Petroleum coke blending quantitative control system
By designing a quantitative control system for petroleum coke dosing, the problems of low efficiency, insufficient accuracy and environmental pollution of traditional dosing technology are solved, and an efficient, accurate and environmentally friendly petroleum coke dosing process is achieved.
Patent Information
- Application Number
- CN202411888679.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-20
- Publication Date
- 2025-03-21
AI Technical Summary
Traditional petroleum coke dosing technology relies on manual operations and simple mechanical equipment, resulting in low dosing efficiency and insufficient accuracy, making it difficult to achieve large-scale automated production, and at the same time there are problems of high energy consumption and carbon emissions.
A petroleum coke doping quantitative control system is designed, including a modular silo array, high-precision electronic belt metering scale, intelligent prediction and optimization engine, real-time data monitoring and adaptive feedback system, and an automated control center, combining the Internet of Things communication module and an environmentally friendly energy-saving doping algorithm to achieve accurate doping and automated control.
It significantly improves the efficiency and accuracy of the doping process, reduces errors caused by human operations, improves product quality and stability, and at the same time reduces energy consumption and carbon emissions, achieving a more efficient and smarter petroleum coke doping process.
Smart Images
Figure CN119668222A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of petroleum coke blending technology, and in particular to a petroleum coke blending quantitative control system. Background Art
[0002] In the field of petroleum coke processing and utilization, the traditional blending process often relies on manual operation and empirical judgment, resulting in low blending efficiency, insufficient accuracy, and difficulty in achieving large-scale automated production. In addition, with the increasing global attention to environmental protection and sustainable development, energy consumption and carbon emissions in the petroleum coke processing process are becoming increasingly prominent. Therefore, it is particularly important to develop an efficient, accurate and environmentally friendly intelligent petroleum coke blending quantitative control system.
[0003] Traditional petroleum coke blending technology mainly relies on manual operation and simple mechanical equipment. Due to the errors and uncertainties of manual operation and the limitations of mechanical equipment precision, the blending process is difficult to accurately control and the product quality is unstable. At the same time, traditional blending technology lacks intelligent and automated means, making it difficult to achieve large-scale and efficient production.
[0004] Moreover, in the traditional petroleum coke blending process, due to the lack of effective remote monitoring and collaborative operation means, it is difficult to achieve information sharing and collaborative operation between various production links. This not only leads to low production efficiency, but also increases operating costs. In addition, due to the existence of information islands, it is difficult for enterprises to achieve comprehensive monitoring and management of the entire production process; with the increasing global attention to environmental protection and sustainable development, energy consumption and carbon emissions in the petroleum coke processing process have become key factors restricting the development of the industry. Due to the lack of environmental protection concepts and energy-saving means, traditional blending technology leads to high energy consumption and large carbon emissions, which not only pollutes the environment, but also restricts the sustainable development of enterprises. For this reason, we provide a petroleum coke blending quantitative control system. Summary of the invention
[0005] In order to solve the above problems, the present invention proposes a petroleum coke blending quantitative control system to more accurately solve the problems raised in the above background technology.
[0006] The present invention is achieved through the following technical solutions: The invention proposes a petroleum coke blending quantitative control system, which includes: Modularly designed batching silo array, each batching silo is independently configured to store petroleum coke with different sulfur content or trace element ratios. A high-precision electronic belt weighing scale is integrated at the bottom of each batching silo to monitor and accurately control the flow and weight of petroleum coke in real time; Intelligent prediction and optimization engine, which is based on deep learning algorithms and integrates historical blending data, market trends, petroleum coke composition analysis and user-specific needs to predict future blending needs and generate the optimal blending solution. At the same time, the engine has the ability to continuously learn and self-optimize, and continuously improves prediction accuracy by continuously learning historical data, real-time feedback and user feedback; Real-time data monitoring and adaptive feedback system, which continuously monitors key parameters in the blending process, including but not limited to the flow rate, weight, sulfur content and trace element content of petroleum coke, and feeds back real-time data to the control core. At the same time, the system has built-in anomaly detection and adaptive adjustment algorithms, which can automatically identify and report abnormal data or deviations in the blending process, trigger instant alarms and automatically execute corrective measures; The automated control center dynamically adjusts the discharge rate of the batching silo array and the metering parameters of the electronic belt weighing scale based on the prediction results of the intelligent prediction and optimization engine and the information of the real-time data monitoring and adaptive feedback system to achieve precise blending.
