Optimization and recommendation method and system for acid rolling unit and capacity associated optimal parameter

Abstract

The invention discloses an optimization and recommendation method for an optimal parameter associated with an acid rolling unit and productivity, and the method comprises the following steps: S1, building an acid pickling process parameter recommendation model, and giving a recommendation result; s2, establishing a rolling process parameter recommendation model, and giving a recommendation result; s3, collecting and recording technological parameter data of the steel coil; s4, processing the steel coil process parameter data; s5, optimal production parameters are recommended in the steel coil process parameter data meeting the conditions; and S6, outputting a recommendation result. The invention further discloses a system for optimizing and recommending the optimal parameters associated with the acid rolling unit and the productivity. According to the method, the problem that optimization and recommendation of optimal parameters are not easy to perform due to many influence factors at present is solved.

Description

Technical Field

[0001] The present invention relates to a steel coil production technology, and more specifically, to a method and system for finding and recommending optimal parameters associated with a pickling mill unit and production capacity. Background Art

[0002] Steel coil is a commonly used metal product, which is widely used in automobile, construction and other fields. Steel plate processing is a very critical link in steel coil production. First, the steel plate needs to be cut into the required specifications and sizes. Next, it is cold rolled or hot rolled to transform the steel plate into the required coil shape. In order to protect the surface of the steel coil from corrosion and oxidation, the manufacturer will perform surface treatment and then cut the steel coil to ensure that it meets customer needs. Cutting methods include shearing, sawing, mechanical cutting, etc.

[0003] There are many factors that affect the optimal parameters of the pickling mill and its production capacity, as well as its output and energy consumption, including steel type, specification, process conditions, etc. These factors have complex relationships, making it difficult to find and recommend the optimal parameters.

[0004] Parameter values ​​related to production capacity directly affect the production output and energy consumption of steel coils. Parameter optimization and recommendation are used to find production parameter values ​​that meet the maximum production capacity and minimum energy consumption, thereby improving the production efficiency of the unit. Existing parameter values ​​are currently set through manual experience. Summary of the invention

[0005] In view of the defects existing in the prior art, the purpose of the present invention is to provide a method and system for optimizing and recommending the optimal parameters associated with a pickling mill and production capacity, so as to solve the problem that it is difficult to optimize and recommend the optimal parameters due to many influencing factors.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a method for optimizing and recommending optimal parameters associated with a pickling mill and production capacity, comprising the following steps:

[0008] S1. Establish a pickling process parameter recommendation model and give the recommendation results;

[0009] S2. Establish a rolling process parameter recommendation model and give the recommendation results;

[0010] S3, collecting and recording the process parameter data of the steel coil;

[0011] S4, processing the steel coil process parameter data;

[0012] S5. recommending the best production parameters from the steel coil process parameter data that meet the conditions;

[0013] S6. Output the recommendation results.

[0014] Preferably, the step S1 establishes a pickling process parameter recommendation model, and the recommendation results specifically include:

[0015] The pickling process parameter recommendation model is established by using characteristic parameters related to pickling speed and pickling energy consumption and historical data of process parameters, and recommendation results in two dimensions of optimal production capacity and minimum energy consumption are given respectively.

[0016] Preferably, the step S2 establishes a rolling process parameter recommendation model, and the recommendation results specifically include:

[0017] The rolling process parameter recommendation model is established by using characteristic parameters related to rolling speed and rolling energy consumption and historical data of process parameters, and recommendation results in two dimensions of optimal production capacity and minimum energy consumption are given respectively.

[0018] Preferably, the step S3 of collecting and recording the process parameter data of the steel coil specifically includes:

[0019] The production line server of the pickling mill sends the steel coil process parameter data in real time through TCP communication at a frequency of once per second. The data acquisition program monitors the port and establishes a TCP connection with the production line server. After receiving the steel coil process parameter data, the data is parsed and converted into corresponding fields of the class according to the field order defined in the data definition document;

[0020] Determine whether the unit speed is greater than 0. If the unit speed is 0, the data will be discarded. The pickling section determines the production line speed, and the rolling mill section determines the rolling speed of the frame. If the unit speed is greater than 0, save the collected data to the corresponding table of the MongoDB database, the pickling section process data of the pickling mill unit is saved to pl_process_parameter, and the rolling mill section process parameters of the pickling mill unit are saved to tcm_process_parameter. The data collection is completed, and wait for the next second of data to be received before repeating the above steps.

