Production process of petroleum coke for aluminum smelting anode

Abstract

The invention belongs to the technical field of petroleum coke production, and particularly relates to a production process of petroleum coke for an aluminum smelting anode, which comprises the following steps: a raw material intelligent analysis stage: performing deep analysis on key impurity elements such as sulfur, calcium, sodium, vanadium and the like on green petroleum coke from various sources by adopting an advanced artificial intelligence algorithm in combination with a high-precision spectral analysis technology to obtain a crude petroleum coke sample; accurate identification and classification of raw material components are realized; and a dynamic ratio optimization stage: based on the raw material analysis result, dynamically adjusting the petroleum coke mixing ratio by using a machine learning model, and by introducing an advanced artificial intelligence algorithm, a high-precision spectrum analysis technology and the machine learning model, accurate identification and dynamic ratio optimization of raw material components are realized. Therefore, the utilization rate of the raw materials is remarkably improved, and accurate control of the content of key impurity elements in the mixed dry material is ensured, so that the comprehensive performance of the anode material is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum coke production, and particularly to a production process of petroleum coke for aluminum smelting anodes. Background Art

[0002] In the traditional production process of petroleum coke for aluminum smelting anodes, the raw material analysis and proportioning link faces many challenges. First of all, the analysis of raw material components mainly relies on manual experience and traditional chemical analysis methods. This method not only takes a long time but also has limited accuracy, making it difficult to accurately control the content of key impurity elements in the raw materials, and these elements have a crucial impact on the performance of anode materials. Secondly, each process in the production process is often carried out in isolation, lacking effective systematic management, resulting in low production efficiency and unstable product quality control. In addition, the traditional production process has a low utilization rate of raw materials, further restricting the improvement of production efficiency and product quality.

[0003] With the increasing global awareness of environmental protection and growing emphasis on energy efficiency, the production process of petroleum coke for aluminum smelting anodes faces the dual challenges of intelligent management and energy conservation and emission reduction. On the one hand, the traditional production management method leads to the inability to collect and analyze production data in real time, and the production process is difficult to achieve visual monitoring and intelligent scheduling. This not only affects production efficiency but also increases energy consumption and emissions. On the other hand, there are lack of effective technical means for the treatment of pollutants such as waste gas, waste water and solid waste, resulting in prominent environmental pollution problems. At the same time, the traditional production mode has a low energy utilization efficiency, making it difficult to meet the requirements of modern industry for green production and sustainable development.

[0004] Facing the increasingly fierce market competition and changing customer needs, the production technology of petroleum coke for aluminum smelting anodes faces an urgent need for innovation and upgrading. The traditional production process and equipment configuration are difficult to meet the market demand for high-performance anode materials. At the same time, there is a lack of interdisciplinary R & D teams and systematic process evaluation and improvement mechanisms in the industry, resulting in a slow pace of technological innovation and difficulty in significantly improving product quality and production efficiency. In addition, the application of digital and intelligent technologies in the production of petroleum coke for aluminum smelting anodes is not yet extensive, restricting the visualization and intelligent management of the production process and affecting the decision-making support and strategic planning of enterprises. Therefore, promoting the innovation and upgrading of the production technology of petroleum coke for aluminum smelting anodes has become an inevitable trend in the industry's development. For this reason, we provide a production process of petroleum coke for aluminum smelting anodes. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a production process of petroleum coke for aluminum smelting anodes to more precisely solve the problems raised in the above background art.

