Optimization method and system for reducing electricity consumption of graphite electrode and improving adhesion of medium-temperature pitch

Abstract

The invention provides an optimization method and system for reducing electricity consumption of a graphite electrode and improving the adhesion of medium-temperature pitch, and relates to the technical field of industrial production. The optimization system for reducing the electricity consumption of the graphite electrode and improving the adhesion of the medium-temperature pitch comprises an electric energy management module which comprises a monitoring unit, a control unit and a feedback unit, and the monitoring unit is used for monitoring the electricity consumption condition of production equipment in real time; the control unit can automatically adjust operation parameters of the equipment according to monitoring data so as to reduce electricity consumption; and the feedback unit feeds back the adjusted electricity consumption data to the data processing module. Through real-time monitoring and intelligent control of the electric energy management module, the electricity consumption in the graphite electrode production process can be effectively reduced, the system can adjust equipment operation parameters according to real-time data, energy waste is avoided, the energy utilization efficiency is improved, and through proportion adjustment and process parameter optimization of the adhesion optimization module, the production efficiency is improved. The bonding degree of the medium-temperature asphalt can be obviously improved, and process parameters can be monitored and adjusted in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production, and in particular to an optimization method and system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt. Background Art

[0002] Graphite electrodes are widely used in modern industry, especially in high-temperature, high-energy consumption industries such as electric arc furnace steelmaking, metallurgy and chemical industry. The performance of graphite electrodes directly affects the production efficiency and product quality of these industries. However, the production process of graphite electrodes is complex and energy-intensive. Among them, electricity consumption and medium-temperature asphalt adhesion are two key factors that directly affect the production cost and product quality of graphite electrodes.

[0003] The production of graphite electrodes consumes a large amount of electricity, especially during the arc furnace heating and graphitization process. Traditional production methods often rely on fixed equipment operating parameters and lack monitoring and analysis of real-time production data, resulting in low energy efficiency and high electricity consumption. The high electricity bills not only increase production costs, but also have a negative impact on the economic benefits of enterprises. In addition, with the global emphasis on energy conservation and emission reduction, reducing electricity consumption in the production process of graphite electrodes has become an inevitable trend in the development of the industry; medium-temperature asphalt is an important binder in the production of graphite electrodes, and its adhesion has a vital impact on the performance of the electrode. Insufficient adhesion will lead to a decrease in the mechanical strength and conductivity of the electrode, thereby affecting its service life and product quality. In the existing production process, the proportion and process parameters of medium-temperature asphalt often rely on experience, lack of scientific data support and real-time adjustment, resulting in unstable adhesion, affecting the consistency and stability of product quality.

[0004] However, although the existing optimization methods and optimization systems can solve some problems, there are still some shortcomings, such as lack of real-time monitoring and intelligent control, low energy utilization efficiency, unstable adhesion and low data utilization. Therefore, technicians in this field provide an optimization method and system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt to solve the problems raised in the above background technology. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides an optimization method and system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt, which solves the problems of lack of real-time monitoring and intelligent control, low energy utilization efficiency, unstable adhesion and low data utilization in the existing optimization system.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an optimization system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt, comprising:

[0009] The power management module includes a monitoring unit, a control unit and a feedback unit, wherein the monitoring unit is used to monitor the power consumption of the production equipment in real time; the control unit can automatically adjust the equipment operating parameters to reduce power consumption according to the monitoring data; the feedback unit feeds back the adjusted power consumption data to the data processing module;

[0010] The bonding optimization module includes a ratio adjustment unit, a process parameter adjustment unit and a quality inspection unit. The ratio adjustment unit can adjust the ratio of medium-temperature asphalt with other raw materials according to the physical and chemical properties of the medium-temperature asphalt; the process parameter adjustment unit can improve the bonding by optimizing the process parameters of heating temperature, pressure and time; the quality inspection unit performs quality inspection on the bonded graphite electrode to ensure that its performance meets the standards;

[0011] The data processing module includes a data collection unit, a data analysis unit and an optimization suggestion unit, wherein the data collection unit collects data from the power management module and the adhesion optimization module; the data analysis unit performs statistical analysis on the collected data to identify optimization space; the optimization suggestion unit provides optimization suggestions based on the analysis results and feeds back to relevant modules.