[0007] Furthermore, the system is further integrated with an Internet of Things communication module to support real-time communication with other remote servers, control systems or user terminals, and realize remote monitoring, data sharing and remote control functions. At the same time, the system can be integrated with other petroleum coke processing equipment or systems to build a fully automated and intelligent petroleum coke processing production line, improve overall processing efficiency and product quality, and realize cross-regional and cross-system collaborative operations.
[0008] Furthermore, the system has a built-in environmentally friendly energy-saving blending algorithm. When generating the optimal blending plan, the algorithm not only takes into account the sulfur content, trace element ratio and user needs of petroleum coke, but also comprehensively considers environmental factors such as energy consumption and carbon emissions. By optimizing the blending ratio and processing process, energy conservation and emission reduction can be achieved, thereby improving the environmental friendliness of the system.
[0009] Furthermore, the intelligent prediction and optimization engine further includes a user-defined module, allowing the user to input blending conditions according to specific needs to customize the optimization prediction results.
[0010] Furthermore, the real-time data monitoring and adaptive feedback system can automatically identify and report abnormal data or deviations in the blending process, triggering immediate alarms and suggesting or executing corrective actions.
[0011] Furthermore, the automated control center integrates advanced data analysis and optimization algorithms to continuously fine-tune the blending process based on real-time feedback and predicted data to ensure the stability and consistency of the blending results.
[0012] Furthermore, the system also includes an integrated blending platform, which receives petroleum coke from the batching silo array and transmits it to subsequent processing equipment through an integrated belt conveyor; the platform is equipped with advanced screening and crushing devices, which can adjust operating parameters according to the instructions of the control center to accurately screen and crush the petroleum coke.
[0013] Furthermore, the operating parameters of the screening and crushing device are dynamically adjusted by the automation control center according to the intelligent prediction results and real-time data feedback.
[0014] Furthermore, the system is equipped with an intuitive user interface for displaying intelligent prediction results, real-time data, blending status information and system alarms, which facilitates user monitoring and management.
[0015] Furthermore, the system is highly modular and scalable, and can be integrated with other petroleum coke processing equipment or systems to build a fully automated and intelligent petroleum coke processing production line, thereby improving overall processing efficiency and product quality.
[0016] Beneficial effects of the present invention: The present invention realizes precise control and real-time monitoring of the petroleum coke blending process by integrating key components such as a high-precision electronic belt metering scale, an intelligent prediction and optimization core, and a real-time data monitoring and adaptive control system. This highly automated and intelligent system can significantly improve the efficiency and accuracy of the blending process, reduce errors and uncertainties caused by human operations, and thus improve product quality and stability.
[0017] The present invention supports real-time communication and data transmission with other remote servers, control systems or user terminals by integrating an Internet of Things (IoT) communication module; this remote monitoring and collaborative operation capability enables the system to be integrated with other petroleum coke processing equipment or systems to build a fully automated and intelligent petroleum coke processing production line; through cross-regional and cross-system collaborative operations, the system can improve overall processing efficiency, reduce operating costs, and achieve a more efficient and intelligent petroleum coke blending process.
[0018] The present invention sets up an environmentally friendly energy-saving blending algorithm. When generating an optimal blending scheme, the algorithm not only considers the sulfur content, trace element ratio and user needs of petroleum coke, but also comprehensively considers environmental factors such as energy consumption and carbon emissions. By optimizing the blending ratio and processing process, the system can reduce energy consumption and carbon emissions, and reduce the impact on the environment. This concept of environmental protection and sustainable development not only meets the current social requirements for green manufacturing, but also provides strong support for the sustainable development of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Figure 1 A schematic diagram of a control system according to an embodiment of the present invention. DETAILED DESCRIPTION
[0020] In order to more clearly and completely illustrate the technical solution of the present invention, the present invention is further described below in conjunction with the accompanying drawings.
[0021] An embodiment of the present invention provides a petroleum coke blending quantitative control system, wherein a modularly designed batching bin system includes a plurality of independent and interchangeable batching bin modules, each of which is designed to store petroleum coke with a specific sulfur content or trace element ratio. These batching bins are made of high-quality stainless steel materials to ensure corrosion resistance and stability for long-term use.
