Methods, apparatus and equipment for monitoring catalyst extrusion molding
By monitoring various process parameters during catalyst extrusion molding and using a catalyst parameter control model for automated adjustment, the problems of parameter adjustment lag and reliance on human experience in existing technologies have been solved, and stable production of catalyst preforms has been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUXI LONGYUAN ENVIRONMENTAL TECH CO LTD
- Filing Date
- 2026-01-06
- Publication Date
- 2026-05-26
AI Technical Summary
The existing catalyst extrusion molding process relies on manual experience, which leads to lag in parameter adjustment and difficulty in quantification, making it impossible to achieve timely and accurate quality control.
By monitoring various process parameters during the catalyst extrusion molding process, such as humidity, viscosity, pressure, and preform images, the catalyst parameter control model is used for automated adjustment, achieving fully automated monitoring and intelligent adjustment.
It achieves automated monitoring and intelligent adjustment of the catalyst extrusion molding process, timely detection and quantitative adjustment of quality problems, stable production of qualified catalyst preforms, and avoids the shortcomings of human experience judgment.
Smart Images
Figure CN122077976A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of catalysts, and more particularly to the field of catalyst extrusion molding monitoring technology. Background Technology
[0002] Currently, the extrusion molding process of SCR catalysts in the industry generally relies on a manual, empirical phase model. A typical implementation method is as follows: 1. Parameter preset and manual intervention: Before production, experienced process engineers preset basic parameters such as the moisture content of the mud, the proportion of binder, and the screw speed of the extruder according to the batch of raw materials and environmental conditions.
[0003] 2. Intermittent quality inspection: During the extrusion process, operators periodically cut a sample of the preform from the production line and visually inspect its surface for cracks or deformation, or judge its hardness by touch. The judgment of the clay's condition largely relies on the worker's subjective experience in "kneading" the clay.
[0004] 3. Delayed Adjustment: When quality problems are found in the billet (such as rough surface, distorted pores, insufficient strength), the operator then adjusts the amount of water added or the screw speed based on experience. This adjustment is often delayed, and the adjustment amount is difficult to quantify precisely.
[0005] Therefore, how to automatically monitor the catalyst extrusion molding process in a timely manner so as to make timely and rapid parameter adjustments has become an urgent problem to be solved. Summary of the Invention
[0006] This disclosure provides a method, apparatus, equipment, and storage medium for monitoring catalyst extrusion molding.
[0007] According to a first aspect of this disclosure, a method for monitoring catalyst extrusion molding is provided. The method includes: Monitor various process parameters during the catalyst extrusion molding process; Call the catalyst parameter control model; The various process parameters are input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions; The various process parameters are adjusted according to the catalyst parameter adjustment instructions.
[0008] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the monitoring of multiple process parameters during the catalyst extrusion molding process includes: Detect the moisture content of the catalyst slurry entering the extruder; Monitor the viscosity of the catalyst slurry entering the extruder; Monitor the internal pressure and extrusion pressure of the extruder; Images of the catalyst preform in the extruder die are monitored.
[0009] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the monitoring of multiple process parameters during the catalyst extrusion molding process further includes: After monitoring the image of the catalyst preform, the image of the catalyst preform is identified to obtain the dimensional parameters of the preform; Invoke the preset defect detection algorithm; The preset defect detection algorithm is used to identify defects in the image of the catalyst preform and determine the defect parameters of the catalyst preform.
[0010] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes: Obtain the preset parameters corresponding to the various process parameters respectively; The various process parameters are compared with the corresponding preset parameters to obtain the comparison results; Based on the comparison results, it is determined whether the catalyst extrusion molding has excessive deviation.
[0011] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the various process parameters are input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions, including: The various process parameters and the comparison results are input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions within a preset time period in the future.
[0012] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein adjusting the various process parameters according to the catalyst parameter adjustment instruction includes: If the catalyst parameter adjustment command is a humidity adjustment command, then the flow rate of the additive entering the mixer is adjusted. The mixer is connected to the extruder. The catalyst powder enters the mixer and becomes catalyst sludge before entering the extruder. If the catalyst parameter adjustment command is a viscosity adjustment command, then the rotational speed of the mixer screw is adjusted.
[0013] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes: Obtain the adjusted process parameters; The adjusted process parameters are compared with the corresponding preset parameters to determine whether the process parameters need to be readjusted.
