Order Process Model Research Articles

A cake-washing model with an overall cation transfer in kraft pulp washing

Cake washing of freely mobile ions was described by an advection–dispersion equation combined with the Donnan equilibrium and an overall ion transfer model between the external liquid phase and the fiber phase of kraft pulp. The ion exchange rate coefficients between the phases were determined from the earlier published leaching test results for an oxygen delignified Scandinavian hardwood kraft pulp. The time constant of the first order process model was about 19 s for calcium and manganese and about 6 s for sodium and magnesium. The calculated results for various elements during the acid displacement were compared with the published displacement experiments. Calculated concentrations and pH profiles in wash effluent and cake were shown. Moreover, changes in pH values and redistribution of ions between the external phase and the fiber phase in the cake during the acid displacement were also demonstrated.

A hierarchical, objectives-based framework for the digital investigations process

Digital investigations, whether forensic in nature or not, require scientific rigor and are facilitated through the use of standard processes. Such processes can be complex in nature. A more comprehensive, generally accepted digital investigation process framework is therefore sought to enhance scientific rigor and facilitate education, application, and research. Previously proposed frameworks are predominantly single-tier, higher order process models that focus on the abstract, rather than the more concrete principles of the investigation. We contend that these frameworks, although useful in explaining overarching concepts, fail to support the inclusion of additional layers of detail needed by various framework users. We therefore propose a multi-tier, hierarchical framework to guide digital investigations. Our framework includes objectives-based phases and sub-phases that are applicable to various layers of abstraction, and to which additional layers of detail can easily be added as needed. Our framework also includes principles that are applicable in varied ways to all phases. The data analysis function intended to identify and recover digital evidence is used as an example of how the framework might be further populated and used. The framework is then applied using two different case scenarios. At its highest level, the proposed framework provides a simplified view and conceptual understanding of the overall process. At lower levels, the proposed framework provides the granularity needed to achieve practicality and specificity goals set by practitioners and researchers alike.

Estimation, Control and Optimization of Curing in Thick-Sectioned Composite Parts

A nonlinear model-based control method is proposed and validated for controlling, estimating and optimizing the cure in composite parts. During the cure, the exothermic reaction causes temperature gradients through the thickness that can lead to a nonuniform cure and high residual stress. A predictive control and optimization approach is proposed to ensure that temperature gradients are kept within acceptable limits and the cure state is fairly uniform, regardless of the part thickness. A reduced order process model is derived and used to formulate a dynamic inversion controller. A nonlinear observer is constructed to estimate the unknown temperature and cure states within the composite. An on-line optimizer determines the maximum allowable heating rate. The optimizer is run at discrete intervals throughout the process to account for process and part variability. The control, estimation and optimization algorithms were validated through a series of simulations and experiments of composite parts cured in a press. Parts that were cured with the proposed control method were compared with parts cured following a manufacturer’s recommended cure cycle. The results demonstrate the success of the proposed control method in achieving uniform temperature and cure, and in decreasing the residual stress, without increasing cycle time.

Robust PID Tuning Using Closed-Loop Identification

Abstract Identification based PID tuning is studied. The proposed approach consists of the identification of linear or non linear process model and model based control design. The identification test can be performed in both open loop and closed-loop. The so-called ASYM method is used to solve the identification problem. The method identifies a low order process model with a quantification of model errors (uncertainty). The PID tuning is based on internal model control (IMC) tuning rules. Two case studies will be performed to demonstrate the methodology. The first one is the adaptive control of the dissolved oxygen of a bioreactor; the second one is the nonlinear PID control of a pH process.

Computer simulation of the processing of engineering materials with lasers—theory and first applications

Current numerical simulations of laser materials processing are usually based on a greatly simplified process model in order to allow for short computation times. This significantly decreases their flexibility and ability for simulation of the great variation of today's processes and, consequently, accounting for their subtle but important differences. The theory utilized for building a simulation tool presented in this paper can be said to be truly three-dimensional as opposed to other reported work that uses symmetric boundary conditions. The interaction of the free surface of the liquid face is modelled which provides a significant increase in the amount of process detail captured. The investigators contend that numerical modelling of the above can only be achieved credibly using high performance computing methods, in particular parallelization techniques.

