气相聚乙烯工业装置多牌号质量指标鲁棒滤波估计

针对气相聚乙烯工业装置多牌号质量指标实时估计的复杂性,运用聚乙烯工业装置质量指标实时预测模型,提出了一种鲁棒滤波器设计方法。该方法根据实验室分析数据反馈修正模型预测并实时估计多牌号质量指标。在气相聚乙烯工业装置上的应用结果和与美国联碳技术的应用对比证实了该方法的有效性和可行性。     

基于粒子滤波联合估计的气相聚乙烯质量指标在线估计

针对气相聚乙烯生产中各种复杂的工况造成在线估计精度下降的现象,基于乙烯聚合机理并利用特征建模方法建立了聚乙烯质量指标预测模型,结合扩展卡尔曼滤波,提出了粒子滤波联合估计方法,即将状态和修正系数组成增广状态向量,实现对质量指标预测模型的在线滤波修正,并分析了基于粒子滤波估计的收敛性。所提方法在中石化某气相聚乙烯装置的长周期运行结果证实了所提方法的可行性和有效性,为实施聚乙烯装置的先进控制奠定了基础。     

基于推断模型的工业气相聚乙烯装置多牌号质量指标在线控制

针对气相法聚乙烯装置生产中的缺乏质量指标在线测量、大时间滞后和频繁的牌号切换等特性,基于质量指标推断模型,提出一种基于质量指标推断模型的多牌号质量指标在线控制方法。所提方法已成功应用于中石化某气相聚乙烯装置,长周期的运行结果证实了所提方法可以有效地减少产品质量波动,减轻了操作人员的工作强度,取得了显著的经济效益。     

淤浆法高密度聚乙烯装置催化剂国产化

介绍了淤浆法高密度聚乙烯 (HDPE)装置国产催化剂———BCH催化剂在扬子石化股份有限公司HDPE装置的应用 ,从工业应用、产品质量及技术经济等方面对国产催化剂进行评价 ,用BCH催化剂生产出的产品完全符合质量指标 ,并可使装置生产能力提高约 10 %。     

高透明纳米棚模专用料

天津石化公司研制成功高透明线型低密度聚乙烯（LLDPE）棚模专用料DFDA-9086，并在聚乙烯装置上成功实现了工业化生产，产品主要质量指标均达到预期目标。     

高透明纳米棚模专用料在天津投产

天津石化公司研制成功纳米高透明线型低密度聚乙烯（LLDPE）棚模专用料DFDA－9086，并在聚乙烯装置上成功实现了工业化生产，产品主要质量指标均达到预期目标。     

大型铝合金料仓的设计

大型料仓在聚乙烯装置中承担着重要的作用，铝舍金越来越多地用来制造大型料仓。结合某300kt／a聚乙烯装置，介绍了聚烯烃装置中大型铝合金料仓的选材、结构设计、设计计算方法，以及制造、安装的技术要求。     

双峰高密度聚乙烯树脂的开发前景

HDPE装置采用淤浆法生产高密度聚乙烯,描述了双峰高密度聚乙烯树脂的特点,介绍了双峰HDPE产品物性参数、聚合工艺和质量指标,概括了影响双峰高密度聚乙烯树脂物理性质的三个主要因素(聚合方式、所用共聚单体、所用稳定剂),阐述了双峰高密度聚乙烯树脂的研制开发前景。     

天津分公司成功开发高透明纳米棚模专用树脂

中国石油化工股份有限公司天津分公司（简称天津分公司）研制成功纳米高透明线型低密度聚乙烯（LLDPE）棚模专用树脂DFDA-9086，并在聚乙烯装置上成功实现了工业化生产．产品主要质量指标均达到预期目标。     

利安德巴赛尔工业公司采用新技术建设拉波特的HDPE装置

<正>利安德巴赛尔工业公司于2016年9月9日宣布,已经选择在美国得克萨斯州拉波特生产基地建设50万t/年的高密度聚乙烯(HDPE)装置。该装置将首先采用利安德巴赛尔工业公司Hyperzone级联气相聚乙烯技术。建设预计在2017年年初开始,并计划在2019年投产,投资费用估计为7亿美元。使用Hyperzone技术生产的高密度聚乙烯(HDPE)树脂将拥有改进的性质,例如改进的耐应力开裂性,和刚度与抗冲击强度之间改进的平衡。     

工业流化床聚乙烯树脂性能模型的研究

本文首先分析了聚乙烯树脂熔融指数和密度变化的影响因素,建立了流化床动态过程中聚合物性能指标的递椎关系和工业流化床乙烯气相聚合过程产品质量的预测模型,利用乙烯气相（共）聚合过程中不同工艺条件下树脂的质量数据,对模型参数进行了优化,提出由于影响因素的相互关联,熔融指数和密度的控制应该联合进行;熔融指数和密度的计算值与测量值之间的平均误差分别为8.19%和0.13％。预测精度满足要求,模型可以应用于具体的工业生产过程。     

Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements☆

State estimation of biological process variables directly influences the performance of on-line monitoring and op-timal control for fermentation process. A novel nonlinear state estimation method for fermentation process is proposed using cubature Kalman filter (CKF) to incorporate delayed measurements. The square-root version of CKF (SCKF) algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithm is provided and the implementation of the proposed algorithm is constructed. Then a nonlinear state space model for fermentation process is established and the SCKF algorithm incorporating delayed measurements based on fermentation process model is presented to implement the nonlinear state estimation. Finally, the proposed nonlinear state estimation methodology is applied to the state estimation for penicillin and industrial yeast fermentation processes. The simulation results show that the on-line state estimation for fermentation process can be achieved by the proposed method with higher esti-mation accuracy and better stability.     