[0006] The present invention is achieved through the following technical solutions: The present invention provides a production process for petroleum coke used in aluminum smelting anodes, comprising the following steps: Intelligent raw material analysis stage: By using advanced artificial intelligence algorithms combined with high-precision spectral analysis technology, in-depth analysis of key impurity elements such as sulfur, calcium, sodium, and vanadium in green petroleum coke from various sources is carried out to achieve accurate identification and classification of raw material components; Dynamic ratio optimization stage: Based on the raw material analysis results, a machine learning model is used to dynamically adjust the mixing ratio of petroleum coke to ensure that the weight content of sulfur in the mixed dry material is accurately controlled between 0.5% and 3.0%, and the weight ratios of sulfur to calcium, sodium, and vanadium are maintained within the ranges of 150 - 200, 150 - 200, and 75 - 100 respectively, so as to maximize the comprehensive performance of the anode material; Efficient calcination and precise temperature control stage: Using an advanced pyrolysis-combustion integrated calcination furnace combined with a closed-loop temperature control system, precise calcination within the range of 1200°C - 1400°C is achieved, effectively removing volatile components and impurities while maintaining the integrity of the microstructure of the petroleum coke; Intelligent crushing, screening, and particle size control stage: An adaptive crusher and an intelligent screening system are adopted. According to the preset particle size distribution model, the crushing force and the screen aperture are automatically adjusted to ensure the uniformity and consistency of petroleum coke particles; Precision batching and kneading and forming stage: Through precise metering and automated kneading equipment, the screened petroleum coke particles and the optimized binder are mixed in proportion to form a highly uniform anode paste, and then it is formed under high pressure in a precision forming mold to ensure the density and dimensional accuracy of the green anode; Advanced roasting and quality control stage: An energy-saving roasting furnace is used, combined with atmosphere control technology, to ensure that the green anode is roasted in a suitable redox environment. At the same time, an on-line quality monitoring system is introduced to detect key parameters during the roasting process in real time to ensure the stable quality of the final product.

[0007] Preferably, in the intelligent raw material analysis stage, it further includes: Intelligent in-depth mining and prediction module: Using deep learning technology to construct a multi-layer neural network model to conduct in-depth mining and prediction of the microstructure and performance of petroleum coke, and achieve accurate evaluation and classification of raw material quality; Raw material supply prediction module: Based on big data analysis, combined with historical data and market trends, predict the future raw material supply situation to provide a scientific basis for production planning and inventory management.

[0008] Preferably, in the dynamic ratio optimization stage, it further includes: Global search and iterative optimization module: Introduce optimization algorithms such as genetic algorithms to conduct global search and iterative optimization of the ratio strategy to find the optimal ratio plan; Batch performance difference adjustment module: Considering the performance differences between raw materials of different batches, adjust the mixing ratio strategy in real time to ensure the stable performance of the mixed dry materials.

[0009] Preferably, in the high-efficiency calcination and precise temperature control stage, it further includes: Fuzzy control and neural network control module: Combining fuzzy control theory and neural network control technology to achieve precise regulation and rapid response of the calcination temperature; Waste gas treatment and waste heat recovery module: Purify the waste gas generated during the calcination process, and at the same time recover waste heat for preheating raw materials or heating other process links.

[0010] Preferably, in the intelligent crushing, screening and particle size control stage, it further includes: Machine vision and deep learning recognition module: Introduce machine vision technology and deep learning algorithms to identify and analyze the material characteristics in real time, and automatically adjust the crushing force and screen aperture; Ultrasonic cleaning module: Perform ultrasonic cleaning on the crushed petroleum coke particles to remove impurities and pollutants attached to the surface.

[0011] Preferably, in the precise batching and kneading and forming stage, it further includes: Ultrasonic dispersion and high-frequency vibration module: Introduce ultrasonic dispersion technology and high-frequency vibration technology to improve the uniformity and stability of the anode paste; High-precision mold and surface treatment technology: Adopt high-precision machining technology and surface treatment technology to ensure the dimensional accuracy and shape consistency of the green anode, and improve the wear resistance and service life of the mold; Online monitoring module: Conduct real-time monitoring and data analysis on the kneading process and forming process to ensure that the product quality meets the standards.

[0012] Preferably, in the advanced roasting and quality control stage, it further includes: Intelligent control and Internet of Things technology module: Realize the automatic and intelligent management of the roasting process; Machine learning algorithm and data mining module: Deeply mine and analyze the production data to provide a scientific basis for quality traceability and process optimization; Nitrogen protection module: Provide an inert atmosphere for the anode during the roasting process to prevent oxidation and corrosion.

[0013] Preferably, it further includes a green circular technology module, including: Wastewater treatment and recycling system: Resourcefully utilize or harmlessly treat the wastewater generated during the production process; Solid waste resource utilization technology: Resourcefully utilize solid waste and other pollutants.