Adaptive process monitoring via multichannel EIV lattice filters

AbstractOnline monitoring of multivariable processes is crucial to operational safety and product quality. For this, multivariable statistical analysis methods, such as principal‐component analysis (PCA), partial least squares, and canonical variate analysis have been widely applied. However, few recursive monitoring techniques have been developed for fully dynamic and time‐varying processes. Recursive PCA has been successfully applied to monitor static time‐varying processes, but does not work for fully dynamic processes. Dynamic PCA has been developed, but its recursive variant is not available. Many processes operate in dynamic states and are often time‐varying, and the time‐varying property includes the variation of parameters and of process structure, e.g., the change of model order. A novel approach to the adaptive monitoring of multivariate dynamic and time‐varying processes by the recursive multichannel instrumental variable (IV) lattice filters was developed using the errors‐in‐variables (EIV) state space model to represent a dynamic process. To show the relationship between EIV state‐space representation of the process and a multichannel IV lattice filter, the lattice filter was used to generate a residual vector for process monitoring. By using lattice filter's ability of recursively updating the process model both in time and order, a real time, on‐line algorithm was used to update the residual vector with newly sampled process data, including a practical approach to recursive determination of the process model order. Based on the residual vector, the Hotelling T2 statistic and the associated confidence limits are used as the monitoring index. The proposed scheme was evaluated on a simulation example and a pilot plant to support the theoretical results.

Sparse on-line gaussian processes.

We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian on-line algorithm, together with a sequential construction of a relevant subsample of the data that fully specifies the prediction of the GP model. By using an appealing parameterization and projection techniques in a reproducing kernel Hilbert space, recursions for the effective parameters and a sparse gaussian approximation of the posterior process are obtained. This allows for both a propagation of predictions and Bayesian error measures. The significance and robustness of our approach are demonstrated on a variety of experiments.

Process Monitoring in Grinding

The grinding process is characterised by a high number of cutting edges undergoing non-uniform wear, this also typifies a highly non-stationary process. Pure process modelling in order to understand the grinding mechanisms and to predict the work result is difficult. In view of this, several approaches by academia and by industry were made to address this issue. One such approach is the development and implementation of process monitoring in grinding. The work presented in this paper summarises the various elements and approaches in process monitoring during grinding. The paper deals with the relevant quantities that describe the grinding process itself and the ensuing work result. Furthermore the different sensing techniques are introduced to measure the relevant quantities. Finally an overview is presented as to the strategies and techniques involved in the interpretation of the measured data and the subsequent control action initiated in order to close the process control loop.

Designing a MIMO PI controller using the multiple integration approach

The large majority of existing tuning methods for multivariable PI controllers require a good quality process model in order to achieve reliable tuning. Modelling and identification of an accurate process model is time-consuming and requires extensive experimentation. In this paper, a new parameterisation of the process, requiring only a multiple integration of the process step-response, is proposed. It is shown that the proposed parameterisation also interlaces very well with one of the existing tuning methods for multivariable processes. Experiments on two process models showed that the results of the proposed method are comparable with or better than certain other tuning methods requiring more demanding modelling and identification. One experiment performed on a laboratory plant shows that the method can also be easily applied in practice.

Characterizing the Decision Process Leading to Enrollment in Master's Programs: Further Application of the Enrollment Process Model

ABSTRACT As part of an ongoing market research function, the administration at a private university's business school implemented a project to better understand the composition of its master's students. This research was an extension of a similar study conducted for the school's doctoral programs. Like the earlier study on doctoral students, a behavioral approach that employed a theoretical model of the decision process leading to enrollment was applied to master's students. This enrollment process model was utilized to guide the implementation of a survey that sampled the school's current students. Application of this model to questionnaire development procedures is also presented. Based on the information collected, the decision process leading to enrollment in the master's business programs was characterized. Quadrant analysis was applied to selected data derived from the enrollment process model in order to develop a two dimensional profile of students. In practice, this depiction provided insight to student perception of ideal versus actual program characteristics. The usefulness and applicability of the enrollment process model in achieving enrollment management objectives is also discussed.

