Process and system for production preparation optimization of rubber vulcanization accelerators

Abstract

The application discloses a production preparation optimization method and system for rubber vulcanization accelerators, and relates to the technical field of production control. The method comprises the following steps: determining a rubber vulcanization system according to vulcanization requirements, and matching and determining accelerator characteristics; making optimization decisions on compatibility mode and process stage reconstruction according to the accelerator characteristics, and determining a reconstructed production process; triggering the reconstructed production process as the production line processing starts, and performing microflow stage control based on micro-control cooperation and dynamic combination switching based on intermittent reaction progress, performing in-situ separation and purification control based on first-order physical screening based on a molecular sieve membrane microchannel and second-order virtual screening based on a descriptor, and performing control of digital optimization processing of the accelerators based on judgment shunting of a packaging production line and a feedback processing production line. The technical problems of low production efficiency and unstable quality of rubber vulcanization accelerators in the prior art are solved, and the technical effects of improving production efficiency and quality are achieved.

Description

Technical Field

[0001] This invention relates to the field of production control technology, and specifically to an optimized method and system for the production and preparation of rubber vulcanization accelerators. Background Technology

[0002] Rubber vulcanization accelerators are crucial additives in the manufacturing of rubber products. Their selection and formulation directly affect the vulcanization rate, crosslinking density, and the physicochemical properties of the final product. Current rubber accelerator production typically relies on fixed formulations and single processes for mass production, lacking adaptation mechanisms for different vulcanization systems and failing to meet diverse product demands. Furthermore, traditional production processes generally suffer from insufficient control precision, response delays, and high energy consumption in raw material selection, reaction control, and product purification, hindering the quality stability and processing efficiency of the accelerators. Especially with the increasing automation and digitalization, current technologies have not yet developed a solution that can dynamically match accelerator characteristics to vulcanization requirements and reconstruct and synergistically optimize the production process. Summary of the Invention

[0003] This application provides an optimized method and system for the production and preparation of rubber vulcanization accelerators, which solves the technical problems of low production efficiency and unstable quality of rubber vulcanization accelerators in the prior art.

[0004] A first aspect of this application provides an optimized method for the production and preparation of a rubber vulcanization accelerator, the method comprising:

[0005] Based on vulcanization requirements, a rubber vulcanization system is determined, and the characteristics of the accelerator are matched and determined. These accelerator characteristics include intrinsic and modified properties. Based on the accelerator characteristics, optimization decisions are made regarding the compatibility methods and process stages, resulting in a reconstructed production process. This reconstructed production process includes a microfluidic stage, a two-stage filtration stage, and a judgment and diversion treatment stage. As production line processing begins, the reconstructed production process is triggered. Microfluidic stage control is executed using microcontroller coordination and dynamic combination switching based on intermittent reaction processes. In-situ separation and purification control is performed using first-stage physical filtration based on molecular sieve membrane microchannels and second-stage virtual filtration based on descriptors. Digital optimization processing control of the accelerator is achieved through judgment and diversion based on the packaging production line and feedback processing production line.

[0006] A second aspect of this application provides an optimized system for the production and preparation of rubber vulcanization accelerators, the system comprising:

[0007] Matching Module: Based on vulcanization requirements, determine the rubber vulcanization system and match and determine the characteristics of the accelerator, wherein the accelerator characteristics include intrinsic characteristics and modified characteristics; Production Process Reconfiguration Module: Based on the accelerator characteristics, make optimization decisions on the compatibility method and process stage reconfiguration, and determine the reconfigured production process, wherein the reconfigured production process includes a microfluidic stage, a two-stage filtration stage, and a judgment and diversion treatment stage; Control Module: As production line processing begins, trigger the reconfigured production process, and execute microfluidic stage control by switching between microcontroller coordination and dynamic combination based on the intermittent reaction process, execute in-situ separation and purification control by first-stage physical filtration based on molecular sieve membrane microchannels and second-stage virtual filtration based on descriptors, and perform digital optimization processing control of the accelerator by judgment and diversion based on the packaging production line and feedback processing production line.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, based on vulcanization requirements, the rubber vulcanization system is determined, and the characteristics of the accelerator are matched and determined. These accelerator characteristics include both intrinsic and modified properties. Then, based on the accelerator characteristics, optimization decisions are made regarding the compatibility methods and process stages, determining the reconstructed production process. This reconstructed production process includes a microfluidic stage, a two-stage filtration stage, and a judgment and diversion treatment stage. Finally, with the start of production line processing, the reconstructed production process is triggered. Microfluidic stage control is executed through coordinated and dynamic switching of microcontrollers based on the batch reaction process. In-situ separation and purification control is performed through first-stage physical filtration based on molecular sieve membrane microchannels and second-stage virtual filtration based on descriptors. Digital optimization processing control of the accelerator is achieved through judgment and diversion based on the packaging and feedback processing lines. This solves the technical problems of low production efficiency and unstable quality of rubber vulcanization accelerators in existing technologies, achieving the technical effect of improving both production efficiency and quality. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the optimized production and preparation method for rubber vulcanization accelerators provided in the embodiments of this application;

