Automatic control system and method for waste cloth dissolving equipment in plasticizer production process

Abstract

The invention discloses an automatic control system and method for waste cloth dissolving equipment in a plasticizer production process, belonging to the field of automatic control technology, and specifically comprises: preparing cloth raw materials, initializing sensor and controller parameters, setting and preheating a screw extruder; dynamically adjusting the parameters of the screw extruder by real-time monitoring the temperature and pressure in a dissolving tank; after the cloth pieces are melted and extruded by the screw extruder, they are mixed with octanol, and a dual-control mixing control algorithm is used to automatically adjust the amount of octanol added, and a cleaning program is automatically started; a machine learning model is used to monitor the state of the mixed liquid in real time, and the dissolution completion condition is automatically determined; at the same time, the operating state of the equipment is monitored in real time, and an alarm is triggered and safety measures are taken in case of abnormality; after the production is completed, the system automatically shuts down the equipment and records the production data, so as to realize efficient, safe and accurate automatic production.

Description

Technical Field

[0001] The invention belongs to the technical field of automatic control, in particular to an automatic control system and method for waste cloth dissolving equipment in a plasticizer production process. Background Art

[0002] Plasticizers are key additives widely used in industries such as plastics, rubber, and coatings. Raw material processing and waste management in their production processes are particularly critical. Traditional methods of dissolving waste cloth mainly rely on manual operation, which not only leads to low production efficiency, but also is accompanied by energy consumption problems. Manual monitoring and adjustment of parameters such as temperature and pressure during the dissolution process are often difficult to achieve precise control, resulting in high energy consumption and increased production costs. Secondly, manual operation also brings safety hazards. Under high temperature and high pressure environments, manual contact with dissolution equipment and chemical solvents can easily lead to safety accidents such as burns and poisoning, especially when dealing with flammable and explosive organic solvents.

[0003] For example, the patent with the authorization announcement number CN205983128U discloses an operating system for controlling the automatic operation of sewage treatment equipment, including: detection module, dissolved oxygen detector, electromagnetic flowmeter, clean water flowmeter, liquid level difference meter, liquid level meter, control module, programmable control unit, data processing unit, conversion unit, comparison unit, automatic operation module, frequency converter, aerator, air valve, timer, screen machine, lifting pump, dosing pump, touch screen module, display unit, storage module, fault detection module, alarm module, mobile terminal and camera. This technical solution can perform various tests on sewage through the detection module, and automatically judge and adjust the operating parameters through the automatic operation module to achieve the purpose of making the sewage stable and unattended; the screen machine can be timed to work through the timer; at the same time, the main steps of sewage treatment such as sewage lifting, fan aeration and sludge return are automatically controlled to ensure that the effluent is stable and meets the standards.

[0004] The above existing technologies all have the following problems: 1) There are limitations in application scenarios; 2) The functional complexity and cost are high; 3) The flexibility and scalability are poor. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes an automatic control system and method for waste cloth dissolution equipment in a plasticizer production process, including preparing cloth raw materials, initializing sensor and controller parameters, setting and preheating a screw extruder; dynamically adjusting the screw extruder parameters by real-time monitoring the temperature and pressure in the dissolution tank; after the cloth pieces are melted and extruded by the screw extruder, they are mixed with octanol, and a dual-control mixing control algorithm is used to automatically adjust the amount of octanol added, and a cleaning program is automatically started; a machine learning model is used to monitor the state of the mixed liquid in real time, and the dissolution completion conditions are automatically determined; at the same time, the equipment operating status is monitored in real time, and an alarm is triggered and safety measures are taken in case of abnormalities; after the production is completed, the system automatically shuts down the equipment and records the production data, so as to realize efficient, safe and accurate automated production.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The automatic control method of waste cloth dissolving equipment in the plasticizer production process includes:

[0008] Step S1: prepare cloth raw materials, initialize sensor and controller parameters, set working parameters of the screw extruder, and preheat the screw extruder to a preset temperature;

[0009] Step S2: monitoring the temperature and pressure in the dissolving tank in real time through a temperature sensor and a pressure sensor, and dynamically adjusting the parameters of the screw extruder according to the real-time monitoring data;

