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In this paper we propose an approach to derive the formal semantics of WS-BPEL processes compositionally using Reo and constraint automata. We map each WS-BPEL process into a Reo circuit and then construct the corresponding constraint automaton which shows the behavior of the process. The constraint automaton can be used for analyzing the process behavior. Our work covers the core part of the WS-BPEL language including basic and structured activities, correlation sets, variables, and links.

Feedforward control has the theoretical potential for perfect control; therefore, research to simplify and improve the use of such a control strategy for possible industrial applications has potential value. A general model for simulation of controlled distillation columns is used to reproduce operating trends of some monitored variables for an industrial column. Comparisons between simulated and operating data show a general good agreement. When a rigorous model is available and can be proved reliable for practical purposes, the design of feedforward control schemes becomes viable and can be incorporated in the more conventional approach. A specific industrial example shows the practical implementation problems and the real economic value of a feedforward control strategy to optimize the process behavior. In particular, the feedforward control action can reduce the inherent error from changes in feed composition when a basic feedback control structure is used to infer composition.

The supervision of technical processes is aimed at showing the present state, indicating undesired or unpermitted states, and taking appropriate actions to avoid damage or accidents. The deviations from normal process behavior result from faults and errors, which can be attributed to many causes. They may result in some shorter or longer time periods with malfunctions or failures if no counteractions are taken. One reason for supervision is to avoid these malfunctions or failures. In the following article the basic tasks of supervision are shortly described.

The paper describes a method for identification of mechanisms and process trends based on combination of subject-driven document clustering, shape analysis, trends understanding and relevant context retrieval via semantic analysis. The goal is to extract potentially interesting knowledge from a set of technical information based on analysis of graphical information in order to find explanation for a specific process behavior.

This paper presents the development and application of a neural networks-based scheme for fault diagnosis, including detection and classification, for the steam generator of a fossil electric power plant. The scheme is constituted by two components: residual generation and fault classification. The first component generates residuals via the difference between measurements coming from the plant and a neural network predictor. The neural network predictor is trained with healthy data collected from a full-scale simulator reproducing reliably the process behavior. For the second component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. The fault patterns are stored in an associative memory based on a recurrent neural network. The scheme is evaluated on-line via a full-scale simulator to diagnose the main faults appearing in this kind of power plants.

A combination of Multivariate Statistical Process Control (MSPC) and an automatic classification algorithm is applied to monitor a Waste Water Treatment Plant (WWTP). The goal of this work is to evaluate the capabilities of these techniques for assessing the actual state of a WWTP. The research was performed in a pilot WWTP operating with a Sequencing Batch Reactor (SBR). The results obtained refer to the dependence of process behavior with environmental conditions and the identification of specific abnormal operating conditions. It turned out that the combination of tolls yields better classifications compared with those obtained by using methods based on Partial Least Squares.

The impact of mask CD non-uniformity on the accuracy of optical proximity correction (OPC) models has been observed on several critical levels. In the current OPC model calibration flow, the mask effect is not explicitly separated from the optical and resist models. Instead, the resist model is compensating for the mask errors. In this paper, we report a detailed study of the effect of mask CD non-uniformity on OPC model accuracy using the established OPC model calibration flow. The influence of mask CD non-uniformity on the through process behavior of an OPC model is also discussed. A possible OPC flow to take the systematic mask CD error into consideration is proposed and a detailed study of mask modeling is present.

Hybrid laser welding technologies can be defined as the combination of a high quality laser heat source with an additional secondary heat source. Based on such a definition, known hybrid techniques are classified by the type of the heat sources used as well as their arrangement relative to each other. The classification is followed by a discussion of different theoretical aspects occuring during a hybrid laser beam welding process concerning the plasma characteristics and the resultant temperature distribution of the superposed heat sources. Finally, some practical examples demonstrate how the potential of hybrid technologies can be used to extend the application spectrum of the laser beam welding process.

Supervisory control and data acquisition (SCADA) systems are widely used in industrial control and automation. Modern SCADA protocols often employ TCP/IP to transport sensor data and control signals. Meanwhile, corporate IT infrastructures are interconnecting with previously isolated SCADA networks. The use of TCP/IP as a carrier protocol and the interconnection of IT and SCADA networks raise serious security issues. This paper describes an architecture for SCADA network forensics. In addition to supporting forensic investigations of SCADA network incidents, the architecture incorporates mechanisms for monitoring process behavior, analyzing trends and optimizing plant performance.