[0012] Figure 2 This is a schematic diagram of an optimized system structure for the production and preparation of rubber vulcanization accelerators provided in an embodiment of this application.

[0013] Explanation of reference numerals in the attached diagram: Matching module 11, Production process reconfiguration module 12, Control module 13. Detailed Implementation

[0014] This application provides an optimized method and system for the production and preparation of rubber vulcanization accelerators, which solves the technical problems of low production efficiency and unstable quality of rubber vulcanization accelerators in the prior art.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, this application provides an optimized method for the production and preparation of a rubber vulcanization accelerator, wherein the method includes:

[0018] Based on the vulcanization requirements, the rubber vulcanization system is determined, and the characteristics of the accelerator are matched and determined. The characteristics of the accelerator include intrinsic characteristics and modified characteristics.

[0019] Vulcanization requirements include parameters such as crosslinking density, vulcanization rate, heat resistance, aging resistance, and processing window. Based on these requirements, combined with the desired rubber type (e.g., natural rubber, styrene-butadiene rubber, EPDM rubber) and filler system, the required vulcanization system type is determined, including selecting appropriate sulfur types and the ratio of primary to secondary accelerators. Based on this vulcanization system, the required accelerator type is matched, and the accelerator characteristics of the selected accelerator are further analyzed. Accelerator characteristics include intrinsic and modified characteristics. Intrinsic characteristics refer to the natural physicochemical properties of the accelerator in its unprocessed state, such as molecular structure, reactivity, volatility, polarity, and environmental stability. Modified characteristics refer to additional properties introduced through structural modification, blending modification, or carrier composites, including enhanced targeted reaction sites, delayed release characteristics, selective catalytic effects, or low-toxicity ecological indicators.

[0020] Based on the characteristics of the accelerator, optimization decisions are made on the compatibility method and process stage reconstruction to determine the reconstructed production process, which includes a microfluidic stage, a two-stage filtration stage, and a judgment and diversion treatment stage.

[0021] Combining the intrinsic and modified properties of accelerators, a coupled analysis of various characteristic parameters and chemical reaction behavior in existing rubber vulcanization systems is conducted to construct an accelerator compatibility model. This model is used to simulate and evaluate various compatibility methods, selecting the optimal compatibility structure with balanced performance in reaction synergy, thermal stability, and processing adaptability. Based on this, and considering production efficiency, raw material utilization, and product performance consistency requirements, the existing accelerator preparation process is deconstructed and restructured in stages to form a modular production stage system, determining the reconstructed production process. This reconstructed production process includes: a microfluidic stage for precise control and dispersion mixing of reaction components at the microscale; a two-stage filtration stage for sequentially performing primary sieving based on physical molecular size and virtual sieving based on structure-activity relationship to achieve high-precision separation of main products and by-products; and a judgment and diversion stage for dynamically determining the product processing path based on the filtration results, enabling packaging and transfer of qualified products and feedback reprocessing of secondary products, thereby constructing an intelligent production process with adjustability and responsiveness.

[0022] Furthermore, the optimization decision-making for the compatibility method and process stage reconstruction based on the characteristics of the accelerator includes:

[0023] For the intrinsic characteristics, the first process element is determined by direct indexing; for the modified characteristics, the second process element is determined by directional optimization based on the greedy principle; and the matching method and process stage reconstruction are performed based on the first process element and the second process element.

