【作者】 陈贵华；

【导师】 王振雷； 王昕；

【作者基本信息】 华东理工大学 ， 控制科学与工程， 2012， 硕士

【摘要】 软测量技术可以解决现代复杂工业过程中较难甚至无法由硬件在线测量参数的问题,是实时估计的有效手段。乙烯裂解炉裂解过程十分复杂,具有非线性、时变、大滞后等特点,为了提高原料的利用率,根据原料组成变化实时调整操作参数,使得双烯(丙烯、乙烯)收率最大化,需要把裂解炉的裂解深度控制在较优的数值。本文在乙烯裂解深度在线测量现状的基础上,分析油品数据属性和现场操作参数,采用自适应模糊核聚类的方法对其进行模糊划分,建立了多个子模型和基于模糊隶属度加权的切换策略,针对乙烯裂解过程中双烯收率和裂解深度的软测量问题,提出了一种基于差分进化算法-最小二乘支持向量机的多模型建模方法。本文针对模糊核聚类算法的初始聚类中心、个数优选和聚类性能进行了研究,提出了一种自适应模糊核聚类算法,并通过Iris数据集和石脑油属性数据验证了新算法的有效性。在自适应模糊核聚类算法的基础上,针对LSSVM建模时存在的超参数选取问题,提出用差分进化算法进行全局寻优,并提出具体的优化策略,通过聚酯酯化率的的建模验证了该算法具有良好的预测性能和泛化能力。针对双烯收率和裂解深度软测量建模问题,提出基于DE-LSSVM的多模型建模方法,经过机理分析选取软测量的辅助变量和主导变量,建立基于各个子工况的多模型和相应的切换策略,最后通过仿真实验验证了新算法的有效性。更多还原

【Abstract】 In modem complicated industrial process, some variables are very hard to be measured or even cannot be measured on-line by existing instruments and sensors. Soft sensor is an effective means of implementing the on-line evaluation of these variables. The ethylene cracking process is complex, characterized of nonlinearity and time-variant properties.In order to improve the utilization of raw materials, change raw material composition according to operating parameters in real time and make the ethylene and propylene yield maximization, the cracking severity must be controlled on better values.This research is based on the current state of ethylene cracking severity online measurement and analysising the disadvantages of traditional soft sensor modeling methods. To address the problem with the complexity and volatility of Naphtha feedstock components, adaptive fuzzy kernel clustering method was developed to divide the naphtha database optimally. After establishing multiple models of least squares support vector machine, in order to improve the model accuracy and generalization ability, differential evolution algorithm was used to determine the proper parameters of LSSVM model.In this paper, to address the issue of the initial clustering centers and the number of clustering, we proposed an adaptived fuzzy kernel clustering algorithm. Finally, through the experiment of Iris data set and naphtha attribute data, we qualify the effectiveness of our new algorithm. To solve the problem of parameters selecting of LSSVM, differential evolution algorithm is proposed. We also propose specific optimization strategy to apply this algorithm. Simulation on polyester esterification rate results shows that the model has stronger ability to generalization. To address the problem of ethylene cracking severity soft-sensor modeling, we introduced the background and process of ethylene cracking process and proposed a multi-model modeling method-LSSVM After selecting auxiliary variables and leading variables through mechanism analysis soft sensor, we established each sub-model based on the condition of sub-condition in chemical process. also the switching strategy is based on weighted value. Simulation results show that the proposed model is precise.更多还原