Intelligent production system and method for polyester resins for powder coatings

Abstract

This invention discloses an intelligent production system and method for polyester resin used in powder coatings. The system includes a metering and batching system, a production line allocation system, an online monitoring system, a process control system, a laboratory analysis system, and a customer feedback system. During production, the metering and batching system dispenses materials according to a specified formula, and the production line allocation system allocates materials based on the production plan and production line characteristics. After the polyester resin is synthesized, the finished product is made into powder coating. The laboratory analysis system tests the product performance and collects customer feedback on the subsequent use of corresponding batches of products. The system initially accumulates production, testing, and feedback data to form a database; in the middle stage, it introduces artificial intelligence to analyze big data, builds algorithm models, optimizes formulas and process parameters based on coating application performance, and compares differences in materials from different production lines and suppliers; ultimately, it can determine the optimal formula ratio, suitable supplier materials, and optimal synthesis process parameters according to customer needs, ensuring that product test results meet requirements.

Description

Technical Field

[0001] This invention belongs to the field of chemical synthesis and production technology. Specifically, this invention relates to an intelligent production system and method for polyester resin used in powder coatings. Background Technology

[0002] Powder coatings are 100% solids content, zero VOC "4E" (high efficiency, energy saving, environmental protection, and economy) coatings. They have the characteristics of energy saving, environmental protection and excellent comprehensive performance, and have an extremely wide range of applications. Polyester resin is one of the most important raw materials for powder coatings. The formulation design and synthesis process of polyester resin are important factors affecting the performance and quality of powder coatings and their coatings.

[0003] The existing synthesis routes for powder-processed polyester resins are as follows: 1. In terms of formulation design, it is an empirical design developed by R&D personnel through extensive and repeated experiments over a long period of time. The designed formulation is usually in the designer's mind, and this formulation design requires a large number of trial-and-error experiments, which is time-consuming, wastes resources, and has relatively low work efficiency. Furthermore, when achieving mass production, the synergistic effects between different suppliers and different materials cannot be considered. 2. In terms of production line allocation, scheduling personnel arrange production lines based on their schedules and availability. This allocation does not take into account the differences in the characteristics of equipment on different production lines, which may result in some products being unsuitable for production on certain lines. 3. Regarding online monitoring, although the current traditional polyester resin synthesis routes use DCS (Distributed Control System) for automatic control and precise temperature control, other parameters such as time and pressure are not effectively monitored, and the data from this DCS control is not analyzed later. Data obtained from traditional polyester resin synthesis routes only forms a separate record for product quality traceability. It fails to connect all data from the entire synthesis route for big data collection and analysis, thus failing to leverage the full potential of the data. This reliance on traditional routes to synthesize polyester resin has numerous drawbacks, including long R&D cycles, slow response times to customer information, poor coordination between different stages or key nodes, and low efficiency.

[0004] This invention provides an intelligent production system for polyester resin used in powder coatings, and specifically relates to how to achieve intelligent synthesis and production of polyester resin for powder coatings to improve production efficiency and product quality. Summary of the Invention

[0005] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention provides an intelligent production system for polyester resin used in powder coatings, with the purpose of realizing intelligent synthesis and production of polyester resin for powder coatings, thereby improving production efficiency and product quality.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: an intelligent production system for polyester resin used in powder coatings, comprising: Workstations and servers are used to store product formulas, process parameters, and historical production data, and to generate production instructions based on the production plan; A metering and batching system is used to meter and feed various raw materials according to the production instructions. The production line allocation system is used to obtain the operating status of multiple production lines and select the target production line according to the production instructions. An online monitoring system is used to collect production process parameters in the target production line in real time; The process control system is used to detect and analyze key control parameters in the polyester resin synthesis process and establish parameter correlations; and Laboratory analysis system for performance testing of finished polyester resin products; The workstation and server are respectively connected to the metering and batching system, the production line distribution system, the online monitoring system, the process control system, and the laboratory analysis system, and are used to receive data from each system and send control commands to each system.

[0007] The workstation and server are equipped with a data analysis module, which is used to update the product formula or process parameters and generate new production instructions based on the data fed back from the online monitoring system, process control system and laboratory analysis system.

[0008] The metering and batching system includes a solid material metering unit and a liquid material metering unit. The solid material metering unit is used to feed solid raw materials into the corresponding production line silos, and the liquid material metering unit is used to transport liquid raw materials to the reaction vessel via a flow meter and a conveying device.

