端边云协同的氧化铝生产过程苛性碱浓度智能预报方法  

Intelligent Forecasting Method of Caustic Concentration in Alumina Production Process Based on End-edge-cloud Coordination

作　　者：高愫婷 柴天佑 GAO Su-Ting;CHAI Tian-You(State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang 110819;National Engineering Research Center of Metallurgy Automation,Shenyang 110819)

机构地区：[1]东北大学流程工业综合自动化国家重点实验室,沈阳110819 [2]国家冶金自动化工程技术研究中心,沈阳110819

出　　处：《自动化学报》2023年第5期964-973,共10页Acta Automatica Sinica

基　　金：2020年度辽宁省科技重大专项计划(2020JH1/10100008);国家自然科学基金委重大项目(61991404,61991400);一体化过程控制学科创新引智基地2.0(B08015)资助。

摘　　要：苛性碱溶液浓度是氧化铝生产过程中的重要运行指标,由于苛性碱溶液的温度和浓度频繁波动,导致目前的浓度检测仪表检测精度低,只能采用人工化验获得苛性碱浓度值,化验结果的严重滞后导致无法实现苛性碱浓度的自动控制,影响氧化铝产品质量.在分析苛性碱溶液浓度控制过程动态特性的基础上建立了由线性模型和未知非线性动态系统描述的苛性碱浓度预报模型,将参数辨识与自适应深度学习相结合,提出端边云协同的氧化铝生产过程苛性碱浓度智能预报方法,并采用氧化铝生产企业的实际生产数据对本文所提方法进行应用验证.应用结果表明,所提的苛性碱浓度智能预报方法可以实时、准确预报苛性碱浓度,为实现苛性碱浓度的闭环运行优化控制创造了条件.Caustic concentration is an important operating indicator in alumina production process.Due to frequent fluctuations in terms of the temperature and concentration of caustic solution,the precision of current concentration meters can not be guaranteed.High precision caustic concentration can only be obtained through manual assay.However,the severe lag of assay results will lead to the failure of automatic control of caustic concentration,which affects the quality of alumina products.In this paper,the dynamic characteristics of caustic concentration are analysed,and a caustic concentration forecasting model described by a linear model and an unknown nonlinear dynamic system is established.Then a novel intelligent forecasting method for caustic concentration of alumina production process based on end-edge-cloud cooperation is established by incorporating parameter identification with adaptive deep learning.The application verification of the proposed method is performed on actual production data from an alumina manufacturer.The results show that the proposed intelligent forecasting method is able to forecast caustic concentration accurately in real time,providing conditions for achieving the closed-loop optimal control of caustic concentration.

关 键 词：苛性碱浓度 未知非线性动态系统 端边云 自适应深度学习 长短周期记忆 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TQ133.1[自动化与计算机技术—控制科学与工程]