[0012] Preferably, the power management module comprises the following steps:

[0013] S1. Monitoring unit, by installing high-precision current sensors and voltage sensors on key power-consuming equipment to collect power consumption data in real time, and transmit the sensor data to the central control system in real time through industrial Ethernet or wireless communication technology, establish a database, and store historical power consumption data for subsequent analysis and optimization;

[0014] S2. The control unit automatically adjusts the equipment operation parameters according to the real-time power consumption data and the preset target value by adopting intelligent control algorithms, including but not limited to PID control, fuzzy control and machine learning algorithms, adjusts the current, voltage and power factor parameters of the arc furnace, optimizes the heating curve and holding time of the heating furnace, and ensures that the equipment operates within a safe range when adjusting the parameters, so as to avoid equipment damage or safety accidents caused by parameter adjustment;

[0015] S3. Feedback unit ensures that the adjusted power consumption data is updated synchronously with the monitoring data, maintains the real-time and accuracy of the data, establishes a standardized data interface, and supports the exchange and sharing of data with other modules.

[0016] Preferably, the adhesion optimization module comprises the following steps:

[0017] S1. Ratio adjustment unit, which analyzes the composition of medium-temperature asphalt and other raw materials to determine their physical and chemical properties. Through experiments and data analysis, it determines the best raw material ratio, including adjusting the ratio of medium-temperature asphalt to graphite powder and additives. According to the real-time data in the production process, it dynamically adjusts the raw material ratio to ensure the stability and consistency of adhesion;

[0018] S2. Process parameter adjustment unit, which analyzes the existing process parameters, determines the main factors affecting the adhesion, determines the optimal heating temperature, pressure and time through experiments and simulations, and monitors the process parameters in real time during the production process to ensure that they meet the optimized standards;

[0019] S3. Quality inspection unit, through the use of non-destructive testing technology and physical properties test, the quality of graphite electrodes is inspected, and the inspection results are fed back to the ratio adjustment unit and the process parameter adjustment unit for further optimization, establishment of quality control standards, marking and processing of unqualified products, and ensuring that the products shipped meet the quality requirements.

[0020] Preferably, the data processing module comprises the following steps:

[0021] S1. Data collection: real-time data collection from the power management module and the adhesion optimization module, including equipment power consumption, raw material ratio, process parameters and adhesion test results;

[0022] S2. Data preprocessing: clean and preprocess the collected raw data to remove noise data, fill missing values ​​and standardize data. Missing value processing uses interpolation to delete data rows with missing values. Data standardization uses Z-score standardization to standardize the data to the same scale. The calculation formula is:

[0023]

[0024] Among them, x is the original data, μ is the mean, and σ is the standard deviation;

[0025] S3. Data analysis: Statistical analysis is performed on the pre-processed data to identify the main factors affecting power consumption and adhesion, calculate the correlation coefficients between the variables, and identify the variables with high correlation. The calculation formula is:

[0026]

[0027] Among them, x i and i are the data points of two variables, are their means respectively;

[0028] Linear Regression Model:

[0029] y=β 0 +β 1 x 1 +β 2 x 2 +...+β n x n +∈

[0030] Among them, y is the target variable, x 1 , x 2 , ..., x n is the independent variable, β 0 , β 1 , ..., β n is the regression coefficient, ∈ is the error term;

[0031] S4. Optimization suggestion unit provides optimization suggestions based on the analysis results and feeds back to relevant modules. Based on the data analysis results, it generates optimization suggestions, adjusts equipment operating parameters, raw material ratios and process parameters, and feeds back the optimization suggestions to the power management module and the adhesion optimization module to guide actual production. Based on the feedback results, it continuously optimizes the suggestions to achieve continuous improvement of the system.

[0032] An optimization method for reducing the power consumption of graphite electrodes and improving the medium-temperature asphalt adhesion optimization system comprises the following steps:

[0033] S1. Start the system, start the power management module and the adhesion optimization module, ensure the normal operation of each module, check the connection and status of each sensor, controller and data processing unit, and ensure the stable operation of the system;

[0034] S2. Real-time monitoring: real-time monitoring of power consumption and medium-temperature asphalt adhesion data during graphite electrode production to ensure normal operation of sensors and testing equipment, and real-time data transmission and update;

[0035] S3. Adjust parameters, adjust the parameters of the power management module and the adhesion optimization module according to the monitoring data;

[0036] S4. Data processing: collect and analyze data, provide optimization suggestions, use statistical analysis methods and machine learning algorithms to analyze data and generate optimization suggestions;

[0037] S5. Continuous optimization: further adjust system parameters according to optimization suggestions to achieve continuous optimization. Based on feedback results, continuously optimize system parameters to ensure continuous improvement of system performance and efficiency.

