Generic reconstruction technology based on RST for multivariate time series of complex process industries

In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology.Then,the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space,and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space,Finally,the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model.Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application.

An Incremental Model Transfer Method for Complex Process Fault Diagnosis

Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.

Prediction of tri-modal microstructure under complex thermomechanical processing history in isothermal local loading forming of titanium alloy

To control the tri-modal microstructure and performance,a prediction model of tri-modal microstructure in the isothermal local loading forming of titanium alloy was developed.The staged isothermal local loading experiment on TA15alloy indicates that there exist four important microstructure evolution phenomena in the development of tri-modal microstructure,i.e.,the generation of lamellarα,content variation of equiaxedα,spatial orientation change of lamellarαand globularization of lamellarα.Considering the laws of these microstructure phenomena,the microstructure model was established to correlate the parameters of tri-modal microstructure and processing conditions.Then,the developed microstructure model was integrated with finite element(FE)model to predict the tri-modal microstructure in the isothermal local loading forming.Its reliability and accuracy were verified by the microstructure observation at different locations of sample.Good agreements between the predicted and experimental results suggest that the developed microstructure model and its combination with FE model are effective in the prediction of tri-modal microstructure in the isothermal local loading forming of TA15alloy.

Recent progress on equation-oriented optimization of complex chemical processes

Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.However,computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application.Recently,progress has been made in using a pseudo-transient(PT)modeling method to address these difficulties,providing a fresh way forward in EO-based optimization.Nevertheless,research in this area remains open,and challenges need to be addressed.Therefore,understanding the state-of-the-art research on the PT method,its principle,and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization.For this purpose,the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper.Several typical applications,e.g.,complex distillation processes,cryogenic processes,and optimizations under uncertainty,are presented as well.Finally,we identify several main challenges and give prospects for the development of the PT based optimization methods.

Predicting Model for Complex Production Process Based on Dynamic Neural Network

Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.

Parameter Estimation for Complex Ornstein-Uhlenbeck Processes

The drift parameter estimation problem of the complex Ornstein-Uhlenbeck process driven by a complexα-stable motion is considered.Based on discrete observations,an estimator of the unknown drift parameter is constructed by using the least squares method.Moreover,the strong consistency and the asymptotic distribution of the least squares estimator are derived under some assumptions.

RFID Complex Event Processing: Applications in Real-Time Locating System

Complex event processing (CEP) can extract meaningful events for real-time locating system (RTLS) applications. To identify complex event accurately in RTLS, we propose a new RFID complex event processing method GEEP, which is based on the timed automata (TA) theory. By devising RFID locating application into complex events, we model the timing diagram of RFID data streams based on the TA. We optimize the constraint of the event streams and propose a novel method to derive the constraint between objects, as well as the constraint between object and location. Experiments prove the proposed method reduces the cost of RFID complex event processing, and improves the efficiency of the RTLS.

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

Inner-Sphere Reorganization Eneregy For Metal Ion-Benzene Complex in Electron-Transfer Process

Based on the capture force field potential model and the adiabatic invariant proposed by Bates, adopting improved average dipole orientation (IADO) theory, the force constants between transition metal ions and benzene (bz) and also a series of inner-sphere reoganization energy (REin) were calculated. The reasons for the differences between theoretical predictions and experimental results were discussed.

Phase Transition in Recovery Process of Complex Networks

The dynamic characteristic of complex network failure and recovery is one of the main research topics in complex networks. Real world systems such as traffic jams and Internet recovery could be described by the complex network theory. We propose a model to study the recovery process in complex networks. Two different recovery mechanisms are considered in three kinds of networks： external recovery and internal recovery. By simulating the process of the nodes recovery in networks, it is found that the system exhibits the feature of first-order phase transition only when the external recovery is considered. Internal recovery cannot induce such a kind of transitions. As external recovery and internal recovery coexist on networks, the systems will retain the most efficient part of external recovery and internal recovery. Meanwhile, a hysteresis could be observed when increasing or decreasing the failure probability. Finally, a largest degree node protection strategy is proposed for improving the robustness of networks.

A Further Investigation into Intramolecular Process of Mg( Ⅱ )-, Sc( Ⅲ )-, Y( Ⅲ )-, In ( Ⅲ )-and Pb( Ⅲ )-CyDTA Complexes by the Method of Dynamic NMR

The intramolecular process of Mg ( Ⅱ ) - , Sc ( Ⅲ ) - , Y ( Ⅲ ) - , In(Ⅲ)-and Pb ( Ⅱ )-trans-1,2-cyclohexanediaminetetraacetic acid (CyDTA) complexes was studied by the method of proton dynamic NMR(DNMR). The top limit of. the experimental temperature range of deuterium oxide solutions was extended from 100℃ to 167 ℃ instead of the previous 100 ℃ by using sealed, thick-walled tubes to observe experimental phenomena further. It was found that another intramolecular process, i. e. B, R (Bonded State, Rotational State) conversion, exists in these complexes formed by polyaminepolycarboxylic ligands in addition to △,A conversion and nitrogen inversion. A quantitative treatment was proposed to estimate the relative populations of acetate groups in B and R states.