We are researching advanced operation support system which can help operators and provide them with novel knowledge. Advanced operation support system needs more essential information of plants such as temperature of reactor inside, real-time data of composition, prediction of the process behavior and so on. We use simulators as a way of getting such essential information of plants from the experience that we have developed process simulator. Our process simulator is strictly based on chemical engineering principle, namely, rigorous model which makes accurate simulation possible. However, it is hard to keep simulation highly-accurate against many reasons, e.g., change of process characteristics with degradation of plant equipments or catalysts. For this reason, we have proposed tracking simulator which runs in parallel with an actual process and adjust its parameters online. We developed an experimental steam reforming plant generating hydrogen from methane gas and the corresponding model for demonstration of tracking simulator. And then, we run tracking simulator with the process to visualize composition of reactor in real-time as an example of operation support.

Although multi-way Fisher discriminant analysis (MFDA) has been successfully applied in monitoring and fault diagnosis of batch processes, it has to estimate the future measured data of the batch processes during online monitoring and fault diagnosis. The estimated values not exactly follow the dynamic process behavior and they lead to false detection. In this paper, a novel method of monitoring and diagnosis for batch processes, named as multi-model FDA, is presented. The proposed method not only overcomes the need to estimating or filling up the future unmeasured data from the current time to the end of the batch, but also directly identifies the assignable causes of process abnormalities. Therefore, more accurate diagnostic decisions are made via multi-model FDA for online fault diagnosis. The method is proved to be effective by the application to monitoring and diagnosis of a multi-stage streptomycin fermentation process.

The drying of kraft paper at forced air conditions is provided and the influence of the drying air operational conditions on the process behavior is analyzed through the generalized drying curves proposal. The experimental apparatus consists of a metallic box where hot air was canalized over both surfaces of the paper sample. The influence of the samples thickness and the drying air temperatures and velocities in the drying curves and drying rate curves behavior is studied through the methodology of generalized drying curves and normalized drying rate curves, respectively. Finally, the possibility of fitting these curves is discussed.

Discusses partitions in asynchronous message-passing systems. In such systems, slow processes and slow links can lead to virtual partitions that are indistinguishable from real ones. To overcome the impossibility of detecting crashed processes in an asynchronous system, the system model incorporates a failure suspector to detect (possibly erroneously) process failures. Based on failure suspicions, the authors give a definition of partitions that accounts for real partitions as well as virtual ones. It is shown that under certain assumptions about the process behavior, any incorrect failure suspicion inevitably partitions the system. It is then shown how to interpret the absence-of-partition assumption. >

The presented Nonlinear Model Predictive Control (NMPC) scheme based on stable Wiener and Hammerstein models retains the characteristics of conventional linear Model Predictive Control (MPC) including capabilities to control stable nonlinear processes. The future process behavior is predicted by the q-th degree Wiener and Hammerstein model represented by Volterra-series. The optimal manipulated variable is calculated minimizing a quadratic cost function depending on the nonlinear predictor subject to input and output constraints.

Entecting anomaly in a system or a process behavior is very important in many real-world applications such as manufacturing, monitoring, signal processing etc. This chapter presents an anomaly Entection algorithm inspired by the negative-selection mechanism of the immune system, which discriminates betweensellandother.Here self is Enfined to benormal data patternsand non-self is any Enviation exceeding an allowable variation. Experiments with this anomaly Entection algorithm are reported for two data sets - time series data, generated using the Mackey-Glass equation and a simulated signal.Compared to existing methods, this method has the advantage of not requiring prior knowledge about all possible failure moEns of the monitored system. Results are reported to display the performance of the Entection algorithm

The objective of this research is to develop cost-effective surfactant flooding technology by using surfactant simulation studies to evaluate and optimize alternative design strategies taking into account reservoir characteristics, process chemistry, and process design options such as horizontal wells. Task 1 is the development of an improved numerical method for our simulator that will enable us to solve a wider class of these difficult simulation problems, accurately and affordably. Task 2 is the application of this simulator to the optimization of surfactant flooding to reduce its risk and cost. The objective of Task 2 is to investigate and evaluate, through a systematic simulation study, surfactant flooding processes that are cost-effective. We previously have reported on low tension polymer flooding as an alternative to classical surfactant/polymer flooding. In this reporting period, we have studied the potential of improving the efficiency of surfactant/polymer flooding by coinjecting an alkali agent such as sodium carbonate under realistic reservoir conditions and process behavior. The alkaline/surfactant/polymer (ASP) flood attempts to take advantage of high pH fluids to reduce the amount of surfactant needed by the chemical reactions between injection fluid and formation fluid or formation rocks.

