Shankar Narasimhan S

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.

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.

In process design, the insights obtained by applying pinch technology have played an important role in maximizing energy efficiency through energy integration [1]. Pinch technology exclusively addresses temperature variations in process streams resulting from indirect heat exchange. The analysis does not take into account temperature changes in process streams induced by changes in stream pressure. In this work, a systematic procedure for applying pinch analysis to the Liquid Air Energy Storage (LAES) process is proposed. In this process, air undergoes significant pressure changes, which results in phase changes as well as wide variations in the specific heat capacity. Since the air temperature varies from well above ambient to well below ambient conditions, multiple minimum approach temperature specifications have to be imposed. A parameterized version of the Grand Composite Curves (GCCs) is proposed for pinch analysis that takes into account for all of these special features. The parameterized GCCs are used to identify the feasible design space for a LAES process.

A reliable dynamic model is essential for model–based control, monitoring, and optimization of reaction systems. Hence, a change in a part or whole of the reaction kinetics of these systems leads to poor performance. In this work, the problem of model–plant mismatch in open reaction system is studied. We propose an online fault diagnosis and rectification framework for solving the problem of model–plant mismatch for open reaction systems. The framework combines the concept of the extents of reaction and flowrate in reaction systems and incremental model identification approach for isolation and rectification of the deficient part of the model. The proposed framework will be demonstrated via a simulation example of the acetoacetylation of pyrrole in a semi–batch reactor for two scenarios: (i) shift in the change of one of the reaction rates, and (ii) change in the inlet flowrate.

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.

Ensuring adequate supply of clean drinking water and electricity in several parts of the world continues to be a formidable challenge. In coastal areas facing this problem, desalination of sea water using Reverse Osmosis (RO) driven by solar power without batteries can be an appropriate technological solution. Variability in incident solar power is a significant operational issue. The focus of this work is optimal operation and control of the RO plant with guaranteed water purity. A steady state model is developed and validated using 'ROSA', a black-box software programme commonly used for simulating RO plants. Analysis of the optimal solution reveals that the feasible space (of available power) consists of two regions where different set of constraints are active. In one region salt concentration constraint is active and in another, the pressure constraint is active. Hence the optimal operation strategy can be implemented by active constraint control. © 2012 Elsevier B.V.

Optimal operation of municipal Water Distribution Networks (WDNs) is based on optimizing one or more performance metrics while meeting consumer demands and satisfying supply side and storage constraints. This can be achieved by implementing advanced control schemes such as Model Predictive Control (MPC). With the alarming decrease in fresh water supplies, the primary focus of online control strategies should be to conserve water. A novel control strategy that can handle both water sufficient and deficient cases is proposed for WDNs with storage facilities. Performance of the developed model based online control strategy is tested by numerical simulations of an illustrative WDN. © 2012 Elsevier B.V.