Aluminum electrolytic production technology optimization method based on BP neural network and MBFO algorithm

Abstract

The invention discloses an aluminum electrolytic production technology optimization method based on a BP neural network and an MBFO algorithm. The aluminum electrolytic production technology optimization method comprises the following steps that step one: statistics of parameters having high influence on current efficiency, ton aluminum energy consumption and perfluoro-compound emissions is performed and the parameters act as decision variables X; step two: an aluminum electrolytic production process model is established by using the BP neural network; step three: the decision variables are optimized within the value range of the decision variables by using the MBFO algorithm; and step four: field control is performed according to the optimal decision variables. The beneficial effects are that the optimal values of the technological parameters can be determined and can be applied to actual production so that current efficiency in the aluminum electrolytic production process can be enhanced, and ton aluminum energy consumption and perfluoro-compound emissions can be reduced and thus the objectives of energy conservation, consumption reduction and emission reduction can be achieved.

Description

technical field [0001] The invention relates to the field of aluminum electrolysis industrial production, in particular to an aluminum electrolysis production process optimization method based on BP neural network and MBFO algorithm. Background technique [0002] Aluminum electrolysis is a complex industrial production process, which is usually smelted by the Bayer method, but this method consumes a lot of energy, has low efficiency, and produces a large amount of greenhouse gases, 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, complex material chemical changes inside the aluminum electrolytic cell and various external uncertain operating factors lead to...

AI Technical Summary

Problems solved by technology

However, complex material chemical changes inside the aluminum electrolytic cell and various external uncertain operating factors lead to many parameters in the cell, and the parameters are characterized by nonlinearity and strong coupling, and parameters such as pole distance and insulation material thickness are difficult Real-time measurement and adjustment bring certain difficulties to the control optimization of aluminum electrolysis production process

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