Showing papers by "Raghunathan Rengaswamy published in 2014"

Data Reconciliation and Dynamic Modeling of a Sour Water Gas Shift Reactor

Abstract: Integrated gasification combined cycle (IGCC) plants with CO2 capture have strong potential in the future carbon-constrained world. In these plants, the CO2 and COS content of the syngas at the inlet of the acid gas removal process should be within certain limits in order to satisfy the environmental limits on sulfur and CO2 emissions. To satisfy these constraints, the syngas from the gasification process is passed through water gas shift reactor(s). A premium is placed on sulfur-tolerant catalysts since the syngas may contain COS and H2S. In comparison to the sweet-shift process, the sour-shift process results in higher overall efficiency because of the higher temperature of the feed syngas and requirement of less additional steam for the shift reactions. The optimal operating conditions and the dimensions of the sour shift reactors can be obtained by considering the effect of a number of key variables. With this motivation, a 1-D mathematical model of a sour water gas shift (WGS) reactor has been develo...

Origin of periodic and chaotic dynamics due to drops moving in a microfluidic loop device.

Abstract: Droplets moving in a microfluidic loop device exhibit both periodic and chaotic behaviors based on the inlet droplet spacing. We observe that the periodic behavior is an outcome of carrier phase mass conservation principle, which translates into a droplet spacing quantization rule. This rule implies that the summation of exit spacing is equal to an integral multiple of inlet spacing. This principle also enables identification of periodicity in experimental systems with input scatter. We find that the origin of chaotic behavior is through intermittency, which arises when drops enter and leave the junctions at the same time. We derive an analytical expression to estimate the occurrence of these chaotic regions as a function of system parameters. We provide experimental, simulation, and analytical results to validate the origin of periodic and chaotic behavior.

Understanding drop-pattern formation in 2-D microchannels: a multi-agent approach

Abstract: We propose a modeling strategy to simulate drop movement in a two-phase flow inside a 2-D diverging–converging microchannel. These are planar channels that allow 2-D movement of drops. The increasing cross-sectional area of the diverging section decelerates the drop, and the decreasing cross-sectional area of the converging section accelerates it. These drops as they slow down approach each other and start to interact hydrodynamically and form 2-D arrangements inside the microchannel. We propose interacting drop-traffic models, that are phenomenological in nature, to characterize the different interactions of a drop with the neighboring drops, continuous phase and the channel walls. By incorporating these models into a multi-agent simulation, that employs Newton’s second law of motion along with the creeping flow approximation, we are able to predict the positions and velocities of all the drops inside the microchannel. The time evolution of the dynamic 2-D patterns formed by the drops inside the microchannel is investigated. We are able to qualitatively understand the features in a microchannel that aid the formation of the 2-D patterns. The simulation strategy helps us to understand the layering phenomena that results in the formation of the 2-D structures near the diverging section and the breaking patterns of drops near the converging section of the microchannel.

Data driven approach for performance assessment of linear and nonlinear Kalman filters

Abstract: A new technique is developed for assessing the performance of linear and nonlinear Kalman filter based state estimators. The proposed metric will indicate the performance of these state estimators which will be primarily influenced by: (i) difference between the model dynamics and process dynamics and, (ii) various approximations of the nonlinear plant dynamics used in nonlinear Kalman filters. Currently, there exists no such quantification method to analyze the performance of linear and nonlinear Kalman filters, a key requirement for improvement and a practical benchmark for comparison of these state estimation algorithms. The proposed technique uses the generalized Hurst exponent of the prediction errors (difference in measured output and a posteriori estimates) obtained from the state estimators to quantify the performance. This technique could be implemented on-line as it requires only plant operating data and the predicted outputs (from the linear and nonlinear Kalman filters) to assess the performance. Several simulation studies demonstrate the applicability of the proposed performance metric to both linear and non-linear Kalman filters.

Optimal Sensor Placement for Contamination Detection and Identification in Water Distribution Networks

Abstract: 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.

Derivation of an equivalent electrical circuit model for degradation mechanisms in high temperature pem fuel cells in performance estimation

Abstract: This paper presents the development of an equivalent electrical circuit model that represents the characteristics of a high temperature PEM fuel cell under various fault mechanisms. The three main degradation mechanisms that can reduce long term and short term performance are investigated and the specific electrochemical parameters that are affected are identified. A semi-empirical modelling approach based on captured experimental data is used to model the degradation mechanisms in order to reduce complexity. In particular, the effect of ripple current from the power electronics that accelerates degradation of the specific model parameters is discussed. The mechanism is also included in the model in order to increase functionality. Each voltage loss component and the associated degradation phenomena are equated to a circuit element and the final circuit model is presented. The simulated results are compared to experimental data to determine accuracy. The model is able to estimate performance loss based on operating time and conditions allowing for life cycle estimation and system optimization.

System and method for simulation and design of discrete droplet microfluidic systems

Abstract: A computerized method for designing a discrete droplet microfluidic system: (a) provides an initial set of droplet based networks; (b) codes each droplet based network into a data structure such that all the data structures form a current set of data structures; (c) creates new data structures by performing one or more genetic operators on the current set of data structures; (d) adds new data structures to the current set of data structures; (e) creates a new set of data structures that satisfies one or more design parameters; (f) replaces the current set of data structures with the new set of data structures; (g) repeats steps (c), (d), (e) and (f) until the new set of data structures has been created a third number of times; and (h) displays/outputs the current set of data structures as possible designs for the discrete droplet microfluidic system to one or more output devices.

Understanding control in microchannels to manipulate drop-drop interactions

Abstract: Recently we proposed a simple, computationally inexpensive multi-agent simulation strategy to understand drop movement in a microchannel with Hele-Shaw flow geometry (2D). This has helped us understand the collective behavior of the drops in these systems. However, transitioning these systems to practice would require that strategies are developed to combat the inherent fluctuations that are a part of any physical system. In the present work we utilize the simulation strategy for understanding the impact of design choices in minimizing the effect of uncertainties, and the sensitivity of the device behavior to operational variables that can potentially be used as controlled variables. We study a control metric, time of contact, which is defined as the time spent by a drop in the microchannel when its distance from its neighboring drops is less than some critical value. By simplifying the model further we are able to analyze the contact time dynamics as a function of geometric parameters and operating conditions analytically. We also carry out the complete numerical simulation of our models to understand active control of drop clusters.