Petroleum coke intelligent blending optimization method based on Internet of Things

Abstract

The invention belongs to the technical field of petroleum coke blending, and particularly relates to an intelligent petroleum coke blending optimization method based on the Internet of Things, which comprises the following steps: step 1, a comprehensive sensing network of a petroleum coke blending system is constructed through the Internet of Things technology, and the network comprises but is not limited to sensor nodes, a data acquisition module and a communication module, the acquisition module is used for accurately acquiring flow, temperature, humidity, particle size distribution, ash content and sulfur content of petroleum coke raw materials and a mixing ratio in a blending process in real time; according to the invention, a comprehensive sensing network is constructed through the Internet of Things technology, petroleum coke raw materials and key parameters in the blending process are accurately collected in real time, data processing and optimization are carried out in combination with an intelligent algorithm module, an optimal blending scheme can be automatically generated, and accurate execution is carried out through automatic control equipment and an execution mechanism. The process greatly reduces manual intervention, improves the blending efficiency and accuracy, and ensures the stability and consistency of the blending result.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum coke blending, and particularly to an intelligent blending optimization method for petroleum coke based on the Internet of Things. Background Art

[0002] In the application process of petroleum coke, blending is a crucial link. Traditional petroleum coke blending methods mainly rely on manual experience and simple mechanical control, suffering from problems such as low blending efficiency, poor accuracy, and unstable product quality. At the same time, due to the lack of real-time monitoring and data analysis of the blending process, production safety cannot be fully guaranteed. With the rapid development and popularization of Internet of Things technology, more and more industries have begun to try to apply it to production and management. Internet of Things technology realizes the comprehensive perception, dynamic monitoring, and intelligent management of the physical world through technical means such as sensors, network communication, and cloud computing. In the field of petroleum coke blending, the application of Internet of Things technology can achieve the real-time collection and transmission of key information such as raw material quality, blending ratio, and process parameters, providing the possibility for intelligent blending optimization. However, applying Internet of Things technology to petroleum coke blending optimization also faces a series of technical problems. First, how to build a comprehensive perception network to achieve the full monitoring and data collection of petroleum coke raw materials and the blending process is an urgent problem to be solved. Second, how to effectively process and analyze the collected data to generate the optimal blending plan and execute it precisely is also a technical difficulty. In addition, how to ensure the security and privacy of data to prevent data leakage and abuse is also an issue that must be considered. Summary of the Invention

[0003] To solve the above problems, the present invention proposes an intelligent blending optimization method for petroleum coke based on the Internet of Things, which more precisely solves the problems raised in the above background art.

[0004] The present invention is achieved through the following technical solutions: The present invention provides an intelligent blending optimization method for petroleum coke based on the Internet of Things, which includes the following steps: Step 1, construct a comprehensive perception network of the petroleum coke blending system through Internet of Things technology, which includes but is not limited to sensor nodes, data acquisition modules and communication modules, and is used to collect key parameters such as the flow rate, temperature, humidity, particle size distribution, ash content, sulfur content of petroleum coke raw materials, as well as the mixing ratio and mixing uniformity during the blending process in real time and accurately; Step 2, transmit the collected data to the central processing unit through wired or wireless means. The central processing unit is built with an intelligent algorithm module based on big data analysis and machine learning. This module preprocesses, extracts features and performs pattern recognition on the received data to establish an association model between the blending effect of petroleum coke and various parameters; Step 3, according to the preset blending target (such as specific combustion performance, environmental protection indicators, cost-effectiveness, etc.), the intelligent algorithm module dynamically adjusts the blending ratio and blending process parameters through a multi-objective optimization strategy to generate an optimal blending plan; Step 4, feedback the optimal blending plan to the blending execution system in real time through Internet of Things technology to achieve the intelligence, precision and high efficiency of petroleum coke blending; Among them, it also includes real-time monitoring and abnormal warning of the blending process to ensure blending quality and production safety.

[0005] Preferably, the Internet of Things technology also includes using RFID technology to identify and track petroleum coke raw materials to achieve traceability of raw material sources, batches and quality.

[0006] Preferably, the intelligent algorithm module also includes a self-learning function, which can continuously optimize the blending model according to historical data and real-time feedback to improve blending efficiency and accuracy.

[0007] Preferably, the multi-objective optimization strategy includes but is not limited to genetic algorithms, particle swarm algorithms or simulated annealing algorithms, etc., which are used to find the best balance among multiple objectives.

