Self‐optimization for smelting process of fused magnesium furnace based on operation status assessment

Abstract

Based on operation status assessment and self‐optimizing regulation, an optimization strategy for the smelting process of a fused magnesium furnace is proposed to improve the benefits of smelting magnesia. In the online evaluation stage, according to the qualitative information, the data is divided by the fuzzy model recognition method, and then the posterior probability of online data in different Gaussian mixture models is calculated to obtain the evaluation results. When the evaluation results are not optimal, the non‐optimal variables are obtained by calculating the contribution rate, and then the self‐optimizing adjustment is carried out by case search. In order to improve the recovery rate of self‐optimizing regulation, a time series prediction method is used to predict the operation status, and the corresponding operation schemes for different prediction results are proposed. Experimental results show that the proposed method is accurate and effective.

Process operation performance assessment of electro‐fused magnesium furnace based on deep auto‐encoder transfer generative adversarial network

“…In recent years, research based on the assessment of operation performance has been widely used in industrial processes. The operation POPA for fused magnesium smelting process is proposed based on the Gaussian mixture model in the work of Niu et al [ 7 ] However, the non‐Gaussian characteristic is more universal in most industrial process data, which makes the application of this method limited. For non‐Gaussian and nonlinear processes, based on the independent kernel component analysis, Liu et al [ 8 ] proposed a performance evaluation method.…”