Hybrid approach integrating case-based reasoning and Bayesian network for operational adjustment in industrial flotation process

Abstract In the industrial flotation process, the operational adjustment is still manual, which mainly relies on the operator’s observation of the flotation froth. Due to limited experience or operation lag, technical indexes such as concentrate grade are difficult to control within the qualified range. In the era of big data, case-based reasoning (CBR) and Bayesian network (BN) are two advanced technologies that can realize intelligent operational adjustment. Although CBR is highly reliable, it is rough and has poor generalization performance. Besides, BN is challenging in responding to multi-working conditions and strong nonlinearities. Inspired by the advantages of the integrated models, a two-step meticulous operational adjustment approach for the flotation process combining CBR and BN is proposed in this article. A case library is constructed in the offline stage, consisting of cases whose technical index has been improved by an operational adjustment in history. After introducing a new case, the rough operational adjustment solution is first determined by CBR. Based on this, a new incremental database is constructed and used for online training of the BN model. After receiving the evidence of the new case’s problem attribute, the precise operational adjustment can be determined by BN reasoning. The final case solution to be performed is the sum of the rough and precise operational adjustment received in the two steps. Experiments in a real-world copper flotation process verify the performance and merit of the proposed hybrid approach. The results show that intelligent operational adjustment can significantly improve the copper concentrate grade index.