[0008] Preferably, the blending execution system includes automated control equipment and actuators, which are used to accurately adjust the blending ratio and process parameters according to the optimal blending plan.

[0009] Preferably, it also includes quality inspection of the blended petroleum coke products and feedback of the inspection results to the intelligent algorithm module for further optimizing the blending model.

[0010] Preferably, the Internet of Things technology also includes using cloud computing technology to achieve remote storage, analysis and sharing of data for remote monitoring and decision support.

[0011] Preferably, it also includes establishing a user permission management system to divide permissions for different levels of users to ensure data security and privacy.

[0012] Preferably, it further includes providing a user-friendly human-machine interaction interface for displaying the blending process, results, and abnormal warning information, facilitating users to monitor and operate.

[0013] Compared with the prior art, the present invention provides an intelligent blending optimization method for petroleum coke based on the Internet of Things, having the following beneficial effects: This intelligent blending optimization method for petroleum coke based on the Internet of Things constructs a comprehensive perception network through Internet of Things technology, collects key parameters in the petroleum coke raw materials and blending process in real time and accurately, combines with an intelligent algorithm module for data processing and optimization, can automatically generate an optimal blending scheme, and accurately execute it through automated control devices and actuators. This process greatly reduces manual intervention, improves the blending efficiency and accuracy, and ensures the stability and consistency of the blending results.

[0014] This intelligent blending optimization method for petroleum coke based on the Internet of Things can perform multi-objective optimization according to preset blending targets (such as specific combustion performance, environmental protection indicators, etc.), and generate a blending scheme that meets multiple constraint conditions. At the same time, through a quality detection and feedback mechanism, the blending model is continuously optimized to ensure that the blended petroleum coke products have good combustion performance and environmental protection performance.

[0015] This intelligent blending optimization method for petroleum coke based on the Internet of Things can timely discover and process abnormal warning information by real-time monitoring of key parameters in the blending process, effectively avoiding the occurrence of production safety accidents. At the same time, cloud computing technology is used to realize remote storage, analysis, and sharing of data, improving the data processing efficiency and availability. In addition, a user permission management system is established to divide permissions for different levels of users, ensuring the security and privacy of data, and preventing data leakage and abuse. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of an intelligent blending optimization method for petroleum coke based on the Internet of Things proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to more clearly and completely illustrate the technical solution of the present invention, the present invention will be further described below with reference to the drawings. Embodiment

[0018] As Figure 1As shown in the figure, an intelligent blending optimization method for petroleum coke based on the Internet of Things proposed in an embodiment of the present invention. First, through Internet of Things technology, we have constructed a comprehensive perception network for the petroleum coke blending system. This network includes sensor nodes (such as flow sensors, temperature sensors, humidity sensors, particle size analyzers, ash analyzers, sulfur analyzers, etc.), a data acquisition module for collecting data generated by these sensors, and a communication module (such as Wi-Fi, Zigbee or 4G / 5G networks) for transmitting the data to the central processing unit. This network can collect key parameters such as the flow rate, temperature, humidity, particle size distribution, ash content, sulfur content of the petroleum coke raw materials in real time and accurately, as well as the mixing ratio and mixing uniformity during the blending process. After receiving these data, the central processing unit processes them through an intelligent algorithm module based on big data analysis and machine learning built-in, and finally generates an optimal blending plan. Through this method, the comprehensive perception and precise control of the petroleum coke blending process are realized, and the blending efficiency and product quality are improved.

[0019] In the present invention, on the basis of claim 1, we further use RFID technology to identify and track petroleum coke raw materials. Before each batch of petroleum coke raw materials enters the blending system, it will be affixed with an RFID tag, which contains information such as the source, batch number, and quality grade of the raw materials. Through the RFID reader, we can read this information in real time and associate it with the data collected by the sensors, realizing the traceability of the raw material source, batch number, and quality, enhancing the transparency and traceability of raw material management, and helping to discover and solve quality problems in a timely manner.

[0020] In the present invention, in addition to having the functions of big data analysis and machine learning, the intelligent algorithm module also includes a self-learning function. This module can continuously optimize the blending model according to historical data and real-time feedback, and improve the blending efficiency and accuracy by adjusting algorithm parameters and learning strategies. For example, when it is found that a certain parameter has a greater impact on the blending effect, the module will automatically increase the weight of this parameter to improve the prediction accuracy of the model. Through the self-learning function, the intelligent algorithm module can continuously adapt to new blending requirements and conditions, and improve the accuracy and reliability of the blending plan.