Optimal filtering and Bayesian detection for friction-based diagnostics in machines

Non-model-based diagnostic methods typically rely on measured signals that must be empirically related to process behavior or incipient faults. The difficulty in interpreting a signal that is indirectly related to the fundamental process behavior is significant. This paper presents an integrated non-model and model-based approach to detecting when process behavior varies from a proposed model. The method, which is based on nonlinear filtering combined with maximum likelihood hypothesis testing, is applicable to dynamic systems whose constitutive model is well known, and whose process inputs are poorly known. Here, the method is applied to friction estimation and diagnosis during motion control in a rotating machine. A nonlinear observer estimates friction torque in a machine from shaft angular position measurements and the known input voltage to the motor. The resulting friction torque estimate can be analyzed directly for statistical abnormalities, or it can be directly compared to friction torque outputs of an applicable friction process model in order to diagnose faults or model variations. Nonlinear estimation of friction torque provides a variable on which to apply diagnostic methods that is directly related to model variations or faults. The method is evaluated experimentally by its ability to detect normal load variations in a closed-loop controlled motor driven inertia with bearing friction and an artificially-induced external line contact. Results show an ability to detect statistically significant changes in friction characteristics induced by normal load variations over a wide range of underlying friction behaviors.

Building Agile Systems with Mobile Code

Mobile code has been championed as a solution to a plethora of software problems. This paper describes investigative work undertaken in order to evaluate the mobile code abstractions of Mobile Agents and Mobile Objects, and to understand the implications of using these abstractions to build distributed systems. We describe two systems built to support the Sales Order Process of a distributed manufacturing enterprise, using IBM's Aglets Software Development Kit. The Sales Order Process model and the requirements for agility used as the basis for these implementations are derived from data collected in an industrial case study. Both systems are evaluated using the Goal/Question/Metric methodology. Two new metrics for Semantic Alignment and Change Capability are presented and used to evaluate each system with respect to the degree of system agility supported. The systems are evaluated through a set of scenarios generated during the case study in an attempt to see if they support system integration and agility in the manufacturing domain. Further we examine the implications of using a mobile code abstraction when compared with the abstraction offered by traditional distribution technology. The work described provides evidence that both Mobile Agent and Mobile Object systems have inherent properties that can be used to build agile distributed systems. Further, Mobile Agents with their additional autonomy provide marginally greater support.

Parameter Estimation of a Second Order Model in The Frequency Domain from Closed Loop Data

This paper presents a method for estimation of parameters of a second order plus dead time process model. The estimation procedure is based on transforming the closed loop data into the Fourier frequency domain. Then, using a fairly pedestrian approach the transformed data is used with a linear least-squares estimator to obtain the estimated values. In contrast to former least-squares estimation methods, no iterations or search procedures are implied. This is achieved by using the PID tuning practice that regards self-regulating processes as a first or second order dynamic. Thus the resulted method is attractive for self tuning controllers and related process control applications. Moreover, since closed-loop estimation is adopted, the model parameters are less sensitive to disturbances and measurement noise occurring during the test stage. However, this requires a knowledge of the controller transfer function and settings. The proposed method is applicable to both under- and over-damped closed loops and is not limited only to step test signals. Since the method is simple, its results can be compared against closed-loop reaction curve methods. This is done using a comparative basis from the literature.

Identifying weld pool dynamics for gas metal arc fillet welds

Arc welding processes are conventionally modelled under static (steady state) conditions to provide a relationship between the controllable process parameter values and the characteristic weld geometry parameters that result. While this is appropriate for open loop control of the process, should closed loop (feedback) control of the process be required, the dynamic (or transient) response must also be identified. In the present paper, the crosscorrelation approach for system identification is evaluated for developing a dynamic process model of the relationship between welding voltage and weld pool width. Based on the identified impulse response, a second order process model is assumed. The non-linearity of the model is demonstrated and the gain, damping, and natural frequency are estimated.