[0009] The production line allocation system is used to acquire the operating status parameters of each production line, lock the target production line according to the production instructions, and send the operating parameters of the target production line to the online monitoring system.

[0010] The online monitoring system is used to collect one or more of the following production process parameters: Reactor temperature, reactor pressure, stirring speed, motor current, reaction time, and material viscosity.

[0011] The process control system is used to detect sampling points, vacuum time, intermediate acid value and viscosity during the polyester resin synthesis process, and to establish the correlation between key control node parameters based on the detection data.

[0012] The laboratory analysis system is used to mix polyester resin products with curing agents, pigments and fillers in a preset ratio, form powder coatings after melt extrusion, and perform performance testing on the resulting powder coatings or cured coatings.

[0013] The intelligent production system for polyester resin used in powder coatings also includes: The customer feedback system is used to receive customer feedback on the application of polyester resin products and to transmit the feedback data unidirectionally to the workstation and server.

[0014] The present invention also provides an intelligent production method for polyester resin for powder coating based on the system, comprising the following steps: S1. The workstation retrieves the product formula and process parameters according to the production plan and generates production instructions; S2. According to the production instruction, the raw materials are metered and fed into the batching system. S3. Select the target production line through the production line allocation system; S4. During the synthesis process, production process parameters are collected through an online monitoring system; S5. Detect and analyze the parameters of key control nodes in the synthesis process through the process control system; S6. After synthesis is completed, the finished polyester resin product is tested using a laboratory analysis system; S7. Feed back the data obtained in steps S4 to S6 to the workstation for the generation of subsequent production instructions.

[0015] In step S7, the workstation adjusts the product formula or process parameters based on historical production data and testing data, and generates updated production instructions. The workstation establishes a predictive model from the target performance to the formulation design and synthesis process based on the input target product performance requirements, and generates at least one feasible polyester resin synthesis path accordingly. The small-batch production data and testing data corresponding to the synthesis path are fed back to the workstation to update the prediction model.

[0016] The intelligent production system for polyester resin used in powder coatings of the present invention, through the coordinated operation of multiple subsystems, can realize the intelligent synthesis and production of polyester resin for powder coatings, reduce the trial and error costs in the research and development process, improve production efficiency and product quality, and at the same time provide support for enterprises to establish a big data platform for products and rapid research and development of new products. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure are briefly described below. The flowcharts used in this application to illustrate the operations performed by the system in the embodiments of this application should be understood as follows: the subsystems above or below do not necessarily need to be executed precisely in sequence. Instead, one or more subsystems can be selectively executed simultaneously as needed; it is not necessary to execute the operations of all subsystems every time. Furthermore, other production-related systems can be added to these processes, or processes with little correlation can be removed from them.

[0018] Figure 1 A schematic diagram of the composition of an intelligent production system for polyester resin for powder coating provided in an embodiment of the present invention; Figure 2 The process control system of the intelligent synthesis and production system and method for polyester resin for powder coating provided in the embodiments of the present invention uses a Bayesian analysis model to draw a process parameter correlation diagram. Figure 3 A flowchart illustrating the process of deep learning and self-optimization for AI algorithm models; The diagram is labeled as follows: 100, metering and batching system; 200, production line distribution system; 300, online monitoring system; 400, process control system; 500, laboratory analysis system; 600, customer feedback system; 700, workstation; 800, server. Detailed Implementation

[0019] The above description is merely an overview of the technical solution of the present invention. In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and listed embodiments. It should be understood that the following embodiments are only used for further explanation of the present invention, and not all embodiments. Any non-essential improvements and adjustments made by those skilled in the art based on the principles expounded in the present invention are within the protection scope of the present invention.