[0022] The bottom of each batching bin is equipped with a high-precision electronic belt weighing scale, which uses advanced laser ranging technology and high-precision sensors to monitor and accurately control the flow and weight of petroleum coke in real time, with the error rate controlled within ±0.5%.
[0023] Intelligent prediction and optimization core: The intelligent prediction and optimization core is built based on deep learning algorithms. It generates the optimal blending solution by integrating historical blending data, market dynamic information, petroleum coke composition analysis and user-specific needs. The core uses high-performance GPU accelerated computing to improve prediction speed and accuracy.
[0024] In order to continuously optimize the prediction model, the core also has the ability to continuously learn and self-optimize. By regularly receiving feedback data from users and real-time blending data, the core can automatically adjust model parameters to improve the accuracy and reliability of prediction results.
[0025] Real-time data monitoring and adaptive control system: Real-time data monitoring and adaptive control system continuously monitors key parameters in the blending process, such as the flow rate, weight, sulfur content and trace element content of petroleum coke, by integrating a variety of high-precision sensors and monitoring equipment.
[0026] The system has built-in advanced anomaly detection algorithms and adaptive adjustment mechanisms that can automatically identify and report abnormal data or deviations during the blending process. Once an abnormality is detected, the system will immediately trigger an alarm and automatically adjust the discharge rate of the batching bin or the metering parameters of the electronic belt weighing scale to correct the deviation and restore stable blending.
[0027] Automation control center: The automation control center is the core control unit of the system. It is responsible for receiving the prediction results of the intelligent prediction and optimization core and the information of the real-time data monitoring and adaptive control system, and dynamically adjusting the discharge rate of the batching bin system and the metering parameters of the electronic belt weighing scale based on this information.
[0028] The control center uses advanced control algorithms and data processing technologies, such as adaptive PID control algorithms or model predictive control (MPC), to ensure the accuracy and stability of the blending process. At the same time, the control center also has a remote communication interface to support real-time communication and data transmission with other remote servers, control systems or user terminals.
[0029] Furthermore, remote monitoring and collaborative operation based on the Internet of Things (IoT): The system integrates the Internet of Things (IoT) communication module to support real-time communication and data transmission with other remote servers, control systems or user terminals. Through the Internet of Things technology, the system can transmit data and information during the blending process in real time, realizing remote monitoring and remote control functions.
[0030] At the same time, the system can be integrated with other petroleum coke processing equipment or systems to build a fully automated and intelligent petroleum coke processing production line. Through cross-regional and cross-system collaborative operations, the system can improve overall processing efficiency and product quality, and achieve a more efficient and intelligent petroleum coke blending process.
[0031] In order to ensure the security and reliability of data transmission, the system uses advanced encryption technology and data verification mechanism to prevent data leakage and transmission errors.
[0032] Environmentally friendly energy-saving blending strategy: The system has a built-in environmentally friendly energy-saving blending algorithm. When generating the optimal blending plan, the algorithm not only considers the sulfur content, trace element ratio and user needs of petroleum coke, but also comprehensively considers environmental factors such as energy consumption and carbon emissions.
[0033] By optimizing the blending ratio and processing process, the system can reduce energy consumption and carbon emissions and improve the environmental friendliness of the system. For example, the system can select the optimal blending ratio and processing parameters based on the sulfur content and trace element ratio of petroleum coke to reduce pollutant emissions generated during the combustion process.
[0034] At the same time, the system also has energy monitoring and statistical functions, which can monitor and record the energy consumption in the blending process in real time, providing users with a reference for energy saving and consumption reduction.
[0035] Furthermore, we conducted system testing and verification: In order to ensure the stability and reliability of the system, we conducted system testing and verification. During the test, we simulated the blending process of petroleum coke with different sulfur content and trace element ratios, and monitored the key parameters of the blending process in real time. The test results show that the system can accurately and stably realize the blending process of petroleum coke, and has a high level of automation and intelligence.
[0036] At the same time, we also tested the system's remote monitoring and collaborative operation capabilities. The test results show that the system can communicate and transmit data in real time with other remote servers, control systems or user terminals, and realize remote monitoring and remote control functions. In addition, the system can also be integrated with other petroleum coke processing equipment or systems to build a fully automated and intelligent petroleum coke processing production line.
[0037] During the testing process, we also conducted a rigorous evaluation of the stability and reliability of the system. Through long-term continuous operation and simulation tests, we verified the stability and reliability of the system and ensured the reliability and safety of the system in actual applications.
