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Method for inversion of aluminum reduction cell damage based on machine learning algorithms

The present application relates to the technical field of aluminum electrolysis, and provides an aluminum electrolysis cell damage inversion method based on a machine learning algorithm, comprising the following steps: obtaining current data of an aluminum electrolysis cell through a fiber-optic current sensor, and obtaining temperature data of the aluminum electrolysis cell; performing discharge area division according to a discharger of the aluminum electrolysis cell; performing area division on the current data and the temperature data, and performing feature extraction on the current data and the temperature data; establishing a physical information forward model based on a neural network, and associating and mapping a damage state vector with current feature data and temperature feature data through the physical information forward model; constructing a cathode joint loss function and an anode joint loss function; respectively performing iterative solution on the cathode joint loss function and the anode joint loss function through an optimization algorithm, outputting an optimal damage state vector, and generating a damage space distribution result of the electrolysis cell. The present application can realize online inversion and spatial distribution visualization of anode and cathode damage states.
Owner:GUANGXI ACAD OF SCI +3