We design an intelligent cooperative system to support operators in their task of supervision in nuclear plants application. In this situation, cooperation has two modes, namely a waking state and a participating state. During the waking state, the system observes the operator's behavior and the consequences on the process behavior. During the participating state, the cooperative system builds jointly with the user a solution to a problem. The cooperation depends of the system capabilities to explain and to incrementally acquire knowledge. This implies a revision of the design and the development of cooperative systems. We develop these ideas in our application.

This paper aims to explore the environment and the implementation of creative accounting events related to the context of ethical behavior and find solutions and ways to deal with matters creative accounting. Exploration carried out related to the definition of creative accounting, creative accounting in nature, ethics, reason for doing creative accounting practices, process behavior in creative accounting, as well as some summary results of empirical research about the events of creative accounting. Recent findings in this paper is to provide solutions and ways to prevent and solve problems in creative accounting accounting practices.

In recent years, multilayer feedforward neural networks have been used for chemical process identification [9]. One argument used repeatedly against neural network models is that they are “black-box” input/output models. The exact nature of the mapping from inputs to outputs is not easily understood. However, the advantage of neural network models is that they are able to predict process behavior accurately without any a priori knowledge of the process. Alternatives to input/output models are first principles (FP) models. The FP models describe the details of a process, but they are hard to develop, complex and difficult to solve. This paper discusses an approach to integrating FP models and neural network models, Integrated Neural Network (INN) models. Model mismatch between the FP model and the process is captured by the neural network. A polymer reactor system is chosen as a case study to demonstrate the technique.

Trajectory encoding is a method for on-line monitoring of manufacturing processes with complex discrete and continuous dynamics and infrequent, uncertain observations. Using a behavioral model specification of the acceptable process behavior, an evolution graph is constructed and updated iteratively as observations are received from the system. The evolution graph efficiently represents the (possibly infinite) set of valid system trajectories which are consistent with the observations. This paper focuses on the formal definition of the trajectory encoding method and the analytical results on the encoding as it relates to fault monitoring. The concluding section briefly describes applications of the method and directions for extensions to aid in fault diagnosis as well as detection.

A methodological approach to the reuse of process behavior specifications is presented, from a design-for-reuse perspective. More precisely, we present modeling and methodological tools for preparing reusable behavioral components to be exploited for designing dynamic specifications of applications not from scratch, but tailoring and adapting existing components. Reusable behavioral components are defined as generic process classes with associated meta-process classes providing guidelines for reuse in a given application.

In gas–solid heterogeneous photocatalytic oxidation (PCO), volatile organic compounds (VOCs) present in process air or air vents can be rapidly and completely oxidized to innocuous by‐products over a near‐ultraviolet (UV) illuminated titanium dioxide thin film catalyst at room temperature. This class of advanced oxidation processes appears to be well‐suited for point‐of‐use VOC abatement in the microelectronics manufacturing industry. In this article, we review industrial requirements and unresolved technical issues in the context of the recently published Semiconductor Industry Association roadmap. The specific requirements for VOC abatement from a typical photolithography track are presented. Bench‐scale PCO kinetics for target VOCs are reviewed to demonstrate the typical process behavior expected.

Packed bed reactors are commonly used for carrying out heterogeneous chemical reactions, and the attendant complexities arising out of the interactions of the momentum, heat, and mass transport processes make the design of such units a very involved and cumbersome task. The strategy for rational design depends strongly on the nature of the reaction scheme and its sensitivity to perturbations in operating conditions. Models ranging from simple one-dimensional pseudohomogeneous to two-dimensional heterogeneous are used to describe process behavior. Reviews detailing these aspects of the packed-bed have appeared from time to time.