[0038] (III) Beneficial effects

[0039] The present invention provides an optimization method and system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt. It has the following beneficial effects:

[0040] 1. In the present invention, through real-time monitoring and intelligent control of the power management module, the power consumption in the production process of graphite electrodes can be effectively reduced. The system can adjust the equipment operating parameters according to real-time data to avoid energy waste and improve energy utilization efficiency.

[0041] 2. In the present invention, the viscosity of medium-temperature asphalt can be significantly improved by adjusting the ratio of the viscosity optimization module and optimizing the process parameters, and the process parameters can be monitored and adjusted in real time to ensure the consistency and stability of product quality.

[0042] 3. In the present invention, by adopting intelligent control algorithms and optimization strategies, it is possible to automatically adjust equipment operating parameters and process parameters, reduce manual intervention, and be able to monitor and respond to various changes in the production process in real time to ensure the continuity and stability of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the overall system flow of the invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Embodiment 1:

[0046] like Figure 1 The embodiment of the present invention provides an optimization system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt, comprising:

[0047] The power management module includes a monitoring unit, a control unit and a feedback unit, wherein the monitoring unit is used to monitor the power consumption of the production equipment in real time; the control unit can automatically adjust the equipment operating parameters to reduce power consumption according to the monitoring data; the feedback unit feeds back the adjusted power consumption data to the data processing module;

[0048] The bonding optimization module includes a ratio adjustment unit, a process parameter adjustment unit and a quality inspection unit. The ratio adjustment unit can adjust the ratio of medium-temperature asphalt with other raw materials according to the physical and chemical properties of the medium-temperature asphalt; the process parameter adjustment unit can improve the bonding by optimizing the process parameters of heating temperature, pressure and time; the quality inspection unit performs quality inspection on the bonded graphite electrode to ensure that its performance meets the standards;

[0049] The data processing module includes a data collection unit, a data analysis unit and an optimization suggestion unit, wherein the data collection unit collects data from the power management module and the adhesion optimization module; the data analysis unit performs statistical analysis on the collected data to identify optimization space; the optimization suggestion unit provides optimization suggestions based on the analysis results and feeds back to relevant modules.

[0050] The power management module includes the following steps:

[0051] S1. Monitoring unit, by installing high-precision current sensors and voltage sensors on key power-consuming equipment to collect power consumption data in real time, and transmit the sensor data to the central control system in real time through industrial Ethernet or wireless communication technology, establish a database, and store historical power consumption data for subsequent analysis and optimization;

[0052] S2. The control unit automatically adjusts the equipment operation parameters according to the real-time power consumption data and the preset target value by adopting intelligent control algorithms, including but not limited to PID control, fuzzy control and machine learning algorithms, adjusts the current, voltage and power factor parameters of the arc furnace, optimizes the heating curve and holding time of the heating furnace, and ensures that the equipment operates within a safe range when adjusting the parameters, so as to avoid equipment damage or safety accidents caused by parameter adjustment;

[0053] S3. Feedback unit ensures that the adjusted power consumption data is updated synchronously with the monitoring data, maintains the real-time and accuracy of the data, establishes a standardized data interface, and supports the exchange and sharing of data with other modules.

[0054] The adhesion optimization module includes the following steps:

[0055] S1. Ratio adjustment unit, which analyzes the composition of medium-temperature asphalt and other raw materials to determine their physical and chemical properties. Through experiments and data analysis, it determines the best raw material ratio, including adjusting the ratio of medium-temperature asphalt to graphite powder and additives. According to the real-time data in the production process, it dynamically adjusts the raw material ratio to ensure the stability and consistency of adhesion;

[0056] S2. Process parameter adjustment unit, which analyzes the existing process parameters, determines the main factors affecting the adhesion, determines the optimal heating temperature, pressure and time through experiments and simulations, and monitors the process parameters in real time during the production process to ensure that they meet the optimized standards;

[0057] S3. Quality inspection unit, through the use of non-destructive testing technology and physical properties test, the quality of graphite electrodes is inspected, and the inspection results are fed back to the ratio adjustment unit and the process parameter adjustment unit for further optimization, establishment of quality control standards, marking and processing of unqualified products, and ensuring that the products shipped meet the quality requirements.

