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.