[0024] For the intrinsic properties of accelerators, such as chemical structure, reactivity, decomposition temperature, polarity parameters, and blending compatibility, the system establishes a mapping database between intrinsic properties and process parameters. Using a direct indexing method, it quickly matches and extracts standardized process parameters suitable for these intrinsic properties, determining the first process element. This first process element includes operational parameters highly coupled to basic physicochemical behavior, such as temperature control settings, stirring rate, and reaction pH range. For the modification properties of accelerators, a greedy optimization algorithm is used to rapidly evaluate the modification parameters of accelerators with specific functional modifications (such as delayed exothermic reactions and enhanced selective catalysis). It prioritizes the target property that contributes the most under current conditions, gradually constructing the optimal combination path to finally determine the second process element. This second process element includes dynamic control parameters highly dependent on the modification properties, such as reaction sequence, blending ratio, and stage residence time. Based on the synergy between the first and second process elements, the existing preparation process is deconstructed and reconstructed, the production segment sequence and control boundaries are reset, and the compatibility method and process stage are jointly optimized to form a reconstructed production scheme adapted to the current characteristics of the accelerator.

[0025] Furthermore, the second process element is determined by a greedy algorithm-based directional optimization, including:

[0026] The modified characteristics are traversed to determine multiple greedy targets. Oriented optimization is performed using these greedy targets to determine multiple target process elements, wherein each modified characteristic corresponds one-to-one with the multiple greedy targets. The accelerator characteristics are traversed to determine mutual influence relationships. Based on these mutual influence relationships, the multiple target process elements are iteratively adjusted to determine the second process element, wherein a preset step size is used as a constraint for each adjustment.

[0027] First, the modification characteristics of the accelerators are thoroughly investigated, mapping each modification characteristic (such as sustained-release performance, selective catalytic ability, structural stability, and eco-friendliness) to a specific greedy objective, i.e., the performance index that is prioritized for improvement in the current optimization stage. Based on these greedy objectives, targeted optimization is performed in the corresponding process parameter space. Through local optimum advancement, multiple target process elements matching each greedy objective are determined. These target process elements include reaction time window, compatibility order, additive proportion, and structural stability adjustment mechanism. Second, the accelerator characteristics are comprehensively investigated to identify the interaction relationships between different intrinsic and modified characteristics, constructing a parameter dependency graph to clarify the coupling or conflict relationships between multiple target process elements. Finally, based on these interaction relationships, with a preset step size as the adjustment constraint, multiple target process elements are iteratively adjusted and linked for correction, ensuring that the local optimization results are coordinated and unified at the system level. Ultimately, the second process element after comprehensive optimization is determined as the basis for dynamic control supporting process stage reconfiguration.

[0028] Furthermore, the reconfiguration of the production process includes a microfluidic phase, comprising:

[0029] Based on the characteristics of the promoter, the control elements of the batch reaction preparation process are decoupled to determine the decoupling elements; for the decoupling elements, a microfluidic controller is constructed, wherein the microfluidic controller corresponds one-to-one with the decoupling elements; based on the dynamics of the batch reaction preparation process, the dynamic combination of the microfluidic controller is executed to determine the microfluidic module, wherein the dynamics include nonlinear characteristics and time-varying characteristics.

[0030] First, based on the characteristics of the accelerator, the control elements of the batch reaction preparation process to which it is adapted are decoupled. The coupled and interactive control factors in the original process flow (including material injection rate, temperature regulation range, pressure response curve, shear rate distribution, etc.) are separated by feature mapping to identify multiple decoupled elements with independent control capabilities. Second, a microfluidic controller is constructed for each decoupled element. The microfluidic controller is an integrated microscale execution unit, and its structure can adopt a multi-channel micropump, a thermosensitive flow control module, or a flexible electronically controlled valve array to ensure real-time and precise adjustment of each unit variable at the microscale, realizing controller functionality. Each element corresponds to a decoupling element. Finally, based on the dynamic characteristics of the batch reaction process itself, including nonlinear changes in parameters (such as the nonlinear response relationship between rate and concentration) and time-varying changes (such as the conditional migration and interaction structure changes of the reaction over time), the system dynamically combines and configures each microfluidic controller according to the current operating conditions and the target reaction path, and determines its activation sequence, working state and adjustment mode in real time, thereby constructing a microfluidic module that adapts to the target reaction requirements. The microfluidic module supports online adjustment and multi-path switching during the preparation process, effectively improving reaction accuracy, conversion efficiency and system stability.

