Safety control modeling method based on Bayesian network transfer learning for the thickening process of gold hydrometallurgy

Abstract

When the data information of target domain is very limited, it is difficult to establish the accurate model to analyze the target problem. For the safety control modeling problem, this paper develops a new Bayesian network (BN) transfer learning strategy for the thickening process of gold hydrometallurgy. First of all, the safety control modeling problem in this process is analyzed deeply. When the data information of abnormality is insufficient, the safety control modeling problem is transformed into the BN transfer learning problem. Furthermore, the new BN transfer learning strategy is proposed, which includes the structure and parameters transfer learning methods. For the structure transfer learning, by integrating the common structural information of multiple sources and the useful information of target, the final structure of target is determined. For the parameters transfer learning, by distinguishing the similarity of multiple sources, the parameters of target are obtained by the fusion algorithm. Finally, the proposed method is verified by the Asia network and it is applied to establish the safety control model for the thickening process of gold hydrometallurgy. The simulation results demonstrate that the proposed method is effective and owns the better performances than the traditional modeling method.