[0020] This invention relates to the field of chemical synthesis and production technology, specifically to an intelligent production system and method for polyester resin used in powder coatings. The intelligent synthesis and production system includes: a metering and batching system 100, a production line distribution system 200, an online monitoring system 300, a process control system 400, a laboratory analysis system 500, and a customer feedback system 600, etc. First, the metering and batching system 100 feeds materials according to the specified formula. Production lines are allocated based on the production plan and the characteristics of each line. Throughout the entire polyester resin synthesis process, including esterification, polycondensation, cooling and crushing, and packaging, relevant process parameters such as pressure, temperature, acid value, viscosity, time, and vacuum degree are monitored in real time. After the finished product is made into powder coating, the laboratory tests its gelation time, coating gloss, impact, and other properties. Finally, customer feedback (if any) is collected after using the corresponding batch. This intelligent synthesis production system initially accumulates a large amount of data through data collection and statistical analysis, forming a database. In the middle stage, artificial intelligence is introduced to analyze big data, build algorithm models, and optimize the formula and process parameters based on the actual application performance of the coating. Simultaneously, differences between different production lines and materials from different suppliers are compared. The ultimate goal is to design the optimal formula ratio, specify supplier materials and production lines, and optimize synthesis process parameters according to customer requirements, ensuring that the final coating meets customer requirements. This achieves multiple benefits, including precise control of polyester resin synthesis production, reduced trial-and-error costs in the R&D process, improved production efficiency and product quality, and reduced customer complaints.

[0021] Specifically, in the first aspect, such as Figure 1 As shown, this embodiment of the invention provides an intelligent production system for polyester resin used in powder coatings, comprising: Workstation 700 and server 800 are used to store product formulas, process parameters and historical production data, and generate production instructions according to the production plan; The metering and batching system 100 is used to meter and feed various raw materials according to production instructions; The production line allocation system 200 is used to acquire the operating status of multiple production lines and select the target production line according to the production instructions. The online monitoring system 300 is used to collect production process parameters in the target production line in real time. The process control system 400 is used to detect and analyze key control node parameters in the polyester resin synthesis process and establish parameter correlations; and Laboratory analysis system 500 is used for performance testing of finished polyester resin products; Workstation 700 and server 800 are respectively connected to metering and batching system 100, production line distribution system 200, online monitoring system 300, process control system 400 and laboratory analysis system 500, and are used to receive data from each system and send control commands to each system.

[0022] In this embodiment of the invention, the focus is on introducing AI, intelligent algorithm models, and chemical structure simulation technologies into the synthesis route of polyester resin for powder coatings in the chemical synthesis subfield. A PDCA cycle is formed from formulation design and synthesis route to application testing and customer feedback. First, during data collection, the AI ​​continuously learns and analyzes, establishing algorithmic models for existing formulation designs and process routes, and continuously optimizing and adjusting these models to ultimately form an optimal synthesis route, which is then shared with other subsystems, making the synthesis of polyester resin more modular, digital, and intelligent. Second, based on customer requirements, new products are redesigned. Using customer requirements as input, the AI ​​system redesigns a new product from the existing database, quickly screening out several formulas and synthesis routes with the best potential from millions of permutations and combinations, and then conducting targeted experimental verification, thereby greatly shortening the R&D cycle, reducing R&D costs, and improving efficiency.

[0023] In this embodiment of the invention, the workstation 700 and server 800 are configured with a data analysis module, used to update the product formula or process parameters and generate new production instructions based on data fed back from the online monitoring system 300, the process control system 400, and the laboratory analysis system 500. For example... Figure 1 As shown, workstation 700, server 800, and other systems are connected via data cables, enabling real-time data transmission between systems and data storage and backup on workstation 700.

[0024] In this embodiment of the invention, the metering and batching system 100 includes a solid material metering unit and a liquid material metering unit. The solid material metering unit is used to feed solid raw materials to the silo of the corresponding production line, and the liquid material metering unit is used to transport liquid raw materials to the reaction vessel via a flow meter and a conveying device, which can be a feed pump.

[0025] The metering and batching system 100 is installed at the top of each production line. Initially, the system meters and premixes various raw materials according to pre-set formulas and proportions. The metering and batching system 100 has two data links: one end is connected to the workstation 700 and the server 800, and the other end is connected to the production line distribution system 200.

[0026] In this embodiment of the invention, the production line allocation system 200 is used to obtain the operating status parameters of each production line, lock the target production line according to the production instructions, and send the operating parameters of the target production line to the online monitoring system 300.

[0027] The production line allocation system 200 can store existing, mature formulas and can also receive optimized formulas from the workstation 700's algorithm model after analysis. Located behind the metering system, the production line allocation system 200 monitors the real-time status of each production line while receiving instructions from the workstation 700. Based on these instructions, it allocates production lines and simultaneously transmits online monitoring parameters for each production line to the online monitoring system 300. The production line allocation system 200 has three data interfaces: interface 1, interface 2, and interface 3. Interface 1 connects to the workstation 700 and server 800; interface 2 connects to the metering and batching system 100; and interface 3 connects to the online monitoring system 300.