[0010] Step S3: feeding the cloth sheet raw material into a preheated screw extruder, the screw extruder starts working, melting and extruding the cloth sheet, the molten material directly flows into the area where it is mixed with octanol through a preset outlet, and a dual-control mixing control algorithm is used to automatically adjust the amount of octanol added according to the temperature and flow data of the molten material. At the same time, after the melt extrusion, the cleaning program is automatically started;

[0011] Step S4: real-time monitoring of the state of the mixed solution, using a machine learning model to comprehensively analyze the monitoring data, and automatically determining whether the mixed solution has reached a preset dissolution completion condition. If the preset dissolution completion condition has been reached, the system automatically confirms that the dissolution is complete;

[0012] Step S5: Real-time monitoring of the equipment operation status in the production process. If an abnormality is detected, an alarm is immediately triggered and safety measures are automatically taken;

[0013] Step S6: After confirming that the mixed liquid has been dissolved and the equipment is in a safe stop state, the system automatically shuts down the screw extruder, dissolving tank equipment and valve, enters a standby state, and waits for the next operation instruction. At the same time, the production data is recorded.

[0014] Specifically, the specific steps of step S3 include:

[0015] S3.1: The operator feeds the prepared cloth sheet raw material into the feed port of the preheated screw extruder. At the same time, the screw extruder is started, the heating system maintains the set temperature, and the screw starts to rotate; the cloth sheet raw material gradually melts under the extrusion and shearing action of the screw to form a molten material;

[0016] S3.2: The molten material flows out through the preset outlet of the screw extruder and enters the mixing area with octanol to prepare for mixing;

[0017] S3.3: Set the target values ​​of temperature and flow rate, and monitor the temperature and flow rate data of the molten material in real time through the temperature sensor and flow rate sensor to obtain the deviation between the target value and the real-time monitoring data ,in, Indicates the temperature and flow data of the molten material monitored in real time. Indicates the temperature and flow target values, t Indicates the current moment.

[0018] Specifically, the specific steps of step S3 also include:

[0019] S3.4: Use the control strategy to calculate and output the control signal based on the real-time monitoring of the temperature and flow data of the molten material. The calculation formula for the output control signal is: ;

[0020] in, Represents the output control signal, represents the proportional gain, and represents the time constant, represents the sensitivity adjustment parameter to the deviation, represents the nonlinear integral term coefficient, Representation bias The average value within the time window, Representation bias The amount of change between successive time steps, and , represents the nonlinear coefficient, c represents the sensitivity parameter, e represents nonlinear gain;

[0021] S3.5: Perform analog-to-digital conversion and compare the converted analog-to-digital and ,

[0022] like , then continue to adjust the amount of octanol added according to the control strategy, and use the AI ​​algorithm to optimize the control strategy parameters according to the adjusted temperature and flow data;

[0023] like , then the melt extrusion task is completed;

[0024] S3.6: Set the task completion conditions. After the melt extrusion task is completed, the system automatically detects and confirms the task completion and automatically starts the cleaning program.

[0025] Specifically, the specific steps of step S4 include:

[0026] S4.1: Deploy a sensor network to monitor the liquid level, temperature, viscosity and pH value of the mixed liquid in real time, and perform preprocessing to obtain mixed liquid parameter data ,in, represents the Nth mixed liquid parameter data, where N represents the number of mixed liquid parameter data;

[0027] S4.2: Load the pre-built dissolution completion condition judgment model, and use the historical mixed liquid parameter data to train the dissolution completion condition judgment model;

[0028] S4.3: Inputting the mixed solution parameter data collected in real time into the trained dissolution completion condition judgment model to automatically judge whether the mixed solution reaches the preset dissolution completion condition;

[0029] If the dissolution completion condition judgment model determines that the mixed liquid has reached the dissolution completion condition, the system automatically confirms that the dissolution is complete;

[0030] If the dissolution completion condition judgment model determines that the mixed liquid does not meet the dissolution completion condition, the system continues to monitor the state of the mixed liquid and updates the mixed liquid parameter data in real time.

[0031] Specifically, the dissolution completion condition judgment formula in S4.3 is: ;

[0032] in, Indicates that dissolution is complete. Indicates that dissolution is not complete. express t The liquid level error at the time, Indicates the threshold value of liquid level error, express t The temperature of the moment, and Represent the minimum and maximum values ​​of temperature respectively. express t The viscosity of time, and Represent the minimum and maximum values ​​of viscosity, express t pH value at the moment, and Represent the minimum and maximum pH values ​​respectively.

