Intelligent daily chemical production line online control system

Abstract

The invention discloses an intelligent daily chemical production line online control system, which comprises a central control terminal, a data monitoring terminal, a data storage and cloud analysis platform, a self-adaptive production scheduling system and a scheduling data uploading module, the central control terminal is in signal connection with the sensor, the actuator and the production equipment through the wireless data communication module. The data monitoring terminal is arranged at the rear ends of the sensor, the actuator and the production equipment; when the intelligent daily chemical production line on-line control system is applied to the daily chemical production line, the production efficiency is improved, the consistency of the product quality is ensured, the manual intervention in the production process is reduced, and meanwhile, the advantages of energy conservation and consumption reduction are brought to enterprises. Through comprehensive application of technologies such as a sensor, data analysis, machine learning and an optimization algorithm, the production process can be dynamically adjusted and optimized, and good adaptability and continuous improvement capability are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of daily chemical production, in particular to an intelligent online control system for a daily chemical production line. Background Art

[0002] The full name of daily chemical products is daily-use chemical products. Strategic upgrading of local daily chemical companies is imperative. Local companies should seize the opportunity, increase investment in science and technology and product research and development, continuously improve marketing methods, and create a high-end and fashionable brand image, so as to occupy a place in the fierce market competition.

[0003] With the development of daily chemical products, how to improve the efficiency of its production line has become an inevitable content of the industry. Both efficiency and effect need to be controlled, so an online control system for daily chemical production lines is needed.

[0004] The prior art has the following defects or problems:

[0005] The existing daily chemical production line control system requires a large investment of manpower and material resources. All equipment, including the raw material storage environment, needs to be manually inspected. The manual inspection error is large and the consistency of product quality cannot be guaranteed. At the same time, the enterprise's energy consumption cannot be effectively controlled.

[0006] It should be noted that the above contents belong to the technical knowledge of the inventor and do not necessarily constitute prior art. Summary of the invention

[0007] The purpose of the present invention is to provide an intelligent online control system for daily chemical production line to solve the problems raised in the background technology in view of the shortcomings of the prior art.

[0008] To achieve the above object, the present invention provides the following technical solution: an online control system for an intelligent daily chemical production line, comprising:

[0009] A central control terminal, wherein the central control terminal has signal connections with sensors, actuators and production equipment respectively through a wireless data communication module;

[0010] A data monitoring terminal, which is disposed at the rear end of the sensor, actuator and production equipment and is used to monitor the operating status of the sensor, actuator and production equipment;

[0011] Data storage and cloud analysis platform, which is used to store various types of data, and uses Alibaba Cloud as the basic platform. The data storage and cloud analysis platform has built-in data comparison modules for comparing the operating status of sensors, actuators and production equipment with the old data. The output ends of the data comparison modules are respectively connected to the data abnormality alarm module and the data normal display module;

[0012] An adaptive production scheduling system, which is used to control various production parameters and make rapid response scheduling;

[0013] The scheduling data uploading module transmits the scheduling information of the adaptive production scheduling system to the data storage and cloud analysis platform for storage.

[0014] As a preferred technical solution of the present invention, the sensors include temperature, pressure, flow and liquid level sensors, and each group of the sensors is provided with a sensor data acquisition module, the output end of the sensor data acquisition module is connected to a sensor data upload module, and each group of the sensors uploads the collected data to the data monitoring terminal through the sensor data upload module to complete the monitoring.

[0015] As a preferred technical solution of the present invention, the actuator is specifically a control valve and a driving device;

[0016] The actuators include:

[0017] An actuator operation control module is provided inside each of the actuators to control the operation of each actuator;

[0018] The actuator operation status data acquisition module is used to detect the operation status of each actuator and upload the data to the data monitoring terminal through the post-installed actuator operation status data upload module.

[0019] As a preferred technical solution of the present invention, the production equipment is specifically a heater, a stirring device and a conveying device;

[0020] The production equipment includes:

[0021] A production equipment operation control module, the production equipment operation control module is used to control the operation of each production equipment;

[0022] A production equipment temperature control module, which is used to control the temperature of the heater and the stirring equipment and adjust the production environment accordingly;

[0023] The production equipment operation status data collection module is used to collect the production equipment operation status information and upload it through the production equipment operation status data upload module to import the data into the data monitoring terminal.