[0028] In this embodiment of the invention, the online monitoring system 300 is used to collect one or more of the following production process parameters: Reactor temperature, reactor pressure, stirring speed, motor current, reaction time, and material viscosity.

[0029] The online monitoring system 300 is located in the DCS automatic control room, monitoring the real-time stirring speed, reactor temperature and pressure, viscosity, etc., of each production line, and plotting the correlation curves between these parameters and time. Traditional polyester resin synthesis production rarely focuses on these parameters; therefore, the initial stage mainly involves data collection and analysis, followed by optimal parameter settings based on AI intelligent model analysis and algorithms. The online monitoring system 300 has three data links: interface 1, interface 2, and interface 3. Interface 1 connects to workstation 700 and server 800; interface 2 connects to production line distribution system 200; and interface 3 connects to process control system 400.

[0030] In this embodiment of the invention, the process control system 400 is used to detect sampling nodes, vacuum time, intermediate acid value and viscosity during the polyester resin synthesis process, and to establish the correlation between key control node parameters based on the detection data.

[0031] The process control system 400 is located in the workshop process control room. It primarily uses chemical analysis to analyze parameters at key process control nodes of the polyester resin. Traditionally, this method relies on operator experience to determine the next steps. In this embodiment, the process control system 400 employs a Bayesian analysis model to identify the correlations between parameters at various key control nodes and plot correlation curves. The process control system 400 has two data links: a first interface and a second interface. The first interface connects to the workstation 700 and the server 800, while the second interface connects to the online monitoring system 300.

[0032] In this embodiment of the invention, the laboratory analysis system 500 is used to mix the finished polyester resin with curing agent, pigment and filler in a preset ratio, form a powder coating after melt extrusion, and perform performance testing on the obtained powder coating or the cured coating.

[0033] The laboratory analysis system 500 is installed in the pre-shipment testing stage of polyester resin. The laboratory mixes the finished polyester resin with curing agents, pigments, and fillers according to a preset formula, melts and extrudes the mixture to produce powder coatings, and evaluates the usability of the polyester resin by testing the powder coating and the powder coating after spraying and curing. The customer feedback system 600 has one data link interface, which connects to workstation 700 and server 800.

[0034] like Figure 1 As shown, the intelligent production system for polyester resin used in powder coatings according to an embodiment of the present invention further includes: The customer feedback system 600 is used to receive customer feedback on the application of polyester resin products and to transmit the feedback data unidirectionally to workstation 700 and server 800.

[0035] The customer feedback system 600 is located at the end of the entire system and does not exchange data with other subsystems; it only transmits information unidirectionally to the workstation 700.

[0036] Secondly, embodiments of the present invention also provide an intelligent production method for polyester resin for powder coatings based on the above-mentioned intelligent production system, comprising the following steps: S1. Workstation 700 retrieves product formulas and process parameters based on the production plan and generates production instructions; S2. According to the production order, the raw materials are measured and fed into the metering and batching system 100; S3. Select the target production line through the production line allocation system 200; S4. During the synthesis process, production process parameters are collected through the online monitoring system 300; S5. The process control system 400 is used to detect and analyze the parameters of key control nodes in the synthesis process; S6. After synthesis is completed, the finished polyester resin product is tested using a laboratory analysis system 500. S7. Feed back the data obtained in steps S4 to S6 to workstation 700 for the generation of subsequent production instructions.

[0037] In step S1 above, the existing formula is retrieved from workstation 700 and server 800, and instructions are sent to metering and dispensing system 100.

[0038] In step S2 above, the metering and batching system 100 selects each material from the designated supplier according to the instructions issued by the workstation 700 and premixes them in proportion.

[0039] In step S3 above, workstation 700 sends a specific target production line to production line allocation system 200 based on multiple factors such as the status and compatibility of each production line. Upon receiving the instruction, production line allocation system 200 activates production line standby, and metering and batching system 100 allocates premixed materials to each production line.

[0040] In step S4 above, during the synthesis process, after the metering and dispensing of materials and the allocation to the production line are completed, the online monitoring system 300 starts online monitoring of parameters such as time, temperature, pressure, and online viscosity. Simultaneously, the monitoring data is transmitted back to the workstation 700 for storage and backup in real time.