[0014] Preferably, it also includes a continuous technological innovation and process optimization mechanism, including: Interdisciplinary R & D team: Form an interdisciplinary R & D team, combine the industry development trend and customer needs, and continuously introduce new technologies, new materials and new methods; Process evaluation and improvement activities: Deeply analyze and solve the bottleneck problems and quality problems in the production process.

[0015] Preferably, it also includes the comprehensive application of digital and intelligent technologies, including: Digital factory and intelligent management system: Realize functions such as real-time collection and analysis of production data, visual monitoring and intelligent scheduling of the production process, online monitoring and early warning of product quality, etc.; Decision support and strategic planning module: Provide a scientific basis for the decision support and strategic planning of the enterprise, and promote the transformation and upgrading and sustainable development of the enterprise.

[0016] Compared with the prior art, the present invention provides a production process of petroleum coke for aluminum smelting anodes, which has the following beneficial effects: In the production process of petroleum coke for aluminum smelting anodes, by introducing advanced artificial intelligence algorithms, high-precision spectral analysis technologies and machine learning models, the present invention realizes the accurate identification of raw material components and the optimization of dynamic proportioning; this not only significantly improves the utilization rate of raw materials, but also ensures the precise control of the content of key impurity elements in the mixed dry materials, thereby improving the comprehensive performance of the anode materials, including electrical conductivity, corrosion resistance and mechanical strength; in addition, advanced calcination technologies, intelligent crushing, screening and particle size control systems, precision batching and kneading and forming processes, etc., further ensure the stability and controllability of the production process, making the density of the green anodes uniform and the dimensional accuracy meet the design requirements; the comprehensive application of these technologies significantly improves the production efficiency and product quality of petroleum coke for aluminum smelting anodes, meeting the market demand for high-performance anode materials.

[0017] In the production process of petroleum coke for aluminum smelting anodes, by integrating intelligent deep mining and prediction modules, raw material supply prediction modules, global search and iterative optimization modules, etc., the intelligent management and optimization of the production process are realized; these modules can analyze production data in real time, predict the raw material supply situation, and optimize the proportioning strategy, thereby reducing production costs and improving production efficiency; at the same time, the application of intelligent control and Internet of Things technology modules realizes the automatic and intelligent management of the roasting process, improving production efficiency and product quality; in addition, the introduction of waste gas treatment and waste heat recovery modules, wastewater treatment and recycling systems, and solid waste resource utilization technologies effectively reduces the waste emissions in the production process, achieving the goals of energy conservation, emission reduction and resource recycling, and conforming to the concept of sustainable development.

[0018] The production process of petroleum coke for aluminum smelting anodes has successfully developed a number of innovative technologies and products by forming an interdisciplinary R & D team, combining industry development trends with customer needs, and continuously introducing new technologies, new materials and new methods. At the same time, through in-depth analysis and solution of process evaluation and improvement activities, the production process flow and equipment configuration have been optimized, improving production efficiency and product quality. In addition, the construction of a digital factory and intelligent management system has realized functions such as real-time collection and analysis of production data, visual monitoring and intelligent scheduling of the production process, and online monitoring and early warning of product quality, providing a scientific basis for the decision-making support and strategic planning of the enterprise. The implementation of these innovative measures not only enhances the core competitiveness of the enterprise, but also promotes the transformation and upgrading and sustainable development of the entire petroleum coke production industry for aluminum smelting anodes. Brief Description of the Drawings

[0019] Figure 1 It is a schematic flow chart of a production process of petroleum coke for aluminum smelting anodes proposed by the present invention; Detailed Embodiments

[0020] 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 accompanying drawings.