[0033] Specifically, the task completion condition in S3.6 refers to reaching a preset output or time.

[0034] The automatic control system of waste cloth dissolving equipment in the plasticizer production process includes: initialization module, dynamic adjustment module, mixing control module, status monitoring module and early warning module;

[0035] The initialization module is responsible for the preparation work before the system starts;

[0036] The dynamic adjustment module is used to dynamically adjust the parameters of the screw extruder according to the temperature and pressure data in the dissolving tank monitored in real time;

[0037] The mixing control module is used to control the screw extruder to melt and extrude the cloth sheet, mix it with octanol at the same time, automatically adjust the amount of octanol added, and start the cleaning program after the melt extrusion;

[0038] The state monitoring module is used to monitor the state of the mixed liquid in real time, including liquid level, temperature, viscosity and pH value, and use a machine learning model to determine whether the mixed liquid has reached a preset dissolution completion condition;

[0039] The early warning module is used to monitor the operating status of all key equipment in the production process in real time, and immediately trigger an alarm and take safety measures once an abnormality is found.

[0040] Specifically, the mixing control module includes a mixing control unit, which is equipped with a PID and AI dual-control mixing control algorithm. The PID and AI dual-control mixing control algorithm is used to automatically adjust the amount of octanol added according to the temperature and flow data of the molten material.

[0041] Specifically, the state monitoring module includes: a multi-parameter monitoring unit and a judgment unit;

[0042] The multi-parameter monitoring unit is used to monitor the state of the mixed liquid in real time through multiple sensors including a liquid level sensor, a temperature sensor, a viscometer and a pH meter;

[0043] The judgment unit is used to use a machine learning model to comprehensively analyze the monitoring data and automatically determine whether the mixed liquid has reached the dissolution completion condition.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The invention provides an automatic control method for waste cloth dissolving equipment in a plasticizer production process, wherein cloth sheet raw materials are directly fed into a screw extruder, thereby eliminating a foam material production process and achieving higher efficiency; the molten material at the outlet of the screw extruder is directly dissolved in octanol, the temperature is automatically controlled, the dissolution is fast and complete, and the waste block processing process is eliminated.

[0046] The present invention proposes an automatic control method for waste cloth dissolving equipment in a plasticizer production process, which realizes precise control of the production process by real-time monitoring of the temperature and pressure in a dissolving tank, dynamically adjusting the parameters of a screw extruder, and automatically adjusting the amount of octanol added by a dual-control mixing control algorithm. This not only improves production efficiency, but also ensures the stability and consistency of product quality. At the same time, the automatic start of a cleaning program also reduces manual intervention and reduces production costs.

[0047] The present invention proposes an automatic control method for waste cloth dissolution equipment in the plasticizer production process, which automatically determines whether the mixed liquid reaches the preset dissolution completion condition through a machine learning model, monitors the equipment operation status in real time, triggers alarms and takes safety measures in case of abnormalities, further improving the safety and reliability of production; after confirming that the mixed liquid has been dissolved and the equipment has stopped safely, the system automatically closes related equipment and valves, enters standby mode, and waits for the next operation instruction, while recording the production data of this time, providing support for production management and quality traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the automatic control method of the waste cloth dissolving equipment in the plasticizer production process of the present invention;

[0049] Figure 2 This is a principle flow chart of the automatic control method of the waste cloth dissolving equipment in the plasticizer production process of the present invention;

[0050] Figure 3 This is a diagram of the automatic control system architecture of the waste cloth dissolving equipment in the plasticizer production process of the present invention. DETAILED DESCRIPTION

[0051] Example 1: Please refer to Figure 1-Figure 2 , an embodiment provided by the present invention: an automatic control method for waste cloth dissolving equipment in a plasticizer production process, comprising the following steps:

[0052] Step S1: prepare cloth raw materials, initialize sensor and controller parameters, set working parameters of the screw extruder, and preheat the screw extruder to a preset temperature;

[0053] Furthermore, the specific steps of step S1 include:

[0054] (1) Screen the cloth raw materials, remove impurities and unqualified products, ensure the purity and quality of the raw materials, and cut or sort the cloth raw materials;