[0041] In step S5 above, the process control system 400 controls the process parameters such as vacuum degree, vacuum time, acid value, viscosity, secondary feeding temperature, and holding time at key nodes of the polyester resin synthesis process, and at the same time, transmits all relevant control parameters back to the workstation 700 for storage and backup.

[0042] In step S6 above, after the polyester resin product is produced, it is made into powder coating and coating layer. The comprehensive properties of the powder coating, such as gelation time, coating gloss, film thickness, impact, bending, boiling, acid resistance, and alkali resistance, are tested. At the same time, all test data are sent back to workstation 700 for storage backup.

[0043] In this embodiment of the invention, the main control unit of workstation 700 has a built-in artificial intelligence processing chip. In step S7 above, workstation 700 adjusts the product formula or process parameters based on historical production datasets and historical inspection datasets, and generates updated production instructions; Workstation 700 establishes a predictive model from target performance to formulation design and synthesis process based on the input target product performance index requirements, and generates at least one feasible polyester resin synthesis process path based on the predictive model. The small-batch production data and testing data corresponding to the synthesis process path are fed back to workstation 700 to update the prediction model.

[0044] like Figure 3 As shown, the prediction model was built and optimized by the artificial intelligence algorithm analysis module built into the workstation 700 through the following steps: (1) Data acquisition; (2) Model building; (3) Parameter optimization; (4) Experimental verification; (5) Deep learning; (6) Model iteration.

[0045] Workstation 700 uses material molecular simulation analysis technology to construct an intelligent synthesis production model based on data transmitted from various subsystems (including metering and batching system 100, production line distribution system 200, online monitoring system 300, process control system 400, laboratory analysis system 500, and customer feedback system 600). Subsequently, based on the accumulated new data, the artificial intelligence algorithm analysis module iteratively optimizes the intelligent synthesis production model through self-learning, and finally outputs the optimal polyester resin synthesis process path that meets the performance requirements of the target product.

[0046] Finally, the customer feedback system 600 collects feedback information on the corresponding batches and models of materials during actual use and sends the information back to the workstation 700 for storage and backup. Example 1

[0047] This embodiment provides an intelligent production method for polyester resin used in powder coatings, comprising the following steps: Select a polyester resin with a mature formula and matching synthesis process, and designate it as polyester resin A; store the formula data of polyester resin A in the storage unit of workstation 700 in advance.

[0048] Workstation 700 checks the existing inventory of materials based on the bill of materials and data in the formula. If all materials are in stock, it proceeds to the next step; if materials are insufficient, it stops the operation and issues an alert.

[0049] When there are sufficient materials, workstation 700 sends instructions to metering and batching system 100, production line distribution system 200, online monitoring system 300 and process control system 400 respectively based on the real-time data fed back by production line distribution system 200 and online monitoring system 300.

[0050] The metering and batching system 100 selects materials from designated suppliers according to instructions, and meters and batches them according to the formula. Solid materials go directly into the designated production line's silo, while liquid materials are pumped into the designated production line's reactor using flow meters and feed pumps. The feeding sequence follows the workflow.

[0051] The production line allocation system 200 locks onto the designated production line and determines the target production line based on the instructions issued by the workstation 700.

[0052] Based on instructions from workstation 700, the online monitoring system 300 initiates online monitoring of parameters such as reactor temperature, pressure, stirring speed, motor current, and time from the moment materials begin entering the reactor. This monitoring data is then transmitted back to the data center of workstation 700 in real time.

[0053] The process control system 400, based on the process control operation instructions for polyester resin A, is operated by skilled personnel to control the process, including sampling points, vacuum time, intermediate control acid value, viscosity, etc., and transmits these monitoring data back to the data center of workstation 700 in real time.

[0054] According to the testing instructions for polyester resin A, the laboratory analysis system 500 allows testing personnel to mix the weighed polyester resin, curing agent, pigment, and filler according to the recommended powder coating formula in the instruction manual for polyester resin A. The mixture is then melt-extruded, cooled, crushed, and sieved. The gelation time and mechanical properties (impact, bending) of the powder coating are tested, and the test data is transmitted back to the data center of workstation 700 in real time.

[0055] Example 1 illustrates the data collection process of this invention, which provides data support for the subsequent establishment of AI algorithm models. Example 2

[0056] In this embodiment, workstation 700, based on the production-related data collected and accumulated in the early stage, optimizes the existing formula and synthesis process, and initiates the production of polyester resin B, planning to start the production of polyester resin B.