[0021] Preferably, the steel coil process parameter data includes the steel coil's process parameters and energy consumption data in the pickling section and rolling mill section of the pickling mill group.

[0022] Preferably, the step S4 of processing the steel coil process parameter data specifically includes:

[0023] First, query the records of unprocessed process parameters in the coil information table, traverse the records, query all the pickling process parameter data and energy consumption data of the coil from the pickling process parameter and energy consumption data table pl_process_parameter according to the coil number, calculate the average line speed linespeed in the pickling process parameter, and update it to the coil information table coilInfo;

[0024] Traverse the pickling energy consumption field, and obtain the consumption of the steel coil in the pickling section by subtracting the energy consumption data at the beginning from the energy consumption data at the end. If the consumption is a negative number, it means that the energy consumption data has been reset during the production process. It is necessary to calculate the consumption data by subtracting the starting value from the maximum value and adding the ending value. After serializing the data, update it to the pickling energy consumption field plConsumption of the steel coil information table;

[0025] Calculate the weighted average value of pickling energy consumption and update it to the pickling average energy consumption field avgPlConsumption of the steel coil information table. Calculate the rolling mill energy consumption data in the same way and update the rolling mill energy consumption field tcmConsumption and the rolling mill average energy consumption field avgTcmConsumption of the steel coil information table. Query all the rolling mill process parameter data of the steel coil from the rolling mill process parameter data table tcm_process_parameter according to the steel coil number, calculate the average value of the frame speed no5StandSpeed ​​in the rolling mill process parameters, and update it to the steel coil information table coilInfo. Serialize the pickling section process parameters and the rolling mill section process parameters at the end of the steel coil and update them to the pickling section process parameter field plParam and the rolling mill section process parameter field tcmParam of the steel coil information table respectively. Finally, update the process parameter processing status in the steel coil information table to "completed", and judge whether all the records in the steel coil information table have been processed. If there are still unprocessed records, continue to process the process parameters of the next steel coil, otherwise the process parameters of this steel coil are completed.

[0026] Preferably, the step S5 of recommending the optimal production parameters from the steel coil process parameter data that meets the conditions specifically includes:

[0027] In the steel type information table coilInfo, select the matching records by steel type, width range, and export thickness range, then sort them in descending order by average speed and in ascending order by average energy consumption, and then take out the pickling or rolling mill parameter data of the first 5 records, among which the first record is the recommended optimal production parameter.

[0028] The second aspect of the present invention provides a system for searching and recommending optimal parameters associated with a pickling mill and production capacity, comprising:

[0029] Process parameter and energy consumption data acquisition module, used to collect and record the process parameter data of steel coils;

[0030] A steel coil process parameter processing module, used for processing the steel coil process parameter data;

[0031] A parameter optimization and recommendation module is used to provide the optimal production parameters among the steel coil process parameter data that meet the conditions;

[0032] The system for finding and recommending the optimal parameters associated with the acid rolling mill group and the production capacity realizes the method for finding and recommending the optimal parameters associated with the acid rolling mill group and the production capacity.

[0033] Preferably, the steel coil process parameter processing module runs once every 30 seconds.

[0034] Preferably, the parameter optimization recommendation module adopts an API interface form.

[0035] The present invention provides a method and system for optimizing and recommending the optimal parameters associated with a pickling mill and production capacity. Compared with the prior art, the method and system can automatically optimize and recommend the optimal parameters by utilizing a large amount of data collected by a production line automation system through the use of artificial intelligence, so that the results of the optimization and recommendation are more accurate, thereby facilitating the production and processing of steel coils. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic flow chart of a method for optimizing and recommending the optimal parameters associated with a pickling mill unit and production capacity according to the present invention;

[0037] Figure 2 It is a schematic flow chart of the collection of process parameters and energy consumption data of the pickling section in the method for optimizing and recommending the optimal parameters associated with the pickling mill unit and the production capacity of the present invention;

[0038] Figure 3 It is a schematic flow chart of the collection of mill section process parameters and energy consumption data in the method for optimizing and recommending the optimal parameters associated with the pickling mill group and the production capacity of the present invention;

[0039] Figure 4 It is a schematic flow chart of steel coil process parameter processing in the method for optimizing and recommending the optimal parameters associated with the pickling mill unit and the production capacity of the present invention;

[0040] Figure 5 It is a schematic diagram showing the process parameter recommendation results in the method for finding and recommending the optimal parameters associated with the pickling mill and production capacity. DETAILED DESCRIPTION

