parameter set for each application scenario in the scenario set; Demand Scope Set Unit: Based on the demand parameter set of each application scenario in the scenario set and the demand scope of all demand parameters in the application demand data, determine the demand scope set of each application scenario in the scenario set.

3. The anthraquinone fluidized bed hydrogen peroxide production system according to claim 1, characterized by, The second analysis module includes: Clustering Unit: Based on all environmental parameters, environmental parameter values ​​for each environmental parameter, all equipment parameters, and equipment parameter values ​​for each equipment parameter in the historical production data of all historical production sub-data of each production formula sub-data in the production formula data, cluster analysis is performed on the historical production sub-data of all historical production sub-data of each production formula sub-data in the production formula data to determine multiple formula environment equipment sets for each production formula sub-data in the production formula data. The formula environment equipment set includes historical production sub-data of multiple historical productions, fitted environmental parameter value vectors, and fitted equipment parameter value vectors. Production parameter set unit: Based on all production parameters in the historical production data of all historical production sub-data of each production formula sub-data in the production formula data, determine the production parameter set of each production formula sub-data in the production formula data; Production Scope Unit: Based on the production parameter values ​​of each production parameter in the historical production sub-data of all historical productions in each formula environment equipment set of each production formula sub-data in the production formula data, the production scope of each production parameter in each formula environment equipment set of each production formula sub-data in the production formula data is determined; Production Scope Set Unit: Based on the production parameter set of each formula environment equipment set for each formula sub-data in the production formula data and the production scope of each production parameter, determine the production scope set of each formula environment equipment set for each formula sub-data in the production formula data.

4. The anthraquinone-based fluidized bed hydrogen peroxide production system according to claim 3, characterized in that, The module to be determined includes: The first calculation unit: Based on the demand parameter set and demand range set of each application scenario in the scenario set, and the production parameter set, all formula environment equipment sets, historical production sub-data of all past productions of each formula environment equipment set, and production range set of each production formula sub-data in the production formula data, it calculates each formula environment equipment set of each production formula sub-data in the production formula data, based on the first sub-candidate value of each application scenario in the scenario set, and calculates the candidate value of each production formula sub-data in the production formula data based on each application scenario in the scenario set; Candidate formula data unit: All production formula sub-data in the production formula data are sorted from largest to smallest based on the candidate values ​​of each application scenario in the scenario set. Based on the production formula sub-data corresponding to the first specified number of candidate values ​​after sorting, the candidate formula data for each application scenario in the scenario set is determined. The candidate formula data includes multiple production formula sub-data.

5. The anthraquinone-based fluidized bed hydrogen peroxide production system according to claim 4, characterized in that, The calculation module includes: Real-time production data unit: acquires real-time production data of hydrogen peroxide production, including real-time scene, real-time environmental value vector and real-time equipment value vector; Scene tag unit: Based on the scene tags of the real-time scene and all application scenes in the scene set, determine the real-time scene and scene tag of the current hydrogen peroxide production; Real-time candidate data unit: Based on the real-time scenario and the candidate formula data of all application scenarios in the scenario set, determine the real-time candidate data for the current production of hydrogen peroxide; Second calculation unit: If the scene label of the real-time scene is severe, based on the real-time environment value vector, the real-time device value vector, the first sub-candidate value of each formula environment device set of all production formula sub-data in the production formula data, and the fitted environment parameter value vector and fitted device parameter value vector in all production formula sub-data and all formula environment device sets of each production formula sub-data in the current real-time candidate data of hydrogen peroxide production, calculate the real-time formula data and real-time environment device set of the current hydrogen peroxide production. Selection Unit: If the scene label of the real-time scene is loose, select the production formula sub-data with the largest candidate value in the current real-time candidate data for producing hydrogen peroxide as the current real-time formula data for producing hydrogen peroxide. Select the first sub-candidate value of the formula environment equipment set with the largest candidate value in the current real-time candidate data for producing hydrogen peroxide as the current real-time environment equipment set for producing hydrogen peroxide.

6. The anthraquinone-based fluidized bed hydrogen peroxide production system according to claim 5, characterized in that, The production module includes: Operation parameter set unit: Based on all operation parameters in the historical production data of all production formula sub-data in the candidate formula data of each application scenario in the scenario set, determine the operation parameter set of the candidate formula data for each application scenario in the scenario set; Operation parameter value set unit: Based on each operation parameter in the operation parameter set of each production formula sub-data in the candidate formula data of each application scenario in the scenario set, and the operation parameter value of each operation parameter in the historical production sub-data of all historical productions in each formula environment equipment set, determine the operation parameter value set of each operation parameter in the operation parameter set of each formula environment equipment set in the candidate formula data of each application scenario in the scenario set. The optimal operation value unit is determined by inputting the operation parameter value set of each operation parameter in the operation parameter set of each production formula sub-data of each application scenario in the scenario set, the production parameter value of all production parameters in the historical production sub-data of the second historical production in the formula environment and equipment set, and the direction label and direction value of all demand parameters of each application scenario into the Bayesian optimization model. Based on the output of the Bayesian optimization model, the optimal operation value of each operation parameter in the operation parameter set of each production formula sub-data of each application scenario in the scenario set is determined. Optimized Operation Value Unit: Based on the optimal operation value of each operation parameter in the operation parameter set of each production formula sub-data of each application scenario in the scenario set, and the real-time scenario, real-time formula data, and real-time environment equipment set of the current hydrogen peroxide production, determine the optimized operation value of each operation parameter in the operation parameter set.

7. The anthraquinone-based fluidized bed hydrogen peroxide production system according to claim 6, characterized in that, The production module also includes: Production unit: Based on the real-time formula data, real-time environmental equipment set, and fluidized bed, hydrogen peroxide is produced in real time; Control unit: Real-time monitoring of operation data during the production process, including the monitored operation value of each operation parameter in the operation parameter set, and the control unit adjusts each operation parameter in the operation parameter set based on the optimized operation value and the monitored operation value.

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