Characterization of Feed Properties for Conceptual Process Design Involving Complex Mixtures, Such as Natural Extracts

The number of products used as agro-chemicals, food additives, flavors, aromas, pharmaceuticals and nutraceuticals which are made by fermentation or extraction from plants has increased significantly. Despite this growth, initial predictions for a potential product purification process for these complex mixtures remains entirely experimentally based. The present work represents an initial study to demonstrate the benefits of a systematic approach. For process development of chemically well-studied systems model based process design methods are already available. Therefore the proposed approach focuses on a method for the efficient characterization of the physical properties of the key components. Once this is adequately defined, unit operations and their potential to separate the feed components can be modeled. The current state of research is discussed. Based on this evaluation the most efficient method for conceptual process development has been identified and further developed. The resulting methodology consists of model-based cost accounting accompanied by experimental model-parameter determination. The latter is carried out at in miniaturized laboratory-scale measurement cells for each unit operation using the complete original feed. The model-based modelparameter determination from these experiments is accompanied by a comprehensive error analysis. The experimental plan currently includes the determination of thermodynamic equilibrium conditions in the mixture directly from the raw material mixture. Transport kinetics and fluid dynamic parameters are first estimated from known correlations or preexisting knowledge. Later on these parameters are determined exactly in mini-plant experiments. Furthermore, biological and botanical-based guidelines are developed to identify thermodynamically favored basic operations. Finally, the developed approaches are successfully validated using two plant extracts. Firstly, it could be proven that the botanical pre-selection can reduce the experimental plan significantly. Secondly, it was shown that the experimental equilibrium data of the kinetics and fluid dynamics can have a significant impact on the separation costs. Therefore, detailed rigorous modeling approaches have to be chosen instead of short-cut methods in order to make any valid process development conclusions or to further optimize the system.

Agglomerate control in the complexing sol-gel process

Nanometer ZrO2 - 8% Y2O3 (mole fraction, % ) powders were prepared by the EDTA (ethylenediaminetetraacetic acid) sol-gel process. Effects of the addition of ethylene glycol on agglomerate control was investigated. The results showed that because of the replacement of hydrogen bonds with ethylene glycol in the polymerized gel, gel stabilization and homogeneity were improved and close approach of gel particles was prevented, which led to reduction of hard agglomerates to some extent. Calcined at 4OO t for 2 h and 700 C for 2 h, the powders had a specific surface area of 35 m2/g, average particle size of 28 nm, and median particle size (d50) of 0. 44um with very sharp distribution, mostly being soft agglomerates.

COPOLYMERIZATION PROCESS OF TRIPHENYLMETHYL METHACRYLATE AND MMA WITH CHIRAL ANIONIC COMPLEX INITIATOR

The copolymerization process of triphenylmethyl methacrylate (TrMA) and methylmethacrylate (MMA) using chiral anionic complex initiator (-) SP-FlLi (Scheme 1) has beenstudied in toluene and THF, respectively. The copolymer obtained in toluene possessed muchhigher specific rotation than that in THF. These copolymers have shown a tendency to a random and a like alternating structure, respectively.

甜玉米芯多糖铁配合物的工艺优化及体外活性

甜玉米芯多糖(SCP80)与三氯化铁共热合成了甜玉米芯多糖铁配合物(SCP),在单因素实验基础上,采用响应面法优化了SCP制备工艺。通过SEM、XRD、TG、纳米粒度及Zeta电位分析仪、UV-Vis和FTIR对SCP进行了形貌和结构表征,并测定了其体外抗氧化及降糖活性。结果表明,50 mL质量浓度为1 g/L的SCP80水溶液合成SCP的最佳合成工艺为:反应温度75℃、反应时间64.6 min、pH=8.5、m(SCP80)∶m(柠檬酸三钠)=3.64∶1。在该条件下制备的SCP中铁质量分数为27.89%±0.35%。与SCP80相比,SCP表面更光滑,粒径从43.8 nm(SCP80)增至164.0 nm,具有更好的热稳定性,更好的清除羟基自由基能力和还原能力,质量浓度为4.0 g/L的SCP对羟基自由基的清除率为57.51%;SCP对α-淀粉酶半抑制浓度(IC_(50))为(1.20±0.11) g/L,对α-葡萄糖苷酶的IC_(50)为(0.92±0.07) g/L,其体外降糖活性明显优于SCP80。

