Determining a dynamic model for flotation circuits using plant data to implement a Kalman filter for data reconciliation

Since measurements of process variables are subject to measurements errors as well as process variability, data reconciliation is the procedure of optimally adjusting measured date so that the adjusted values obey the conservation laws and constraints. Thus, data reconciliation for dynamic systems is fundamental and important for control, fault detection, and system optimization. Attempts to successfully implement estimators are often hindered by serve process nonlinearities, complicated state constraints, and un-measurable perturbations. As a constrained minimization problem, the dynamic data reconciliation is dynamically carried out to product smoothed estimates with variances from the original data. Many algorithms are proposed to solve such state estimation such as the extended Kalman filter (EKF), the unscented Kalman filter, and the cubature Kalman filter (CKF). In this paper, we investigate the use of CKF algorithm in comparative with the EKF to solve the nonlinear dynamic data reconciliation problem. First we give a broad overview of the recursive nonlinear data dynamic reconciliation (RNDDR) scheme, then present an extension to the CKF algorithm, and finally address the issue of how to solve the constraints in the CKF approach. The CCRNDDR method is proposed by applying the RNDDR in the CKF algorithm to handle nonlinearity and algebraic constraints and bounds. As the sampling idea is incorporated into the RNDDR framework, more accurate estimates can obtained via the recursive nature of the estimation procedure. The performance of the CKF approach is compared with EKF and RNDDR on nonlinear process systems with constraints. The conclusion is that with an error optimization solution of the correction step, the reformulated CKF shows high performance on the selection of nonlinear constrained process systems. Simulation results show the CCRNDDR is an efficient, accurate and stable method for real-time state estimation for nonlinear dynamic processes.

This article describes a new framework for data reconciliation in generalized linear dynamic systems, in which the well‐known Kalman filter (KF) is inadequate for filtering. In contrast to the classical formulation, the proposed framework is in a more concise form but still remains the same filtering accuracy. This comes from the properties of linear dynamic systems and the features of the linear equality constrained least squares solution. Meanwhile, the statistical properties of the framework offer new potentials for dynamic measurement bias detection and identification techniques. On the basis of this new framework, a filtering formula is rederived directly and the generalized likelihood ratio method is modified for generalized linear dynamic systems. Simulation studies of a material network present the effects of both the techniques and emphatically demonstrate the characteristics of the identification approach. Moreover, the new framework provides some insights about the connections between linear dynamic data reconciliation, linear steady state data reconciliation, and KF. © 2009 American Institute of Chemical Engineers AIChE J, 2010

Data reconciliation for the overall thermal system of a steam turbine power plant

The amount of greenhouse gases (GHG), especially, nitrous oxide (N2O) emitted from wastewater treatment plants (WWTP) using data reconciliation and closed-loop mass balance was estimated. This study is based on a flowbased emission estimation approach which depends on the accuracy of the measurement data. To reduce the (random) measurement error, data reconciliation was used to enhance the accuracy of the flow measurements. After performing data reconciliation, N2O emission was estimated with more precision by using the closed-loop mass balance. The results in both pilot-scale and full-scale plants show that the suggested method can easily obtain the precise flow measurement for GHG emission, which in turn, results in the accurate estimation of the N2O amounts emitted from WWTP. Moreover, it is shown that the estimated flowrate values can be used as a software sensor, which can replace the faulty sensors and can validate the existing field measurements.

Data reconciliation (DR) and gross error detection are two common tools used in industry to provide accurate and reliable data, which is useful to analyse plant performance and basis for troubleshooting. DR techniques improve the accuracy of measurements by using redundancies in material and energy balances. This provides reliable information that could help decision making regarding plant operation, which potentially leads to financial benefit. This paper presents the utilization of plant data to perform troubleshooting of ammonia reactor, in particular the profile of catalyst activity. Bad plant data are collected and then analysed using DR to produces reconciled data, which could be used to detect and identify the gross error measurements. The input data for DR and gross error detection were gathered from Aspen HYSYS V8.8 simulations by modelling the single-bed ammonia reactor. The result presents that bad plant data could define actual system condition such as gross error measurements in normal condition or catalyst activity problem. Both conditions are modelled by DR to indicate actual system condition using statistical analysis and to perform troubleshooting. Appropriate troubleshooting could save time and provide financial benefits by avoiding wrong accusation of system problem, specifically in ammonia reactor evaluated in this paper.

Data Reconciliation in Reaction Systems using the Concept of Extents

The presence of measurement bias and random noise significantly deteriorates the information quality of plant data. Data reconciliation techniques for steady‐state processes have been widely applied to processing industries to improve the accuracy and precision of the raw measurements. This paper develops an algorithm for simultaneous bias correction and data reconciliation for dynamic processes. The algorithm considers process model error as an important contributing factor in the estimation of the measurement bias and process state variables. It employs black‐box models for the process as would be done when phenomenological models are difficult or impractical to obtain. Simulation results of a distillation column demonstrated that this algorithm effectively compensates constant and non‐constant measurement biases yielding much improved reconciled values of process variables. It has computational advantages over previously proposed algorithms based on non‐linear dynamic data reconciliation because an analytical solution is available when using linear process models to approximate the process.