[0024] As a preferred technical solution of the present invention, the adaptive production scheduling system includes:

[0025] System historical operation data analysis module, which is used to collect and analyze historical data in the production process, and discover potential production rules, process bottlenecks and optimization opportunities through data mining, including data such as production speed, raw material usage of production equipment, temperature and pressure changes;

[0026] Machine learning prediction model, which uses machine learning algorithms to train historical data and establish a prediction model for the production process. For example, based on historical data of parameters such as temperature, pressure, flow, etc., the output or quality of a production process is predicted so that timely adjustments can be made;

[0027] PID control and adaptive control module, the PID control and adaptive control module is used to adjust the conventional production process parameters and automatically adjust the equipment operation. Intelligent PID control can add adaptive algorithms to enable it to dynamically adjust PID parameters according to real-time data and optimize control response speed and stability.

[0028] As a preferred technical solution of the present invention, the adaptive production scheduling system further includes:

[0029] Control algorithm optimization module, the control algorithm optimization module uses model predictive control and genetic algorithm and particle swarm optimization. The model predictive control is based on the prediction model. The MPC control algorithm can optimize the production process under multiple variables and constraints, calculate the optimal control input (such as temperature, flow, pressure, etc.), and adjust the production equipment parameters in real time. The genetic algorithm and particle swarm optimization are used for optimization problems such as production planning and scheduling, resource allocation, etc. For example, in production scheduling, the genetic algorithm can optimize the working order and equipment scheduling of the production line to maximize production efficiency;

[0030] Adaptive production scheduling module: The system can automatically optimize production scheduling and adjust the process sequence or production plan of the production line according to the real-time production capacity and market demand changes. The system can automatically adjust the priority of the production line according to the real-time production capacity, raw material inventory, equipment status and other factors;

[0031] Fault prediction and preventive maintenance module, which can predict potential equipment failures by monitoring the equipment's operating data and combining historical fault data with machine learning models, and perform maintenance or adjustments in advance to avoid production losses caused by downtime;

[0032] Closed-loop feedback and continuous optimization module. The closed-loop feedback control in the closed-loop feedback and continuous optimization module is intended to make the entire intelligent optimization and automatic adjustment system form a closed-loop system, that is, the system continuously collects real-time data during operation, analyzes and feeds back to the control system, and the control system then adjusts the production process based on the feedback data. The continuous optimization module can be used to optimize the side rate.

[0033] Compared with the prior art, the present invention provides an intelligent online control system for daily chemical production line, which has the following beneficial effects:

[0034] This kind of intelligent daily chemical production line online control system can effectively collect production environment and raw material conditions through sensors, transmit them to the data storage and cloud analysis platform through the data monitoring terminal, and compare various data with previous data conditions to effectively adjust the raw materials and production environment. Similarly, the actuator and production equipment data are also uploaded and compared. When the data is abnormal, the data abnormality alarm module completes the response and alarm, and the staff can make equipment maintenance adjustments. When the data is normal, the operating status of each device is adjusted through the adaptive production scheduling system, and the adjustment log is uploaded and retained through the scheduling data upload module;

[0035] The application of this in daily chemical production lines not only improves production efficiency, but also ensures the consistency of product quality, reduces manual intervention in the production process, and brings energy-saving and consumption-reducing advantages to enterprises. Through the comprehensive application of sensors, data analysis, machine learning, optimization algorithms and other technologies, the production process can be dynamically adjusted and optimized, and has good adaptability and continuous improvement capabilities. At the same time, the entire optimization and adjustment system constitutes a closed-loop system. In this way, the production process can be maintained in the best state. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0037] Figure 1 This is a schematic diagram of an online control system for an intelligent daily chemical production line of the present invention;

[0038] Figure 2 Schematic diagram of the adaptive production scheduling system of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1 In this embodiment: an intelligent daily chemical production line online control system, including:

[0041] Central control terminal: The central control terminal has signal connections with sensors, actuators and production equipment through wireless data communication modules;

[0042] Data monitoring terminal, which is set at the back end of sensors, actuators and production equipment to monitor the operating status of sensors, actuators and production equipment;

[0043] The sensors include temperature, pressure, flow and liquid level sensors, and each group of sensors is provided with a sensor data acquisition module. The output end of the sensor data acquisition module is connected to a sensor data upload module. Each group of sensors uploads the collected data to the data monitoring terminal through the sensor data upload module to complete the monitoring.

