基于自适应模糊核聚类的多模型软测量建模研究
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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。
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.
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。
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.
引文
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[3]俞金寿,刘爱伦,张克进.软测量技术及其在石油化工中的应用[M].化学工业出版社,2000
[4]丁云,于静江,周春晖.原油蒸馏塔的质量估计和优化管理[J].石油炼制与化工1994,25(5)：23-28
[5]张克进,沈雁鸣,俞金寿.软测量在分馏塔粗汽油干点推断控制中的应用[J].自动化仪表.1999,20(11)：17-19
[6]黄洁,王忠诚.塔板持液量的测定与计算[J].化学工程1993,21(4)：6-10
[7]颜学峰.基于径向基函数-加权偏最小二乘回归的干点软测量[J].自动化学报,2007,33(2)：193-196
[8]杜锋,刘全,雷鸣.软测量技术及其在发酵过程中的应用[J].食品科学,2002,23(8)：352-356
[9]Tham M T, Montague G A, Morris A J, Lant P A. Soft-sensors for process estimation and inferential control[J]. Journal of Process Control,1991,1(1):3-14
[10]叶楠,吕勇哉.模式识别在状态估计中的应用-一类软测量技术[J].仪器仪表学报,1988,9(4)：368-374
[11]穆罕默德-阿塔,蒋慰孙.在线精馏塔板效率的模式识别[J].信息与控制,1993,22(1)：47-49
[12]王旭东,邵惠鹤.神经元网络建模技术与软测量技术[J].化工自动化及仪表,1996,23(2)：28-31
[13]许勇刚,杨辉.基于RBF网络的稀土萃取过程组分含量软测量[J].稀土2007,28(5)：19-22
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[16]王李东,侍宏博.基于SVR算法的短期快速预测研究[J].继电器.2005,33(9)：17-21
[17]杜树新,吴铁军.回归型加权支持向量机方法及其应用[J].浙江大学学报,2004,38(3)：302-306
[18]张绍武,潘泉,张洪才等.基于支持向量机和贝叶斯方法的蛋白质四级结构分类研究[J].生物物理学报,2003,19(2)：171-175
[19]郭宗明,张治洲,潘宇曦等.利用支持向量机预测生物膜蛋白类型[J].上海交通大学学报,2004,38(5)：806-809
[20]李颖新,朱云华,阮晓刚.基于支持向量机的肿瘤亚型识别[J].计算机工程与应用,2004,(17)：1-3
[21]邵信光,杨慧中,石晨曦.ε不敏感支持向量回归在化工数据建模中的应用[J].东南大学学报(自然科学版),2004,34(s1)：215-218
[22]刘斌,苏宏业,褚健.一种基于最小二乘支特向量机的预测控制竹法[J].控制与策,2004,19(2)：1399-1402
[23]常玉清,王福利,王小刚等.基于支持向量机的生物发酵过程软测量建模[J].东北大学学报(自然科学版),2005,26(11)：1025-1028
[24]张立岩,岳恒,柴天佑等.基于小二乘-支持向量机的制粉过程煤粉细度软测量模型[J].清华大学学报(自然科学版),2007,47(s1)：1932-1935
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