Recursive data-based prediction and control of product quality for a PMMA batch process

In many batch processes, frequent process/feedstock disturbances and unavailability of direct on-line quality measurements make it very difficult to achieve tight control of product quality. Motivated by this, we present a simple data-based method in which measurements of other process variables are related to end product quality using a historical data base. The developed correlation model is used to make on-line predictions of end quality, which can serve as a basis for adjusting the batch condition/time so that desired product quality may be achieved. This strategy is applied to a methyl methacrylate (MMA) polymerization process. Important end quality variables, the weight average molecular weight and the polydispersity, are predicted recursively based on the measurements of reactor cooling rate. Subsequently, a shrinking-horizon model predictive control approach is used to manipulate the reaction temperature. The results in this study show promise for the proposed inferential control method.     

基于在线子空间辨识的自适应预测控制

针对实际工业工程中存在非线性、时变的特点,提出一种基于子空间辨识的自适应预测控制方法。利用滚动窗口在线更新R阵,得到新的预测模型参数矩阵,通过比较更新前和更新后的预测误差来决定是否更新预测模型。将此控制方法应用于2-CSTR过程控制的仿真试验,通过与自适应模糊控制、PID控制器的比较,说明了该方法的优越性。     

一种基于增量式SVR学习的在线自适应建模方法

训练样本的数量与质量对于过程建模至关重要,在很大程度上影响所建模型的质量。基于增量式支持向量回归(SVR)学习算法,提出一种在线自适应建模方法以实现有选择地添加和删除训练样本。该方法利用SVR模型的KKT条件选择出那些包含足够多新信息的样本进行增量学习,能够在保证模型泛化能力的同时降低模型更新频率。另外,为快速准确地跟踪过程特性的变化,将通过评价当前模型对新增训练样本的学习能力来决定是否需要删除旧样本。当需要删除样本时,基于样本间的相似度,选择淘汰与当前过程特性差别最大的旧样本。将该方法用于建立工业聚丙烯熔融指数预报模型,结果表明,与其他方法相比,获得的预测模型具有更好的泛化性能,且模型更新频率明显降低,能有效地适应工况的变化。     

Enhancing dynamic data reconciliation performance through time delays identification

In order to improve the performance of data reconciliation methods, an efficient Genetic algorithm (GA) for determining time delays has been developed. Delays are identified by searching the maximum correlation among the process variables. The delay vector (DV) is integrated within a dynamic data reconciliation (DDR) procedure based on Kalman filter through the measurements error model. The proposed approach can be satisfactorily applied not only off-line but also on-line. It was firstly validated in a dynamic process with recycles and feedback control loops. Then, the methodology was successfully applied to a highly non-linear and complex challenging control case study, the Tenessee Eastman benchmark process, demonstrating its robustness in complex industrial problems. This case study required to implement an extended Kalman filter to deal with the existing non-linearities.     

Predictive control of particlesize distribution of crystallization process using deep learning based image analysis

The challenges to regulate the particle-size distribution (PSD) stem from on-line measurement of the full distribution and the distributed nature of crystallization process. In this article, a novel nonlinear model predictive control method of PSD for crystallization process is proposed. Radial basis function neural network is adopted to approximate the PSD such that the population balance model with distributed nature can be transformed into the ordinary differential equation (ODE) models. Data driven nonlinear prediction model of the crystallization process is then constructed from the input and output data and further be used in the proposed nonlinear model predictive control algorithm. A deep learning based image analysis technology is developed for online measurement of the PSD. The proposed PSD control method is experimentally implemented on a jacketed batch crystallizer. The results of crystallization experiments demonstrate the effectiveness of the proposed control method.     

Complex process quality prediction using modified kernel partial least squares

In this paper, kernel partial least squares (KPLS) method is modified based on orthogonal independent component analysis (O-ICA). Then it is applied to quality prediction of industrial processes.In ICA, the extracted components are assumed to be mutually statistically independent instead of uncorrelated. Independence is much stronger than uncorrelativity. Those extracted ICs may thus provide more informative statistical explanations and better reflect the inner properties of measurement data. However, disturbing variation can be extracted since ICA uses entropy theory to extracts high-order statistics. Hence, first, O-ICA is proposed for signal correction of non-Gaussian processes. Then KPLS is modified for quality prediction of non-Gaussian processes based on O-ICA, which is called O-ICA-KPLS. Advantages of the proposed O-ICA-KPLS are: (1) has the ability to give high-order representations for non-Gaussian data compared to original KPLS, and (2) provides more accurate statistical analysis and on-line monitoring because independent signals are corrected.The proposed methods are applied to the quality prediction in fermentation process and Tennessee Eastman process. Applications indicate that the proposed approach effectively captures the relations in the process variables and use of O-ICA-KPLS instead of original KPLS improves the predictive ability.