[0057] The AI ​​algorithm prediction model built into the workstation 700 outputs an optimized formula, denoted as Formula 2, through virtual screening and calculation using big data. Formula 2 includes the optimal raw material selection scheme and raw material ratio scheme. At the same time, the AI ​​algorithm prediction model combines the inherent characteristics of each production line and the real-time operating status to output the recommended result of the optimal production line. The human-machine interaction unit of the workstation 700 generates and displays a confirmation interaction window for the operator to confirm whether to use the optimal scheme output by the AI ​​algorithm prediction model. After the operator completes the confirmation operation, the workstation 700 issues corresponding production control instructions to each subsystem.

[0058] The metering and batching system, production line distribution system 200, and online monitoring system 300 will execute according to the new instructions and parameters. The AI ​​algorithm prediction model can process real-time data from sensors on the production line, dynamically adjust process parameters, ensure that the production process is always in the optimal state, and improve product quality consistency and energy utilization efficiency.

[0059] The Bayesian analysis model configured in the process control system 400 generates a process parameter correlation map (as shown in Figure 2) and integrates the corresponding operation instructions of the process control into the automatic control logic of the DCS.

[0060] According to the testing instructions for polyester resin B, the laboratory analysis system 500 allows testing personnel to mix the weighed polyester resin, curing agent, pigment, and filler according to the recommended powder coating formula in the instruction manual for polyester resin B. The mixture is then melt-extruded, cooled, crushed, and sieved. The gelation time and mechanical properties (impact, bending) of the powder coating are tested, and the test data is transmitted back to the data center of workstation 700 in real time.

[0061] Example 2 corresponds to the third stage of the present invention—the parameter optimization stage. This stage is based on the production data collection results of the first stage and the AI ​​algorithm prediction model construction results of the second stage. The following optimization operations are performed through the AI ​​algorithm prediction model: (1) Raw material screening: Screening of raw material components used in the synthesis of polyester resin: One or more mixtures of candidate polyols were selected as polyol raw materials. The candidate polyol group included neopentyl glycol, 1,4-cyclohexanediol, 2-butyl-2-ethyl-1,3-propanediol, 2,2,4-trimethyl-1,3-pentanediol, 2-methyl-1,3-propanediol, 1,6-hexanediol, trimethylolethane, and trimethylolpropane. One or more mixtures of candidate polybasic acids are selected as polybasic acid raw materials. The candidate polybasic acid group includes terephthalic acid, isophthalic acid, 1,4-cyclohexanedicarboxylic acid, trimellitic anhydride, pyromellitic acid, and adipic acid. (2) Supplier selection: For the same category of raw materials, select the supplier with the best fit from the preset supplier set (including supplier A, supplier B, supplier C, and supplier D); (3) Optimization of reaction conditions: This includes screening of reaction equipment and optimization of process parameters. Screening of reaction equipment involves selecting suitable synthesis reaction equipment from existing reactors (production lines). Optimization of process parameters involves iteratively optimizing parameters such as the order of material feeding, reaction temperature inside the reactor, reaction pressure, stirring speed, and vacuum treatment time.

[0062] Through the above optimization process, the AI ​​algorithm prediction model outputs a set of Pareto optimal production solutions for relevant technical personnel to choose from.

[0063] After the technicians selected a solution, they proceeded to the fourth stage—testing and verification.

[0064] AI algorithm prediction model through Figure 3The process shown continuously involves deep learning and self-optimization. Example 3

[0065] In this embodiment, information from customer feedback regarding the insufficient compatibility of existing polyester resin products is obtained, clarifying the need to develop a new polyester resin C to meet the target product performance requirements proposed by the customer.

[0066] Input the target product performance requirements into the AI ​​algorithm prediction model built into the workstation 700, and start the reverse synthesis analysis function of the model; the AI ​​algorithm prediction model completes the target decomposition based on the target product performance requirements, generates at least one feasible polyester resin synthesis path, and constructs a correlation prediction model between formulation design, synthesis process route and target product characteristics.

[0067] The AI ​​algorithm prediction model integrates digital twin technology and recurrent neural network technology to simulate and predict the performance of polyester resins after powder coating for each synthesis path, as well as the key process parameters in the synthesis process. It outputs a reference list containing the above simulation prediction results for researchers to consult.