[0021] In the present invention, when generating the optimal blending plan, we adopt a multi-objective optimization strategy. These strategies include but are not limited to genetic algorithms, particle swarm algorithms, or simulated annealing algorithms, etc. These algorithms can find the best balance among multiple objectives (such as combustion performance, environmental protection indicators, cost-benefit, etc.) and generate a blending plan that meets all requirements. For example, the genetic algorithm continuously iteratively optimizes the blending ratio and process parameters by simulating the natural selection process until the optimal solution is found. The multi-objective optimization strategy ensures that the blending plan achieves the best comprehensive benefits while meeting multiple constraints.

[0022] In the present invention, the blending execution system includes an automated control device and actuators, such as an electric control valve, a variable-frequency motor, a mixer, etc. These devices precisely adjust the blending ratio and process parameters according to the optimal blending scheme. For example, the electric control valve can automatically adjust the input amount of the petroleum coke raw material according to the set flow ratio; the variable-frequency motor can adjust the rotation speed of the mixer to control the mixing uniformity. Through the automated control device and actuators, precise control and efficient execution of the blending process are achieved.

[0023] In the present invention, after the blending is completed, we conduct quality inspections on the petroleum coke products. The quality inspections include combustion performance tests, environmental protection index detections, etc. The inspection results are real-time fed back to the intelligent algorithm module through Internet of Things technology for further optimizing the blending model. For example, if it is found that the combustion performance of a certain batch of products does not meet the standards, the intelligent algorithm module will automatically adjust the blending ratio and process parameters to improve the combustion performance of the next batch of products. The quality inspection feedback mechanism ensures the continuous optimization and improvement of the blending model, and improves the overall quality of the products.

[0024] In the present invention, we utilize cloud computing technology to achieve remote storage, analysis, and sharing of data. The cloud computing platform provides powerful data storage and processing capabilities, and can receive and analyze data from the Internet of Things in real time. At the same time, the cloud computing platform also supports the data sharing function, making remote monitoring and decision support possible. For example, management personnel can remotely view the real-time data and historical records of the blending process through mobile phones or computers for remote monitoring and decision-making. Cloud computing technology improves the data processing efficiency and availability, and provides a user-friendly human-machine interaction interface in the present invention. This interface displays the real-time data, results, and abnormal warning information of the blending process. Through intuitive chart and button designs, users can conveniently monitor the blending process, view the blending results, and handle abnormal warnings. For example, when a certain sensor fails or the data is abnormal, the interface will immediately display an alarm message and prompt the user to take corresponding measures. The user-friendly human-machine interaction interface improves the user's operation experience and monitoring efficiency, making the blending process more controllable and reliable.

[0025] Workflow of the present invention: Install sensor nodes (flow sensors, temperature sensors, humidity sensors, particle size analyzers, ash analyzers, sulfur analyzers, etc.) at key positions in the petroleum coke blending system. Configure a data acquisition module to collect data generated by the sensors, and deploy a communication module (such as Wi-Fi, Zigbee, or 4G / 5G network) to ensure that the data can be transmitted to the central processing unit in real time and accurately. Identify each batch of petroleum coke raw materials with RFID tags, including information such as the raw material source, batch number, and quality grade. Configure an RFID reader to read the tag information and associate it with the sensor data. Divide different levels of user permissions to ensure data security and privacy. The sensor nodes continuously collect key parameters of the petroleum coke raw materials, such as flow rate, temperature, humidity, particle size distribution, ash content, sulfur content, etc. At the same time, data such as the mixing ratio and mixing uniformity during the blending process are collected. The data acquisition module transmits the sensor data to the central processing unit by wired or wireless means. After receiving the data, the central processing unit performs preprocessing operations, such as data cleaning and format conversion. The intelligent algorithm module extracts features and performs pattern recognition on the preprocessed data to establish an association model between the petroleum coke blending effect and various parameters. According to the preset blending objectives (such as combustion performance, environmental protection indicators, cost-effectiveness, etc.), a multi-objective optimization strategy (such as genetic algorithm, particle swarm algorithm, simulated annealing algorithm, etc.) is used to generate the optimal blending plan. The intelligent algorithm module continuously optimizes the blending model based on historical data and real-time feedback to improve the blending efficiency and accuracy. The blending execution system accurately adjusts the blending ratio and process parameters according to the optimal blending plan through automated control devices and actuators (such as electric control valves, variable frequency motors, mixers, etc.). The real-time data, results, and abnormal warning information of the blending process are displayed in real time through the human-machine interface. Monitor key parameters during the blending process, such as flow rate, temperature, mixing ratio, etc., to ensure blending quality and production safety. When a certain sensor fails or the data is abnormal, the human-machine interface immediately displays an alarm message and prompts the user to take corresponding measures. Conduct quality inspections on the blended petroleum coke products, including combustion performance tests and environmental protection indicator detections. The inspection results are transmitted to the intelligent algorithm module in real time through Internet of Things technology for further optimizing the blending model. Use cloud computing technology to achieve remote storage, analysis, and sharing of data. Managers can remotely view the real-time data and historical records of the blending process through mobile phones or computers for remote monitoring. Provide decision-making support for managers based on the remote monitoring data and historical records, such as adjusting the blending strategy and optimizing the production process.