[0021] As Figure 1As shown in the figure, a production process of petroleum coke for aluminum smelting anodes proposed in an embodiment of the present invention includes the following steps: Raw material intelligent analysis stage: Advanced artificial intelligence algorithms are combined with high-precision spectral analysis technology to deeply analyze green petroleum coke from different mining areas. Through the algorithm model, key impurity elements such as sulfur, calcium, sodium, and vanadium in the petroleum coke are successfully identified and classified, achieving precise identification of the raw material composition. The results show that the impurity content of the raw material is highly consistent with the predicted value, providing a reliable basis for subsequent proportioning; Dynamic proportioning optimization stage: Based on the raw material analysis results, a machine learning model is used to dynamically adjust the mixing ratio of petroleum coke. After multiple iterations of optimization, the weight content of sulfur in the mixed dry material is accurately controlled between 0.5% and 3.0%, and the weight ratios of sulfur to calcium, sodium, and vanadium are maintained within the ranges of 150 - 200, 150 - 200, and 75 - 100 respectively. After testing, the comprehensive performance of the anode material is significantly improved, including electrical conductivity, corrosion resistance, and mechanical strength; High-efficiency calcination and precise temperature control stage: An advanced pyrolysis-combustion integrated calcination furnace is used, combined with a closed-loop temperature control system, to achieve precise calcination in the range of 1200°C - 1400°C. The volatile components and impurities in the calcined petroleum coke are effectively removed, while maintaining the integrity of the microstructure. Observed by scanning electron microscopy, the calcined petroleum coke particles exhibit a uniform pore structure, which is beneficial to improving the performance of the anode material; Intelligent crushing, screening, and particle size control stage: An adaptive crusher and an intelligent screening system are used to automatically adjust the crushing force and screen aperture according to the preset particle size distribution model. After crushing and screening, the uniformity and consistency of the petroleum coke particles are good, and the particle size distribution meets the preset requirements. Detected by a particle size analyzer, the particle size distribution range of the particles is narrow, which is beneficial to improving the uniformity and compactness of the anode material; Precision batching and kneading molding stage: Through precise metering and automated kneading equipment, the screened petroleum coke particles and the optimized binder are mixed in proportion to form a highly uniform anode paste. Subsequently, it is formed under high pressure in a precision molding die, ensuring the density and dimensional accuracy of the green anode. After testing, the density of the green anode is uniform, and the dimensional accuracy meets the design requirements; Advanced roasting and quality control stage: An energy-saving roasting furnace is used, combined with atmosphere control technology, to ensure that the green anode is roasted in a suitable redox environment. At the same time, an online quality monitoring system is introduced to real-time detect key parameters during the roasting process, such as temperature, atmosphere concentration, etc. After roasting, the performance of the anode material is stable, and the quality meets the industry standards.

[0022] Furthermore, the intelligent in-depth mining and prediction module: constructs a multi-layer neural network model using deep learning technology to deeply mine and predict the microstructure and properties of petroleum coke. After model training, accurate evaluation and classification of raw material quality have been successfully achieved. The prediction results are highly consistent with the experimental data, providing strong support for production decision-making; the raw material supply prediction module: based on big data analysis, combines historical data with market trends to predict future raw material supply. The prediction results show that the raw material supply will maintain stable growth, providing a scientific basis for production planning and inventory management.

[0023] Furthermore, the global search and iterative optimization module: introduces optimization algorithms such as genetic algorithms to globally search and iteratively optimize the formulation strategy. After multiple iterations, the optimal formulation plan has been found, making the comprehensive performance of the anode material reach the optimal level; the batch performance difference adjustment module: considers the performance differences between raw materials of different batches and adjusts the formulation strategy in real time. After testing, the performance of the mixed dry material is stable, and the differences between batches have been effectively controlled.

[0024] Furthermore, the fuzzy control and neural network control module: combines fuzzy control theory with neural network control technology to achieve precise regulation and rapid response of the calcination temperature. Through comparative experiments, this module has significantly improved the stability of the calcination temperature and reduced energy consumption; the waste gas treatment and waste heat recovery module: purifies the waste gas generated during the calcination process and at the same time recovers the waste heat for preheating raw materials or heating other process links. After detection, the waste gas emissions meet the standards, and the waste heat recovery efficiency has reached the expected goal.

[0025] Furthermore, the machine vision and deep learning recognition module: introduces machine vision technology and deep learning algorithms to identify and analyze material characteristics in real time. Through this module, the crushing force and screen aperture are automatically adjusted to ensure the uniformity and consistency of petroleum coke particles. After detection, the uniformity of the particles has been significantly improved; the ultrasonic cleaning module: ultrasonically cleans the crushed petroleum coke particles. The surface of the particles after cleaning is clean without impurities, which is beneficial to improving the purity of the anode material.

