Data traceability and benchmarking system and method for secondary aluminum production based on big data

Abstract

The invention discloses a system and method for tracing and benchmarking recycled aluminum production data based on big data, comprising a programmable controller, a first switch, a first photoelectric converter, a second switch, a second photoelectric converter, a third Switches and servers; the server is equipped with SCADA platform software connected to the programmable controller signals, and the SCADA platform software is used to establish collection points and establish communication connections with the programmable controllers to display the collected data; the programmable controllers are used to collect, process and Output the collected data; input the standard operation data in the SCADA platform software, compare the collected data with the standard operation data in real time, form the production benchmarking data and display it in real time. The invention transforms the programmable controller and touch screen program of the furnace group, selects corresponding detection components and sensors, and automatically records the production process of the brand name through the SCADA platform software, forming an electronic version of the production process tracking card, and gets rid of the dependence on manual labor. , to improve data authenticity and controllability.

Description

technical field [0001] The invention relates to a production process tracking and benchmarking system and an operation method, in particular to a production data tracing and benchmarking system and method based on big data, and belongs to the technical field of secondary aluminum. Background technique [0002] Technology status: It is the first domestic initiative in the recycled metal industry, which automatically collects the duration of the process in the recycled metal industry and the corresponding temperature of molten aluminum for benchmarking with standard operating procedures. [0003] At present, in the domestic recycled metal industry, the duration of the grades in the furnace group production process (melting, alloying, stirring, alloying holding time, slag cleaning, degassing, standing, online degassing and casting, heat preservation and other processes) and the corresponding The temperature of molten aluminum) is mainly recorded manually, which will waste a lot...

AI Technical Summary

Problems solved by technology

[0003] At present, in the domestic recycled metal industry, the duration of the grades in the furnace group production process (melting, alloying, stirring, alloying retention time, slag removal, degassing, standing, online degassing, casting, heat preservation, etc.) Aluminum liquid temperature) is mainly recorded manually, which will waste a lot of material and financial resources, and will also produce certain errors in the process of data recording, resulting in a serious decrease in controllability

[0004] During the smelting process of recycled metals, there are one or more operations in the same process, and the records of the on-site processes are completely dependent on manual filling, which not only increases the workload of the on-site operators, but also has low reliability in obtaining the data of the brand production process, which is compared with standard operations. The reliability of the label is low, affecting the quality of the brand, prolonging the production cycle, reducing the controllability, increasing the energy consumption of the furnace group and the production cost of the brand

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