The updating strategy for the safe control Bayesian network model under the abnormity in the thickening process of gold hydrometallurgy

To adapt to the change in the environment, the model needs to own the ability to update to ensure the performance of the decision. In this paper, a new updating strategy is proposed for the safe control Bayesian network (BN) model under the abnormity in the thickening process of gold hydrometallurgy. First of all, the abnormality in the thickening process of gold hydrometallurgy is analyzed deeply. The new safe control BN model is established for three main abnormities. Furthermore, the general framework of BN model updating strategy based on the new expert knowledge and the new dataset is proposed, which mainly includes the parameter updating learning and the structure updating learning. For the structure updating learning, the useful information in the established model is reserved, and only the partial structure which does not adapt to the change in the environment is changed by searching for the unconformable nodes as the target nodes. Finally, the proposed method is applied to update the safe control BN model in the thickening process of gold hydrometallurgy. The simulation results demonstrate that it is effective and owns the better performances to update the established model as the change of the dosage of flocculants