[0026] Furthermore, the ultrasonic dispersion and high-frequency vibration module: introduces ultrasonic dispersion technology and high-frequency vibration technology to improve the uniformity and stability of the anode paste. After testing, the uniformity of the paste has been significantly improved; the high-precision mold and surface treatment technology: adopts high-precision machining technology and surface treatment technology to ensure the dimensional accuracy and shape consistency of the green anode. After detection, both the dimensional accuracy and shape consistency of the green anode have reached the design requirements; the online monitoring module: conducts real-time monitoring and data analysis on the kneading process and the forming process. Through this module, abnormal situations in the production process are promptly detected and processed, ensuring the stability of product quality.

[0027] Furthermore, the intelligent control and Internet of Things technology module: realizes the automated and intelligent management of the roasting process. Through this module, real-time monitoring and intelligent scheduling of the roasting process are achieved, improving production efficiency and product quality; The machine learning algorithm and data mining module: deeply mines and analyzes production data. Through this module, potential problems and improvement points in the production process are discovered, providing a scientific basis for quality traceability and process optimization; The nitrogen protection module: provides an inert atmosphere protection for the anode during the roasting process. After testing, no oxidation or corrosion occurred to the anode material during the roasting process, and its good performance was maintained.

[0028] Furthermore, the wastewater treatment and recycling system: resourcefully utilizes or harmlessly treats the wastewater generated during the production process. The treated wastewater meets the discharge standards or the requirements for recycled water quality; The solid waste resource utilization technology: resourcefully utilizes pollutants such as solid waste. Through this technology, the goals of solid waste reduction, resource utilization, and harmless treatment are achieved.

[0029] Furthermore, the interdisciplinary R & D team: forms an interdisciplinary R & D team, combines industry development trends with customer needs, and continuously introduces new technologies, new materials, and new methods. Through the efforts of the team, a number of innovative technologies and products have been successfully developed; The process evaluation and improvement activities: deeply analyze and solve the bottleneck problems and quality problems in the production process. Through the evaluation and improvement activities, the production process flow and equipment configuration are optimized, improving production efficiency and product quality.

[0030] Furthermore, the digital factory and intelligent management system: realizes functions such as real-time collection and analysis of production data, visual monitoring and intelligent scheduling of the production process, and online monitoring and early warning of product quality. Through this system, the goals of digital management and intelligent control of the production process are achieved; The decision-making support and strategic planning module: provides a scientific basis for the enterprise's decision-making support and strategic planning. Through this module, the enterprise can timely understand market dynamics and industry development trends, formulate scientific and reasonable strategic plans and decision-making schemes, and promote the transformation, upgrading, and sustainable development of the enterprise.

[0031] 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 "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 explicitly 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.

Claims

1. A production process for petroleum coke for aluminum anode smelting, characterized in that: The following steps are involved: Raw material intelligent analysis stage: using advanced artificial intelligence algorithms combined with high-precision spectral analysis technology to conduct in-depth analysis of key impurity elements such as sulfur, calcium, sodium, and vanadium in raw petroleum coke from various sources, to achieve accurate identification and classification of raw material components; Dynamic ratio optimization stage: Based on the raw material analysis results, the machine learning model is used to dynamically adjust the petroleum coke mixing ratio to ensure that the weight content of sulfur in the mixed dry material is accurately controlled between 0.5% and 3.0%, and the weight ratios of sulfur to calcium, sodium, and vanadium are maintained in the ranges of 150-200, 150-200, and 75-100, respectively, to maximize the comprehensive performance of the anode material; Efficient calcination and precise temperature control stage: Using advanced pyrolysis-combustion integrated calcination furnaces, combined with closed-loop temperature control systems, precise calcination within the range of 1200°C-1400°C is achieved, effectively removing volatiles and impurities while maintaining the microstructural integrity of petroleum coke; Intelligent crushing, screening and particle size control stage: Adopting adaptive crusher and intelligent screening system, according to the preset particle size distribution model, the crushing force and screen aperture are automatically adjusted to ensure the uniformity and consistency of petroleum coke particles; Precision batching and kneading stage: Through precise metering and automated kneading equipment, the screened petroleum coke particles are mixed with the optimized binder in proportion to form a highly uniform anode paste, which is then molded under high pressure in a precision molding mold to ensure the density and dimensional accuracy of the green anode; Advanced roasting and quality control stage: Use energy-saving roasting furnaces, combined with atmosphere control technology, to ensure that the raw anode is roasted in a suitable redox environment. At the same time, introduce an online quality monitoring system to detect key parameters in the roasting process in real time to ensure the stable quality of the final product.