[0055] (2) Check the connection status of the temperature sensor, pressure sensor, and liquid level sensor to ensure that they are working properly, and calibrate the sensors. At the same time, load the controller program and set the initial parameters;

[0056] (3) Collect historical production data, including the operating parameters, production efficiency, and product quality of the screw extruder, and use big data analysis technology to mine and analyze the historical production data to find out the key factors affecting production efficiency and product quality. Among them, big data analysis technology is the existing technology content in this field and is not the inventive solution of this application, so it is not elaborated here;

[0057] (4) Based on the analysis results, combined with the current production demand and raw material characteristics, the optimal screw extruder operating parameters are predicted through a machine learning prediction model, and the predicted operating parameters are input into the controller as the operating parameters of the screw extruder. The machine learning prediction model is the prior art content in this field and is not the inventive solution of the present application, and is not described in detail here;

[0058] (5) According to production requirements and product characteristics, set the preheating temperature of the screw extruder. At the same time, start the heating system of the screw extruder to heat the screw, barrel and other components;

[0059] (6) The temperature of the screw extruder is monitored in real time through a temperature sensor to ensure that the temperature can rise to the preset temperature evenly and stably. At the same time, when the temperature reaches the preset value, stable heating is maintained for a period of time to ensure that all components of the screw extruder are fully preheated.

[0060] Step S2: monitoring the temperature and pressure in the dissolving tank in real time through a temperature sensor and a pressure sensor, and dynamically adjusting the parameters of the screw extruder according to the real-time monitoring data;

[0061] Furthermore, the specific steps of step S2 include:

[0062] Install temperature sensors and pressure sensors at appropriate locations in the dissolving tank to ensure that they can accurately measure the temperature and pressure in the tank, and connect the sensors to the data acquisition system or control system to ensure that real-time data can be transmitted to the control system;

[0063] Configure temperature and pressure monitoring modules in the control system and set monitoring parameters, including monitoring frequency and alarm threshold;

[0064] According to the production process requirements and the characteristics of the screw extruder, the control logic is written, wherein the control logic includes a mapping relationship or algorithm between temperature-pressure and parameters such as screw speed, extrusion pressure, and heating power, and the specific mapping relationship or algorithm is the prior art content in this field, which is not the inventive solution of the present application and will not be described in detail here;

[0065] Start the monitoring system, start receiving the temperature and pressure data in the dissolving tank in real time, and display the temperature and pressure data in real time on the control interface;

[0066] According to the real-time monitored temperature and pressure data, the control system automatically or dynamically adjusts the parameters of the screw extruder according to the operator's instructions, wherein the adjusted parameters include screw speed, extrusion pressure, and heating power, so as to maintain the temperature and pressure in the dissolving tank within a preset range.

[0067] Step S3: feeding the cloth sheet raw material into a preheated screw extruder, the screw extruder starts working, melting and extruding the cloth sheet, the molten material directly flows into the area where it is mixed with octanol through a preset outlet, and a dual-control mixing control algorithm is used to automatically adjust the amount of octanol added according to the temperature and flow data of the molten material. At the same time, after the melt extrusion, the cleaning program is automatically started;

[0068] Step S4: real-time monitoring of the state of the mixed solution, using a machine learning model to comprehensively analyze the monitoring data, and automatically determining whether the mixed solution has reached a preset dissolution completion condition. If the preset dissolution completion condition has been reached, the system automatically confirms that the dissolution is complete;

[0069] Step S5: Monitor the operating status of the equipment in the production process in real time. If an abnormality is detected, an alarm is triggered immediately and safety measures are automatically taken.

[0070] Furthermore, the specific steps of step S5 include:

[0071] (1) Install sensors and monitoring equipment on key equipment in the production process to collect real-time operating status data of the equipment;

[0072] (2) According to the characteristics of the equipment and production requirements, set monitoring parameters such as temperature, pressure, vibration, and current, and set thresholds for each parameter to determine whether the equipment is in an abnormal state;

[0073] (3) The monitoring system collects equipment operating status data in real time and performs preprocessing, such as filtering and denoising, and compares the processed data with the preset threshold to determine whether the equipment is abnormal;

[0074] (4) If the monitored equipment status data exceeds the preset threshold, the abnormal detection mechanism will be triggered and the staff will be notified immediately through various means such as sound and light alarms, text messages, and emails;

[0075] (5) When the alarm is triggered, the system automatically performs corresponding operations according to the preset safety measures, such as shutting down the machine and cutting off the power supply, to ensure that the safety measures are implemented quickly and accurately to prevent the situation from further deteriorating.

