Petroleum coke blending process optimization system

Abstract

The invention belongs to the technical field of petroleum coke blending process optimization, and particularly relates to a petroleum coke blending process optimization system, which comprises a multi-dimensional raw material database construction module used for collecting, integrating and standardizing multi-dimensional data including physical and chemical properties, production places, production processes, historical use effects, environmental adaptability and the like of petroleum coke raw materials, a comprehensive raw material database is formed; a deep learning-driven intelligent blending decision-making module which uses a deep learning algorithm to train a blending prediction model, combines a multi-objective optimization algorithm, comprehensively considers product quality, cost benefits, environmental protection requirements and market trends, and generates an optimal blending scheme; advanced technologies such as deep learning, a multi-objective optimization algorithm and automation equipment integration are introduced, so that the whole optimization system can automatically complete the steps of raw material proportioning, conveying, blending and the like in the petroleum coke blending process, and the intelligent level and the production efficiency of the blending process are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum coke blending process optimization, and in particular to a petroleum coke blending process optimization system. Background Art

[0002] In the petroleum coke processing industry, the traditional blending process mainly relies on manual operation and simple mechanized equipment, which not only leads to low production efficiency, but also makes it difficult to ensure the stability and consistency of product quality. Manual operation is easily affected by subjective factors such as fatigue and experience differences, making it difficult to achieve precise control. At the same time, the degree of automation of mechanized equipment is limited, and it lacks flexibility and intelligence, making it difficult to cope with market fluctuations and changes in raw material characteristics.

[0003] In recent years, with the rapid development of information technology, especially the rise of artificial intelligence technologies such as deep learning and machine learning, the intelligent transformation of the petroleum coke blending process has become possible. However, most of the existing technologies only stay at the level of data collection, storage and preliminary analysis, and lack the ability of deep learning and multi-objective optimization. Although these technologies can provide some basic data support, they cannot be dynamically adjusted according to market changes, raw material price fluctuations, customer demand and other factors, thus limiting the further improvement of production efficiency.

[0004] Moreover, in the process of petroleum coke blending, the problem of raw material waste and loss has always troubled enterprises. The traditional blending method lacks precise control and monitoring means, resulting in low raw material utilization and high costs. At the same time, unreasonable blending schemes may also produce a large amount of harmful substances emissions, pollute the environment, and violate the concept of sustainable development. In order to reduce production costs and achieve green production, enterprises need to seek an intelligent solution that can comprehensively optimize the blending process, reduce raw material waste and loss, and reduce emissions. However, most existing technologies can only achieve the optimization of a single goal, such as reducing costs or improving product quality, but cannot consider multiple aspects such as cost, quality, and environmental protection at the same time. This single-goal optimization often leads to the sacrifice of other goals, thereby limiting the company's sustainable development capabilities.

[0005] Finally, in the process of petroleum coke blending, the decision-making efficiency and satisfaction of users have an important impact on the market competitiveness of enterprises. Traditional decision-making methods often rely on manual experience and simple data analysis, lacking intelligent assistance and recommendation functions, resulting in low decision-making efficiency and low user satisfaction. With the continuous development of big data and artificial intelligence technologies, more and more companies are beginning to explore the application of intelligent technologies in the user decision-making process. However, most existing technologies can only provide a single data display and analysis function, lacking personalized recommendations and decision-making assistance capabilities. Although these technologies can provide users with some basic data support, they cannot make intelligent recommendations and assist decisions based on information such as users' actual needs, preferences, and historical behaviors. To this end, we provide a petroleum coke blending process optimization system. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a petroleum coke blending process optimization system to more accurately solve the problems raised in the above background technology.

[0007] The present invention is achieved through the following technical solutions: The invention proposes a petroleum coke blending process optimization system, which includes: A multi-dimensional raw material database construction module is used to collect, integrate and standardize multi-dimensional data of petroleum coke raw materials, including physical and chemical properties, origin, production process, historical use effects and environmental adaptability, to form a comprehensive raw material database; A deep learning-driven intelligent blending decision module, which uses deep learning algorithms to train blending prediction models and combines them with multi-objective optimization algorithms to generate the optimal blending plan by comprehensively considering product quality, cost-effectiveness, environmental requirements and market trends; Highly integrated dynamic execution and intelligent monitoring module, which integrates advanced automation equipment and Internet of Things technology to achieve accurate execution of blending instructions and real-time monitoring of key parameters during the blending process, including flow, temperature, pressure and environmental adaptability indicators; Adaptive learning and knowledge graph construction module, which is based on real-time monitoring data and product feedback, and uses reinforcement learning mechanism to continuously correct and optimize the intelligent blending decision-making model. At the same time, it constructs a knowledge graph in the field of petroleum coke blending, integrates industry knowledge and experience, and provides richer and more accurate decision support for intelligent decision-making.

