基于数据驱动技术和工艺机理模型的PTA生产过程软测量建模方法  

Data-driven technology and mechanism model based soft sensor modeling in PTA process

作　　者：姚科田[1] 邵之江[1] 陈曦[1] 纪彭[1] 蒋鹏飞[1] 

机构地区：[1]浙江大学控制科学与工程系,浙江杭州310027

出　　处：《计算机与应用化学》2010年第10期1329-1332,共4页Computers and Applied Chemistry

基　　金：国家高技术研究发展计划(863)(2008AA042902);国家自然科学基金(40740420661和60974007);浙江省自然科学基金(Y1080406);中央高校基本科研业务费专项资金

摘　　要：PTA生产过程物耗指标和质量指标实时准确的监控,是PTA生产企业降低生产成本、提高市场竞争力的重要途径。现存的PTA工艺机理模型无法实时有效地对PTA生产过程进行监控,本文提出了1种基于数据驱动技术和工艺机理模型的软测量建模方法,通过采用主元分析和模糊C均值聚类(PCA-FCM)算法将PTA历史工况进行分类,建立BP神经网络和偏最小二乘(PLS)的组合多工况模型,对PTA生产过程的物耗指标和质量指标进行预测。以某化工厂的投运效果分析,该方案有效地对PTA重要指标作了实时准确的预测,其中质量指标4CBA的相对误差控制在3%左右,有效地帮助现场操作人员将生产装置稳定运行在较优工况点。Accurately real-time monitoring for consumption and quality indicators in PTA process,is an important way to reduce production cost and increase market competitiveness for the PTA enterprise.The existing mechanism model for PTA process cannot be real-time and effective on the monitoring.A soft sensor model based on data-driven technique and process mechanism is presented in this paper.By using primary component analysis and fuzzy C means clustering（PCA-FCM）algorithm to classify PTA historical stages,the combined model including BP neural networks model and partial least squares model is established to predict product quality indicators and consumption indicators.The actual operating results proved the validity of the proposed diagnosing method.The relative error of 4CBA quality indicator is about 3%,which help process engineers stabilize the plant in optimum working condition effectively.

关 键 词：数据驱动 主元分析 模糊C均值聚类 BP神经网络 偏最小二乘 

分 类 号：TQ015.9[化学工程] TP391.9[自动化与计算机技术—计算机应用技术]