[0076] Step S6: After confirming that the mixed liquid has been dissolved and the equipment is in a safe stop state, the system automatically shuts down the screw extruder, dissolving tank equipment and valve, enters a standby state, and waits for the next operation instruction. At the same time, the production data is recorded.

[0077] Among them, production data includes mixed liquid composition data, dissolution process parameters, equipment status data, energy consumption data, and quality inspection results.

[0078] The specific steps of step S3 include:

[0079] S3.1: The operator feeds the prepared cloth sheet raw material into the feed port of the preheated screw extruder. At the same time, the screw extruder is started, the heating system maintains the set temperature, and the screw starts to rotate; the cloth sheet raw material gradually melts under the extrusion and shearing action of the screw to form a molten material;

[0080] S3.2: The molten material flows out through the preset outlet of the screw extruder and enters the mixing area with octanol to prepare for mixing;

[0081] S3.3: Set the target values ​​of temperature and flow rate, and monitor the temperature and flow rate data of the molten material in real time through the temperature sensor and flow rate sensor to obtain the deviation between the target value and the real-time monitoring data ,in, Indicates the temperature and flow data of the molten material monitored in real time. Indicates the temperature and flow target values, t Indicates the current moment;

[0082] S3.4: Use the control strategy to calculate and output the control signal based on the real-time monitoring of the temperature and flow data of the molten material. The calculation formula for the output control signal is: ;

[0083] in, Represents the output control signal, represents the proportional gain, and represents the time constant, represents the sensitivity adjustment parameter to the deviation, represents the nonlinear integral term coefficient, Representation bias The average value within the time window. In practical applications, this average value can be obtained by calculating the sum of the errors of the past few time steps and then dividing by the number of time steps. Representation bias The amount of change between successive time steps, and , represents the nonlinear coefficient, c represents the sensitivity parameter, e represents nonlinear gain;

[0084] S3.5: Perform analog-to-digital conversion and compare the converted analog-to-digital and ,

[0085] like , then continue to adjust the amount of octanol added according to the control strategy, and use the AI ​​algorithm to optimize the control strategy parameters according to the adjusted temperature and flow data;

[0086] like , then the melt extrusion task is completed;

[0087] Furthermore, the specific steps of using AI algorithms to optimize control strategy parameters include:

[0088] (1) Collect adjusted temperature and flow data, as well as historical data, and clean the data to handle missing values ​​and outliers;

[0089] (2) Feature extraction is performed based on the problem type and data characteristics. At the same time, new features are constructed based on domain knowledge and data analysis results to improve the performance of the model. The principal component analysis method is selected for feature extraction. The calculation formula of the principal component analysis method is the prior art content in this field and is not an inventive solution of the present application, so it is not elaborated here.

[0090] (3) Select the linear regression AI model based on the data type and problem complexity;

[0091] (4) Using the training data to train the linear regression AI model, and evaluating the performance of the linear regression AI model through a cross-validation method, wherein the training process and cross-validation method of the linear regression AI model are both prior art contents in the art, and are not the inventive solutions of the present application, and are not described in detail here;

[0092] (5) Optimizing the parameters of the linear regression AI model using a grid search method, and evaluating the optimized linear regression AI model. The grid search method is a prior art in the art and is not an inventive solution of the present application, and is not described in detail herein.

[0093] (6) Based on the evaluation results, continuously adjust the parameters until the performance of the linear regression AI model reaches the optimal level, and deploy the optimized linear regression AI model to the production environment. At the same time, continuously monitor the performance and stability of the model so as to adjust the parameters in a timely manner.

[0094] S3.6: Set the task completion conditions. After the melt extrusion task is completed, the system automatically detects and confirms the task completion and automatically starts the cleaning program.

[0095] The task completion condition in S3.6 refers to reaching the preset output or time.

[0096] Among them, the cleaning procedure includes cleaning the screw, barrel, mixing area and other parts to remove residues and impurities. Moreover, during the cleaning process, cleaning agents and water and other media can be used for flushing and wiping.