2. The production process of petroleum coke for aluminum anode according to claim 1, characterized in that: The raw material intelligent analysis stage further includes: Intelligent deep mining and prediction module: Use deep learning technology to build a multi-layer neural network model to deeply mine and predict the microstructure and performance of petroleum coke, and achieve accurate evaluation and classification of raw material quality; Raw material supply forecasting module: Based on big data analysis, combined with historical data and market trends, it predicts future raw material supply and provides a scientific basis for production planning and inventory management.

3. The production process of petroleum coke for aluminum anode according to claim 1, characterized in that: The dynamic ratio optimization stage further includes: Global search and iterative optimization module: introduces optimization algorithms such as genetic algorithms to perform global search and iterative optimization on the matching strategy to find the optimal matching solution; Batch performance difference adjustment module: Consider the performance differences between different batches of raw materials and adjust the ratio strategy in real time to ensure the stable performance of the mixed dry materials.

4. The production process of petroleum coke for aluminum anode according to claim 1, characterized in that: The efficient calcination and precise temperature control stage further includes: Fuzzy control and neural network control module: Combining fuzzy control theory and neural network control technology to achieve precise control and rapid response of calcining temperature; Waste gas treatment and waste heat recovery module: The waste gas generated during the calcination process is purified and the waste heat is recovered for preheating raw materials or heating other process links.

5. The production process of petroleum coke for aluminum anode according to claim 1, characterized in that: The intelligent crushing, screening and particle size control stage further includes: Machine vision and deep learning recognition module: Introducing machine vision technology and deep learning algorithms to identify and analyze material characteristics in real time, and automatically adjust the crushing force and screen aperture; Ultrasonic cleaning module: Ultrasonic cleaning is performed on the crushed petroleum coke particles to remove impurities and pollutants attached to the surface.

6. The production process of petroleum coke for aluminum anode according to claim 1, characterized in that: The precision batching and kneading stage further includes: Ultrasonic dispersion and high-frequency vibration module: Introducing ultrasonic dispersion technology and high-frequency vibration technology to improve the uniformity and stability of anode paste; High-precision mold and surface treatment technology: high-precision processing technology and surface treatment technology are used to ensure the dimensional accuracy and shape consistency of the raw anode, and improve the wear resistance and service life of the mold; Online monitoring module: Real-time monitoring and data analysis of the kneading and molding processes to ensure that product quality meets standards.

7. The production process of petroleum coke for aluminum anode according to claim 1, characterized in that: The advanced roasting and quality control stage further includes: Intelligent control and Internet of Things technology module: realize the automation and intelligent management of the roasting process; Machine learning algorithm and data mining module: conduct in-depth mining and analysis of production data to provide a scientific basis for quality traceability and process optimization; Nitrogen protection module: provides an inert atmosphere for the anode during the baking process to prevent oxidation and corrosion.

8. A process for producing petroleum coke for aluminum anode according to any one of claims 1 to 7, characterized in that: Also included are green cycle technology modules, including: Wastewater treatment and recycling system: Reuse or harmlessly treat wastewater generated during the production process; Solid waste resource utilization technology: resource utilization of solid waste and other pollutants.

9. The process for producing petroleum coke for aluminum anode smelting according to claim 8, characterized in that: It also includes continuous technological innovation and process optimization mechanisms, including: Interdisciplinary R&D team: Establish an interdisciplinary R&D team, combine industry development trends with customer needs, and continuously introduce new technologies, new materials and new methods; Process evaluation and improvement activities: Conduct in-depth analysis and resolution of bottlenecks and quality issues in the production process.

10. The process for producing petroleum coke for aluminum anode smelting according to claim 9, characterized in that: It also includes the comprehensive application of digital and intelligent technologies, including: Digital factory and intelligent management system: realize real-time collection and analysis of production data, visual monitoring and intelligent scheduling of production process, online monitoring and early warning of product quality, etc. Decision support and strategic planning module: Provide scientific basis for the decision support and strategic planning of enterprises, and promote the transformation, upgrading and sustainable development of enterprises.

Images