Method for improving aluminum electrolysis production efficiency based on data driving
A production efficiency, data-driven technology, applied in knowledge-based computer systems, chemical data mining, neural learning methods, etc., can solve problems such as complex processes, difficult modeling, analysis and judgment, and avoid data redundancy.
Pending Publication Date: 2022-06-28
QINGDAO HONGJIN E COMMERCE CO LTD
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[0003] The purpose of the present invention is to provide a data-driven method for improving the production efficiency of aluminum electrolysis, to solve the problem of difficult modeling, analysis and judgment due to the complex process in the production process of the existing aluminum electrolysis cell, and to realize the improvement of the current of the electrolysis cell through the model Efficiency, the purpose of reducing energy consumption
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[0047] In order to make the objectives, technical solutions and advantages of the present invention more clear, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.
[0048] like figure 1 As shown, a data-driven method for improving the production efficiency of aluminum electrolysis includes the following steps:
[0049] S1. Obtain the production data of an aluminum electrolytic cell in a factory. The production data is a daily sample from March 1, 2021 to August 31, 2021, including pole distance, current intensity, current efficiency, anode effect coefficient, Anode current density, average voltage of electrolytic cell, electrolyte level, aluminum level, cell temperature, electrolyte molecular ratio, alumina concentration, aluminum output, number of instal...
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The invention discloses a method for improving aluminum electrolysis production efficiency based on data driving, and relates to the technical field of aluminum electrolysis production. Obtaining production process parameters in the aluminum electrolysis cell, analyzing the importance of each variable in the original data set, and deleting 16 variables in the original data set; the polar distance, the cell temperature, the aluminum oxide concentration, the anode effect coefficient, the anode current density, the average voltage of the electrolytic cell, the electrolyte level, the aluminum level, the electrolyte molecular ratio and the current efficiency which can influence the current efficiency are selected to establish a prediction model, and data redundancy is avoided; a data driving model is obtained through a multi-layer perceptron MLP neural network, a particle swarm optimization algorithm is used for solving, the controllable variable polar distance, the cell temperature and the aluminum oxide concentration of the aluminum electrolysis cell serve as decision variables, the remaining uncontrollable variables are responded, the optimal operation condition of the controllable variables is obtained, and the purposes of energy conservation and consumption reduction of the aluminum electrolysis cell are achieved.
Description
technical field [0001] The invention relates to the technical field of aluminum electrolysis production, in particular to a data-driven method for improving the production efficiency of aluminum electrolysis. Background technique [0002] Modern aluminum industrial production mainly adopts cryolite-alumina molten salt electrolysis method, and the chemical reaction equation is: 2Al 2 O 3 +3C=4Al+3CO 2 ↑. The anode products are mainly carbon dioxide and carbon monoxide gas, and the cathode products are aluminum liquid, which is extracted from the tank by vacuum ladle and sent to the foundry. Electrolytic aluminum is a high energy-consuming industry. At present, each ton of electrolytic aluminum consumes about 13000-14000KWh of electricity. In addition to the main raw materials and the energy consumption of the production process, each ton of electrolytic aluminum will consume 6-8 tons of standard coal, reducing ton of aluminum. Power consumption is the goal pursued over th...
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IPC IPC(8): G16C20/10G16C20/70G06N3/04G06N3/08G06N5/00C25C3/20
CPCG16C20/10G16C20/70G06N3/086C25C3/20G06N5/01G06N3/048G06N3/045
Inventor 寇腾跃孙延贞于忠清
Owner QINGDAO HONGJIN E COMMERCE CO LTD