[0097] The specific steps of step S4 include:

[0098] S4.1: Deploy a sensor network to monitor the liquid level, temperature, viscosity and pH value of the mixed liquid in real time, and perform preprocessing to obtain mixed liquid parameter data ,in, represents the Nth mixed liquid parameter data, where N represents the number of mixed liquid parameter data;

[0099] S4.2: Load the pre-built dissolution completion condition judgment model, and use the historical mixed liquid parameter data to train the dissolution completion condition judgment model;

[0100] Furthermore, the construction process of the dissolution completion condition judgment model includes:

[0101] (1) The standard for determining the completion of dissolution, which is usually defined as the solute reaching a predetermined solubility in the solvent or reaching saturation;

[0102] (2) Collect historical mixed liquid parameter data and perform preprocessing, including cleaning and data normalization;

[0103] (3) Extracting features from historical mixed liquid parameter data to obtain characteristic data of solvent type, solute type, temperature, and pressure, wherein the feature extraction adopts the principal component analysis method;

[0104] (4) loading the pre-built neural network model, using the characteristic data of the historical mixed liquid parameter data to train the neural network model, and obtaining a dissolution completion condition judgment model;

[0105] (5) Deploy the dissolution completion condition judgment model.

[0106] S4.3: Inputting the mixed solution parameter data collected in real time into the trained dissolution completion condition judgment model to automatically judge whether the mixed solution reaches the preset dissolution completion condition;

[0107] If the dissolution completion condition judgment model determines that the mixed liquid has reached the dissolution completion condition, the system automatically confirms that the dissolution is complete;

[0108] If the dissolution completion condition judgment model determines that the mixed liquid does not meet the dissolution completion condition, the system continues to monitor the state of the mixed liquid and updates the mixed liquid parameter data in real time.

[0109] The formula for judging the dissolution completion condition in S4.3 is: ;

[0110] in, Indicates that dissolution is complete. Indicates that dissolution is not complete. express t The liquid level error at the time, Indicates the threshold value of liquid level error, express t The temperature of the moment, and Represent the minimum and maximum values ​​of temperature respectively. express t The viscosity of time, and Represent the minimum and maximum values ​​of viscosity, express t pH value at the moment, and Represent the minimum and maximum pH values ​​respectively.

[0111] Example 2: Please refer to Figure 3 Another embodiment provided by the present invention is an automatic control system for waste cloth dissolving equipment in a plasticizer production process, comprising:

[0112] Initialization module, dynamic adjustment module, hybrid control module, status monitoring module, and early warning module;

[0113] Initialization module, responsible for the preparation before system startup;

[0114] Dynamic adjustment module, used to dynamically adjust the parameters of the screw extruder according to the real-time monitored temperature and pressure data in the dissolving tank to ensure the stability and efficiency of the melting process;

[0115] A mixing control module is used to control the screw extruder to melt and extrude the fabric sheet, mix it with octanol at the same time, automatically adjust the amount of octanol added, and start the cleaning program after the melt extrusion;

[0116] The status monitoring module is used to monitor the status of the mixed liquid in real time, including liquid level, temperature, viscosity and pH value, and use the machine learning model to determine whether the mixed liquid has reached the preset dissolution completion condition;

[0117] The early warning module is used to monitor the operating status of all key equipment in the production process in real time. Once an abnormality is found, an alarm will be triggered immediately and safety measures will be taken.

[0118] The initialization module includes: a raw material preparation unit, an initialization unit, and a parameter setting unit;

[0119] The raw material preparation unit is responsible for the screening, cleaning and pretreatment of cloth raw materials to ensure the quality of raw materials;

[0120] Initialization unit, used to initialize the temperature sensor, pressure sensor, level sensor and controller to ensure that they can accurately monitor and control the production process;

[0121] The parameter setting unit is used to intelligently set the working parameters of the screw extruder and preheat it to the preset temperature based on historical data and current production needs through big data analysis.

[0122] The mixing control module includes: a melt extrusion unit, a mixing control unit, and a cleaning program unit;

[0123] The melt extrusion unit is used to feed the cloth sheet raw material into the preheated screw extruder for melting and extrusion;

[0124] The mixing control unit is used to use PID and AI dual-control mixing control algorithms to automatically adjust the amount of octanol added according to the temperature and flow data of the molten material to achieve precise mixing;

[0125] The cleaning program unit is used to automatically start the cleaning program after the melt extrusion task is completed to clean the residues in the screw extruder.

