A safe control scheme under the abnormity for the thickening process of gold hydrometallurgy based on Bayesian network

This paper develops a safe control scheme under the abnormity based on Bayesian network (BN) for the thickening process of gold hydrometallurgy. By analyzing the causes and corresponding solutions of the abnormity, the operator experience of removing the abnormity is transformed to BN which is used to make safe decisions automatically. The proposed BN-based approach can provide real-time decision support when the abnormity happens. The BN combines the expert knowledge with quantitative data analysis for the abnormity which can make decisions to remove the abnormity. The BN is established off-line and used to infer on-line. After receiving abnormal phenomena as evidences, the posterior probabilities of the decision variables with different grades can be obtained by BN reasoning, which provide real-time safety analysis. The posterior probabilities can be updated using the abnormal latest information. If the abnormity is not removed, once again the BN analysis is invoked based on the newly established situation and the appropriate set of decisions are obtained accordingly. The application results show that the proposed approach can make the safe operation control decisions to remove the abnormity in the thickening process effectively