Provided is a dynamic evolution modeling method for
aluminum electrolysis process electrolytic bath technology
energy consumption. The method is characterized by including the following steps of step 1, collecting data [XN, Y], step 2, carrying out normalization
processing on the collected data, step 3, carrying out modeling on the data after the normalization
processing by strongly tracking a square root trackless Kalman neural network, and step 4, estimating an
electrolysis process
energy consumption value by applying an established model to obtain a technology
energy consumption value of the
electrolysis process at the moment. The method has the advantages that advantages of strong tracking filtering and square root filtering are combined, convergence rates of the model and tracking ability on electrolytic bath
mutation states are improved, the
algorithm is stable, accuracy is high, tracking ability on the electrolytic bath
mutation states is strong, therefore, real
time estimation on the
aluminum electrolysis process electrolytic bath technology energy consumption is achieved, technology operations on the
aluminum electrolysis process can be optimized, and the purposes of saving energy and reducing emission can be achieved.