Multiphase batch process monitoring based on higher-order cumulant analysis

In this paper, a two-step phase partitioning strategy is proposed. Firstly, the number of phases is automatically determined according to the intra-class and inter-class similarity of feature space data, thus avoiding excessive manual intervention. Secondly, the phases are partitioned by step-wise adding the kernel entropy extended load matrix (KEELM), avoiding the wrong division of phases caused by unstable state of working condition conversion. A process monitoring model based on multiway kernel entropy independent component analysis (MKEICA) is constructed in each sub-phase to deal with complex batch processes with nonlinear and non-Gaussian properties. A new statistics index based on the idea of high order cumulant analysis (HCA) is constructed in each sub-phase for process monitoring. Compared with the traditional second-order statistics, it can obtain high-order statistical information. Finally, the proposed method is applied to the penicillin simulation platform process and compared with the traditional multiway kernel independent components analysis (MKICA) and HCA methods to verify the effectiveness of the method that is mentioned above.     

Rapid synthesis of Gd2Zr2O7 ceramics by flash sintering and its aqueous durability

The high radiation resistance and long time stability of Gd₂Zr₂O₇ ceramics make it a promising candidate for high level waste (HLW) immobilization materials. In this study, single phase nanocrystalline Gd₂Zr₂O₇ was successfully synthesized and consolidated at temperatures around 1050 °C for only 1 min by flash sintering for the first time. The phase evolution and microstructural development during flash sintering were systematically studied and compared with the conventionally sintered samples. The flash sintered Gd₂Zr₂O₇ exhibit defect fluorite structure, and a following heat treatment at 1400 °C could transform the Gd₂Zr₂O₇ ceramics from defect fluorite phase into pyrochlore phase. The MCC-1 leaching test shows that the flash sintered Gd₂Zr₂O₇ samples exhibit good aqueous durability.     

小型特种精细化工过程自动化和信息化研究发展趋势探讨

针对采取小批量间歇性批次生产方式、工艺介质腐蚀性强、危险性大的小型特种精细化工生产工艺研究试验过程自动化、信息化程度不高,导致工艺研究试验中获取数据较少、过程机理研究不够透彻、人工操作多、安全风险高、研究试验消耗大及效率不高等问题,综述了期待通过智能控制、在线分析、模拟仿真和虚拟制造、工况监测及预测性维护以及信息管理和生产调度等关键技术的研究和应用,提高小型特种精细化工生产工艺过程的自动化和信息化程度,实现小型特种精细化工生产工艺过程的数字化、虚拟化和智能化,降低生产安全风险和试验消耗,提高小型特种精细化工生产工艺研究试验的成功率和效率,达到小型特种精细化工生产工艺过程的数字化设计和精准生产的目标。     

混炼胶门尼粘度的预测与控制

使用ＭＧＫＪ型密炼机混炼微机监控系统，在ＢＢ２７０型密炼机上预测混炼胶的门尼粘度值，实现了胎面胶门尼粘度值的预测。预测值与实测值的差平均为０８（相对误差１％）。借助预测门尼粘度的数学模型，对排胶点进行控制，使混炼胶门尼粘度的波动范围为－３～＋３。     

红外回转窑燃烧胴体温度监测系统的设计与实现

采用红外扫描系统对回转窑燃烧胴体温度进行实时监测的周期较长,所得温度场数据量很大,直接进行传输,效率较低且易出错。为此设计红外回转窑燃烧胴体温度监测系统,介绍系统的构成和原理;采用基于LZ77数据压缩算法的程序对回转窑温度场数据进行传输,并使解压后的数据与原始温度场数据保持一致;同时,设计并实现了工业窑炉燃烧过程的监测界面,包括燃烧过程中各种气体浓度、温度场数据等信息。实践证明:该系统达到了设计要求。     

Multivariate statistical process monitoring of batch‐to‐batch startups

In batch processes, multivariate statistical process monitoring (MSPM) plays an important role for ensuring process safety. However, despite many methods proposed, few of them can be applied to batch‐to‐batch startups. The reason is that, during the startup stage, process data are usually nonstationary and nonidentically distributed from batch to batch. In this article, the trajectory signal of each process variable is decomposed into a series of components corresponding to different frequencies, by adopting a nonparametric signal decomposition technique named ensemble empirical mode decomposition. Then, through instantaneous frequency calculation, these components can be divided into two groups. The first group reflects the long‐term trend between batches, which extracts the batch‐wise nonstationary drift information. The second group corresponds to the short‐term intrabatch variations. The variable trajectory signals reconstructed from the latter fulfills the requirements of conventional MSPM. The feasibility of the proposed method is illustrated using an injection molding process. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3719–3727, 2015     

Consistent Monitoring of Cointegrating Relationships: The US Housing Market and the Subprime Crisis

We propose a consistent monitoring procedure for structural change in a cointegrating relationship. The procedure is inspired by Chu et al. (1996) by being based on parameter estimation on a prebreak ‘calibration’ period. We use three modified least squares estimators to obtain nuisance parameter‐free limiting distributions. We study the asymptotic and finite sample properties of the procedures and finally apply the approach to monitor two‐fundamentals‐driven US housing prices cointegrating relationships over the period 1976:Q1–2010:Q4 using the data of Anundsen (2015). Depending on the relationship considered and the estimation method used, a break point is detected as early as 2003:Q2, that is, well before US housing prices started to fall in 2007.     

An iterative two‐step sequential phase partition (ITSPP) method for batch process modeling and online monitoring

Operating at different manufacturing steps, multiphase modeling and analysis of the batch process are advantageous to improving monitoring performance and understanding manufacturing processes. Although many phase partition algorithms have been proposed, they have some disadvantages and cause problems: (1) time sequence disorder, which requires elaborate post‐treatments; (2) a lack of quantitative index to indicate transition patterns; and (3) tunable parameters that cannot be quantitatively determined. To effectively overcome these problems, an iterative two‐step sequential phase partition algorithm is proposed in the present work. In the first step, initial phase partition results are obtained by checking changes of the control limit of squared prediction error. Sequentially, the fast search and find of density peaks clustering algorithm is employed to adjust the degradation degree and update the phase partition results. These two steps are iteratively executed until a proper degradation degree is found for the first phase. Then, the remaining phases are processed one by one using the same procedure. Moreover, a statistical index is quantitatively defined based on density and distance analysis to judge whether a process has transitions, and when the transition regions begin and end. In this way, the phases and transition patterns are quantitatively determined without ambiguity from the perspective of monitoring performance. The effectiveness of the proposed method is illustrated by a numerical example and a typical industrial case. Several typical phase partition algorithms are also employed for comprehensive comparisons with the proposed method. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2358–2373, 2016