Thermal experimental results on the prototype for high power neutron converter for low energy proton/deuteron beams

The cryogenic system [1] for the Large Hadron Collider accelerator is presently in its final phase of commissioning at nominal operating conditions. The refrigeration capacity for the LHC is produced using eight large cryogenic plants and eight 1.8 K refrigeration units installed on five cryogenic islands. Machine cryogenic equipment is installed in a 26.7-km circumference ring deep underground tunnel and are maintained at their nominal operating conditions via a distribution system consisting of transfer lines, cold interconnection boxes at each cryogenic island and a cryogenic distribution line. The functional analysis of the whole system during all operating conditions was established and validated during the first sector commissioning in order to maximize the system availability. Analysis, operating modes, main failure scenarios, results and performance of the cryogenic system are presented.

Following an unforeseen disturbance in a power system, the system state should be known to extract the emergency voltage restoration strategy. Such a condition does not usually hold in distribution systems due to the lack of enough measurements. Here, the joint probability density function ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pdf</small> ) of uncertain parameters is extracted from historical data. This <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pdf</small> is updated to comply with the measurements. A set of samples is then generated that best models the updated <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pdf</small> . These data reconciliation and sampling techniques enable the functioning of the emergency voltage control ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evc</small> ) with measurement scarcity. They are designed to be fast enough to meet the quasi-real-time requirements of the intended application. The <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evc</small> problem is cast as a stochastic programming problem. The resultant problem is a mixed-integer non-convex (and hence, NP-hard) optimization problem. It is solved using five distinct <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sm</small> s based on relaxation/approximation of the non-convex constraints. The results show that a novel combination of relaxation and approximation aimed at respectively mitigating the emergency under- and over-voltages outperforms the others. The propounded stochastic <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evc</small> is validated through numerical studies.

The Open Science Lab at Technische Informationsbibliothek (TIB) uses Wikidata, and the underlying open-source software Wikibase, for reconciliation and data enrichment across diverse linked open data projects in the humanities and cultural heritage sector. In the context of the Consortium for Cultural Heritage part of the National Infrastructure for Research Data in Germany (NFDI4Culture), TIB develops the dedicated Wikibase4Research service in close collaboration with researchers and museum and library professionals. This paper outlines our hands-on approach towards data reconciliation and enrichment workflows using the Wikibase4Research service, and related tooling ecosystem, including the open source OpenRefine reconciliation service, Wikidata’s SPARQL query service and Wikibase extensions. We discuss the outcomes of data enrichment and federation across three architectural datasets structured in separate Wikibase repositories. We elaborate on the advantages of integrating Wikidata data in humanities projects, such as its breadth of cross-disciplinary data, mappings to authority file identifiers, and access to additional data such as images, geo coordinates, historical dates, and more. We also address typical workflow challenges, such as duplicate data entries, missing data, multiple possible matches, as well as the potential for automation of manual tasks. Lastly, the paper also showcases the capabilities of federated queries and their relevance to different research scenarios.

Thrust Stand Characterization of the NASA Evolutionary Xenon Thruster

Incremental identification of hybrid process models

We present a model based algorithm for simultaneous validation of online analysers and process simulators. Reconciled on-line and historical process data satisfying balance and model equations provides the opportunity to validate and improve process models and soft sensors. Accurate simulation results and laboratory measurements can be used for the validation of online analysers. Validated and reconciled data can be used to the iterative and interactive identification of the unknown parameters of the simulator, e.g. for the determination of kinetic parameters. This method can also be used for monitoring and diagnostics of complex processes because situations when the operating conditions have been significantly changed can be discovered. The approach is illustrated by the analysis of an industrial hydrogenation system. We present the proposed iterative and interactive procedure of model development and analyser validation, the applied data reconciliation method, and the details of the case study. The results show the applicability of the proposed scheme in industrial environment and the benefits of the extracted information in the maintenance and monitoring of advanced model based process engineering tools. The developed tool can increase operating efficiency that is the key of reducing energy consumption and environmental impact. This is especially true in hydrocarbon industry where the operation of the technology is supported by process simulator and on-line analyser based advanced process control systems.

This paper introduces novel approaches for the estimation of the traffic stream density. First, an artificial neural network (ANN) data-driven approach is developed to estimate the level of market penetration (LMP) of connected vehicles at two fixed locations. Then, the estimated values are used as inputs to a Kalman filter (KF) approach to estimate the vehicle count between these two locations. Second, three data-driven approaches are developed to directly estimate the vehicle count using only connected vehicle data, an ANN, a k-nearest neighbor (k-NN), and a random forest (RF). A congested signalized roadway in downtown Blacksburg, Virginia, is used to test and compare the performance of the estimation approaches. Results demonstrate that the ANN approach produces reasonable errors in estimating the LMPs; however, integrating the ANN with the KF results in larger errors than the errors produced from using the KF with a predefined fixed average value obtained from historical data. The results also demonstrate that the data-driven approaches provide accurate vehicle count estimates, with the ANN being the most accurate of the three approaches. Lastly, the paper compares the three developed data-driven approaches with model-driven approaches (i.e., KF), showing that the ANN outperforms all other approaches. However, taking into consideration that the difference is not large, the computational time needed to train the ANN, the large amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the KF approach is recommended in the estimation of traffic stream density due to its simplicity and applicability in the field.