加氢裂化尾油在SRT-IV（HC）型裂解炉上裂解建模及应用

介绍了加氢裂化尾油的裂解性能，并针对鲁姆斯SRT—IV（HC）型工业裂解炉，对加氢裂化尾油裂解过程而建立基于Hirato模型的分子动力学模型，模型的一次反应选择性系数的调整采用随机搜索算法，既可以满足碳氢平衡和又可以保证较高的模拟精度。采用特雷纳数值积分算法对所建立的模型作了模拟计算，通过改变操作条件，包括炉管出口温度、停留时间和汽／油比，研究了裂解炉出口产物收率分布的变化情况，对照实际生产数据作分析，证实模型合理，对生产具有参考价值。     

加氢裂化尾油在SRT-Ⅳ(HC)型裂解炉上裂解建模及应用

介绍了加氢裂化尾油的裂解性能．并针对鲁姆斯SRT-IV(HC)型工业裂解炉,对加氢裂化尾油裂解过程而建立基于Hirato模型的分子动力学模型,模型的一次反应选择性系数的调整采用随机搜索算法,既可以满足碳氢平衡和又可以保证较高的模拟精度。采用特雷纳数值积分算法对所建立的模型作了模拟计算,通过改变操作条件,包括炉管出口温度、停留时间和汽/油比,研究了裂解炉出口产物收率分布的变化情况,对照实际生产数据作分析。证实模型合理,对生产具有参考价值。     

页岩油中芳烃加氢集总反应动力学研究

基于对页岩油芳烃加氢过程的分析,建立了页岩油加氢八集总动力学模型和反应网络。根据不同温度和空速下的页岩油加氢小试实验数据,采用MATLAB优化算法回归得到加氢动力学模型参数。随后模型的验证结果表明计算值和实验值相吻合。在此基础上模型可有效用于加氢产品分布的预测及工艺条件的优化,该工作为页岩油加氢过程的设计提供了指导。     

基于ASPEN PLUS的超临界汽油中低温煤焦油加氢裂解工艺模拟(英文)

为了尽快实现超临界汽油中低温煤焦油加氢裂解工艺的工业化,利用ASPEN PLUS模拟煤焦油在超临界溶剂中的加氢过程,以获得整个工艺运行的基本参数。本文基于超临界汽油中低温煤焦油加氢裂解的中试试验数据,对超临界汽油中低温煤焦油加氢裂解工艺进行模拟。首先,煤焦油代表组份选自煤焦油中含量较高的组分,汽油溶剂在模拟中根据其沸点曲线定义为虚拟组分;其次,根据碳离子反应机理,所有的煤焦油代表组分发生裂解反应,并将所有反应输入模型;加氢裂解过程主要选用RK-SOAVE和BK10物性方法,超临界汽油在模拟过程中定义为亨利组分:最后,根据煤焦油加氢裂解的反应特征,模型中主要包含3个基本模块,即RYIELD、Separtor和PetroFrac,分别模拟加氢裂解、气液分离和常压分馏。模拟时将超临界汽油中低温煤焦油加氢裂解中试实验条件数据输入模型,模拟结果表明:模型预测值与实验值基本一致,表明该模型能较好的反映超临界汽油中低温煤焦油加氢裂解工艺过程。并利用模型对年处理15万吨的超临界汽油中煤焦油加氢裂解工艺进行优化设计,获得了工艺的基本参数和能耗,为超临界汽油中低温煤焦油加氢裂解工艺工业化提供了理论依据和设计参考。     

重油加氢反应器后期压降模型及其应用

滴流床是一类气、液、固三相反应器,在石油化工领域应用非常广泛,尤其在加氢裂化过程中常作为主要的反应器使用。本文分析了在重油加氢反应器的实际生产中,引起其压降随生产时间的增加而逐步抬升现象的主要原因,并在前期模型的基础上通过引入铁元素流经总量作为新输入变量,建立了可针对反应器压降随时间变化规律进行有效预测的后期神经网络压降模型,提出了样本筛选的原则。该模型可用于反应器因压降而停工的日期的预测。     

柴油加氢工艺动态仿真中相平衡模型的研究

在柴油加氢工艺动态仿真的研究中,相平衡模型是柴油加氢工艺动态仿真中重要的数学模型.由于柴油加氢动态仿真中相平衡模型求解对于计算速度和精度的要求较高,传统方法无法解决计算稳定性和计算速度这一难点问题.所以提出了一种采用简化模型逼近严格模型的新的求解方法,应用到柴油加氢动态仿真的相平衡模型计算中,计算结果与实际数据吻合较好,证明提出的方法在计算速度、收敛范围和计算精度等方面均优于传统的求解方法,表明了新方法在柴油加氢动态仿真中的有效性和实用性.     

