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.

在工业浮选过程中，操作调整仍然是手动的，这主要取决于操作员对浮选泡沫的观察。由于经验有限或操作滞后，精矿品位等技术指标难以控制在合格范围内。在大数据时代，基于案例的推理（CBR）和贝叶斯网络（BN）是可以实现智能操作调整的两项先进技术。尽管CBR高度可靠，但它粗糙且泛化性能差。此外，BN在应对多种工作条件和强大的非线性方面也具有挑战性。受到集成模型优势的启发，本文提出了结合CBR和BN的浮选过程的两步精细操作调整方法。脱机阶段构建了一个案例库，该案例库包含通过历史操作调整而提高了技术指标的案例。引入新案例后，首先由CBR确定粗略的业务调整解决方案。在此基础上，构建了一个新的增量数据库，并将其用于BN模型的在线培训。收到新案件的问题属性的证据后，可以通过BN推理确定精确的操作调整。要执行的最终方案是两个步骤中收到的粗略和精确的操作调整之和。在现实世界中的铜浮选过程中的实验验证了所提出的混合方法的性能和优点。结果表明，智能操作调整可以显着提高铜精矿品位指标。