化工进展2026，Vol.45Issue(2)：672-684,13.DOI:10.16085/j.issn.1000-6613.2025-0202
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
Improved FCM-based soft sensor method for scaling parameters in multi-condition alumina evaporation process
摘要
Abstract
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.关键词
蒸发结垢过程/时序约束/模糊C均值聚类/多工况预测/机理数据联合驱动Key words
evaporation scaling process/temporal constraints/fuzzy C-means clustering/multi-operating condition prediction/mechanism-data hybrid driven分类
信息技术与安全科学引用本文复制引用
韩洁,赵灼,朱亮,任超,桂卫华..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[J].化工进展,2026,45(2):672-684,13.基金项目
国家自然科学基金(62473383,62394340) （62473383,62394340）
第九届青托工程(2023QNRC001) （2023QNRC001）
中南大学研究生自主探索创新项目(2024ZZTS0782). （2024ZZTS0782）
