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.

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.

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.

Thermodynamic simulation of complex Pb-Bi concentrate oxidative bath smelting process

The element partitioning in a Pb-Bi concentrate oxygen-rich bath smelting process was studied using thermodynamic equilibrium simulation method. Effects of oxygen to feed ratio(OFR) and sulfur dioxide partial pressure(pSO2) on the partitionings of Bi, Pb, As, Sb, Cu and Ag were analyzed and compared with industrial data. The results suggested that the optimal OFR was between 6.3 and 6.8 kmol/t to maximize Bi, Pb, Cu and Ag partitioning in the metal phase. Further increase of OFR led to the drop of metal partitioning and increase of slag liquidus temperature. High pSO2 led to high deportment of Bi and Pb in the gas phase mainly in the form of sulfides, suggesting that a low pSO2 was conducive for reducing the dust ratio.

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.

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.

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.

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.

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.

Sheeted Dike Complexes in Contemporary Oceanic Crust: Implications for Spreading Processes and the Interpretation of Ophiolites

As anticipated from studies of ophiolite complexes,direct investigations of the oceanic crust confirm that basaltic dikes are an integral part of the upper 2 km of the oceanic crust.Currently available information suggests

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.

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.

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.

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.

A method of manufacturing process modeling based on activity network for large-sized and complex products

Simulation technique is an efficient approach to realize the planning and scheduling of manufacturing process of products. An appropriate and efficient manufacturing process model is the basis and key of manufacturing process simulation. By analyzing the features of large-sized and complex products, a method of manufacturing process modeling based on activity network is presented and a mapping algorithm of translating BOM/BOP into the manufacturing process model is designed in detail.

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.

Measuring the Complexity of Business Organization and Business Software Using Analytic Hierarchy Process （AHP）

The paper shows a proposition of metrics for measuring the complexity of the business organization and business software. The metrics is based on a subjective estimation of complexity of the elements from a part of the structure of business organization or business software in relation to other elements from the observed part. Estimation is performed based on the measuring scale for comparison of complexity of elements, and reaching a final conclusion on the complexity of elements in relation to other elements, through the Analytic Hierarchy Process （AHP）. Defined in this manner, the metrics represents a unique metrics for measuring the complexity of elements of business organization and business software, which enables their comparison. The paper also presents a short overview of existing metrics for measuring the complexity of business organization and business software.

Managing Complexity and Speed of Processes in the Organizations—A Philosophical and Practical Approach

The business challenges of today are managing complexity and the speed of changes with processes and systems since there are continuously new requirements from market, consumers, suppliers, employees, government etc. For example, when product and service innovations are launched on the market, the relevant processes, policies, people, systems, and technologies should have been prepared and set up in advance. Depending on the business sector, the implementation of new processes and systems as well as their continuous improvement can be easy to very complex and of course very costly. Responding to those challenges, there are several methodologies and approaches like Continuous Improvement Process, Lean Management and Kaizen etc. On the other hand, the application of these methodologies to other industries like service and technology companies is not always successful since their conditions and requirements are different than those of manufacturing organizations. Because of these reasons, the management needs to find out （new） ways of working and to implement new solutions in order to master those challenges and problems. This is not always easy and successful. There are different types of solution options that can be applied situational in every organization. Philosophy can help essentially by asking and answering critical questions in order to provide with solution options to reduce the complexity and to increase the speed of processes. There are several proven and new governance concepts, frameworks, and tools which follow their philosophies. This paper demonstrates how those challenges can be responded and managed as well as how a philosophical approach can help primarily to provide with appropriate solutions.

Mass Transfer-Promoted Fe^(2+)/Fe^(3+)Circulation Steered by 3D Flow-Through Co-Catalyst System Toward Sustainable Advanced Oxidation Processes

Realizing fast and continuous generation of reactive oxygen species(ROSs)via iron-based advanced oxidation processes(AOPs)is significant in the environmental and biological fields.However,current AOPs assisted by co-catalysts still suffer from the poor mass/electron transfer and non-durable promotion effect,giving rise to the sluggish Fe^(2+)/Fe^(3+)cycle and low dynamic concentration of Fe^(2+)for ROS production.Herein,we present a three-dimensional(3D)macroscale co-catalyst functionalized with molybdenum disulfide(MoS_(2))to achieve ultra-efficient Fe^(2+)regeneration(equilibrium Fe^(2+)ratio of 82.4%)and remarkable stability(more than 20 cycles)via a circulating flow-through process.Unlike the conventional batch-type reactor,experiments and computational fluid dynamics simulations demonstrate that the optimal utilization of the 3D active area under the flow-through mode,initiated by the convectionenhanced mass/charge transfer for Fe^(2+)reduction and then strengthened by MoS_(2)-induced flow rotation for sufficient reactant mixing,is crucial for oxidant activation and subsequent ROS generation.Strikingly,the flow-through co-catalytic system with superwetting capabilities can even tackle the intricate oily wastewater stabilized by different surfactants without the loss of pollutant degradation efficiency.Our findings highlight an innovative co-catalyst system design to expand the applicability of AOPs based technology,especially in large-scale complex wastewater treatment.