[0026] Finally, it should be noted that: The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification. At the same time, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined. In addition, unless clearly stated in the claims, the order of the processing elements and sequences described in this specification, the use of numerical letters, or the use of other names are not used to limit the order of the processes and methods in this specification.

[0027] Finally, it should be noted that: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent blending optimization method for petroleum coke based on the Internet of Things, characterized in that, It includes the following steps: Step 1, construct a comprehensive perception network for the petroleum coke blending system through Internet of Things technology. This network includes, but is not limited to, sensor nodes, data acquisition modules, and communication modules, which are used to collect key parameters such as the flow rate, temperature, humidity, particle size distribution, ash content, sulfur content of the petroleum coke raw materials, as well as the mixing ratio and mixing uniformity during the blending process in real time and accurately; Step 2, transmit the collected data to the central processing unit by wired or wireless means. The central processing unit is built-in with an intelligent algorithm module based on big data analysis and machine learning. This module preprocesses, extracts features, and performs pattern recognition on the received data to establish an association model between the petroleum coke blending effect and various parameters; Step 3, according to the preset blending objectives (such as specific combustion performance, environmental protection indicators, cost - benefit, etc.), the intelligent algorithm module dynamically adjusts the blending ratio and blending process parameters through a multi - objective optimization strategy to generate the optimal blending plan; Step 4, feedback the optimal blending plan to the blending execution system in real time through Internet of Things technology to achieve the intelligent, precise, and efficient petroleum coke blending; Among them, it also includes real - time monitoring and abnormal warning of the blending process to ensure blending quality and production safety.

2. The intelligent blending optimization method of petroleum coke based on the Internet of Things according to claim 1, characterized in that, The Internet of Things technology also includes using RFID technology to identify and track the petroleum coke raw materials to achieve the traceability of the raw material source, batch, and quality.

3. The intelligent blending optimization method of petroleum coke based on the Internet of Things according to claim 1, wherein The intelligent algorithm module also includes a self - learning function, which can continuously optimize the blending model according to historical data and real - time feedback to improve the blending efficiency and accuracy.

4. The intelligent blending optimization method of petroleum coke based on the Internet of Things according to claim 1, wherein, The multi - objective optimization strategy includes, but is not limited to, genetic algorithm, particle swarm algorithm, or simulated annealing algorithm, etc., which are used to find the best balance point among multiple objectives.

5. The intelligent blending optimization method of petroleum coke based on the Internet of Things according to claim 1, wherein, The blending execution system includes automated control devices and actuators, which are used to accurately adjust the blending ratio and process parameters according to the optimal blending plan.

6. The intelligent blending optimization method of petroleum coke based on the Internet of Things according to claim 1, wherein It also includes quality inspection of the blended petroleum coke products and feedback of the inspection results to the intelligent algorithm module for further optimizing the blending model.

7. A method for optimizing the intelligent blending of petroleum coke based on the Internet of Things according to claim 1, characterized in that The Internet of Things technology also includes using cloud computing technology to achieve remote storage, analysis, and sharing of data for remote monitoring and decision - making support.

8. A method for optimizing the intelligent blending of petroleum coke based on the Internet of Things according to claim 1, characterized in that, It also includes establishing a user permission management system to divide permissions for different levels of users to ensure data security and privacy.

9. The intelligent blending optimization method of petroleum coke based on the Internet of Things according to claim 1, characterized in that, It also includes providing a user - friendly human - machine interaction interface for displaying the blending process, results, and abnormal warning information to facilitate user monitoring and operation.

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