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BP neural network and MPSO algorithm-based aluminium electrolysis energy-saving and emission-reduction control method

A BP neural network, energy saving and emission reduction technology, applied in neural learning methods, biological neural network models, etc., can solve the problems of polluting the environment, high energy consumption, low efficiency, etc., to reduce energy consumption per ton of aluminum, reduce emissions, The effect of improving the current efficiency

Active Publication Date: 2016-03-30
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0003] This application provides a control method for energy-saving and emission-reduction of aluminum electrolysis based on BP neural network and MPSO algorithm to solve the problem of huge energy consumption, low efficiency, and Technical problems that seriously pollute the environment

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  • BP neural network and MPSO algorithm-based aluminium electrolysis energy-saving and emission-reduction control method
  • BP neural network and MPSO algorithm-based aluminium electrolysis energy-saving and emission-reduction control method
  • BP neural network and MPSO algorithm-based aluminium electrolysis energy-saving and emission-reduction control method

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Embodiment

[0039] Such as figure 1 As shown, a control method for energy saving and emission reduction of aluminum electrolysis based on BP neural network and MPSO algorithm includes the following steps:

[0040] S1: Select control parameters that affect current efficiency, energy consumption per ton of aluminum, and perfluorinated compound emissions to form a decision variable X=[x 1 ,x 2 ,...,x M ], M is the number of selected parameters;

[0041] The implementation is to count the original variables that have an impact on current efficiency, energy consumption per ton of aluminum, and perfluoride emissions in the production process of aluminum electrolysis, and determine the parameters that have the greatest impact on current efficiency, energy consumption per ton of aluminum, and perfluoride emissions as a decision variable X;

[0042]Through the statistics of the measured parameters in the actual industrial production process, the variables that have the greatest impact on curre...

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Abstract

The invention provides a BP neural network and MPSO algorithm-based aluminum electrolysis energy-saving and emission-reduction control method. Firstly, the modeling is conducted for the aluminum electrolysis production process based on the BP neural network. After that, based on the multi-objective particle swarm optimization (MPSO) algorithm, the model for the aluminum electrolysis production process is optimized to obtain a group of optimal solutions for each of all decision variables, the current efficiency, the energy consumption per ton of aluminum and the discharge of perfluorinated compounds corresponding to the group of optimal solutions. No crossover or mutation operation is required for the MPSO algorithm, so that the coding process is simple and easy in implementation. Meanwhile, compared with other algorithms, the MPSO algorithm has memory. In this way, not only a global optimal value and a local optimal value are retained, but also the optimal value integrity during the group evolution process is ensured. Based on the above method, the process parameters of the aluminum electrolysis production process are ensured to be optimal, and the current efficiency is effectively improved. The energy consumption per ton of aluminum is lowered, and the greenhouse gas emission load is reduced. The purposes of energy saving and emission reduction are really realized.

Description

technical field [0001] The invention relates to an automatic control technology in the production process of aluminum electrolysis, in particular to a control method for energy saving and emission reduction of aluminum electrolysis based on BP neural network and MPSO algorithm. Background technique [0002] Aluminum electrolysis is a complicated industrial production process, which is usually smelted by the Bayer process. However, this method consumes a lot of energy and has low efficiency. At the same time, a large amount of greenhouse gases will be generated in the process of aluminum electrolysis, causing serious environmental pollution. Therefore, on the premise of ensuring the stable production of aluminum electrolytic cells, how to improve current efficiency, reduce energy consumption, and reduce pollutant gas emissions to achieve high efficiency, energy saving, and emission reduction has become the production goal of aluminum electrolysis enterprises. However, comple...

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/08
Inventor 易军李太福何海波黄迪周伟张元涛刘兴华陈实
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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