Shankar Narasimhan S

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.

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.

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.

Water Distribution Networks (WDN) is often exposed to either intentional or accidental contamination. In order to protect against such intrusions, an effective and efficient online monitoring system through sensors is needed. Detection of contaminants in WDN is challenging and it is not possible to place sensors at each and every potential point of intrusion, due to cost and maintenance reasons. Instead, as few sensors as possible, should be located optimally such that intrusions can be detected quickly. This is known as sensor network design problem for intrusion detection in WDNs. Several optimization models and algorithms have been proposed for intrusion detection in a WDN. In this study, we design sensor networks which satisfy the two important properties of observability and identifiability. Observability denotes the ability of the sensor network to detect the occurrence of the intrusion, whereas identifiability refers to the ability to unambiguously deduce the point (or source) of intrusion from the set of sensors affected. A hydraulic analysis of the network is first carried out for a given loading condition to determine the flow directions. The concept of a directed path is then used to construct a bipartite graph, and map the sensor network design problem to that of a minimum vertex set cover problem. Algorithms based on greedy heuristics are used to solve the set covering problem and obtain the corresponding sensor network. The proposed method is illustrated using a fairly large scale urban WDN. © 2014 Elsevier B.V.