[0058] The data processing module includes the following steps:

[0059] S1. Data collection: real-time data collection from the power management module and the adhesion optimization module, including equipment power consumption, raw material ratio, process parameters and adhesion test results;

[0060] S2. Data preprocessing: clean and preprocess the collected raw data to remove noise data, fill missing values ​​and standardize data. Missing value processing uses interpolation to delete data rows with missing values. Data standardization uses Z-score standardization to standardize the data to the same scale. The calculation formula is:

[0061]

[0062] Among them, x is the original data, μ is the mean, and σ is the standard deviation;

[0063] S3. Data analysis: Statistical analysis is performed on the pre-processed data to identify the main factors affecting power consumption and adhesion, calculate the correlation coefficients between the variables, and identify the variables with high correlation. The calculation formula is:

[0064]

[0065] Among them, x i and i are the data points of two variables, are their means respectively;

[0066] Linear Regression Model:

[0067] y=β 0 +β 1 x 1 +β 2 x 2 +...+β n x n +∈

[0068] Among them, y is the target variable, x 1 , x 2 , ..., x n is the independent variable, β 0 , β 1 , ..., β n is the regression coefficient, ∈ is the error term;

[0069] S4. Optimization suggestion unit provides optimization suggestions based on the analysis results and feeds back to relevant modules. Based on the data analysis results, it generates optimization suggestions, adjusts equipment operating parameters, raw material ratios and process parameters, and feeds back the optimization suggestions to the power management module and the adhesion optimization module to guide actual production. Based on the feedback results, it continuously optimizes the suggestions to achieve continuous improvement of the system.

[0070] An optimization method for reducing the power consumption of graphite electrodes and improving the medium-temperature asphalt adhesion optimization system comprises the following steps:

[0071] S1. Start the system, start the power management module and the adhesion optimization module, ensure the normal operation of each module, check the connection and status of each sensor, controller and data processing unit, and ensure the stable operation of the system;

[0072] S2. Real-time monitoring: real-time monitoring of power consumption and medium-temperature asphalt adhesion data during graphite electrode production to ensure normal operation of sensors and testing equipment, and real-time data transmission and update;

[0073] S3. Adjust parameters, adjust the parameters of the power management module and the adhesion optimization module according to the monitoring data;

[0074] S4. Data processing: collect and analyze data, provide optimization suggestions, use statistical analysis methods and machine learning algorithms to analyze data and generate optimization suggestions;

[0075] S5. Continuous optimization: further adjust system parameters according to optimization suggestions to achieve continuous optimization. Based on feedback results, continuously optimize system parameters to ensure continuous improvement of system performance and efficiency.

[0076] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An optimization system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt, characterized in that: include: The power management module includes a monitoring unit, a control unit and a feedback unit, wherein the monitoring unit is used to monitor the power consumption of the production equipment in real time; The control unit can automatically adjust the equipment operating parameters to reduce power consumption based on the monitoring data; the feedback unit feeds back the adjusted power consumption data to the data processing module; The viscosity optimization module includes a ratio adjustment unit, a process parameter adjustment unit and a quality inspection unit. The ratio adjustment unit can adjust the ratio of medium-temperature asphalt with other raw materials according to its physical and chemical properties; The process parameter adjustment unit optimizes the process parameters of heating temperature, pressure and time to improve the bonding degree; the quality inspection unit performs quality inspection on the bonded graphite electrode to ensure that its performance meets the standards; The data processing module includes a data collection unit, a data analysis unit and an optimization suggestion unit, wherein the data collection unit collects data from the power management module and the adhesion optimization module; the data analysis unit performs statistical analysis on the collected data to identify optimization space; The optimization suggestion unit provides optimization suggestions based on the analysis results and feeds back to relevant modules.