[0031] Furthermore, the two-stage screening process includes:

[0032] A molecular sieve membrane microchannel is introduced and deployed at a physical location in the lower-level production line of the microfluidic stage as a first-order filtration node, wherein the main product and by-product are the filtration targets, and the main product is the promoter; a descriptor based on the structure-activity relationship of the promoter is introduced to perform high-throughput virtual filtration as a second-order filtration node; the first-order filtration node and the second-order filtration node are cascaded to deploy the two-order filtration stage.

[0033] After the microfluidic stage, molecular sieve membrane microchannels are introduced downstream of the production line. These microchannels are made of highly selective pore size molecular sieve membrane materials, enabling precise sieving of different molecular sizes or polar groups. The molecular sieve membrane microchannels are deployed as first-order filtration nodes for preliminary separation of the product stream. The main sieving targets are the main products (i.e., the target promoters) and byproducts (such as unreacted intermediates and small molecule impurities), which are physically separated based on differences in molecular size or diffusion coefficient. This improves the purity of the main product and reduces the burden on downstream processing.

[0034] Following the first-order physical screening, a virtual screening mechanism based on a descriptor model of the accelerator structure-activity relationship is introduced as a second-order screening node. This second-order screening node does not rely on physical structure separation but instead uses chemical reaction informatics analysis and high-throughput algorithms to rapidly screen accelerator candidates. Descriptors can include indicators such as reactivity index, spatial conformational stability, charge distribution, and electron affinity. By constructing a judgment model, a binary classification (qualified / unqualified) is achieved to determine whether the accelerator meets the structure-activity requirements, and the screening results are output.

[0035] By cascading first-stage and second-stage filtration nodes on the production line, a two-stage filtration structure of physical filtration and virtual filtration is formed, realizing a full-process promoter separation and purification mechanism from in-situ coarse separation to functional fine separation, providing high-quality raw material guarantee for subsequent diversion control and packaging processing.

[0036] Furthermore, a descriptor based on the structure-activity relationship of promoters is introduced to serve as a second-order filtration node for high-throughput virtual filtration, including:

[0037] The structure-activity relationship of the promoter is determined, and a descriptor is introduced, wherein the descriptor is determined at least based on activity and selectivity; a virtual filter is constructed based on the descriptor and a binary classification decision based on the descriptor; and the second-order filtration node is determined based on the virtual filter.

[0038] Based on historical data and experimental validation, the structure-activity relationship (SPR) of promoters is determined, which is a correlation model between molecular structural characteristics and their activity and selective reaction behavior. The SPR of promoters includes, but is not limited to, molecular skeleton type, functional group arrangement, polarity distribution, and electron cloud density. Based on the SPR of promoters, descriptors are introduced as parameter tools to quantify the SPR. The descriptors contain at least two dimensions: one is activity descriptors, such as reaction energy barrier, transition state stability, and electron density distribution, used to evaluate the catalytic efficiency of promoters in the target reaction; the other is selectivity descriptors, such as steric hindrance index, nucleophilicity and electrophilicity, and reaction pathway divergence probability, used to measure the targeted reaction ability of promoters in the complex system. Using the descriptors as input features, a virtual filter based on binary classification logic is constructed. This filter uses a classification model (such as support vector machine, random forest, neural network, etc.) to perform high-throughput discrimination on candidate promoters, classifying them into two categories: "meets the target SPR" and "does not meet the target SPR", and outputting screening labels and confidence scores. Finally, based on the output of the virtual filter, the second-stage filtration node for deployment in the two-stage filtration system is determined and integrated into the production line control logic as a soft decision module. This enables re-judgment and intelligent identification of the structure-property properties of the products after physical filtration, thereby improving product quality consistency and functional optimization capabilities while ensuring high reaction efficiency.