[0041] In order to better understand the above technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0042] Combination Figure 1 As shown, the present invention provides a method for finding and recommending optimal parameters associated with a pickling mill and production capacity, comprising the following steps:

[0043] S1. Using the characteristic parameters and historical data of process parameters related to pickling speed and pickling energy consumption, a pickling process parameter recommendation model is established to provide the recommended results;

[0044] S2. Using characteristic parameters related to rolling speed and rolling energy consumption and historical data of process parameters, a rolling process parameter recommendation model is established to provide recommendation results;

[0045] S3. Collect the process parameter data of the steel coil. When the steel coil is produced, the database table records all the process parameters and energy consumption data of the pickling section and rolling mill section of the steel coil;

[0046] S4, processing the process parameter data of the steel coil;

[0047] S5. Recommend the best production parameters from the process parameter data of the steel coils that meet the conditions;

[0048] S6. Output the recommendation results.

[0049] In the above steps S1 and S2, weight coefficients of production capacity and energy consumption can be given according to the comprehensive requirements of the site for increasing production capacity and reducing energy consumption, the comprehensive objective function can be calculated, and the parameters can be sorted according to the comprehensive objective function to give comprehensive optimization parameter recommendation results.

[0050] In the above steps S1 and S2, recommendation results based on the two dimensions of optimal production capacity and minimum energy consumption are respectively given.

[0051] The present invention also provides a system for searching and recommending optimal parameters associated with a pickling mill and production capacity, comprising:

[0052] Process parameter and energy consumption data acquisition module, used to collect and record the process parameter data of steel coils;

[0053] The steel coil process parameter processing module is used to process the steel coil process parameter data;

[0054] Parameter optimization and recommendation module, used to give the optimal production parameters among the steel coil process parameter data that meet the conditions;

[0055] The system for optimizing and recommending the optimal parameters associated with the acid rolling mill group and the production capacity of the present invention realizes the method for optimizing and recommending the optimal parameters associated with the acid rolling mill group and the production capacity of the present invention.

[0056] The steel coil process parameter processing module runs every 30 seconds.

[0057] The parameter optimization and recommendation module adopts the API interface to provide the optimal production parameters among the qualified steel coil process parameter data.

[0058] Example

[0059] Refer to Figure 1 As shown, this embodiment provides a method for finding and recommending optimal parameters associated with a pickling mill and production capacity, comprising the following steps:

[0060] S1. Using the characteristic parameters and historical data of process parameters related to pickling speed and pickling energy consumption, a pickling process parameter recommendation model is established, and the recommendation results in terms of optimal production capacity and minimum energy consumption are given respectively. The pickling section focuses on recording, analyzing and diagnosing the elongation of the straightening machine, the gap insertion depth, the internal tension and the motor load of the straightening machine, the temperature, liquid level, Fecl2 and Hcl concentration, and acid addition of the graphite heaters in the four pickling acid tanks, the energy media of the unit such as steam consumption, compressed air consumption, pure water consumption, cooling water consumption, oily wastewater, etc., the tension and motor load of the pickling process section, etc.;

[0061] S2. Using the characteristic parameters and historical data of process parameters related to rolling speed and rolling energy consumption, a rolling process parameter recommendation model is established to give the recommendation results in two dimensions: optimal production capacity and minimum energy consumption. The rolling mill section focuses on recording, analyzing and diagnosing the tension, tension difference, load, reduction rate, HYROP roll gap, inclination, working current and leakage current of each section, bending roll force, rolling force, intermediate roll movement value of each stand, opening setting value and injection pressure performance of the emulsion control valve at the inlet and outlet of each stand, liquid level and temperature of each hydraulic, lubrication and cooling system of the rolling mill section, emulsion steam flow rate, etc.

[0062] S3. The process parameter data of the steel coil is collected through the process parameter and energy consumption data collection module. When the steel coil is produced, all the process parameters and energy consumption data of the pickling section and rolling mill section of the steel coil are recorded in the database table:

[0063] The production line server of the pickling mill sends the process parameter data of the steel coil in real time through TCP communication at a frequency of once per second. The data acquisition program monitors the port and establishes a TCP connection with the production line server. After receiving the process parameter data of the steel coil, it parses and converts the data into the corresponding fields of the class according to the field order defined in the data definition document;

