The modeling based on AMCMCUPFNN for aluminum electrolysis energy consumption

Abstract:

The accuracy of the energy consumption models for aluminum electrolysis is poor due to the intensive noise, unknown distribution types and high-dimensional parameter characteristics in the aluminum electrolysis process. In order to solve the problem, a novel modeling method based on Adaptive Markov Chain Monte Carlo Unscented Particle Filter Neural Network (AMCMC-UPFNN) is proposed. This method firstly used the square term of the κ parameter in the Unscented Transformation (UT) to replace the corresponding regular term in the UPFNN algorithm, avoiding the nonpositive definite situation of the UT matrix due to high dimension, and ensuring the reasonableness of Sigma point sampling in UPFNN; then, an adaptive sampling strategy was introduced on the basis of the traditional MCMC method to maintain the diversity of particles and make the established probability density distribution closer to the true distribution. Finally, the verification experiment of aluminum electrolysis industrial application was carried out to compare the proposed method with related modeling methods. The results show that the relative error rate of the AMCMCUPFNN model does not exceed 1%, and it has achieved better performance indicators than PFNN, UPFNN and MCMC-UPFNN.