化工学报2025，Vol.76Issue(4)：1693-1701,9.DOI:10.11949/0438-1157.20241122
事件驱动的深度信念网络软测量模型设计方法
Design method of event-driven deep belief network soft-sensing model
摘要
Abstract
Aiming at the problem that the key parameters of complex chemical process are difficult to be accurately soft-measured due to the non-stationarity and event-driven characteristics,an event-driven deep belief network(EDDBN)soft-sensing model is proposed.First,the operating data of chemical process is obtained and a deep belief network(DBN)model is built.The DBN model is trained in a data-driven way to obtain a soft sensor model based on DBN.Second,some events are defined based on the training-error characteristics of the DBN model.The learning step of parameters in DBN model will be accelerated when the positive events occur,and skip the current data sample and directly go to the next data sample.This event-driven selective learning strategy not only efficiently optimizes the training process of soft-sensing model,but also reduces the computational complexity.Meanwhile,this paper analyzes the boundedness of difference between performance potentials from two consecutive events by construct Markov chain-based dynamicl earning process,which gives convergence analysis of EDDBN training process.Finally,the EDDBN-based soft-sensing model is used to predict the concentration of SO2 in wet-flue-gas desulfurization system.The results show that it can efficiently and accurately predict the concentration of SO2 under such non-stationary operating conditions,and the computational complexities of data set ① and data set② are nearly reduced by 63.83%and 63.33%,respectively.关键词
事件驱动的学习/深度信念网络/软测量/化工过程/湿法烟气脱硫系统Key words
event-driven learning/deep belief network/soft-sensing/chemical process/wet-flue-gas desulfurization system分类
信息技术与安全科学引用本文复制引用
李征,庄铠泽,赵东杰,宋燕星,王功明..事件驱动的深度信念网络软测量模型设计方法[J].化工学报,2025,76(4):1693-1701,9.基金项目
北京市教育委员会科研计划一般项目(KM202210037003) （KM202210037003）
北京物资学院青年科研基金项目(2022XJQN23) （2022XJQN23）
国家自然科学基金面上项目(62373018) （62373018）
北京市自然科学基金面上项目(4232043) （4232043）
北京市博士后科研活动资助项目(2022ZZ-074) （2022ZZ-074）