[0038] 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 of this specification. Although it is 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 belong to the spirit and scope of the exemplary embodiments of this specification. At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "one embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "one embodiment" or "an alternative embodiment" mentioned twice or more in 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 explicitly stated in the claims, the order of processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not used to limit the order of the processes and methods of this specification.
[0039] 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. A petroleum coke blending quantitative control system, characterized in that: The system includes: Modularly designed batching silo array, each batching silo is independently configured to store petroleum coke with different sulfur content or trace element ratios. A high-precision electronic belt weighing scale is integrated at the bottom of each batching silo to monitor and accurately control the flow and weight of petroleum coke in real time; Intelligent prediction and optimization engine, which is based on deep learning algorithms and integrates historical blending data, market trends, petroleum coke composition analysis and user-specific needs to predict future blending needs and generate the optimal blending solution. At the same time, the engine has the ability to continuously learn and self-optimize, and continuously improves prediction accuracy by continuously learning historical data, real-time feedback and user feedback; Real-time data monitoring and adaptive feedback system, which continuously monitors key parameters in the blending process, including but not limited to the flow rate, weight, sulfur content and trace element content of petroleum coke, and feeds back real-time data to the control core. At the same time, the system has built-in anomaly detection and adaptive adjustment algorithms, which can automatically identify and report abnormal data or deviations in the blending process, trigger instant alarms and automatically execute corrective measures; The automated control center dynamically adjusts the discharge rate of the batching silo array and the metering parameters of the electronic belt weighing scale based on the prediction results of the intelligent prediction and optimization engine and the information of the real-time data monitoring and adaptive feedback system to achieve precise blending.
2. A petroleum coke blending quantitative control system according to claim 1, characterized in that: The system is further integrated with an Internet of Things communication module to support real-time communication with other remote servers, control systems or user terminals, and realize remote monitoring, data sharing and remote control functions. At the same time, the system can be integrated with other petroleum coke processing equipment or systems to build a fully automated and intelligent petroleum coke processing production line, improve overall processing efficiency and product quality, and realize cross-regional and cross-system collaborative operations.
3. A petroleum coke blending quantitative control system according to claim 1, characterized in that: The system has a built-in environmentally friendly energy-saving blending algorithm. When generating the optimal blending plan, the algorithm not only considers the sulfur content, trace element ratio and user needs of petroleum coke, but also comprehensively considers environmental factors such as energy consumption and carbon emissions. By optimizing the blending ratio and processing process, energy conservation and emission reduction can be achieved, thereby improving the environmental friendliness of the system.
4. A petroleum coke blending quantitative control system according to any one of claims 1 to 3, characterized in that: The intelligent prediction and optimization engine further includes a user-defined module, which allows the user to input blending conditions according to specific needs to customize the optimization prediction results.
5. A petroleum coke blending quantitative control system according to any one of claims 1 to 3, characterized in that: The real-time data monitoring and adaptive feedback system can automatically identify and report abnormal data or deviations in the blending process, trigger instant alarms and recommend or implement corrective actions.
6. A petroleum coke blending quantitative control system according to any one of claims 1 to 3, characterized in that: The automated control center integrates advanced data analysis and optimization algorithms to continuously fine-tune the blending process based on real-time feedback and predicted data to ensure the stability and consistency of the blending results.
7. A petroleum coke blending quantitative control system according to any one of claims 1 to 3, characterized in that: The system also includes an integrated blending platform that receives petroleum coke from an array of batching silos and transfers it to subsequent processing equipment via an integrated belt conveyor. The platform is equipped with advanced screening and crushing devices that can adjust operating parameters according to instructions from the control center to accurately screen and crush the petroleum coke.
8. A petroleum coke blending quantitative control system according to claim 7, characterized in that: The operating parameters of the screening and crushing device are dynamically adjusted by the automation control center according to the intelligent prediction results and real-time data feedback.
9. A petroleum coke blending quantitative control system according to any one of claims 1 to 3, characterized in that: The system is equipped with an intuitive user interface to display intelligent prediction results, real-time data, blending status information and system alarms, making it easier for users to monitor and manage.
10. A petroleum coke blending quantitative control system according to any one of claims 1 to 9, characterized in that: The system is highly modular and scalable, and can be integrated with other petroleum coke processing equipment or systems to build a fully automated and intelligent petroleum coke processing production line, thereby improving overall processing efficiency and product quality.