基于数据挖掘的医院综合评价量化建模方法研究

为解决患者就医寻找合适医院难的问题,提出依据医院诊疗数据,建立基于死亡率与综合实力的医院评价模型。在基于死亡率评价模型中,筛选出5个影响治疗效果的病人自身因素并将其量化。以成功治愈样本为基础,计算出各因素对于治愈天数的影响权重,以此计算出所评价医院每一个死亡样本治愈的预估天数。若大于治愈天数的最大阈值,则确定该样本死亡不可避免。否则为可避免,由此得到该医院关于该病症的"不当死亡率",以此评判该医院针对该疾病的诊疗水平。在基于综合实力的评价模型中,对医院综合实力分11个指标进行评价并量化。采用主成分分析法确定各指标在整套评价方案中所占权重,将该指标量化加权后的结果与所有医院相同指标量化加权均值计算相对差,作为该医院该指标得分,加和该医院所有指标得分作为评价分值。采用4家样本医院实际诊疗数据对模型进行了检验,模型评价的结果符合样本医院的实际水平。     

加氢机理模型在生产计划中应用的研究

以某工业柴油加氢处理装置为例,通过加氢过程模拟软件的模拟计算分别得到了进料密度和硫含量的变化引起的产品收率和氢耗量的变化量,进而得到了相应的DELTA-BASE数据。由于考虑了进料性质对收率的影响,应用DELTA—BASE数据与没有应用DELTA—BASE数据的生产计划模型的运算结果有较大的不同。结果表明,应用DELTA—BASE数据后避免了生产计划模型的超前优解,提高了炼厂生产计划的精度。     

主成分分析法在软件静态测试中的研究与应用

随着信息化程度的不断提高以及人们对软件需求的扩大,软件的复杂性也已经远远地超出了以前的水平,大大地增加了软件设计和开发的难度.以软件复杂性为出发点,介绍了主成分分析法(PCA)的基本思想、原理和主要作用,分析了主成分分析法在软件静态测试中的应用价值与可行性,最后通过一个具体的软件进行了详细的算例分析,获得了较好的效果,帮助软件开发人员和测试人员在静态分析中识别复杂性和风险性比较高的函数和模块起到了很好的作用.     

粗糙集理论在简化过程建模参数中的应用

利用粗糙集理论知识约简的特点，以连续催化重整反应过程中重整产品辛烷值监控建模参数选择为例，对生产过程工况特征参数建立决策表，计算条件属性的重要性，对决策表进行属性约简，简化过程建模参数。此外还将该方法与主成分分析法作了比较。最后利用选择的建模参数建立起神经网络模型。     

企业过程工程环境的模型复用技术

给出了基于特定企业过程工程环境的模型复用技术，它以过程模型的拆分和组装技术为基础，在企事业过程的建模过程中能生成及使用模型构件；并在此基础上给出了模型构件库的构建及使用技术。     

Quality prediction and analysis for large-scale processes based on multi-level principal component modeling strategy

This paper proposed a multi-level principal component regression (PCR) modeling strategy for quality prediction and analysis of large-scale processes. Based on decomposition of the large data matrix, the first level PCR model divides the process into different sub-blocks through uncorrelated principal component directions, with a related index defined for determination of variables in each sub-block. In the second level, a PCR model is developed for local quality prediction in each sub-block. Subsequently, the third level PCR model is constructed to combine the local prediction results in different sub-blocks. For process analysis, a sub-block contribution index is defined to identify the critical-to-quality sub-blocks, based on which an inside sub-block contribution index is further defined for determination of the key variables in each sub-block. As a result, correlations between process variables and quality variables can be successfully constructed. A case study on Tennessee Eastman (TE) benchmark process is provided for performance evaluation.     

Dynamic process fault monitoring based on neural network and PCA

A newly developed method, NNPCA, integrates two data driven techniques, neural network (NN) and principal component analysis (PCA), for process monitoring. NN is used to summarize the operating process information into a nonlinear dynamic mathematical model. Chemical dynamic processes are so complex that they are presently ahead of theoretical methods from a fundamental physical standpoint. NN functions as the nonlinear dynamic operator to remove processes' nonlinear and dynamic characteristics. PCA is employed to generate simple monitoring charts based on the multivariable residuals derived from the difference between the process measurements and the neural network prediction. It can evaluate the current performance of the process. Examples from the recent monitoring practice in the industry and the large-scale system in the Tennessee Eastman process problem are presented to help the reader delve into the matter.     