2. The optimization system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt according to claim 1 is characterized by: The power management module comprises the following steps: S1. Monitoring unit, by installing high-precision current sensors and voltage sensors on key power-consuming equipment to collect power consumption data in real time, and transmit the sensor data to the central control system in real time through industrial Ethernet or wireless communication technology, establish a database, and store historical power consumption data for subsequent analysis and optimization; S2. The control unit automatically adjusts the equipment operation parameters according to the real-time power consumption data and the preset target value by adopting intelligent control algorithms, including but not limited to PID control, fuzzy control and machine learning algorithms, adjusts the current, voltage and power factor parameters of the arc furnace, optimizes the heating curve and holding time of the heating furnace, and ensures that the equipment operates within a safe range when adjusting the parameters, so as to avoid equipment damage or safety accidents caused by parameter adjustment; S3. Feedback unit ensures that the adjusted power consumption data is updated synchronously with the monitoring data, maintains the real-time and accuracy of the data, establishes a standardized data interface, and supports the exchange and sharing of data with other modules.

3. The optimization system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt according to claim 1 is characterized by: The adhesion optimization module includes the following steps: S1. Ratio adjustment unit, which analyzes the composition of medium-temperature asphalt and other raw materials to determine their physical and chemical properties. Through experiments and data analysis, it determines the best raw material ratio, including adjusting the ratio of medium-temperature asphalt to graphite powder and additives. According to the real-time data in the production process, it dynamically adjusts the raw material ratio to ensure the stability and consistency of adhesion; S2. Process parameter adjustment unit, which analyzes the existing process parameters, determines the main factors affecting the adhesion, determines the optimal heating temperature, pressure and time through experiments and simulations, and monitors the process parameters in real time during the production process to ensure that they meet the optimized standards; S3. Quality inspection unit, through the use of non-destructive testing technology and physical properties test, the quality of graphite electrodes is inspected, and the inspection results are fed back to the ratio adjustment unit and the process parameter adjustment unit for further optimization, establishment of quality control standards, marking and processing of unqualified products, and ensuring that the products shipped meet the quality requirements.

4. The optimization system for reducing the power consumption of graphite electrodes and improving the adhesion of medium-temperature asphalt according to claim 1 is characterized by: The data processing module comprises the following steps: S1. Data collection: real-time data collection from the power management module and the adhesion optimization module, including equipment power consumption, raw material ratio, process parameters and adhesion test results; S2. Data preprocessing: clean and preprocess the collected raw data to remove noise data, fill missing values ​​and standardize data. Missing value processing uses interpolation to delete data rows with missing values. Data standardization uses Z-score standardization to standardize the data to the same scale. The calculation formula is: Among them, x is the original data, μ is the mean, and σ is the standard deviation; S3. Data analysis: Statistical analysis is performed on the pre-processed data to identify the main factors affecting power consumption and adhesion, calculate the correlation coefficients between the variables, and identify the variables with high correlation. The calculation formula is: Among them, x i and i are the data points of two variables, and are their means respectively; Linear Regression Model: y=β0+β1x1+β2x2+...+β n x n +∈ Where y is the target variable, x1, x2, ..., x n are the independent variables, β0, β1, ..., β n is the regression coefficient, ∈ is the error term; S4. Optimization suggestion unit provides optimization suggestions based on the analysis results and feeds back to relevant modules. Based on the data analysis results, it generates optimization suggestions, adjusts equipment operating parameters, raw material ratios and process parameters, and feeds back the optimization suggestions to the power management module and the adhesion optimization module to guide actual production. Based on the feedback results, it continuously optimizes the suggestions to achieve continuous improvement of the system.

5. An optimization method for reducing the power consumption of graphite electrodes and improving the medium-temperature asphalt adhesion optimization system, characterized in that: The following steps are involved: S1. Start the system, start the power management module and the adhesion optimization module, ensure the normal operation of each module, check the connection and status of each sensor, controller and data processing unit, and ensure the stable operation of the system; S2. Real-time monitoring: real-time monitoring of power consumption and medium-temperature asphalt adhesion data during graphite electrode production to ensure normal operation of sensors and testing equipment, and real-time data transmission and update; S3. Adjust parameters, adjust the parameters of the power management module and the adhesion optimization module according to the monitoring data; S4. Data processing: collect and analyze data, provide optimization suggestions, use statistical analysis methods and machine learning algorithms to analyze data and generate optimization suggestions; S5. Continuous optimization: further adjust system parameters according to optimization suggestions to achieve continuous optimization. Based on feedback results, continuously optimize system parameters to ensure continuous improvement of system performance and efficiency.

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