[0044] The actuator is specifically a control valve and a driving device;

[0045] The actuators include:

[0046] Actuator operation control module: each actuator is provided with an actuator operation control module to control the operation of each actuator;

[0047] Actuator operation status data acquisition module, the actuator operation status data acquisition module is used to detect the operation status of each actuator, and upload the data to the data monitoring terminal through the post-installed actuator operation status data upload module;

[0048] The production equipment specifically includes heaters, stirring equipment and conveying equipment;

[0049] Production equipment includes:

[0050] Production equipment operation control module, the production equipment operation control module is used to control the operation of each production equipment;

[0051] The production equipment temperature control module is used to control the temperature of the heater and the stirring equipment and adjust the production environment accordingly;

[0052] The production equipment operation status data collection module is used to collect the production equipment operation status information and upload it through the production equipment operation status data upload module, and import the data into the data monitoring terminal;

[0053] Data storage and cloud analysis platform: The data storage and cloud analysis platform is used to store various types of data, and Alibaba Cloud is selected as the basic platform. The data storage and cloud analysis platform has built-in data comparison modules to compare the operating status of sensors, actuators and production equipment with the old data. The output ends of the data comparison modules are respectively connected to the data abnormality alarm module and the data normal display module;

[0054] Adaptive production scheduling system, which is used to control various production parameters and make rapid response scheduling;

[0055] The scheduling data upload module transmits the scheduling information of the adaptive production scheduling system to the data storage and cloud analysis platform for storage.

[0056] See also Figure 2 , what needs to be supplemented in this embodiment is:

[0057] The adaptive production scheduling system includes:

[0058] System historical operation data analysis module: The system historical operation data analysis module is used to collect and analyze historical data in the production process, and discover potential production rules, process bottlenecks and optimization opportunities through data mining, including data such as production speed, raw material usage of production equipment, temperature and pressure changes;

[0059] Machine learning prediction model: The machine learning prediction model uses machine learning algorithms to train historical data and establish a prediction model for the production process. For example, based on historical data of parameters such as temperature, pressure, and flow, the output or quality of a production process can be predicted so that timely adjustments can be made;

[0060] PID control and adaptive control module, PID control and adaptive control module is used to adjust the conventional production process parameters and automatically adjust the equipment operation. Intelligent PID control can add adaptive algorithms to enable it to dynamically adjust PID parameters according to real-time data and optimize control response speed and stability;

[0061] The adaptive production scheduling system also includes:

[0062] Control algorithm optimization module, the control algorithm optimization module uses model predictive control and genetic algorithm and particle swarm optimization. Model predictive control is based on the prediction model. The MPC control algorithm can optimize the production process under multiple variables and constraints, calculate the optimal control input (such as temperature, flow, pressure, etc.), and adjust the production equipment parameters in real time. Genetic algorithm and particle swarm optimization are used for optimization problems such as production planning and scheduling, resource allocation, etc. For example, in production scheduling, genetic algorithms can optimize the working order and equipment scheduling of the production line to maximize production efficiency;

[0063] Adaptive production scheduling module: The system can automatically optimize production scheduling and adjust the process sequence or production plan of the production line according to the real-time production capacity and market demand changes. The system can automatically adjust the priority of the production line according to real-time production capacity, raw material inventory, equipment status and other factors;

[0064] Fault prediction and preventive maintenance module: By monitoring the operating data of the equipment and combining historical fault data with machine learning models, the fault prediction and preventive maintenance module can predict potential equipment failures, perform maintenance or adjustments in advance, and avoid production losses caused by downtime;

[0065] Closed-loop feedback and continuous optimization module. The closed-loop feedback control in the closed-loop feedback and continuous optimization module is designed to make the entire intelligent optimization and automatic adjustment system form a closed-loop system, that is, the system continuously collects real-time data during operation, analyzes and feeds back to the control system, and the control system then adjusts the production process based on the feedback data. The continuous optimization module can be used to optimize productivity.

[0066] The working principle and use process of the present invention: When the system is put into use, the production environment and raw material condition data are first collected through sensors, and then transmitted to the data storage and cloud analysis platform through the data monitoring terminal, and the various data are compared with the previous data conditions to effectively adjust the raw materials and production environment. Similarly, the actuator and production equipment data are also uploaded and compared. When the data is abnormal, the data abnormality alarm module completes the response and alarm, and the staff can make maintenance adjustments for the equipment. When the data is normal, the adaptive production scheduling system is used to achieve optimization and adjustment, and the adjustment log is uploaded and retained through the scheduling data upload module.

