An Operational Adjustment Framework for a Complex Industrial Process Based on Hybrid Bayesian Network

The operational variables used to adjust the control level of the copper cleaner flotation process have a noticeable impact on the object variables, e.g., the copper concentrate grade. Currently, due to the complexity of the flotation process, the operational variables, which are controlled by operators, are often not adjusted properly in time. Hence, this article investigates an intelligent operational adjustment framework based on a hybrid Bayesian network (BN). The offline BN model structure and the parameters are established based on process knowledge and real industrial data, respectively. After receiving the expected value of the copper concentrate grade as evidence, an operational adjustment can be obtained online by BN reasoning. To ensure its credibility, the copper concentrate grade after operational adjustment is further predicted. According to the predicted value, the operators can determine whether to implement the operational adjustment or not. Finally, the experimental results show the effectiveness and practical significance of the proposed method. Note to Practitioners—This article was motivated by the problem of operational adjustment in the copper cleaner flotation process. Because of the current manual operational adjustment, controlling the concentrate grade within its acceptable range is difficult. This article suggests a hybrid Bayesian network (BN) approach to address this problem. The hybrid BN model is built offline and used to make inferences online. Compared with a conventional discrete BN, this approach can provide operators with specific adjustment values to qualify the concentrate grade. For the evaluation, data experiments are used. This evaluation shows the efficiency and superiority of the proposed approach in terms of automation, intelligence, and decision-making.