[0008] Furthermore, the multi-dimensional raw material database construction module also includes a data preprocessing and cleaning submodule, which uses advanced machine learning algorithms to clean, integrate and standardize the original data to ensure data quality while improving data processing efficiency.

[0009] Furthermore, the deep learning model in the deep learning-driven intelligent blending decision module adopts a customized neural network architecture, which is optimized for the characteristics of the petroleum coke blending problem, thereby improving the prediction accuracy and generalization ability of the model.

[0010] Furthermore, the multi-objective optimization algorithm adopts a dynamic weight adjustment strategy to dynamically adjust the weight of each optimization objective according to factors such as market changes, raw material price fluctuations and environmental protection policy adjustments to ensure the optimization of the blending scheme.

[0011] Furthermore, the highly integrated dynamic execution and intelligent monitoring module also includes an anomaly detection and early warning submodule, which uses a machine learning algorithm to monitor abnormal data in the blending process in real time. Once an abnormality is found, an early warning signal is immediately issued to ensure the stability and controllability of the blending process.

[0012] Furthermore, the adaptive learning and knowledge graph construction module also includes a knowledge reasoning and decision support submodule, which utilizes the relational network and logical reasoning capabilities in the knowledge graph to provide deeper and more comprehensive decision support for intelligent blending decisions.

[0013] Furthermore, it also includes a user interaction interface, which adopts an advanced graphical user interface design, supports multiple languages, and has permission management functions to ensure data security and operational compliance. At the same time, it provides rich visual display and interactive functions to facilitate users to perform operations such as parameter setting, scheme adjustment and result viewing.

[0014] Furthermore, the user interaction interface also includes intelligent recommendation and decision-making assistance functions, which intelligently recommend the optimal blending scheme and operation strategy according to the user's operating habits and preferences, thereby improving the user's operating efficiency and decision-making accuracy.

[0015] Furthermore, the system can exchange and integrate data with other petrochemical production systems to achieve comprehensive intelligent management of production processes, while supporting cloud computing and edge computing technologies to improve the scalability and flexibility of the system.

[0016] Beneficial effects of the present invention: The present invention introduces advanced technologies such as deep learning, multi-objective optimization algorithms, and automated equipment integration, so that the entire optimization system can automatically complete the steps of raw material proportioning, transportation, blending, etc. in the petroleum coke blending process, which significantly improves the intelligence level and production efficiency of the blending process; and the system can monitor key parameters in the blending process, such as flow, temperature, pressure, etc. in real time to ensure the stability and controllability of the blending process; at the same time, the system can also dynamically adjust the blending plan according to factors such as market changes and raw material price fluctuations to achieve optimal production benefits; this intelligent production method not only reduces manual intervention and waiting time, shortens the production cycle, but also improves product quality and stability, bringing significant economic benefits and market competitiveness to petrochemical enterprises.

[0017] The present invention can make full use of the characteristics of various raw materials and realize the maximum utilization and recycling of resources by optimizing the blending scheme; the system can intelligently adjust the ratio according to the characteristics and market prices of the raw materials, reduce unnecessary waste and loss of raw materials, and thus reduce production costs; at the same time, the optimized blending scheme can also reduce the emission of harmful substances and reduce environmental pollution; the system can monitor the concentration and emission amount of emissions in real time to ensure that the emissions meet environmental protection requirements; this green production method not only helps enterprises to fulfill their social responsibilities and enhance their corporate image, but also brings long-term environmental benefits and sustainable development capabilities to enterprises.

[0018] The present invention provides an intuitive and easy-to-use user interaction interface and powerful decision-making support functions; users can quickly query raw material data, view blending schemes, monitor data in real time, etc. through the interface, so as to conveniently perform production management and decision-making; at the same time, the system can also intelligently recommend the optimal blending scheme and operation strategy according to user needs and preferences, so as to improve the user's decision-making efficiency and accuracy; this personalized service not only improves user satisfaction and loyalty, but also enhances the company's market competitiveness; by continuously optimizing and improving system functions, the company can provide users with better quality and more efficient services, thereby winning more market share and customer trust. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of system control 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. Example

[0021] like Figure 1As shown, a petroleum coke blending process optimization system proposed in one embodiment of the present invention is composed of four core modules: a multi-dimensional raw material database construction module, a deep learning-driven intelligent blending decision module, a highly integrated dynamic execution and intelligent monitoring module, and an adaptive learning and knowledge graph construction module. In addition, the system also integrates a powerful user interaction interface to provide users with an intuitive and efficient interactive experience.

