Simultaneous Measurement Bias Correction and Dynamic Data Reconciliation

Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: Application for an industrial polypropylene reactor

Measured process data inherently contain inaccurate information since the measurements are obtained with imperfect instruments. Data reconciliation is the process of treating a set of measured data so that the derived values obey natural laws. Extensive research has been carried out on static data reconciliation techniques. Much less research however has been directed towards the reconciliation of dynamic data. The problem of identification of systematic bias in dynamic processes has been addressed by a handful of researchers. This paper presents a new algorithm for the detection and identification of systematic bias. The algorithm is used in conjunction with dynamic data reconciliation and applied to a simulated process plant consisting of two Continuous Stirred Tank Reactors (CSTR) connected in series. The results show that the new algorithm, which is practical and intuitive, is capable of accurately detecting and identifying systematic bias.

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.

Life cycle assessment (LCA) is a methodology used in assessing the environmental impacts of products. Life cycle inventory analysis (LCI) is one of the four components of LCA which quantifies flows of materials and pollutants in the entire life cycle (cradle‐to‐grave) of the product. Data are commonly collected from various sources (e.g. publications, databases and site‐specific measurements) which may result in violations of mass and energy balances due to uncertainties. In such cases, the data can be adjusted using reconciliation methods to improve the reliability of the results. Another computational issue in LCI arises when two or more products are involved, known as the multi‐functionality or allocation problem. The problem is how to allocate resource and emission streams to each product. This work describes a model using fuzzy optimization for solving both allocation and data reconciliation problems simultaneously. The model is illustrated on an aluminum industry case study involving open‐loop recycling, and the approach is compared with two sequential allocation and data reconciliation methods. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.

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

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.

Measurement bias invariant moving horizon estimation in the presence of outliers for data reconciliation of nonlinear dynamic systems

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

High variability, stringent effluent permits, and often extreme operating conditions define the practice of wastewater treatment in the chemical industry. This paper reviews the benefits and challenges of applying dynamic simulation to chemical-industry wastewater treatment plants by describing case studies at full-scale wastewater treatment plants (WWTP). The applications range from process troubleshooting to optimization and control. The applications have been valuable and useful in developing a deeper understanding of the plants as integrated systems. However there still remains substantial work to implement the dynamic simulations for daily real-time use by plant engineers and operators. This opportunity to improve plant operations is still largely untapped and will remain so until dynamic state estimation and data reconciliation are incorporated into simulation packages for use in developing the on-line simulations.

Data-driven modeling has been widely studied and employed for quality prediction in batch processes. However, existing research and development of this methodology have found it not sufficiently effective in addressing challenges from the batch-to-batch uncertainty and limitation of noisy modeling data. This work proposes a quality prediction method that combines batch correlation information and dynamic data reconciliation (DDR). The method is based on Gaussian process regression and integrated with canonical correlation analysis to capture batch-to-batch characteristics through examining the correlation between batches. Furthermore, DDR is used to dynamically adjust prediction variance, reduce the impact of noisy process data, and improve the prediction accuracy. A numerical case, a batch crystallization process, and a silica modification process have verified that the proposed method is demonstrated effective performance, compared with the candidates.

Data Reconciliation for Stationary Multi-Phase Multi-Stream Metallurgical Processes

The prelims comprise: Introduction Introductory Concepts for Validation of Plant Data Formulation Software Solution Integration in the Process Decision Chain Optimal Design of Measurement System An Example Conclusions

Sufficient conditions for robust stability of multivariable quadratic dynamic matrix controllers with an end condition (EQDMC) are developed, and the effect of these conditions on closed‐loop performance is examined. Hard and soft constraints on process inputs and outputs, and process modeling uncertainty are present Modeling uncertainty is quantified in the time domain as upper and lower bounds on the coefficients of a finite pulse response process model. The robust stability conditions that we develop involve the prediction and control horizon lengths, and a set of inequalities that the move‐suppression coefficients in the on‐line EQDMC objective function must satisfy. These conditions imply that for processes with modeling uncertainty, the move‐suppression coefficients could be quite large and quite sensitive to the control horizon length and process modeling errors. This could make the EQDMC controller conservative, thus significantly deteriorating performance. To determine the optimal control horizon length corresponding to the best robust performance of the EQDMC controller for a class of disturbances, follow the proposed EQDMC controller design methodolo∼. To illustrate this methodology simulations on an SISO example and a 2 × 2 subsystem of the Shell Standard Control Problem are presented.

Extension of process models to predict batch screening results under the influence of moisture based on DEM simulations

In this paper, a Mahalanobis distance based adaptive unscented Kalman filter (MDAUKF) is proposed, and it is applied to the tightly coupled GPS/MEMS-IMU integration for navigation solution. The MDAUKF aims to address the performance degradation of the unscented Kalman filter due to process model error. Based on a statistical function defined by the Mahalanobis distance of innovation vector, the MDAUKF adopts hypothesis testing to identify process model error and further introduces an adaptive factor into the prediction covariance to decrease the weight of state prediction and make more use of the accurate observation information. The MDAUKF is achievable to suppress the influence of process model error on filtering solution, and improve the adaptability for data fusion in GPS/MEMS-IMU (global positioning system/Micro-electro mechanical system based on inertial measurement unit) integration. Simulation results and comparison analysis demonstrate the effectiveness of the proposed method.