[0014] According to a second aspect of this disclosure, a catalyst extrusion molding monitoring device is provided. The device includes: The monitoring module is used to monitor various process parameters during the catalyst extrusion molding process; The calling module is used to invoke the catalyst parameter control model; The acquisition module is used to input the various process parameters into the catalyst parameter control model to obtain catalyst parameter adjustment instructions; The adjustment module is used to adjust the various process parameters according to the catalyst parameter adjustment instructions.
[0015] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.
[0016] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to a first aspect of this disclosure.
[0017] In this disclosure, after monitoring multiple process parameters during catalyst extrusion molding, these parameters can be input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions. Then, based on these instructions, the multiple process parameters are adjusted. This allows for automatic monitoring of multiple process parameters during catalyst extrusion molding, i.e., automatic judgment of catalyst quality. This facilitates timely adjustments when quality problems occur in the catalyst preform, and allows for quantitative adjustments, avoiding reliance on human experience for judgment and adjustment. This achieves fully automated monitoring and intelligent adjustment during catalyst extrusion molding, thereby ensuring the stable production of qualified catalyst preforms.
[0018] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description
[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A flowchart of a catalyst extrusion molding monitoring method according to an embodiment of the present disclosure is shown; Figure 2A flowchart of another catalyst extrusion molding monitoring method according to an embodiment of the present disclosure is shown; Figure 3 A flowchart of yet another catalyst extrusion molding monitoring method according to an embodiment of the present disclosure is shown; Figure 4 A block diagram of a catalyst extrusion molding monitoring device according to an embodiment of the present disclosure is shown; Figure 5 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0021] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0022] Figure 1 A flowchart of a catalyst extrusion molding monitoring method 100 according to an embodiment of the present disclosure is shown. Method 100 may include: Step 110: Monitor various process parameters during the catalyst extrusion molding process; Various process parameters include, but are not limited to, the moisture content of the catalyst slurry entering the extruder, the viscosity of the catalyst slurry entering the extruder, the internal pressure value and extrusion pressure value of the extruder, and the image of the catalyst preform in the extruder die.
[0023] The catalyst can be an SCR catalyst, which is a catalyst that selectively reduces nitrogen oxides (NOx) in flue gas to harmless nitrogen gas (N2) under aerobic conditions by using ammonia (NH3) and other reducing agents.
[0024] Step 120: Call the catalyst parameter control model; Catalyst parameter control models can include predictive control (MPC) algorithms, etc.
[0025] Step 130: Input the various process parameters into the catalyst parameter control model to obtain catalyst parameter adjustment instructions; the catalyst parameter adjustment instructions may be catalyst moisture adjustment instructions, catalyst additive adjustment instructions, catalyst viscosity adjustment instructions, catalyst temperature adjustment instructions, etc.
[0026] Step 140: Adjust the various process parameters according to the catalyst parameter adjustment instruction.
[0027] After monitoring multiple process parameters during catalyst extrusion molding, these parameters can be input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions. Then, based on these instructions, the multiple process parameters are adjusted. This allows for automatic monitoring of multiple process parameters during catalyst extrusion molding, i.e., automatic judgment of catalyst quality. This facilitates timely adjustments when quality problems occur in the catalyst preform, and allows for quantitative adjustments, avoiding reliance on human experience for judgment and adjustment. This achieves fully automated monitoring and intelligent adjustment during catalyst extrusion molding, thereby ensuring the stable production of qualified catalyst preforms.
[0028] In some embodiments, monitoring multiple process parameters during the catalyst extrusion molding process includes: Detect the moisture content of the catalyst slurry entering the extruder; Microwave or infrared humidity sensors can be installed inside the mixer to monitor the moisture content of the mud entering the extruder in real time, serving as an auxiliary reference parameter.
[0029] Monitor the viscosity of the catalyst slurry entering the extruder; Online rheometer: Installed at the feed trough of the extruder. An online rotary rheometer or a near-infrared (NIR) viscometer can be used. The former directly calculates the apparent viscosity and shear stress of the clay by measuring the relationship between the torque and rotational speed of the clay on a small rotor; the latter indirectly calculates the viscosity value by analyzing the absorption and reflection spectrum of near-infrared light by the clay, offering advantages such as non-contact operation and resistance to dirt.
[0030] Online rheometers can be replaced by an indirect measurement method using torque and speed. This involves monitoring the drive torque of the extruder's main motor and the screw speed, and then indirectly calculating the viscosity of the clay based on a relational model. A common feature is that both require obtaining parameters reflecting the rheological properties of the clay.