[0022] Among them, data source: the system comprehensively collects multi-dimensional data of petroleum coke raw materials from channels such as raw material suppliers, production site monitoring equipment (such as infrared spectrometers, X-ray fluorescence spectrometers, particle size analyzers, etc.), historical databases, and third-party data platforms; Data cleaning and integration: Use advanced ETL tools to clean, deduplicate, transform and load the collected data to ensure data accuracy, completeness and consistency; Data standardization: formulate unified data formats and coding rules, and standardize various types of data to facilitate subsequent analysis and processing; Data storage: Use a high-performance database system to store and manage standardized data to ensure fast access and efficient use of data; Model architecture: A combination of deep neural network (DNN), convolutional neural network (CNN) and recurrent neural network (RNN) is used to extract features and recognize patterns of multi-dimensional raw material data; Input layer: receives preprocessed and standardized raw material data, including physical and chemical properties, origin information, production process parameters, etc. Hidden layer: Through multi-layer nonlinear transformation, the deep features of raw material data are extracted to provide a basis for blending decisions; Output layer: Output the optimal blending scheme based on the extracted features, including the ratio of each raw material, the expected performance after blending, etc.; Goal setting: Comprehensively consider multiple optimization goals such as product quality, cost-effectiveness, environmental protection requirements and market trends, and set a clear optimization objective function; Algorithm implementation: A hybrid strategy of genetic algorithm, particle swarm optimization algorithm and multi-objective optimization algorithm (such as NSGA-II) is used to find the optimal solution through iterative calculation; Dynamic adjustment: Dynamically adjust the weight of each optimization target according to market changes, raw material price fluctuations and other factors to ensure the flexibility and adaptability of the blending scheme; Equipment selection: Use advanced equipment such as high-precision robot arms, intelligent metering devices, and automated conveying systems to achieve accurate execution of blending instructions; Interface development: Develop standardized equipment interfaces to achieve communication and data exchange with the intelligent blending decision module to ensure the real-time and accuracy of information; Monitoring parameters: Real-time monitoring of key parameters such as flow, temperature, pressure, humidity, etc. during the blending process to ensure the stability and controllability of the blending process; Anomaly detection: Use machine learning algorithms (such as isolation forests and support vector machines) to detect anomalies in monitoring data, and promptly identify and handle potential faults and problems; Early warning mechanism: Establish a multi-level early warning mechanism to issue early warning signals in a timely manner based on the results of abnormal detection, and take corresponding emergency measures to ensure production safety; Learning strategy: Use reinforcement learning algorithms (such as Q-learning, Deep Q-Network, etc.) to continuously optimize the intelligent blending decision model based on real-time monitoring data and product feedback; Learning cycle: Set regular learning cycles, dynamically adjust learning strategies and parameters according to production needs and market changes, and ensure continuous optimization and updating of the model; Knowledge extraction: Extract key knowledge and information from literature, patents, expert experience and other sources in the field of petroleum coke blending; Knowledge representation: Use graph database to store knowledge, build knowledge graph in the field of petroleum coke blending, and realize knowledge visualization, structuring and intelligence; Knowledge reasoning: Using the relationship network and logical reasoning capabilities in the knowledge graph to provide deeper and more comprehensive support for intelligent blending decisions, and improve the accuracy and reliability of decisions; Interface design: adopts a concise and clear layout design, providing an intuitive and easy-to-use operation interface, which is convenient for users to get started quickly; Functional realization: Provide functions such as raw material data query, blending scheme viewing, real-time monitoring data display, abnormal early warning prompts, intelligent recommendations and auxiliary decision-making to meet the different needs of users; Permission management: Establish a strict permission management mechanism to ensure the security and privacy of user data.

[0023] like Figure 1 As shown in the figure, the system collects and integrates the multi-dimensional data of raw materials through the multi-dimensional raw material database construction module, and generates the optimal blending plan using the deep learning-driven intelligent blending decision module. Subsequently, the highly integrated dynamic execution and intelligent monitoring module executes the blending instructions and monitors the key parameters of the blending process in real time. The adaptive learning and knowledge graph construction module continuously corrects and optimizes the intelligent blending decision model based on real-time monitoring data and product feedback.