[0126] The status monitoring module includes: a multi-parameter monitoring unit and a judgment unit;

[0127] A multi-parameter monitoring unit is used to monitor the state of the mixed liquid in real time through multiple sensors including a liquid level sensor, a temperature sensor, a viscometer and a pH meter;

[0128] The judgment unit is used to use a machine learning model to comprehensively analyze the monitoring data and automatically determine whether the mixed liquid has reached the dissolution completion condition.

[0129] The early warning module includes: a status monitoring unit, an anomaly detection unit, an execution unit, and a recording unit;

[0130] The status monitoring unit is used to monitor the operating status of the screw extruder, dissolving tank, and valve equipment in real time through an intelligent monitoring system;

[0131] Anomaly detection unit, used to trigger the alarm mechanism and notify the operator when an equipment anomaly is found;

[0132] The execution unit is used to automatically take preset safety measures in abnormal situations, such as emergency shutdown and isolation of fault areas;

[0133] The recording unit is used to automatically shut down the relevant equipment and enter the standby state after confirming that the mixed liquid has been dissolved and the equipment is in a safe stop state, and record the production data at the same time.

[0134] Embodiment 3: A computer-readable storage medium having computer instructions stored thereon, which, when the computer instructions are executed, execute the steps of a method for automatically controlling a waste cloth dissolving device in a plasticizer production process, wherein the storage medium may be a volatile or non-volatile computer-readable storage medium.

[0135] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

Claims

1. Automatic control method for waste cloth dissolving equipment in plasticizer production process, characterized in that: include: Step S1: prepare cloth raw materials, initialize sensor and controller parameters, set working parameters of the screw extruder, and preheat the screw extruder to a preset temperature; Step S2: monitoring the temperature and pressure in the dissolving tank in real time through a temperature sensor and a pressure sensor, and dynamically adjusting the parameters of the screw extruder according to the real-time monitoring data; Step S3: feeding the cloth sheet raw material into a preheated screw extruder, the screw extruder starts working, melting and extruding the cloth sheet, the molten material directly flows into the area where it is mixed with octanol through a preset outlet, and a dual-control mixing control algorithm is used to automatically adjust the amount of octanol added according to the temperature and flow data of the molten material. At the same time, after the melt extrusion, the cleaning program is automatically started; Step S4: real-time monitoring of the state of the mixed solution, using a machine learning model to comprehensively analyze the monitoring data, and automatically determining whether the mixed solution has reached a preset dissolution completion condition. If the preset dissolution completion condition has been reached, the system automatically confirms that the dissolution is complete; Step S5: Real-time monitoring of the equipment operation status in the production process. If an abnormality is detected, an alarm is immediately triggered and safety measures are automatically taken; Step S6: After confirming that the mixed liquid has been dissolved and the equipment is in a safe stop state, the system automatically shuts down the screw extruder, the dissolving tank equipment and the valve, enters a standby state, and waits for the next operation instruction. At the same time, the production data of this time is recorded; The specific steps of step S3 include: S3.1: The operator feeds the prepared cloth sheet raw material into the feed port of the preheated screw extruder. At the same time, the screw extruder is started, the heating system maintains the set temperature, and the screw starts to rotate; the cloth sheet raw material gradually melts under the extrusion and shearing action of the screw to form a molten material; S3.2: The molten material flows out through the preset outlet of the screw extruder and enters the mixing area with octanol to prepare for mixing; S3.3: Set the target values ​​of temperature and flow rate, and monitor the temperature and flow rate data of the molten material in real time through the temperature sensor and flow rate sensor to obtain the deviation between the target value and the real-time monitoring data ,in, Indicates the temperature and flow data of the molten material monitored in real time. Indicates the temperature and flow target values, t Indicates the current moment; The specific steps of step S3 also include: S3.4: Use the control strategy to calculate and output the control signal based on the real-time monitoring of the temperature and flow data of the molten material. The calculation formula for the output control signal is: ; in, Represents the output control signal, represents the proportional gain, and represents the time constant, represents the sensitivity adjustment parameter to the deviation, represents the nonlinear integral term coefficient, Representation bias The average value within the time window, Representation bias The amount of change between successive time steps, and , represents the nonlinear coefficient, c represents the sensitivity parameter, e represents nonlinear gain; S3.5: Perform analog-to-digital conversion and compare the converted analog-to-digital and , like , then continue to adjust the amount of octanol added according to the control strategy, and use the AI ​​algorithm to optimize the control strategy parameters according to the adjusted temperature and flow data; like , then the melt extrusion task is completed; S3.6: Set the task completion conditions. After the melt extrusion task is completed, the system automatically detects and confirms the task completion and automatically starts the cleaning program.

