一类连续反应的SDG HAZOP与故障诊断

在分析苻号有向图（SDG）技术的理论基础上,探讨了SDG定性模型在计算机辅助HAZOP（危险与可操作性分析）和故障诊断中的实际应用,并针对该丙烯聚合反应（溶剂淤浆法）建立SDG模型,利用SDG技术进行HAZOP和故障诊断分析。实验结果表明,基于SDG的HAZOP和故障诊断技术具有完备性好、节省时间、人力、费用等众多优点。     

间歇过程自动化的进展

本文从控制角度分析间歇过程的特征，并介绍了间歇控制装置和控制软件的现状和发展趋势，特别是配方管理和生产调度在线应用。     

间歇化工过程与计算机应用

首先指出间歇过程是一门独立的、有自身特点的、在化学工业中具有独特地位的学科。然后扼要回顾了计算机在间歇过程设计和生产调度中的应用，并介绍了几个典型的用于间歇过程的软件。最后简单介绍了由ＩＳＡ颁布的间歇控制标准ＳＰ８８，以便读者了解该领域的最新国际动态     

间歇生产过程的自动控制

本文简要介绍了间歇生产过程及其特点,结合间歇生产过程对控制的要求,提出了相应的自动控制方案。阐述了间歇生产过程自动化的实现方法。     

基于多级流建模的间歇过程故障定位方法

针对间歇性供水系统故障定位问题的复杂性,提出了一种扩展多级流模型( MFM)的建模、故障警报分析和故障定位新方法.利用多级流模型和Petri网复合的方法,扩展了多级流模型以适应间歇过程的连续变化和离散变化,实现了分布式复杂系统的故障根源搜索和定位.     

单产品间歇化工过程设计

本文修正了单产品间歇化工过程设计的数学模型并提出该问题的启发算法。该算法包括设备尺寸计算与过程综合。将实例计算结果与文献值进行了比较，证实了方法的正确性。     

间歇生产过程的控制系统

论述了间歇生产过程控制的特点、硬件系统的选择原则和软件的开发方法及功能 ,并具体介绍了一个间歇精细化工的特殊生产过程控制的实例 ,对从事间歇生产过程控制开发、设计工作的人员 ,具有实用参考价值     

一种间歇过程HAZOP危险性分析方法及其应用

首先描述了一种简化的间歇过程危险性与可操作性分析HAZOP（hazard and operability analysis）模型,即用Petri网和符号有向图（signed directed graph,SDG）结合的双层结构模型,模型综合了对离散事件有很好描述能力的Petri网和有较强深层推理能力的SDG二者的优点。其次应用此模型对精细化学品D3间歇生产过程操作复杂的萃取提纯工序进行了危险性分析,对生产过程中的一些误操作做了原因分析,并提出了相关的改进性措施。最后,对此方法的优缺点做了总结。     

Integrating data‐based modeling and nonlinear control tools for batch process control

A data‐based multimodel approach is developed in this work for modeling batch systems in which multiple local linear models are identified using latent variable regression and combined using an appropriate weighting function that arises from fuzzy c‐means clustering. The resulting model is used to generate empirical reverse‐time reachability regions (RTRRs) (defined as the set of states from where the data‐based model can be driven inside a desired end‐point neighborhood of the system), which are subsequently incorporated in a predictive control design. Simulation results of a fed‐batch reactor system under proportional‐integral (PI) control and the proposed RTRR‐based design demonstrate the superior performance of the RTRR‐based design in both a fault‐free and faulty environment. The data‐based modeling methodology is then applied on a nylon‐6,6 batch polymerization process to design a trajectory tracking predictive controller. Closed‐loop simulation results illustrate the superior tracking performance of the proposed predictive controller over PI control. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Kinetic characterization and modeling simplification of an anaerobic sulfate reducing batch process

BACKGROUND: A series of kinetic experiments for a sulfate reducing process was carried out in a batch laboratory reactor that allowed simplification of a model initially proposed for this system enabling establishment of the kinetic parameters. RESULTS: It was found that an incomplete oxidation of lactate is the most significant bio‐reaction occurring in the reactor; this fact led to model simplifications that allowed determination of the kinetic parameters. Sulfide and pH inhibition can be described using one inhibition term within the model equation, given the fact that sulfide inhibition is not present in the system. Kinetic parameters were obtained, yielding a maximum specific rate (µ_max/Y) of 2.17 ± 0.08 mg sulfate mg^?1 VSS min^?1. A zero‐order kinetics with respect to sulfate and a Monod affinity constant of 142.7 ± 64.9 mg L^?1 for the lactate were found. CONCLUSIONS: The final model for the process can be described by a single Monod term involving the lactate. This model resulted from the lack of sulfide inhibition in the system and the predominance of the partial lactate oxidation to acetate reaction over the complete lactate and acetate oxidation. Copyright © 2010 Society of Chemical Industry     