[0067] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0068] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent daily chemical production line online control system, characterized in that: include: A central control terminal, wherein the central control terminal has signal connections with sensors, actuators and production equipment respectively through a wireless data communication module; A data monitoring terminal, which is disposed at the rear end of the sensor, actuator and production equipment and is used to monitor the operating status of the sensor, actuator and production equipment; Data storage and cloud analysis platform, which is used to store various types of data and uses Alibaba Cloud as the basic platform. The data storage and cloud analysis platform has built-in data comparison modules for comparing the operating status of sensors, actuators and production equipment with the old data. The output ends of the data comparison modules are respectively connected to a data abnormality alarm module and a data normal display module; An adaptive production scheduling system, which is used to control various production parameters and make rapid response scheduling; The scheduling data uploading module transmits the scheduling information of the adaptive production scheduling system to the data storage and cloud analysis platform for storage.

2. According to claim 1, an intelligent daily chemical production line online control system is characterized in that: The sensors include temperature, pressure, flow and liquid level sensors, and each group of the sensors is provided with a sensor data acquisition module. The output end of the sensor data acquisition module is connected to a sensor data upload module. Each group of the sensors uploads the collected data to the data monitoring terminal through the sensor data upload module to complete the monitoring.

3. According to claim 1, an intelligent daily chemical production line online control system is characterized in that: The actuator is specifically a control valve and a driving device; The actuators include: An actuator operation control module is provided inside each of the actuators to control the operation of each actuator; The actuator operation status data acquisition module is used to detect the operation status of each actuator and upload the data to the data monitoring terminal through the post-installed actuator operation status data upload module.

4. The intelligent daily chemical production line online control system according to claim 1 is characterized in that: The production equipment specifically includes a heater, a stirring device and a conveying device; The production equipment includes: A production equipment operation control module, the production equipment operation control module is used to control the operation of each production equipment; A production equipment temperature control module, which is used to control the temperature of the heater and the stirring equipment and adjust the production environment accordingly; The production equipment operation status data collection module is used to collect the production equipment operation status information and upload it through the production equipment operation status data upload module to import the data into the data monitoring terminal.

5. The intelligent daily chemical production line online control system according to claim 1 is characterized in that: The adaptive production scheduling system comprises: System historical operation data analysis module, which is used to collect and analyze historical data in the production process, and discover potential production rules, process bottlenecks and optimization opportunities through data mining, including data such as production speed, raw material usage of production equipment, temperature and pressure changes; Machine learning prediction model, which uses machine learning algorithms to train historical data and establish a prediction model for the production process. For example, based on historical data of parameters such as temperature, pressure, flow, etc., the output or quality of a production process is predicted so that timely adjustments can be made; PID control and adaptive control module, which is used to adjust conventional production process parameters and automatically adjust equipment operation. Intelligent PID control can add adaptive algorithms to enable it to dynamically adjust PID parameters according to real-time data and optimize control response speed and stability.

6. The intelligent daily chemical production line online control system according to claim 5 is characterized in that: The adaptive production scheduling system also includes: Control algorithm optimization module, the control algorithm optimization module uses model predictive control and genetic algorithm and particle swarm optimization. The model predictive control is based on the prediction model. The MPC control algorithm can optimize the production process under multiple variables and constraints, calculate the optimal control input (such as temperature, flow, pressure, etc.), and adjust the production equipment parameters in real time. The genetic algorithm and particle swarm optimization are used for optimization problems such as production planning and scheduling, resource allocation, etc. For example, in production scheduling, the genetic algorithm can optimize the working order and equipment scheduling of the production line to maximize production efficiency; Adaptive production scheduling module: The system can automatically optimize production scheduling and adjust the process sequence or production plan of the production line according to the real-time production capacity and market demand changes. The system can automatically adjust the priority of the production line according to the real-time production capacity, raw material inventory, equipment status and other factors; Fault prediction and preventive maintenance module, which can predict potential equipment failures by monitoring the equipment's operating data and combining historical fault data with machine learning models, and perform maintenance or adjustments in advance to avoid production losses caused by downtime; Closed-loop feedback and continuous optimization module. The closed-loop feedback control in the closed-loop feedback and continuous optimization module is intended to make the entire intelligent optimization and automatic adjustment system form a closed-loop system, that is, the system continuously collects real-time data during operation, analyzes and feeds back to the control system, and the control system then adjusts the production process based on the feedback data. The continuous optimization module can be used to optimize the side rate.

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