一种面向项目管理信息系统集成的复杂事件处理方法

大型项目管理信息系统中实时产生的海量事件,对深度挖掘系统的隐含特征具有重要意义。通过分析科技计划项目管理的业务逻辑和事件结构,提出项目管理事件模型。为描述事件之间的关系,定义项目管理事件操作符,实现事件的逻辑、算数和时间计算。基于复杂事件处理技术,提出一种面向项目管理信息系统的事件处理算法PMEP,执行事件处理规则,分析系统产生的海量事件,得到深层次的项目管理复杂事件。实验表明,PMEP算法可实现项目管理信息系统中海量事件的实时处理。

基于改进络合铁法的含硫化氢尾气处理工艺模拟与优化

目的有效处理含硫尾气,确保装置“安、稳、长、满、优”运行。方法采用Aspen Plus V11软件基于Peng-Robinson热力学模型对改进络合铁装置的H_(2)S处理过程进行了全流程模拟,并根据Box-Behnken Design对其进行了响应面设计,得到了最佳H_(2)S脱除率及其对应的优化操作参数。结果此模拟流程能够较好地反映改进络合铁装置的实际运行情况,可以作为后续优化研究的基础模型。当循环溶液温度为47.2℃、循环溶液中Fe^(3+)质量分数为8.4100%、循环溶液体积流量为3.12 m^(3)/h、电解槽电压为0.64 V时,改进络合铁装置的H_(2)S脱除率高达99.999988%。此时,外排气中H_(2)S质量浓度为9.5 mg/m^(3),完全可以满足外排气中H_(2)S质量浓度不大于10 mg/m^(3)的约束条件;t检验结果表明,采用响应面法优化得到的预测值和验证值之间不存在显著差异,其准确度一致。结论该研究结果可为进一步提升含H_(2)S尾气处理水平提供准确、可靠的理论支撑和数据来源。

考虑参数空间差异的复合式滑坡推力计算分析

由于复合式滑坡不同滑区同一岩土计算参数数值存在差异性,导致滑坡推力大小判别难度加大。根据复合式滑坡破坏模式,概化构建滑坡地质模型,在此基础上建立滑坡物理力学模型,并对各区参数进行理论分析。根据滑坡失稳特征以及前人对复合式滑坡演化阶段划分的研究,将滑坡演化过程划分为前后裂隙开裂、前后滑带局部软化、前后滑带扩展、中部滑带软化、边坡整体失稳5个阶段,基于传递系数法和静力平衡条件构建不同演化阶段下的滑坡推力计算式。以汉巴南铁路某复合式滑坡为例,揭示了滑坡推力随水平距离增加的变化规律,并将计算结果与不考虑不同滑区同一岩土计算参数数值差异得出的计算结果进行对比分析。研究结果表明:1)在计算复合式滑坡推力时,考虑不同滑区同一岩土计算参数数值差异性的影响,其计算结果更加准确,规避因单一取值而导致计算推力不合理等问题。2)根据复合式滑坡不同演化阶段下的推力变化规律,在滑坡初期应以推移区和牵引区加固为主,当中部锁固区开始滑动,应以中部锁固区加固为主,同时也要加强推移区和牵引区的加固。3)在识别复合式滑坡演化阶段的基础上,根据此阶段构建的滑坡推力计算式得出推力空间变化规律,从而可提出较为合理的防治措施。

逻辑、即兴与批判性抉择——西扎作品东钱湖大师工作室的设计观察(附书面访谈和项目过程笔记)

宁波东钱湖大师工作室是西扎在国内的新作。本文基于大量图纸资料和书面交流,详细梳理了该项目方案设计的多个演进阶段,分析了从基础逻辑到即兴发挥再到批判性抉择的复杂过程。文章进一步讨论了西扎建筑中值得深入辨析的方面,如类型与原型、多维度的连续性、矛盾性与幽默感、现代与后现代等。

深井超深井注入过程井筒温度场模型研究及应用

低温流体的注入过程若使井筒温度场发生较大变化,管柱会因温度变形而产生较大的附加轴向力,导致管柱变形和封隔器失封。为此,考虑深井、超深井井身结构特点及地层传热机理,根据能量守恒定律和热力学定律,建立深井、超深井注入过程井筒温度场模型。采用稳态假设消除时间项并连续使用解析法得到模型解析解,结合某超深井井身结构与施工参数,分析了复杂井身结构和地层温度对计算结果的影响,得到了不同注入温度和排量下井筒温度分布规律。研究结果表明,考虑复杂井身结构后模型精度更高,与井底温度的绝对误差为-0.56%,不考虑复杂井身结构的绝对误差为1.52%;地层温度对井筒温度的影响较小。研究结果可为管柱力学分析提供数据支撑、为作业参数优化提供理论依据。