Shankar Narasimhan S

Concentrations measurements are typically corrupted by noise. Data reconciliation techniques improve the accuracy of measurements by using redundancies in the material and energy balances expressed as relationships between measurements. Since in the absence of kinetic models these relationships cannot integrate information regarding past measurements, they are expressed in the form of algebraic constraints. This paper shows that, even in the absence of a kinetic model, one can use shape constraints to relate measurements at different time instants, thereby improving the accuracy of reconciled estimates. The construction of shape constraints depends on the operating mode of the reactor. Moreover, it is shown that the representation of the reaction system in terms of vessel extents helps identify additional shape constraints. A procedure for deriving shape constraints from measurements is also described. Data reconciliation using both numbers of moles and extents is illustrated via a simulated case study.

This work deals with state estimation in the presence of delayed and infrequent measurements. While most measurements (referred to as secondary measurements) are available frequently and instantaneously, there might be a delay associated with acquiring other measurements (primary measurements) due to long analysis times involved. The primary measurements are usually sampled at irregular intervals and the exact delay is also unknown. The traditional fixed-lag smoothing algorithm, which has been applied for a variety of chemical processes systems, can be computationally inefficient for such situations and alternate methods to handle delays are necessary. In this paper, we analyze several existing methods to incorporate measurement delays and reinterpret their results under a common unified framework (for Extended Kalman Filter). Extensions to handle time-varying and uncertain delays, as well as out of sequence measurement arrival are also presented. Simulation studies on a linear distillation column and a nonlinear polymerization reactor are used to compare the performance of these methods based on RMSE values and computation times. A large scale nonlinear reactive distillation column example is also used to illustrate the practicality of the suggested method. © 2010 Elsevier Ltd. All rights reserved.

An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS) process. For this purpose, a reliable data-driven soft sensor utilizing Support Vector Regression (SVR) was developed and the effects of integrating Vector Quantization (VQ) with Principle Component Analysis (PCA) were studied in the assessment of this soft sensor. First, in the pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR) was better than (PCA-SVR) in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE = 0.0668 and R2 = 0.995) in comparison with investigated models.

Dynamic simulation models can be used along with flow and pressure measurements, for on-line leak detection and identification in gas pipeline networks. In this two part paper, a methodology is proposed for detecting and localizing leaks occurring in gas pipelines. The main features of the proposed methodology are: (i) it is applicable to both single pipelines and pipeline networks and (ii) it considers non-ideal gas mixtures. In order to achieve the desired computational efficiency for on-line deployment, an efficient state estimation technique based on a transfer function model, previously developed by the authors, is embedded in a hypothesis testing framework. In Part-I of this paper, a detailed description of the methodology is presented, and its performance is evaluated using simulations on two illustrative pipeline systems. The proposed method is shown to perform satisfactorily even with noisy measurements and during transient conditions, provided there is sufficient redundancy in the measurements. © 2010 Elsevier Ltd.

Concentrations measured during the course of a chemical reaction are corrupted with noise, which reduces the quality of information. When these measurements are used for identifying kinetic models, the noise impairs the ability to identify accurate models. The noise in concentration measurements can be reduced using data reconciliation, exploiting for example the material balances as constraints. However, additional constraints can be obtained via the transformation of concentrations into extents and invariants. This paper uses the transformation to extents and invariants and formulates the data reconciliation problem accordingly. This formulation has the advantage that non-negativity and monotonicity constraints can be imposed on selected extents. A simulated example is used to demonstrate that reconciled measurements lead to the identification of more accurate kinetic models.

In this contribution we propose an active Fault Tolerant Control (FTC) strategy which enables the isolation and identification of valve stiction and valve blocking, in addition to the additive faults like sensor and actuator biases. The developed method is an extension of the original method proposed by Prakash et al. (2002). This method is based on the Kalman filter and is developed under the assumption that the monitored system is Linear Time Invariant (LTI). It has been shown to work well for additive faults such as sensor and actuator biases. Within this method the fault isolation and identification task is based on the Generalized Likelihood Ratio (GLR) test by which the most plausible fault type in a library of faults is selected following estimation of fault parameters. © 2010 Elsevier B.V.

Kalman filter and its variants have been used for state estimation of systems described by ordinary differential equation (ODE) models. While state and parameter estimation of ODE systems has been studied extensively, differential algebraic equation (DAE) systems have received much less attention. However, most realistic chemical engineering processes are modelled as DAE systems and hence state and parameter estimation of DAE systems is a significant problem. Becerra et al. (2001) proposed an extension of the extended kalman filter (EKF) for estimating the states of a system described by nonlinear differential-algebraic equations (DAE). One limitation of this approach is that it only utilizes measurements of the differential states, and is therefore not applicable to processes in which algebraic states are measured. In this paper, we address the state estimation of constrained nonlinear DAE systems. The novel aspects of this work are: (i) development of a modified EKF approach that can utilize measurements of both algebraic and differential states, (ii) development of a recursive approach for the inclusion of constraints, and (iii) development of approaches that utilize unscented sampling in state and parameter estimation of nonlinear DAE systems; this has not been attempted before. The utility of these estimators is demonstrated using electrochemical and reactive distillation processes. © 2010 Elsevier Ltd.

State estimation is an important problem in process operations. For linear dynamical systems, Kalman Filter (KF) results in optimal estimates. Chemical engineering problems are characterized by nonlinear models and constraints on the states. Nonlinearities in these models are handled effectively by the Extended Kalman Filter (EKF), whereas constraints pose more serious problems. Several constrained estimation problems where the EKF approach fails have been reported in the literature. To address this issue, receding horizon approaches such as the Moving Horizon Estimation (MHE) have been proposed. The MHE approach has been shown to provide the most reliable estimates in several example problems; albeit at a high computational price. Unlike the KF, the MHE formulation does not use an explicit predictor-corrector approach. In this paper, we study the following questions in nonlinear constrained state estimation: (i) can the EKF be extended to include a receding horizon in a simple intuitive fashion? (ii) are there any performance gains over an EKF due to a receding horizon? and, (iii) are there any computational gains over the standard MHE through such an extension? A Receding Nonlinear Kalman (RNK) Filter formulation is proposed to answer these questions. The RNK formulation follows a predictor-corrector approach and uses linearization of the state space model for covariance calculation much like the EKF approach. We demonstrate through examples that inclusion of a receding horizon improves performance over the standard EKF approach. We also discuss the computational properties of RNK in comparison with MHE. © 2011 Elsevier B.V.

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization (HDS) process was proposed. Therefore, an integrated approach using support vector regression (SVR) based on wavelet transform (WT) and principal component analysis (PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance (EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression (MLR), SVR and PCA-SVR.

Optimum use of available energy sources is essential for cost effective and sustainable growth. Fuel cells - due to their ability in efficiently extracting energy from fuels - have gained considerable attention among the various energy conversion alternatives. Systems researchers working in the field of fuel cells have been focusing on optimal stack design and the attendant modeling aspects. In a power network where multiple fuel cell stacks combine together to achieve the required power, it is not enough to focus only on the optimal design of the stacks. While operating the stacks, the problem of optimal sharing of power between the different stacks in a power network is another important problem that needs to be addressed. This optimal power sharing problem is the focus of this paper. We will describe a novel solution approach for this optimization problem, which through prior off-line computations reduces the on-line optimization task to one of solving simple equations. Another major significance of this new approach is that unlike the conventional optimizers, global optimum is guaranteed using this approach. The proposed algorithm uses a data-based model between the voltage and current for optimization. To account for changes in the system characteristics with time, a model updater algorithm that updates the data-based model using newly available data and the previous model is proposed.