[0039] Furthermore, the judgment and triage process includes:

[0040] A flow divider is introduced and deployed at the lower production line control position of the two-stage filtration stage. Taking the result of the second-stage filtration as the cause and the flow divider between the first production line and the second production line as the result, the flow divider is used to determine the flow divider, wherein the first production line is the packaging production line and the second production line is the feedback processing production line.

[0041] A flow divider is introduced into the downstream production line control node of the two-stage screening stage. This flow divider can be a decision unit combining hardware and software, with functions of rule parsing, data reception, logic judgment and production line execution control. It is deployed in the physical structure of the production line and closely connected with the two-stage screening module to receive and process the screening results in real time.

[0042] The system uses the judgment result from the second-order virtual filter as the basis for judgment, that is, marking the accelerator product in each batch or unit of fluid as "structure and function qualified" or "unqualified". Based on the judgment result, the diversion judge executes automatic control logic: if it is judged as "qualified", the product is introduced into the first production line, that is, the formal packaging production line, where the accelerator is subjected to terminal processing operations such as metering and packaging, finished product warehousing or labeling; if it is judged as "unqualified", the product is introduced into the second production line, that is, the feedback processing production line, which is used to perform secondary processing on the accelerator that does not meet the structure and function requirements, such as blending of additives, structural reforming, recirculation mixing or re-screening.

[0043] Furthermore, the second production line includes the incorporation of main products and the output of by-products, wherein the first production line and the second production line have an incorporation point; at the incorporation point, the main products of the second production line are transferred to the first production line for packaging and processing control.

[0044] The second production line is not only used for feedback processing of substandard products, but also includes further classification and flow control of the product results, specifically including: the main product incorporation mechanism and the by-product output pathway. The by-product output pathway is used to directionally remove impurities, degradation components, or non-reusable substances from the reaction process to ensure the purity of the main product and system cleanliness; the main product incorporation pathway is used to re-obtain structure-activity accelerator products during feedback processing.

[0045] Specifically, the system establishes a merging point between the first production line (i.e., the packaging production line) and the second production line. This merging point can be a physical confluence point, a directional valve assembly, or a control switching interface, used to achieve material path fusion between the two production lines. At this merging point, after the second production line completes feedback processing and passes subsequent structure-property verification, the accelerator flow that is determined to meet packaging conditions is transferred from the second production line to the first production line. Standard packaging processing control is then performed in the first production line, including metering, dispensing, sealing, coding, and post-processing steps, thereby achieving the return processing of high-quality accelerators and efficient resource utilization.

[0046] As production line processing begins, the reconfigured production process is triggered. Microfluidic stage control is executed through micro-control coordination and dynamic combination switching based on intermittent reaction process. In-situ separation and purification control is executed through first-order physical filtration based on molecular sieve membrane microchannels and second-order virtual filtration based on descriptors. Digital optimization processing control of accelerators is performed based on judgment diversion between packaging production line and feedback processing production line.

[0047] As production line processing begins, the system automatically triggers the reconfigured production process, initiating a production flow optimized based on accelerator characteristics. First, it enters the microfluidic control stage. Through characteristic analysis of the intermittent reaction process, it invokes previously decoupled control elements, executes a microcontroller coordination mechanism, and dynamically combines and switches multiple microfluidic controllers based on current raw material characteristics, reaction state, and set targets to achieve high-precision adjustment of reaction conditions and continuous process control. Next, the product stream enters the in-situ separation and purification control process, sequentially passing through a first-order physical filtration node and a second-order virtual filtration node deployed downstream. Specifically, based on the molecular sieve membrane microchannel structure, precise physical screening based on particle size and molecular structure is performed to achieve preliminary separation of main and by-products. Then, a virtual filter based on structure-activity descriptors performs high-throughput molecular-level determination of the products, screening out accelerator molecules that do not meet performance indicators from a structure-activity relationship perspective, further improving the purification level. Finally, the production line enters the judgment and diversion control stage. Based on the judgment results of the second-order virtual filtration, the system invokes a diversion judge deployed downstream of the production line to dynamically guide the product stream. For qualified main products, they are guided to the first production line for standard packaging; for materials requiring further processing, they are guided to the second production line for feedback processing. Simultaneously, at the production line integration point, qualified main products processed by the second production line are allowed to flow back to the first production line for final processing, achieving digital optimization control, closed-loop quality feedback, and maximum resource utilization throughout the entire accelerator processing flow.