2. The automatic control method for waste cloth dissolving equipment in the plasticizer production process according to claim 1, characterized in that: The specific steps of step S4 include: S4.1: Deploy a sensor network to monitor the liquid level, temperature, viscosity and pH value of the mixed liquid in real time, and perform preprocessing to obtain mixed liquid parameter data ,in, represents the Nth mixed liquid parameter data, where N represents the number of mixed liquid parameter data; S4.2: Load the pre-built dissolution completion condition judgment model, and use the historical mixed liquid parameter data to train the dissolution completion condition judgment model; S4.3: Inputting the mixed solution parameter data collected in real time into the trained dissolution completion condition judgment model to automatically judge whether the mixed solution reaches the preset dissolution completion condition; If the dissolution completion condition judgment model determines that the mixed liquid has reached the dissolution completion condition, the system automatically confirms that the dissolution is complete; If the dissolution completion condition judgment model determines that the mixed liquid does not meet the dissolution completion condition, the system continues to monitor the state of the mixed liquid and updates the mixed liquid parameter data in real time.

3. The automatic control method for waste cloth dissolving equipment in the plasticizer production process according to claim 2, characterized in that: The formula for judging the dissolution completion condition in S4.3 is: ； in, Indicates that dissolution is complete. Indicates that dissolution is not complete. express t The liquid level error at the time, Indicates the threshold value of liquid level error, express t The temperature of the moment, and Represent the minimum and maximum values ​​of temperature respectively. express t The viscosity of time, and Represent the minimum and maximum values ​​of viscosity, express t pH value at the moment, and Represent the minimum and maximum pH values ​​respectively.

4. The automatic control method for waste cloth dissolving equipment in the plasticizer production process according to claim 3, characterized in that: The task completion condition in S3.6 refers to reaching a preset output or time.

5. An automatic control system for waste cloth dissolving equipment in a plasticizer production process, which is used to implement the automatic control method for waste cloth dissolving equipment in a plasticizer production process according to any one of claims 1 to 4, characterized in that: include: Initialization module, dynamic adjustment module, hybrid control module, status monitoring module, and early warning module; The initialization module is responsible for the preparation work before the system starts; The dynamic adjustment module is used to dynamically adjust the parameters of the screw extruder according to the temperature and pressure data in the dissolving tank monitored in real time; The mixing control module is used to control the screw extruder to melt and extrude the cloth sheet, mix it with octanol at the same time, automatically adjust the amount of octanol added, and start the cleaning program after the melt extrusion; The state monitoring module is used to monitor the state of the mixed liquid in real time, including liquid level, temperature, viscosity and pH value, and use a machine learning model to determine whether the mixed liquid has reached a preset dissolution completion condition; The early warning module is used to monitor the operating status of all key equipment in the production process in real time, and immediately trigger an alarm and take safety measures once an abnormality is found.

6. The automatic control system for waste cloth dissolving equipment in the plasticizer production process according to claim 5, characterized in that: The mixing control module includes a mixing control unit, in which a PID and AI dual-control mixing control algorithm is configured. The PID and AI dual-control mixing control algorithm is used to automatically adjust the amount of octanol added according to the temperature and flow data of the molten material.

7. The automatic control system for waste cloth dissolving equipment in the plasticizer production process according to claim 6, characterized in that: The state monitoring module includes: a multi-parameter monitoring unit and a judgment unit; The multi-parameter monitoring unit is used to monitor the state of the mixed liquid in real time through multiple sensors including a liquid level sensor, a temperature sensor, a viscometer and a pH meter; The judgment unit is used to use a machine learning model to comprehensively analyze the monitoring data and automatically determine whether the mixed liquid has reached the dissolution completion condition.

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