Multi-target optimization method for bath voltage in aluminum electrolysis process

A multi-objective optimization and cell voltage technology, applied in the field of aluminum electrolysis, can solve problems such as time-consuming, high operator dependence, and space reduction

Active Publication Date: 2019-08-16
GUANGXI UNIV
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Problems solved by technology

At present, most factories use heuristics to adjust the set value, but the control effect

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  • Multi-target optimization method for bath voltage in aluminum electrolysis process
  • Multi-target optimization method for bath voltage in aluminum electrolysis process
  • Multi-target optimization method for bath voltage in aluminum electrolysis process

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[0033] The present invention will be described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by specific embodiments.

[0034] In aluminum electrolytic production, controllable parameters include cell resistance, molecular ratio, and alumina concentration, while other parameters are uncontrollable or cannot be precisely controlled. The invention establishes a multi-objective optimization model of the tank voltage, seeks a set of optimal controllable parameter setting values, and achieves the effect of reducing the tank voltage by controlling the set of parameters within the target range, thereby achieving stable and energy-saving production goals. There are three main links in the specific implementation, establishing the objective function model, building the optimization model, and solving the model, as shown in the attached figure 1 shown.

[0035] Reducing cell ...

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Abstract

The invention discloses a multi-target optimization method for bath voltage in an aluminum electrolysis process. The multi-target optimization method comprises the following steps: step 1, collectingand washing data of an aluminum electrolysis production site; step 2, establishing a bath state comprehensive index model related to stability, material balance and energy balance; step 3, establishing an autoregression moving average model for predicating a time sequence of a comprehensive index; step 4, taking a state time sequence solved in step 3 as input and establishing a fuzzy neural network for predicating the bath state; step 5, establishing a bath voltage predication model by adopting an ant lion optimized LSSVM (Least Squares Support Vector Machine); step 6, taking minimum difference between the bath voltage and a target value and a good bath state as targets and taking production operation requirements as constraint conditions to establish a multi-target optimization model forthe bath voltage; and step 7, solving the model by adopting a multi-target ant lion optimization algorithm, so as to solve one group of optimized setting values. The group of optimized setting value can provide guidance for operating workers; and the bath voltage is reduced while the good bath state is ensured and the aims of energy saving and consumption reduction are realized.

Description

technical field [0001] The invention relates to the technical field of aluminum electrolysis, in particular to a multi-objective optimization method for cell voltage in an aluminum electrolysis process. Background technique [0002] In the production process of aluminum electrolysis, under the premise of ensuring the healthy and stable operation of the production, reasonably reducing the cell voltage is an effective way to save energy and reduce consumption. Due to the nonlinear and strong coupling relationship between production parameters, and the tank voltage cannot be directly controlled, it is not possible to simply reduce the set voltage in the tank control machine, which may lead to a decrease in current efficiency and a deterioration of the tank state. If you want to reduce the tank voltage, the operator needs to adjust the set value of the relevant controllable parameters in combination with the current tank status and its trend, so as to achieve the effect of indir...

Claims

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Application Information

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IPC IPC(8): C25C3/20
CPCC25C3/20
Inventor 徐辰华谢春杨继君张进智黄清宝刘斌
Owner GUANGXI UNIV
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