[0048] Furthermore, after controlling the digital optimization of the accelerator processing, the following are included:

[0049] Perform yield rate testing to determine the test results; if the test results are not up to standard, conduct first tracing based on performance defects and second tracing based on the production line processing source to determine the tracing results; based on the tracing results, conduct feedback control of the accelerator production and processing.

[0050] After completing the digital optimization and processing control of the accelerator, the yield rate of the accelerator products after packaging or reprocessing is tested. This test can be based on online analytical instruments (such as infrared spectrometers, chromatography, thermal analysis devices) or offline sampling inspection methods to comprehensively evaluate the product's compliance with key performance indicators such as activity value, purity, particle size distribution, and structure-activity consistency, and generate corresponding test results.

[0051] If the test results fail to meet the standards, the system initiates a dual traceability mechanism to pinpoint the source of the problem from both performance and process dimensions. Firstly, focusing on the substandard performance dimension, a first traceability step is performed to analyze potential structure-property deviations, reaction runaway, or insufficient filtration that could cause the performance defect. Secondly, based on the production line number, process, microfluidic configuration, filtration strategy, and diversion records of the batch of products, a second traceability step is performed to locate the specific processing stage on the production line, including processing time points, microfluidic module combination schemes, and control parameter settings, obtaining precise traceability data. Based on the traceability results, the system intelligently corrects the control parameters, reaction paths, or filtration standards related to the problematic batch, executing targeted feedback control operations, including microfluidic controller parameter tuning, virtual filtration model fine-tuning, modification path priority adjustment, and diversion judgment threshold correction. This enables the suppression and quality recovery of similar anomalies in subsequent processing, constructing a closed-loop optimization system with learning capabilities.

[0052] In summary, the embodiments of this application have at least the following technical effects:

[0053] First, based on vulcanization requirements, the rubber vulcanization system is determined, and the characteristics of the accelerator are matched and determined. These accelerator characteristics include both intrinsic and modified properties. Then, based on the accelerator characteristics, optimization decisions are made regarding the compatibility methods and process stages, determining the reconstructed production process. This reconstructed production process includes a microfluidic stage, a two-stage filtration stage, and a judgment and diversion treatment stage. Finally, with the start of production line processing, the reconstructed production process is triggered. Microfluidic stage control is executed through coordinated and dynamic switching of microcontrollers based on the batch reaction process. In-situ separation and purification control is performed through first-stage physical filtration based on molecular sieve membrane microchannels and second-stage virtual filtration based on descriptors. Digital optimization processing control of the accelerator is achieved through judgment and diversion based on the packaging and feedback processing lines. This solves the technical problems of low production efficiency and unstable quality of rubber vulcanization accelerators in existing technologies, achieving the technical effect of improving both production efficiency and quality.

[0054] Example 2, based on the same inventive concept as the optimized production and preparation method for rubber vulcanization accelerators in the foregoing examples, such as... Figure 2 As shown, this application provides an optimized system for the production and preparation of rubber vulcanization accelerators, wherein the system includes:

[0055] Matching Module 11: Determines the rubber vulcanization system based on vulcanization requirements and matches and determines the characteristics of the accelerator, wherein the accelerator characteristics include intrinsic characteristics and modified characteristics; Production Process Reconstruction Module 12: Makes optimization decisions on the compatibility method and process stage reconstruction based on the accelerator characteristics, and determines the reconstructed production process, wherein the reconstructed production process includes a microfluidic stage, a two-stage filtration stage, and a judgment and diversion treatment stage; Control Module 13: Triggers the reconstructed production process as production line processing begins, performs microfluidic stage control by switching between microcontroller coordination and dynamic combination based on intermittent reaction process, performs in-situ separation and purification control by first-stage physical filtration based on molecular sieve membrane microchannels and second-stage virtual filtration based on descriptors, and performs digital optimization processing control of the accelerator by judgment and diversion based on packaging production line and feedback processing production line.

[0056] Furthermore, the production process reconfiguration module 12 is used to perform the following methods:

[0057] For the intrinsic characteristics, the first process element is determined by direct indexing; for the modified characteristics, the second process element is determined by directional optimization based on the greedy principle; and the matching method and process stage reconstruction are performed based on the first process element and the second process element.

