基于改进FCM的多工况氧化铝蒸发过程结垢参数软测量方法OA

The evaporation process represents a key stage in alumina production,where scaling significantly impairs the heat transfer efficiency of the evaporator and compromises the stability of the system's operation.To address the time-delay characteristics and the complexity of dynamic operating condition transitions in the scaling process,a multi-operating-condition scaling parameter online prediction method based on temporally constrained fuzzy C-means(FCM)clustering was proposed.The proposed method incorporated temporal smoothing constraints to enhance the dynamic adaptability of FCM clustering for operating condition segmentation,effectively capturing the time-dependency characteristics of the scaling process.For each operating condition,a hybrid soft-sensing framework combining mechanistic and data-driven models was used to predict the scaling severity.Additionally,a dynamic smoothing mechanism for operating condition switching was designed based on membership distribution to ensure the continuity and stability of the prediction results.Experimental results demonstrated that the proposed method achieved superior scaling parameter prediction accuracy under multi-operating conditions compared to conventional data-driven methods and mechanistic models,while effectively addressing complex nonlinear behaviors induced by time delays.