Aluminum electrolysis production process multi-objective optimization method based on adaptive-step bacterial foraging algorithm

Abstract

The invention discloses an aluminum electrolysis production process multi-objective optimization method based on an adaptive-step bacterial foraging algorithm. The method includes the following steps that firstly, an aluminum electrolysis production index Y is determined, and a parameter X which has the largest influence on the aluminum electrolysis production index is selected; then the parameter X serves as input, the production index Y serves as output, and modeling is carried out on the aluminum electrolysis process through a back propagation (BP) neutral network, so that an aluminum electrolysis model is obtained; and afterwards, the output Y of aluminum electrolysis is used as a fitness function, the bacterial advancing step length is adjusted in a self-adaptive mode on the basis of Pareto differential entropy, the parameter X is optimized within a value range of the parameter X through the bacterial foraging algorithm, and therefore the optimal aluminum electrolysis production process parameter is obtained. The aluminum electrolysis production process multi-objective optimization method has the beneficial effects that the aluminum electrolysis parameter is optimized based on the bacterial foraging algorithm, so that the aluminum electrolysis production efficiency is effectively improved; the bacterial advancing step length is adjusted in an adaptive-step mode, so that the bacterial foraging algorithm is effectively protected against the locally optimal solution; the flora step length is dynamically adjusted through the Pareto differential entropy, and therefore the optimal parameter of aluminum electrolysis production can be quickly obtained.

Description

technical field [0001] The invention relates to the field of optimal control, in particular to a multi-objective optimization method for aluminum electrolysis production process based on an adaptive step size bacterial foraging algorithm. Background technique [0002] Aluminum electrolysis is a complex industrial production process. The complex chemical changes of materials inside the aluminum electrolytic cell and various external uncertain operating factors lead to many parameters in the cell. The parameters are characterized by nonlinearity and strong coupling, making it difficult to measure in real time. , Adjustment, brings certain difficulty to the control optimization of aluminum electrolysis production process. The current aluminum electrolysis method consumes a lot of energy and is inefficient, and the production process of aluminum electrolysis will generate a lot of greenhouse gases, causing serious environmental pollution. Therefore, on the premise of ensuring t...

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Problems solved by technology

[0002] Aluminum electrolysis is a complex industrial production process. The complex chemical changes of materials inside the aluminum electrolytic cell and various external uncertain operating factors lead to many parameters in the cell. The parameters are characterized by nonlinearity and strong coupling, making it difficult to measure ...

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