[0058] Furthermore, the production process reconfiguration module 12 is used to perform the following methods:

[0059] The modified characteristics are traversed to determine multiple greedy targets. Oriented optimization is performed using these greedy targets to determine multiple target process elements, wherein each modified characteristic corresponds one-to-one with the multiple greedy targets. The accelerator characteristics are traversed to determine mutual influence relationships. Based on these mutual influence relationships, the multiple target process elements are iteratively adjusted to determine the second process element, wherein a preset step size is used as a constraint for each adjustment.

[0060] Furthermore, the production process reconfiguration module 12 is used to perform the following methods:

[0061] Based on the characteristics of the promoter, the control elements of the batch reaction preparation process are decoupled to determine the decoupling elements; for the decoupling elements, a microfluidic controller is constructed, wherein the microfluidic controller corresponds one-to-one with the decoupling elements; based on the dynamics of the batch reaction preparation process, the dynamic combination of the microfluidic controller is executed to determine the microfluidic module, wherein the dynamics include nonlinear characteristics and time-varying characteristics.

[0062] Furthermore, the production process reconfiguration module 12 is used to perform the following methods:

[0063] A molecular sieve membrane microchannel is introduced and deployed at a physical location in the lower-level production line of the microfluidic stage as a first-order filtration node, wherein the main product and by-product are the filtration targets, and the main product is the promoter; a descriptor based on the structure-activity relationship of the promoter is introduced to perform high-throughput virtual filtration as a second-order filtration node; the first-order filtration node and the second-order filtration node are cascaded to deploy the two-order filtration stage.

[0064] Furthermore, the production process reconfiguration module 12 is used to perform the following methods:

[0065] The structure-activity relationship of the promoter is determined, and a descriptor is introduced, wherein the descriptor is determined at least based on activity and selectivity; a virtual filter is constructed based on the descriptor and a binary classification decision based on the descriptor; and the second-order filtration node is determined based on the virtual filter.

[0066] Furthermore, the production process reconfiguration module 12 is used to perform the following methods:

[0067] A flow divider is introduced and deployed at the lower production line control position of the two-stage filtration stage. Taking the result of the second-stage filtration as the cause and the flow divider between the first production line and the second production line as the result, the flow divider is used to determine the flow divider, wherein the first production line is the packaging production line and the second production line is the feedback processing production line.

[0068] Furthermore, the control module 13 is used to perform the following methods:

[0069] The second production line includes the inflow of main products and the outflow of by-products, wherein the first production line and the second production line have an inflow point; at the inflow point, the main products of the second production line are transferred to the first production line for packaging and processing control.

[0070] Furthermore, the control module 13 is used to perform the following methods:

[0071] Perform yield rate testing to determine the test results; if the test results are not up to standard, conduct first tracing based on performance defects and second tracing based on the production line processing source to determine the tracing results; based on the tracing results, conduct feedback control of the accelerator production and processing.

[0072] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0073] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0074] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A process for the production of rubber vulcanization accelerators, characterized in that, The method comprises: determining a rubber vulcanization system according to the vulcanization requirement, matching the determined accelerator characteristics, wherein the accelerator characteristics include intrinsic characteristics and modified characteristics; determining a reconstructed production process according to the optimization decision of the compatibility mode and the process stage reconstruction based on the accelerator characteristics, wherein the reconstructed production process includes a microfluidic stage, a double-stage screening stage, and a judgment and shunt processing stage; triggering the reconstructed production process as the production line processing starts, to perform microfluidic stage control based on the micro-control coordination and dynamic combination switching of the intermittent reaction process, to perform in-situ separation and purification control based on the first-order physical screening of the molecular sieve membrane microchannel and the second-order virtual screening based on the descriptor, and to control the digital optimization processing of the accelerator based on the judgment shunt of the packaging production line and the feedback processing production line; the optimization decision of the compatibility mode and the process stage reconstruction based on the accelerator characteristics comprises: determining a first process element in a direct index call mode for the intrinsic characteristics; determining a second process element in a directional optimization based on the greedy principle for the modified characteristics; reconstructing the compatibility mode and the process stage according to the first process element and the second process element; the directional optimization based on the greedy principle to determine the second process element comprises: traversing the modified characteristics to determine a plurality of greedy targets, and determining a plurality of target process elements in the directional optimization based on the plurality of greedy targets, wherein the modified characteristics and the plurality of greedy targets correspond one by one; traversing the accelerator characteristics to determine the mutual influence relationship; determining the second process element by iteratively adjusting the plurality of target process elements according to the mutual influence relationship, wherein a preset step size is used as a single adjustment constraint.