[0064] Determine whether the unit speed is greater than 0. If the unit speed is 0, the data is discarded. The pickling section determines the production line speed, and the rolling mill section determines the rolling speed of the stand. If the unit speed is greater than 0, save the collected data to the corresponding table of the MongoDB database, the pickling section process data of the pickling mill unit is saved to pl_process_parameter, and the rolling mill section process parameters of the pickling mill unit are saved to tcm_process_parameter. After the data collection is completed, wait for the next second of data to be received and repeat the above steps. Figure 2 and Figure 3 As shown;

[0065] S4. Process the steel coil process parameter data through the steel coil process parameter processing module:

[0066] First, query the records of unprocessed process parameters in the coil information table, traverse the records, query all the pickling process parameter data and energy consumption data of the coil from the pickling process parameter and energy consumption data table pl_process_parameter according to the coil number, calculate the average line speed linespeed in the pickling process parameter, and update it to the coil information table coilInfo;

[0067] Traverse the pickling energy consumption field, and obtain the consumption of the steel coil in the pickling section by subtracting the energy consumption data at the beginning from the energy consumption data at the end. If the consumption is a negative number, it means that the energy consumption data has been reset during the production process. It is necessary to calculate the consumption data by subtracting the starting value from the maximum value and adding the ending value. After serializing the data, update it to the pickling energy consumption field plConsumption of the steel coil information table;

[0068] Calculate the weighted average value of pickling energy consumption and update it to the pickling average energy consumption field avgPlConsumption of the steel coil information table. Calculate the rolling mill energy consumption data in the same way and update the rolling mill energy consumption field tcmConsumption and the rolling mill average energy consumption field avgTcmConsumption of the steel coil information table. Query all the rolling mill process parameter data of the steel coil from the rolling mill process parameter data table tcm_process_parameter according to the steel coil number. Calculate the average value of the rack speed no5StandSpeed ​​in the rolling mill process parameters and update it to the steel coil information table coilInfo. Serialize the pickling section process parameters and the rolling mill section process parameters at the end of the steel coil and update them to the pickling section process parameter field plParam and the rolling mill section process parameter field tcmParam of the steel coil information table respectively. Finally, update the process parameter processing status in the steel coil information table to "completed". Determine whether all records in the steel coil information table have been processed. If there are still unprocessed records, continue to process the process parameters of the next steel coil. Otherwise, the process parameter processing of this steel coil ends. Figure 4 As shown;

[0069] S5. The optimal production parameters are recommended from the steel coil process parameter data that meet the conditions through the parameter optimization recommendation module:

[0070] The page inputs the coil information of the parameters to be recommended: steel type, width range, export thickness range and parameter type (pickling, rolling mill). First, select the records that meet the requirements in the steel type information table coilInfo by steel type, width range and export thickness range, and then sort them in descending order by average speed and ascending order by average energy consumption, and then take out the pickling or rolling mill parameter data of the first 5 records. The first record is the recommended optimal production parameter, such as Figure 5 As shown, the left side of the table shows the name of each row of parameters, including coil number, steel type, width, entry thickness, exit thickness, average speed, energy consumption and parameter information, and the column below indicates recommended information;

[0071] S6. Output the recommendation results.

[0072] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A method for finding and recommending the optimal parameters associated with a pickling mill and production capacity, characterized in that: The following steps are involved: S1. Establish a pickling process parameter recommendation model and give the recommendation results; S2. Establish a rolling process parameter recommendation model and give the recommendation results; S3, collecting and recording the process parameter data of the steel coil; S4, processing the steel coil process parameter data; S5. recommending the best production parameters from the steel coil process parameter data that meet the conditions; S6. Output the recommendation results.

2. The method for finding and recommending the optimal parameters associated with the pickling mill and the production capacity according to claim 1, characterized in that: The step S1 establishes a pickling process parameter recommendation model, and provides a recommendation result, which specifically includes: The pickling process parameter recommendation model is established by using characteristic parameters related to pickling speed and pickling energy consumption and historical data of process parameters, and recommendation results in two dimensions of optimal production capacity and minimum energy consumption are given respectively.

3. The method for finding and recommending the optimal parameters associated with the pickling mill and the production capacity according to claim 1, characterized in that: The step S2 establishes a rolling process parameter recommendation model, and provides a recommendation result, which specifically includes: The rolling process parameter recommendation model is established by using characteristic parameters related to rolling speed and rolling energy consumption and historical data of process parameters, and recommendation results in two dimensions of optimal production capacity and minimum energy consumption are given respectively.