[0031] Monitor the internal pressure and extrusion pressure of the extruder; Pressure sensor array: Multiple high-temperature resistant, high-precision pressure sensors are deployed at key flow path locations in the extruder barrel and die inlet. These sensors form a monitoring network that measures and transmits extrusion pressure values at different locations (pressure at certain locations in the extruder barrel, pressure at the extruder outlet, and pressure at the die outlet) in real time, thereby monitoring the uniformity and stability of pressure distribution.
[0032] Images of the catalyst preform in the extruder die are monitored.
[0033] The catalyst preform can be a honeycomb support: a ceramic or metal support with a cylindrical or cubic shape, many parallel and through channels inside, and a honeycomb-shaped cross-section, used to load catalytically active components.
[0034] Machine vision inspection system: A vision system consisting of a high-speed industrial camera, a high-brightness LED ring light source, and an image processing computer is installed behind the exit of the extrusion die. The camera continuously captures clear images of the cross-section of the honeycomb preform extruded at a uniform speed. The image processing algorithm analyzes the images in real time, accurately calculates the pore size, wall thickness, and other dimensions of the catalyst preform, and automatically identifies defects such as surface cracks, pore blockage, and distortion using defect detection algorithms (such as edge detection and template matching).
[0035] Machine vision systems can be replaced by laser scanners. By scanning the surface of the billet with a laser beam and using triangulation, high-precision three-dimensional contour dimensions are obtained, allowing for the detection of diameter, defects, etc. Commonality: Both require non-contact, online detection of billet geometry and surface defects.
[0036] Monitoring various process parameters may also include monitoring the internal temperature of the extruder, the flow rate of additives in the mixer, and the screw speed of the mixer.
[0037] In some embodiments, monitoring multiple process parameters during the catalyst extrusion molding process further includes: After monitoring the image of the catalyst preform, the image of the catalyst preform is identified to obtain the dimensional parameters of the preform; Dimensional parameters can include hole diameter, wall thickness, and other dimensions.
[0038] Invoke the preset defect detection algorithm; The preset defect detection algorithm can be a predictive control (MPC) algorithm, etc.
[0039] The preset defect detection algorithm is used to identify defects in the image of the catalyst preform and determine the defect parameters of the catalyst preform.
[0040] This means using defect detection algorithms (such as edge detection and template matching) to automatically identify defect parameters such as surface cracks, blocked holes, and distortion.
[0041] In some embodiments, the method further includes: Obtain the preset parameters corresponding to the various process parameters respectively; The various process parameters are compared with the corresponding preset parameters to obtain the comparison results; the preset parameters may be: viscosity range 1200±500 cP, mold inlet pressure stable at 10±2 MPa, and blank size tolerance ±0.1 mm.
[0042] Based on the comparison results, it is determined whether the catalyst extrusion molding has excessive deviation.
[0043] By comparing various process parameters with the corresponding preset parameters, comparison results can be obtained. Then, based on the comparison results, it can be determined whether the catalyst extrusion molding process has excessive deviation. In this way, it is possible to accurately determine whether the catalyst extrusion molding process has excessive dimensional deviation, excessive shape deviation, etc.
[0044] In some embodiments, the various process parameters are input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions, including: The various process parameters and the comparison results are input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions within a preset time period in the future.
[0045] By inputting various process parameters and the comparison results into the catalyst parameter control model, catalyst parameter adjustment instructions within a preset time period can be obtained, thereby facilitating the adjustment of various process parameters using the catalyst parameter adjustment instructions.
[0046] In some embodiments, adjusting the multiple process parameters according to the catalyst parameter adjustment instruction includes: If the catalyst parameter adjustment command is a humidity adjustment command, then the flow rate of the additive entering the mixer is adjusted. The mixer is connected to the extruder. The catalyst powder enters the mixer and becomes catalyst sludge before entering the extruder. If the catalyst parameter adjustment command is a viscosity adjustment command, then the rotational speed of the mixer screw is adjusted.
[0047] The specific adjustment methods are as follows: If the catalyst parameter adjustment command is a humidity control command, a high-precision gear pump or peristaltic pump driven by a servo motor is used to add deionized water or liquid binder to the mixer. It can receive analog signals or communication commands from the controller and dynamically and continuously adjust the flow rate of the additive entering the mixer with milliliter-minute precision. The mixer is connected in front of the extruder and is used to mix the powdered catalyst with the additive to obtain a catalyst slurry, which then enters the extruder.
[0048] If the catalyst parameter adjustment command is a viscosity adjustment command, then the screw speed of the mixer is precisely adjusted using a frequency converter (connected to the main motor of the mixer and extruder). Changes in speed directly affect the shear force on the material and its residence time in the machine, thereby altering the plasticizing effect and extrusion speed of the slurry.