2. The process for production of rubber vulcanization accelerator as claimed in claim 1 wherein, The reconstructed production process includes a microfluidic stage, which comprises: decoupling control elements of the intermittent reaction preparation process according to the accelerator characteristics to determine decoupling elements; constructing a microflow controller for the decoupling elements, wherein the microflow controller corresponds one by one to the decoupling elements; determining a microfluidic module by performing dynamic combination of the microflow controller according to the dynamics of the intermittent reaction preparation process, wherein the dynamics includes nonlinear characteristics and time-varying characteristics.

3. The process for production preparation optimization of rubber vulcanization accelerators as claimed in claim 1 wherein, The double-stage screening stage comprises: introducing a molecular sieve membrane microchannel, which is deployed at a lower production line physical position of the microfluidic stage as a first-order screening node, wherein the main product and the byproduct are used as screening targets, and the main product is the accelerator; introducing a descriptor based on the accelerator structure-activity relationship for high-throughput virtual screening as a second-order screening node; cascading the first-order screening node and the second-order screening node to deploy the double-stage screening stage.

4. The process for the production of rubber vulcanization accelerator as claimed in claim 3 wherein, Introducing a descriptor based on the accelerator structure-activity relationship for high-throughput virtual screening as a second-order screening node comprises: determining the accelerator structure-activity relationship to introduce the descriptor, wherein the descriptor is determined based on at least activity and selectivity; constructing a virtual screening filter based on the binary classification of the descriptor according to the descriptor; determining the second-order screening node according to the virtual screening filter.

5. The method for production preparation optimization of rubber vulcanization accelerators as claimed in claim 1, wherein, The judgment and triage process includes: A flow divider is introduced and deployed at the lower production line control position of the two-stage screening stage. Taking the result of the second-stage filtration as the cause and the diversion of the first production line and the second production line as the result, the production line diversion is determined according to the diversion judge, wherein the first production line is the packaging production line and the second production line is the feedback processing production line.

6. The process for production of rubber vulcanization accelerator as claimed in claim 5 wherein, The second production line includes the incorporation of main products and the output of by-products, wherein the first production line and the second production line have an incorporation point; ​ At the merged location, the main product of the second production line is transferred to the first production line for packaging and processing control.

7. The method for production preparation optimization of rubber vulcanization accelerators as claimed in claim 1, wherein, After controlling the digital optimization process of the accelerator, the following is included: Conduct yield testing to determine the test results; If the test results are not up to standard, the first traceability is based on performance defects, and the second traceability is based on the production line processing source, to determine the traceability result; Based on the traceability results, feedback control is implemented for the production and processing of accelerators.

8. A production preparation optimization system for rubber vulcanization accelerators, characterized by, For implementing the optimized method for the production and preparation of a rubber vulcanization accelerator according to any one of claims 1-7, the system comprises: Matching module: Based on vulcanization requirements, determine the rubber vulcanization system and match and determine the characteristics of the accelerator, wherein the characteristics of the accelerator include intrinsic characteristics and modified characteristics; Production process reconfiguration module: Based on the characteristics of the accelerator, optimize the formulation and process stage reconfiguration to determine the reconfigured production process, which includes a microfluidic stage, a two-stage filtration stage, and a judgment and diversion treatment stage. Control module: As production line processing begins, the reconfigured production process is triggered. Microfluidic stage control is executed through micro-control coordination and dynamic combination switching based on intermittent reaction process. In-situ separation and purification control is executed through first-order physical filtration based on molecular sieve membrane microchannels and second-order virtual filtration based on descriptors. Digital optimization processing control of accelerator is performed based on judgment diversion between packaging production line and feedback processing production line.

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