Regularization‐based statistical batch process modeling for final product quality prediction

Prediction accuracy and model interpretation are two important aspects with regard to regression models. In the field of statistical modeling of chemical batch processes, most research focuses on prediction accuracy, while the importance of the latter aspect is often overlooked. In multiphase batch processes, it is possible that only a few phases are relevant to certain quality indices, while different time points belonging to the same relevant phase usually have similar contribution to the quality. The regression coefficients of batch process model should reflect such process characteristics, that is, the coefficients corresponding to the irrelevant phases should be close to zero, while the coefficients of each variable within the same phase should vary smoothly. In this study, regularization techniques are introduced to statistical modeling of chemical batch processes to achieve both accurate prediction and good interpretation. The application to an injection molding process shows the feasibility of the proposed methods. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2815–2827, 2014     

基于核独立元分析的间歇过程在线监控

针对间歇过程独特的数据特点,提出了一种基于核独立元分析(kernelICA)的局部在线建模监控方法。核独立元分析通过规范相关性将比较函数扩展到一个再生的核希尔伯特空间,并用核的方法在此空间对比较函数进行计算和寻优。对含有多种分布的过程源数据,核独立元分析是一种比独立元分析(ICA)更有效的特征提取方法。对于按批次方向展开的间歇过程历史建模数据,在每一个时间间隔点应用核独立元分析算法提取独立元用于建模,并计算I2和SPE统计量及相应的控制限。此方法不需要对未来测量值进行估计,更重要的是解决了核独立元分析不能直接处理间歇过程高维历史建模数据的难题。仿真结果验证了所提出方法的可行性和有效性,并显示出比传统MICA更好的监控效果。     

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.     

A modeling strategy for integrated batch process development based on mixed-logic dynamic optimization

This paper introduces an optimization-based approach for the simultaneous solution of batch process synthesis and plant allocation, with decisions like the selection of chemicals, process stages, task-unit assignments, operating modes, and optimal control profiles, among others. The modeling strategy is based on the representation of structural alternatives in a state-equipment network (SEN) and its formulation as a mixed-logic dynamic optimization (MLDO) problem. Particularly, the disjunctive multistage modeling strategy by Oldenburg and Marquardt (2008) is extended to combine and organize single-stage and multistage models for representing the sequence of continuous and batch units in each structural alternative and for synchronizing dynamic profiles in input and output operations with material transference. Two numerical examples illustrate the application of the proposed methodology, showing the enhancement of the adaptability potential of batch plants and the improvement of global process performance thanks to the quantification of interactions between process synthesis and plant allocation decisions.     

利用结构逼近式混合神经网络实现间歇反应器的建模

提出了一种新的混合神经网络建模方法——结构逼近式混合神经网络。基于此结构建立的混合神经网络可以充分利用已知非线性系统的结构信息,使神经网络“灰盒”化,更好地解释和描述系统各变量间的因果关系,从而提高网络的建模精度和模型的可靠性。本文介绍了这类神经网络的基本特性、拓扑结构和训练方法。报告了一个典型放热液相二级平行间歇反应的建模过程;并针对间歇反应过程测量滞后的情况,与两种不同的混合神经网络模型作了比较,仿真和比较结果证明了方法的有效性。     

基于测地线距离统计量的多工况间歇过程监测

针对间歇过程数据具有非线性和多工况的特点, 提出一种基于测地线距离统计量(geodesic distance statistic, GDS)的监测方法。首先, 对多工况间歇过程数据按批次方向展开及标准化, 利用主元分析(principal component analysis, PCA)方法进行降维;然后, 在降维空间获得赋权邻接矩阵, 提出采用改进的Dijkstra (improved Dijkstra, IDijkstra)算法使Dijkstra算法更易于实现, 计算各批次之间的测地线距离, 用以表征非线性多工况数据之间的实际最短距离, 更好地体现批次数据之间的局部近邻关系。通过构造测地线距离α次方统计量D^α进行过程监测, 与欧氏距离平方和D²相比将减小边缘训练数据距离的偏离程度。最后, 通过在数值仿真和工业仿真实例中的应用, 验证所提算法的有效性。