Identifying weld pool dynamics for gas metal arc fillet welds

Arc welding processes are conventionally modelled under static (steady state) conditions to provide a relationship between the controllable process parameter values and the characteristic weld geometry parameters that result. While this is appropriate for open loop control of the process, should closed loop (feedback) control of the process be required, the dynamic (or transient) response must also be identified. In the present paper, the crosscorrelation approach for system identification is evaluated for developing a dynamic process model of the relationship between welding voltage and weld pool width. Based on the identified impulse response, a second order process model is assumed. The non-linearity of the model is demonstrated and the gain, damping, and natural frequency are estimated.

Including Geological Uncertainty and Economic Analysis in a Rapid Simulation of Hydrocarbon Exploration

Manly's approximation method has been applied to hydrocarbon discovery process modeling in order to approximate the expected value and the standard deviation of the total discovery volume for a given exploratory effort. A major benefit of this method is that it allows the model to run much faster than the regular simulation method, yet it gives accurate results. This paper extends the benefit of Manly's approach by allowing the approximation method to incorporate the uncertainties in geological parameters that drive Manly's approximation, in order to provide an approximation of the complete distribution of total discovery volume that can result from exploration activity. In addition, it allows the model to include economic parameters into an evaluation of the economic worth of the results of exploration activity, producing distributions of net present value within a short period of time. The offshore Nova Scotia Shelf Basin is selected for demonstrating the methodology.

Continuous casting and rapid solidification of wires produced by a newly developed shape flow casting technique

The shape flow casting (SFC) process was developed for continuous casting of wires with diameters ranging from 1 to 3 mm possessing a refined microstructure achieved by rapid substrate quenching. An instrumented and computerized meltspin facility was designed for a quantitative study of the process parameters including process modelling in order to optimize the casting process under high cooling conditions. The microstructural parameters were correlated with the governing process variables, such as superheat of the melt, velocity of the melt jet, nozzle geometry, and its adjustment. The present paper describes the process principles of the SFC-technology, which enables a flexible production of steel, nickel, or aluminium wires and other novel materials. The microstructural features are quantitatively correlated to the process parameters.

A Process Modelling for Supervision in FMS : Operational Accessibility Graph

Control supervision implies specific functions such as Monitoring, Working Modes Management and Piloting. To manage the complexity of FMS, the process has to be modelled. This paper deals with a process modelling in order to integrate works about supervision. The integration is based on the definition of a library that contains reference elements based on the concept of operation. These elements are used with the notion of accessibility in graph to get the final model. Some algorithms are proposed to exploit the model and examples are given for Recovery integration.

Control of the Crystal Mean Size in a Pilot Plant Potash Crystallizer

On-line measurements of the mean crystal size, the weight percent solids in the product and fines steams, and an estimate of the supersaturation in a 1 m 3 forced-circulation pilot plant crystallizer are performed in this study. From the open loop responses to step changes in the feed, product, recirculation, and fines dissolution flowrates, it is determined that the on-line sensors can provide acceptable measurements of process variables. It is found that the sensors used to measure the weight percent of fines and the supersaturation are sensitive to changes in the brine composition. The open loop responses are also used to design a PI-controller for the control of the mean crystal size. Based on a first order process model of the measured mean size, a cascade Pi-controller is designed using the fines dissolution flow rate as the slave controlled variable. At a temperature difference of 15K between the feed tank and the crystallizer, the controller results in some improvement in the measured mean size of crystals. The controller does not have adequate set point tracking abilities and causes some oscillatory behaviour as a result of disturbances. The controller performance and especially its set point tracking capability was improved when the temperature difference between the feed tank and the crystallizer was reduced to 5K and further modifications to the crystallizer circuit were incorporated.

Industiral Shape Design with Form-Feature-Based Modeling. System Architecture based on Determination Process Model.

To develop a 3D-modeling system that is utilizable for industrial shape design processes, a determination process model for shape creation work and a modeling system architecture based on this model are proposed. Our analysis of the design mock-up process makes it possible to assume that the determination model of shape creation processes is represented by a type of machining process model in order to determine such items as cutting procedures, cutting methods and cut-off-volume. In the feature-based modeling process the items to be determined are combinations of features, definitions of features and values of parameters. Therefore a modeling system for shape creation work can be constructed using the feature-based modeler by correlating these items with those of the determination process model. The applicability of this modeling system has been proven by its utilization for actual shape creation work.