Decisions by process operators are often based on mental models of the behavior of the plant. Intelligent programs to assist the operator will require a similar ability to reason about process behavior. This paper presents a method of qualitative simulation requiring no numerical information which could be used as a model-based “core” for such a reasoning program. The simulator is based on casuality between process variables subject to qualitative constraints. Removal of quantitative information results in a problem which is fundamentally underspecified, potentially resulting in multiple interpretations of a system’s behavior. The current method minimizes these ambiguities. The method is demonstrated by simulating the effect of faults on a process involving interacting control loops, mass and energy feedback, and multiple conflicting causal influences between variables.

Overdesign is applied to chemical processes to account for expected variations in the design data or design conditions. These variables must be treated quantitatively in order to design a process that is certain to perform adequately. Process dependability is introduced as a means of quantifying variable process behavior. Overdesign using the dependability criterion is a stochastic optimization problem. An example problem with three stochastic variables and two design variables is presented to illustrate these procedures.

This study examined the effects of process behavior feedback and goal setting for staff of a human service organization on those process behaviors and intended outcomes. The effects of outcome feedback and goal setting on outcomes intended and specific process behaviors were also studied. Process feedback was found to markedly increase the rate of process behavior but had no effect on intended outcomes. Outcome feedback had no effect on either intended outcomes or the related process behaviors. The implications of these results for the selection of appropriate targets for organizational interventions are discussed.

The efficiency of empirical studies aimed at establishing the cause-and-effect relationships between the manipulable parameters of a manufacturing process and the resultant measurable process behavior can be greatly enhanced by the appropriate application of experimental design methodologies. Some techniques of selecting suitable experimental parameter settings in the face of certain special process characteristics and operational constraints are discussed in this paper with particular reference to fractional factorial designs.

A fully computerized cement rotary kiln process control was tested in a real production line and the results are presented in this paper. The controller design was based on the understanding of the process behavior obtained by careful statistical analyses, and it was realized by using a very efficient statistical identification procedure and the orthodox optimal controller design by the statespace method. All phases of analysis, design and adjustment during the practical application are discussed in detail. Technical impact of the success of the control on the overall kiln installation is also discussed. The computational procedure for the identification is described in an Appendix.

This study focuses on industrial salesmen as interface managers who link their employer with their customers. The linking process behavior of salesmen as an approach to interorganizational coordina...

CO2- and solid-state lasers are the most widely used beam sources. Owing to their different physical beam characteristics, these two types of laser differ fundamentally not only in how the beam is guided but also in their process behavior during deep penetration welding. Almost all industrial applications in thick material > 8 mm have to be full penetration welds to increase fatigue strength, for example in ship building, pipeline construction, train and rail construction or power-train, Holzer et al., 2011 . Therefore process behavior and limits at full penetration will be analyzed in detail for both beam sources.

Dynamic load balancing in a system of loosely-coupled homogeneous processors may employ both judicious initial placement of processes and migration of existing processes to processors with fewer resident processes. In order to predict the possible benefits of these dynamic assignment techniques, we analyzed the behavior (CPU, disk, and memory use) of 9.5 million Unix* processes during normal use. The observed process behavior was then used to drive simulation studies of particular dynamic assignment heuristics.Let F(·) be the probability distribution of the amount of CPU time used by an arbitrary process. In the environment studied we found: (1-F(x)) n rx-c, 1.05

American Institute of Mining, Metallurgical, and Petroleum Engineers Inc. Abstract An experimental program is underway to simulate the in situ gasification of coal. A laboratory combustion tube together with extensive auxiliary equipment have been designed and assembled. This apparatus has been successfully operated and data has been collected which simulates in situ gasification. This laboratory reactor, its operation and some preliminary data are reported. Problems in interpretation of the data are reported. Introduction With the advent of the recent energy crisis various methods for recovery of energy from coal have been suggested. Many of these suggestions have been schemes for gasifying coal to produce a low BTU gas (generally under 200 BTU/SCF). This gas could be used as a fuel, upgraded to pipeline quality gas or used as a chemical pipeline quality gas or used as a chemical feedstock. In general the coal used in these gasification schemes would be strip mined. The processes typically require considerable equipment and are as such quite expensive. In the 19th century, a new process scheme was proposed and has recently attracted considerable attention. This scheme is that of in situ gasification. Conventional strip mining techniques are useful in extracting coal to a depth of approximately 200 feet below ground level. In situ recovery of coal offers the possibility of extracting coal to depths which possibility of extracting coal to depths which are not practical by more conventional techniques. As an example in New Mexico, coal is known to occur to depths in excess of 5000 feet below the surface. In fact, in excess of 95% of the coal in that state is not recoverable by present strip mining methods. The gasification of coal in place provides a potential means of reaching deposits not otherwise available for recovery due to their excessive depth and thin seams. In addition to advantages obtained by reaching deposits not otherwise available, in situ techniques may very well be the most economic method for gasifying coal. It is possible that considerable advantage can be realized by the elimination of surface gasification equipment.