[0024] Among them, precision improvement: the combination of deep learning algorithm and multi-objective optimization algorithm significantly improves the precision of the blending scheme, and the product quality is more stable and reliable. Through comparative experiments, the blending scheme generated by the system is superior to the traditional method in terms of product quality, and the fluctuation range is smaller; Improved efficiency: The integration of automated equipment and the application of real-time monitoring functions have greatly improved the efficiency of the blending process. The system can automatically complete the steps of proportioning, conveying and blending of raw materials, reducing manual intervention and waiting time, and shortening the production cycle; Raw material cost: By optimizing the blending scheme, unnecessary waste and loss of raw materials are reduced, and raw material costs are reduced. The system can intelligently adjust the ratio according to the characteristics and market prices of the raw materials to minimize costs; Maintenance cost: The integration of automated equipment reduces equipment failure rate and maintenance cost. The system can monitor the operating status and performance parameters of the equipment in real time, detect and handle potential failures and problems in a timely manner, and avoid production interruptions and losses caused by equipment failures; Reduced emissions: The optimized blending scheme makes the product more environmentally friendly and reduces the emission of harmful substances. The system can intelligently adjust the ratio and process parameters according to environmental protection requirements and market trends to reduce the emission and concentration of pollutants; Resource utilization: Improves the utilization and recovery rate of raw materials, reduces resource waste and environmental pollution. The system can make full use of the characteristics of various raw materials to achieve maximum utilization and recycling of resources; Convenient operation: The design of the user interface makes the operation more convenient and intuitive, and users can quickly get started and use the system proficiently. Through user surveys and feedback, the system has been highly recognized and praised by users; Decision support: Intelligent recommendation and decision support functions provide users with powerful decision support. The system can intelligently recommend the best blending scheme and operation strategy based on user needs and preferences, improving the efficiency and accuracy of user decision-making.

[0025] The experimental data are shown in the following table: 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.

Claims

1. A petroleum coke blending process optimization system, characterized in that: The system includes: A multi-dimensional raw material database construction module is used to collect, integrate and standardize multi-dimensional data of petroleum coke raw materials, including physical and chemical properties, origin, production process, historical use effects and environmental adaptability, to form a comprehensive raw material database; A deep learning-driven intelligent blending decision module, which uses deep learning algorithms to train blending prediction models and combines them with multi-objective optimization algorithms to generate the optimal blending plan by comprehensively considering product quality, cost-effectiveness, environmental requirements and market trends; Highly integrated dynamic execution and intelligent monitoring module, which integrates advanced automation equipment and Internet of Things technology to achieve accurate execution of blending instructions and real-time monitoring of key parameters during the blending process, including flow, temperature, pressure and environmental adaptability indicators; Adaptive learning and knowledge graph construction module, which is based on real-time monitoring data and product feedback, and uses reinforcement learning mechanism to continuously correct and optimize the intelligent blending decision-making model. At the same time, it constructs a knowledge graph in the field of petroleum coke blending, integrates industry knowledge and experience, and provides richer and more accurate decision support for intelligent decision-making.

2. A petroleum coke blending process optimization system according to claim 1, characterized in that: The multi-dimensional raw material database construction module also includes a data preprocessing and cleaning submodule, which uses advanced machine learning algorithms to clean, integrate and standardize the original data to ensure data quality while improving data processing efficiency.

3. A petroleum coke blending process optimization system according to claim 1, characterized in that: The deep learning model in the deep learning-driven intelligent blending decision module adopts a customized neural network architecture, which is optimized for the characteristics of the petroleum coke blending problem and improves the prediction accuracy and generalization ability of the model.

4. The petroleum coke blending process optimization system according to claim 1, characterized in that: The multi-objective optimization algorithm adopts a dynamic weight adjustment strategy to dynamically adjust the weight of each optimization goal according to factors such as market changes, raw material price fluctuations and environmental protection policy adjustments to ensure the optimization of the blending scheme.

5. The petroleum coke blending process optimization system according to claim 1, characterized in that: The highly integrated dynamic execution and intelligent monitoring module also includes an anomaly detection and early warning submodule, which uses a machine learning algorithm to monitor abnormal data in the blending process in real time. Once an abnormality is found, an early warning signal is immediately issued to ensure the stability and controllability of the blending process.

6. The petroleum coke blending process optimization system according to claim 1, characterized in that: The adaptive learning and knowledge graph construction module also includes a knowledge reasoning and decision support submodule, which utilizes the relationship network and logical reasoning capabilities in the knowledge graph to provide deeper and more comprehensive decision support for intelligent blending decisions.

7. The petroleum coke blending process optimization system according to any one of claims 1 to 6, characterized in that: It also includes a user interaction interface, which adopts an advanced graphical user interface design, supports multiple languages, and has permission management functions to ensure data security and operational compliance. It also provides rich visual display and interactive functions to facilitate users to set parameters, adjust plans, view results and other operations.

8. A petroleum coke blending process optimization system according to claim 7, characterized in that: The user interaction interface also includes intelligent recommendation and decision-making assistance functions, which intelligently recommend the optimal blending scheme and operation strategy according to the user's operating habits and preferences, thereby improving the user's operating efficiency and decision-making accuracy.

9. The petroleum coke blending process optimization system according to any one of claims 1 to 8, characterized in that: The system can exchange and integrate data with other petrochemical production systems to achieve comprehensive intelligent management of the production process, while supporting cloud computing and edge computing technologies to improve the scalability and flexibility of the system.

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