4. The method for finding and recommending the optimal parameters associated with the pickling mill and the production capacity according to claim 1, characterized in that: The step S3 of collecting and recording the process parameter data of the steel coil specifically includes: The production line server of the pickling mill sends the steel coil process parameter data in real time through TCP communication at a frequency of once per second. The data acquisition program monitors the port and establishes a TCP connection with the production line server. After receiving the steel coil process parameter data, the data is parsed and converted into corresponding fields of the class according to the field order defined in the data definition document; Determine whether the unit speed is greater than 0. If the unit speed is 0, the data will be discarded. The pickling section determines the production line speed, and the rolling mill section determines the rolling speed of the frame. If the unit speed is greater than 0, save the collected data to the corresponding table of the MongoDB database, the pickling section process data of the pickling mill unit is saved to pl_process_parameter, and the rolling mill section process parameters of the pickling mill unit are saved to tcm_process_parameter. The data collection is completed, and wait for the next second of data to be received before repeating the above steps.

5. The method for finding and recommending the optimal parameters associated with the pickling mill and the production capacity according to claim 4, characterized in that: The steel coil process parameter data includes the steel coil process parameters and energy consumption data of the pickling section and rolling mill section of the pickling mill group.

6. The method for finding and recommending the optimal parameters associated with the pickling mill and the production capacity according to claim 5, characterized in that: The step S4 of processing the steel coil process parameter data specifically includes: First, query the records of unprocessed process parameters in the coil information table, traverse the records, query all the pickling process parameter data and energy consumption data of the coil from the pickling process parameter and energy consumption data table pl_process_parameter according to the coil number, calculate the average line speed linespeed in the pickling process parameter, and update it to the coil information table coilInfo; Traverse the pickling energy consumption field, and obtain the consumption of the steel coil in the pickling section by subtracting the energy consumption data at the beginning from the energy consumption data at the end. If the consumption is a negative number, it means that the energy consumption data has been reset during the production process. It is necessary to calculate the consumption data by subtracting the starting value from the maximum value and adding the ending value. After serializing the data, update it to the pickling energy consumption field plConsumption of the steel coil information table; Calculate the weighted average of the pickling energy consumption and update it to the pickling average energy consumption field avgPlConsumption of the coil information table. Calculate the rolling mill energy consumption data in the same way and update the rolling mill energy consumption field tcmConsumption and the rolling mill average energy consumption field avgTcmConsumption of the coil information table. Query all the mill section process parameter data of the coil from the mill section process parameter data table tcm_process_parameter according to the coil number. Calculate the average of the stand speed no5StandSpeed ​​in the mill section process parameters and update it to the coil information table coilInfo. Serialize the pickling section process parameters and the mill section process parameters at the end of the coil and update them to the pickling section process parameter field plParam and the mill section process parameter field tcmParam of the coil information table respectively. Finally, update the process parameter processing status in the coil information table to "completed". Determine whether all the records in the coil information table have been processed. If there are still unprocessed records, continue to process the process parameters of the next coil. Otherwise, the process parameters of this coil are finished.

7. The method for finding and recommending the optimal parameters associated with the pickling mill and the production capacity according to claim 6, characterized in that: The step S5 of recommending the optimal production parameters from the steel coil process parameter data that meets the conditions specifically includes: In the steel type information table coilInfo, select the matching records by steel type, width range, and export thickness range, then sort them in descending order by average speed and in ascending order by average energy consumption, and then take out the pickling or rolling mill parameter data of the first 5 records, among which the first record is the recommended optimal production parameter.

8. A system for finding and recommending the optimal parameters associated with a pickling mill and production capacity, characterized in that: include: Process parameter and energy consumption data acquisition module, used to collect and record the process parameter data of steel coils; A steel coil process parameter processing module, used for processing the steel coil process parameter data; A parameter optimization and recommendation module is used to provide the optimal production parameters among the steel coil process parameter data that meet the conditions; The system for optimizing and recommending the optimal parameters associated with the pickling mill group and the production capacity realizes the method for optimizing and recommending the optimal parameters associated with the pickling mill group and the production capacity as described in any one of claims 1-7.

9. The system for searching and recommending the optimal parameters of the pickling mill and the production capacity according to claim 8, characterized in that: The steel coil process parameter processing module runs once every 30 seconds.

10. The system for searching and recommending the optimal parameters of the pickling mill and the production capacity according to claim 8, characterized in that: The parameter optimization recommendation module adopts an API interface form.

Images