Development of correlation-based pattern recognition algorithm and adaptive soft-sensor design

Although soft-sensors have been used for estimating product quality, they do not always function well due to not only changes in process characteristics but also the individual difference of production devices. Correlation-based Just-In-Time (CoJIT) modeling has been proposed to cope with such changes in process characteristics; however it cannot deal with the individual difference. In the present work, a new pattern recognition method, referred to as the nearest correlation (NC) method is proposed to cope with the individual difference. The proposed NC method is integrated with CoJIT modeling. The advantages of the proposed methods are demonstrated through a case study.     

在线式图形建模与仿真研究

该文提出了一种在线式图形建模思想,研究并实现了一套开放式的图形化建模与仿真软件工具,可用来组建适合于热控及其他系统的图形建模环境,以图符方式构造、修改、显示和测试模型。文中简要介绍了其功能及设计与实现的方法。     

网络流量预测模型仿真分析与研究

网络流量的高效准确低冗余度预测,是保证网络安全的关键.网络流量的非线性影响,在突变性较强的网络流量预测中,为了应对突变性,需要大量的先验知识作为预测模型的支撑,导致出现了大量的非正常冗余数据,造成传统的的模型训练样本数量大以及结构复杂,模型不够稳定,导致预测冗余度高,效率低下.提出一种采用支持向量机模型的主成分分析的零冗余度PCA-SVM预测系统,在SVM模型的基础上,采用PCA分析方法对输入系统的一些冗余信息进行清除过滤,提高输入信息数据的贡献率,减少总体的样本训练集数量,降低SVM模型的信息维数,对网络流量的6个因子进行了系统模型构建原始驱动数据,对6个预测因子进行PCA处理,驱动因子之间具有显著的相关关系,完整去除了非相关冗余度.采用SVM-PCA模型进行数学建模,对后来10个月的网络流量进行预测.实验结果表明,改进算法比传统算法,预测偏差降低0.069,预测准确率可以提高17％以上,优化效果明显.     

Process identification based on last principal component analysis

A simple linear identification algorithm is presented in this paper. The last principal component (LPC), the eigenvector corresponding to the smallest eigenvalue of a non-negative symmetric matrix, contains an optimal linear relation of the column vectors of the data matrix. This traditional, well-known principal component analysis is extended to the generalized last principal component analysis (GLPC). For processes with colored measurement noise or disturbances, consistency of the GLPC estimator is achieved without involving iteration or non-linear numerical optimization. The proposed algorithm is illustrated by a simulated example and application to a pilot-scale process.     

组合分析蚀变信息提取方法研究

目前主成分分析法、比值法、光谱角制图法等已广泛应用于遥感矿化蚀变信息提取中,且取得了很好的效果。根据研究区遥感数据特点,采用比值与主成分组合分析的方法,结合FLAASH大气校正、中值滤波以及彩色密度分割等图像处理方法,对新疆且末地区ASTER数据进行蚀变信息提取。经USGS标准矿物波谱库中典型矿物光谱的验证,组合分析方法能够去除数据冗余和噪声,有利于各类蚀变信息的提取。     

Statistical shape modeling in virtual assembly using PCA-technique

The use of virtual assembly tools is one way to understand and improve the geometric product tolerance setting and the conditions for successful manufacturing. Recent developments enable consideration to be given to the deformability of parts (single components or subassemblies) when joined. In order to produce reliable results, the geometric deviations of the mating surfaces must be correctly assumed. In this paper, statistical shape models built on the Principal Component Analysis-technique (PCA) are proposed to be used to describe the part variation. A generalized model is presented and the underlying intentions and implications are discussed. It is demonstrated how the PCA-technique can be applied on bigger structures. The method is exemplified using the software RD&T. In the presented case, a non-rigid sheet metal assembly is modeled and distorted to create a set of sample shapes from which a statistical model is built. In the result, the statistic representation bears a good resemblance to the distorted nominal model when the two are compared.     

Ensemble model of wastewater treatment plant based on rich diversity of principal component determining by genetic algorithm for status monitoring

Wastewater treatment plants (WWTPs) is a complex process, effective process monitoring can make it stable and prevent the destruction of the ecological environment. Principal component analysis (PCA) has been widely used in process monitoring. However, most PCA-based methods construct a single PCA model using several principal components (PCs), causing loss of information on some faults and less generalization ability of the PCA model. Thus, this study proposed a novel ensemble process monitoring method based on genetic algorithm (GA) for selective diversity of PCs. GA is used to determine a set of principal component subspaces with the greatest diversity as the base models. Bayesian inference is adopted to combine the results of base models into a probability index. Cases study on TE benchmark process and an actual WWTP show the excellent performance of the proposed method compared with several PCA-based methods and the strong generalization ability of the ensemble model.