[0068] Researchers conducted small-batch production verification experiments based on the candidate synthesis path schemes in the reference list, and transmitted the verification data generated during the experiment back to the big data storage unit of the workstation 700.

[0069] The AI ​​algorithm prediction model calls back the verification data to iteratively optimize its own model parameters; as the amount of accumulated data increases and the data accuracy improves, the practical application adaptability of the optimized intelligent synthetic production model is improved in tandem.

[0070] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution; or the direct application of the inventive concept and technical solution to other situations without modification, are all within the protection scope of the present invention.

Claims

1. An intelligent production system for polyester resin used in powder coatings, characterized in that, include: Workstations and servers are used to store product formulas, process parameters, and historical production data, and to generate production instructions based on the production plan; A metering and batching system is used to meter and feed various raw materials according to the production instructions. The production line allocation system is used to obtain the operating status of multiple production lines and select the target production line according to the production instructions. An online monitoring system is used to collect production process parameters in the target production line in real time; The process control system is used to detect and analyze key control node parameters in the polyester resin synthesis process and establish parameter correlations. as well as Laboratory analysis system for performance testing of finished polyester resin products; The workstation and server are respectively connected to the metering and batching system, the production line distribution system, the online monitoring system, the process control system, and the laboratory analysis system, and are used to receive data from each system and send control commands to each system.

2. The intelligent production system for polyester resin for powder coatings according to claim 1, characterized in that, The workstation and server are equipped with a data analysis module, which is used to update the product formula or process parameters and generate new production instructions based on the data fed back from the online monitoring system, process control system and laboratory analysis system.

3. The intelligent production system for polyester resin for powder coatings according to claim 1, characterized in that, The metering and dispensing system includes a solid material metering unit and a liquid material metering unit. The solid material metering unit is used to deliver solid raw materials to the corresponding production line's silo. The liquid material metering unit is used to transport liquid raw materials to the reaction vessel via a flow meter and a conveying device.

4. The intelligent production system for polyester resin for powder coatings according to any one of claims 1 to 3, characterized in that, The production line allocation system is used to acquire the operating status parameters of each production line, lock the target production line according to the production instructions, and send the operating parameters of the target production line to the online monitoring system.

5. The intelligent production system for polyester resin for powder coatings according to any one of claims 1 to 3, characterized in that, The online monitoring system is used to collect one or more of the following production process parameters: Reactor temperature, reactor pressure, stirring speed, motor current, reaction time, and material viscosity.

6. The intelligent production system for polyester resin for powder coatings according to any one of claims 1 to 3, characterized in that, The process control system is used to detect sampling points, vacuum time, intermediate acid value and viscosity during the polyester resin synthesis process, and to establish the correlation between key control node parameters based on the detection data.

7. The intelligent production system for polyester resin for powder coatings according to any one of claims 1 to 3, characterized in that, The laboratory analysis system is used to mix polyester resin products with curing agents, pigments and fillers in a preset ratio, form powder coatings after melt extrusion, and perform performance testing on the resulting powder coatings or cured coatings.

8. The intelligent production system for polyester resin for powder coatings according to any one of claims 1 to 7, characterized in that, Also includes: The customer feedback system is used to receive customer feedback on the application of polyester resin products and to transmit the feedback data unidirectionally to the workstation and server.

9. An intelligent production method for polyester resin used in powder coatings based on the system described in any one of claims 1 to 8, characterized in that, Includes the following steps: S1. The workstation retrieves the product formula and process parameters according to the production plan and generates production instructions; S2. According to the production instruction, the raw materials are metered and fed into the batching system. S3. Select the target production line through the production line allocation system; S4. During the synthesis process, production process parameters are collected through an online monitoring system; S5. Detect and analyze the parameters of key control nodes in the synthesis process through the process control system; S6. After synthesis is completed, the finished polyester resin product is tested using a laboratory analysis system; S7. Feed back the data obtained in steps S4 to S6 to the workstation for the generation of subsequent production instructions.

10. The intelligent production method of polyester resin for powder coating according to claim 9, characterized in that, In step S7, the workstation adjusts the product formula or process parameters based on historical production data and testing data, and generates updated production instructions. The workstation establishes a predictive model from the target performance to the formulation design and synthesis process based on the input target product performance requirements, and generates at least one feasible polyester resin synthesis path accordingly. The small-batch production data and testing data corresponding to the synthesis path are fed back to the workstation to update the prediction model.

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