Four different alkaline earth metal oxides i.e. MgO, CaO, SrO and BaO were used as heterogeneous catalysts for biodiesel production from palm oil. Effects of critical process variables i.e. reaction time, methanol to oil ratio and temperature were investigated. The results were then fitted to a historical design to study the Analysis of Variance (ANOVA), to characterize interactions between variables and to simulate the process. MgO did not show good catalytic activity while CaO produced undesired products at longer reaction time. BaO showed the best biodiesel result with a yield of up to 95 %, followed by SrO with a yield of 91 %. A methanol to oil ratio of 9:1 and 60 ºC were found to be the optimum conditions. The experimental data were satisfactorily predicted at 99 % confidence level under various conditions with R2 values higher than 0.92. Characterizations of the catalysts before and after the transesterification process were also performed using a surface analyzer, scanning electron microscopy, Hammett indicator and Atomic Absorption Spectrophotometeric (AAS) methods. The catalytic activity was in the order of BaO > SrO > CaO. However, due to lixiviation of BaO in the product, SrO was found to be the most potential catalyst.

The finest observable and implementable equivalence on concurrent processes is sought as part of a larger program to develop a theory of observable processes where semantics of processes are based on locally and finitely observable process behavior and all process constructs are allowed, provided their operational meaning is defined by realistically implementable transition rules. The structure of transition rules is examined, and several conditions that all realistically implementable rules should satisfy are proposed. It is shown that the ISOS contexts capture exactly the observable behavior of processes. This leads to the result that copy plus refusal equivalence is the finest implementable equivalence. >

In recent years, some promising results about laser beam welding with forced beam oscillation were published. Characteristic feature of this technology is the purposeful beam manipulation by an oscillatory beam deflection in superposition to the commonly uniform and rectilinear feed rate of a conventional laser beam welding process. Due to the increased number of welding variables, the application of oscillation techniques offers manifold possibilities to influence the energy deposition in dependence on the chosen oscillation regime. In such a way, the process behavior as well as the resultant properties of the weld can be favorably changed.An appropriate understanding of the interaction among the relevant welding and oscillation parameters was achieved through the modeling of the underlying physical relationships and the simulation of the transient energy deposition during beam oscillation procedures. Different deflection methods and various one-and two-dimensional oscillation patterns have been allowed for. By derivation of suitable dimensionless quantities, the developed model facilitates the distinction and characterization of different process regimes as well as the determination of appropriate oscillation parameters as function of the chosen welding variables.In recent years, some promising results about laser beam welding with forced beam oscillation were published. Characteristic feature of this technology is the purposeful beam manipulation by an oscillatory beam deflection in superposition to the commonly uniform and rectilinear feed rate of a conventional laser beam welding process. Due to the increased number of welding variables, the application of oscillation techniques offers manifold possibilities to influence the energy deposition in dependence on the chosen oscillation regime. In such a way, the process behavior as well as the resultant properties of the weld can be favorably changed.An appropriate understanding of the interaction among the relevant welding and oscillation parameters was achieved through the modeling of the underlying physical relationships and the simulation of the transient energy deposition during beam oscillation procedures. Different deflection methods and various one-and two-dimensional oscillation patterns have been allowed ...

An industrial ammonia synthesis reactor was studied in order to optimize its operational conditions by means of increasing overall ammonia production. A heterogeneous, one-dimensional model and a two-dimensional rigorous model were utilized to evaluate the process behavior. The simulation results of the two models were compared with data from an industrial ammonia plant. The one-dimensional model was found to be adequate for optimization purposes. Applying the Genetic Algorithm (GA) as a powerful method for complex problems, the model was employed to optimize the reactor performance in varying its quench flows. The optimal temperature profile along the fixed bed reactor was studied by changing independent variables including the quench temperature and the quench flow rates. Optimization results show that the optimum quench temperature is about 615°K and that the optimum quench flows can enhance ammonia production rate by 3.3%.