[0049] Of course, if a deviation occurs (such as an increase in viscosity), the optimal adjustment amount can be calculated immediately, such as increasing the amount of water added, Q.
[0050] In some embodiments, the method further includes: Obtain the adjusted process parameters; The adjusted process parameters are compared with the corresponding preset parameters to determine whether the process parameters need to be readjusted.
[0051] For example, after adjusting various process parameters, the changes in parameters such as viscosity and pressure after adding water can be continuously monitored to verify the control effect and make necessary fine adjustments, forming a dynamic, closed-loop negative feedback control system until the process state returns to and stabilizes within the optimal range.
[0052] like Figure 2 As shown, the catalyst extrusion molding monitoring system is as follows: It mainly consists of three parts: the perception layer, the control layer, and the execution layer. (1) The perception layer is responsible for comprehensively and in real time collecting key parameters that reflect the extrusion molding state.
[0053] • Online rheometer: Installed at the extruder feed trough. An online rotary rheometer or a near-infrared (NIR) viscometer can be used. The former directly calculates the apparent viscosity and shear stress of the clay by measuring the torque and rotational speed relationship generated by the clay on a small rotor; the latter indirectly calculates the viscosity value by analyzing the absorption and reflection spectrum of near-infrared light by the clay, offering advantages such as non-contact operation and resistance to dirt.
[0054] • Pressure sensor array: Multiple high-temperature resistant, high-precision pressure sensors are deployed at key flow path locations in the extruder barrel and die inlet. These sensors form a monitoring network that measures and transmits extrusion pressure values at different locations in real time, thereby monitoring the uniformity and stability of pressure distribution.
[0055] • Machine Vision Inspection System: A vision system consisting of a high-speed industrial camera, a high-brightness LED ring light source, and an image processing computer is installed behind the exit of the extrusion die. The camera continuously captures clear images of the cross-section of the honeycomb preform being extruded at a uniform speed. The image processing algorithm analyzes the images in real time, accurately calculates the dimensions of the preform, such as the aperture and wall thickness, and automatically identifies defects such as surface cracks, blocked pores, and distortion using defect detection algorithms (such as edge detection and template matching).
[0056] • Humidity sensor: A microwave or infrared humidity sensor can be installed inside the mixer to monitor the moisture content of the mud in real time as an auxiliary reference parameter.
[0057] (2) Control layer The control layer is responsible for data processing, status judgment and intelligent decision-making.
[0058] • Hardware platform: This includes a programmable logic controller (PLC) and an edge computing server. The PLC is responsible for direct communication with all sensors and actuators, enabling high-speed and reliable data acquisition and command issuance. The edge computing server carries complex intelligent control algorithms, ensuring real-time performance and reducing the burden on the cloud.
[0059] • Core Algorithm (Intelligent Decision Model): Model Predictive Control (MPC) Algorithm. The core of this algorithm is a process knowledge model (referring to the set of numerical ranges of key process parameters (such as viscosity, pressure, etc.) determined through historical data analysis and process verification to ensure stable production of qualified billets). The algorithm's workflow is as follows: 1. Data fusion: Real-time reception and synchronization of data from multiple sources such as rheometers, pressure sensors, and vision systems.
[0060] 2. Status assessment: Compare the current data with the preset "optimal process window" (viscosity range 1200±500 cP, mold inlet pressure stable at 10±2 MPa, and blank size tolerance ±0.1 mm).
[0061] 3. Prediction and Optimization: The MPC algorithm uses a built-in model to predict the changing trends of key parameters over a future period based on current and historical process conditions. Then, it performs rolling optimization to calculate a set of optimal control commands (such as the amount of water or speed change that needs to be adjusted) that make the future predicted output closest to the ideal target.
[0062] • Human-Machine Interface: Provides a graphical user interface to display all real-time data, alarm information, historical trend curves, and allows engineers to set process target parameters and make necessary manual interventions.
[0063] (3) The execution layer is responsible for accurately executing the instructions issued by the control layer.
[0064] • Precision metering pump: A high-precision gear pump or peristaltic pump driven by a servo motor, used to add deionized water or liquid binder to the mixing system. It can receive analog signals or communication commands from the controller and dynamically and continuously adjust the flow rate of the additive with an accuracy at the milliliter / minute level.
[0065] • Frequency converter: Connected to the main motor of the mixer and extruder, used to precisely adjust the screw speed. Changes in speed directly affect the shear force on the material and its residence time in the machine, thus altering the plasticizing effect of the slurry and the extrusion speed.
