Operational adjustment modeling approach based on Bayesian network transfer learning for new flotation process under scarce data

Abstract

Establishing an intelligent operational adjustment model is significant for optimizing flotation performance and realizing the stable control of the flotation process. When sufficient historical data is available, the Bayesian network (BN) has been used to achieve this goal due to its powerful uncertainty modeling and reasoning capabilities. However, for a new flotation process in the early stage with scarce data, an accurate BN model can hardly be established to make reliable decisions. In this scenario, it is an effective way to transfer data or knowledge from other similar flotation processes to the new flotation process to help complete the target BN modeling task. Therefore, the operational adjustment modeling problem is transformed into the BN transfer learning problem. Moreover, this article proposes a new BN transfer learning approach, including structure and parameters transfer learning techniques. For structure transfer learning, a majority voting mechanism is proposed to get an integrated result of conditional independence tests and generates the network structure based on them. For parameters transfer learning, the sources are judged whether to be used to learn the parameters first, and then the suitable sources are used to learn the network parameters based on the weight determined by the similarity. The Asia network first assesses the proposed approach and then applies it to real-world flotation operational adjustment decisions. Experimental results on the Asia network show that the proposed approach can learn more accurate structure and parameters according to F1 score, Hamming distance, and KL divergence indicators compared to existing advanced methods. Furthermore, the experimental results of the flotation process show that the inference accuracy is improved from 57.1% to 85.7% compared with the traditional modeling method.