[0066] like Figure 3 As shown, the catalyst extrusion molding monitoring method is as follows: 1. Real-time sensing: Each sensor continuously collects data and uploads it to the PLC.
[0067] 2. State Judgment and Decision-Making: The MPC algorithm in the edge server merges data to determine whether the current state deviates from the "optimal window". If a deviation occurs (such as increased viscosity), the algorithm immediately calculates the optimal adjustment amount (e.g., increasing the water replenishment amount Q is required).
[0068] 3. Precise execution: The PLC sends instructions to the precision metering pump, which then precisely executes the water addition operation.
[0069] 4. Closed-loop feedback: The system continuously monitors the changes in viscosity and pressure after water is added, verifies the control effect, and makes necessary fine adjustments to form a dynamic, closed-loop negative feedback control system until the process state returns to and stabilizes within the optimal range.
[0070] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.
[0071] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.
[0072] Figure 4 A block diagram of a catalyst extrusion molding monitoring device 400 according to an embodiment of the present disclosure is shown. Figure 4 As shown, the device 400 includes: Monitoring module 410 is used to monitor various process parameters during the catalyst extrusion molding process; Call module 420 to invoke the catalyst parameter control model; The acquisition module 430 is used to input the various process parameters into the catalyst parameter control model to obtain catalyst parameter adjustment instructions; The adjustment module 440 is used to adjust the various process parameters according to the catalyst parameter adjustment command.
[0073] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0074] According to embodiments of the present disclosure, the present disclosure also provides an electronic device and a non-transitory computer-readable storage medium storing computer instructions.
[0075] Figure 5 A schematic block diagram of an electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.
[0076] Device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.
[0077] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0078] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).
[0079] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0080] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0081] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0082] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0083] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.
[0084] Computing systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.
[0085] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.
[0086] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.
Claims
1. A method for monitoring catalyst extrusion molding, characterized in that, include: Monitor various process parameters during the catalyst extrusion molding process; Call the catalyst parameter control model; The various process parameters are input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions; The various process parameters are adjusted according to the catalyst parameter adjustment instructions.
2. The method as described in claim 1, characterized in that, The monitoring of various process parameters during the catalyst extrusion molding process includes: Detect the moisture content of the catalyst slurry entering the extruder; Monitor the viscosity of the catalyst slurry entering the extruder; Monitor the internal pressure and extrusion pressure of the extruder; Images of the catalyst preform in the extruder die are monitored.
3. The method as described in claim 2, characterized in that, The monitoring of multiple process parameters during the catalyst extrusion molding process also includes: After monitoring the image of the catalyst preform, the image of the catalyst preform is identified to obtain the dimensional parameters of the preform; Invoke the preset defect detection algorithm; The preset defect detection algorithm is used to identify defects in the image of the catalyst preform and determine the defect parameters of the catalyst preform.
4. The method as described in claim 1, characterized in that, The method further includes: Obtain the preset parameters corresponding to the various process parameters respectively; The various process parameters are compared with the corresponding preset parameters to obtain the comparison results; Based on the comparison results, it is determined whether the catalyst extrusion molding has excessive deviation.
5. The method as described in claim 4, characterized in that, The various process parameters are input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions, including: The various process parameters and the comparison results are input into the catalyst parameter control model to obtain catalyst parameter adjustment instructions within a preset time period in the future.
6. The method as described in claim 1, characterized in that, The adjustment of the various process parameters according to the catalyst parameter adjustment command includes: If the catalyst parameter adjustment command is a humidity adjustment command, then the flow rate of the additive entering the mixer is adjusted. The mixer is connected to the extruder. The catalyst powder enters the mixer and becomes catalyst sludge before entering the extruder. If the catalyst parameter adjustment command is a viscosity adjustment command, then the rotational speed of the mixer screw is adjusted.
7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Obtain the adjusted process parameters; The adjusted process parameters are compared with the corresponding preset parameters to determine whether the process parameters need to be readjusted.
8. A catalyst extrusion molding monitoring device, characterized in that, include: The monitoring module is used to monitor various process parameters during the catalyst extrusion molding process; The calling module is used to invoke the catalyst parameter control model; The acquisition module is used to input the various process parameters into the catalyst parameter control model to obtain catalyst parameter adjustment instructions; The adjustment module is used to adjust the various process parameters according to the catalyst parameter adjustment instructions.
9. An electronic device, characterized in that, include: Memory and processor The memory stores a computer program, and when the processor executes the program, it implements the method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor corresponding to the electronic device, the electronic device is able to implement the catalyst extrusion molding monitoring method as described in any one of claims 1-7.