Raghunathan Rengaswamy

Bio: Raghunathan Rengaswamy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Proton exchange membrane fuel cell & Fault detection and isolation. The author has an hindex of 39, co-authored 210 publications receiving 9632 citations. Previous affiliations of Raghunathan Rengaswamy include Indian Institute of Technology Bombay & Bosch.

Dynamic modeling and system identification of a tubular solid oxide fuel cell (TSOFC)

Abstract: Solid Oxide Fuel Cells (SOFCs) are high temperature fuel cells with a strong potential for stationary power house applications. However, considerable challenges are to be overcome to connect these cells to the power grid. The cells have to satisfy the changing demand of the grid without sacrificing their efficiencies and without causing any structural or material damage. Such an operation, coupled with fast and highly nonlinear transients of the transport variables, leads to a very challenging control problem. This requires an efficient and robust controller. For synthesizing such a controller, a well-validated dynamic model is essential. In this work, a dynamic model is validated by using experimental data from an industrial cell. The data are generated over a broad range of cell temperatures, reactant flow rates, DC polarizations, and amplitudes of step. In the process of validation, it is identified that the Knudsen diffusion and an extended active area for the electrochemical reactions play key roles in determining the current transients of the cell. The dynamic model is used for identification of reduced order models that can be solved in real time for implementation in the MPC framework. Several linear and nonlinear models are considered and the best model is chosen according to the AIC values of the models. Both SISO and MIMO models are identified. For the MIMO model, voltage and H2 flow are considered as inputs. Power and utilization factors are considered as outputs. A linear model such as ARX model is found to be satisfactory for most SISO cases. However, a nonlinear model such as NAARX model with more cross terms is found to improve the model performance significantly for the MIMO case. All through this work, efforts have been made to synthesize the simplest, yet representative model that can be used for real-time applications.

INDUSTRIAL EXPERIENCE WITH OBJECT-ORIENTED MODELLING FCC Case Study

Abstract: Process modelling and simulation have emerged as important tools for detailed study and analysis of chemical processes. In activities such as design, optimization and control of processes, realistic process models, which incorporate physics and chemistry of the process in adequate detail, are becoming almost indispensable. Simulation studies also provide guidance in the development of new processes and can reduce both time and capital investment. A difficulty with process models is that they are based on the state of knowledge and simulation objectives defined at the time of their formulation. In addition, it is not easy to modify process models to incorporate new knowledge as it becomes available and as new needs arise. There is a need, therefore, to use advanced modelling and simulation strategies such that refinements and additional capabilities can be incorporated in the model without disproportionate additional effort. This work presents the framework of one such multipurpose process simulator, MPROSIM, an object-oriented process modelling and simulation environment. Though considerable literature is available on process modelling from a subjective or theoretical viewpoint, very little has been published on application of these ideas on complex industrial-scale processes. This being the focus of the paper, a case study of an object-oriented model for automatic generation of a fluid catalytic cracking unit (FCCU) reactor/regenerator is presented. The utility of the framework is illustrated by demonstrating how the model for FCCU could be fine-tuned both structurally and parametrically to represent the behaviour under changing process operating conditions.

Control of proton exchange membrane fuel cells using data driven state space models

Abstract: Proton exchange membrane fuel cells (PEMFCs) are increasingly being researched upon due to their potential toward sustainable energy generation. Toward improved productivity of PEMFCs, it is important to develop systematic approaches for optimization and control of their operations. PEMFCs pose interesting challenges toward these tasks due to their complex behavior such as nonlinearity and spatial variations. While first principles model based approaches could be used, a more mathematically attractive and cost-effective alternative is to use empirical modeling approaches for representing the system dynamics toward optimization and control. In this paper, we propose to use a novel, innovation form of state space models that facilitate the development of advanced control algorithms such as linear quadratic Gaussian (LQG) and model predictive control (MPC), and provide improved disturbance rejection necessary for these applications. We demonstrate the applications of such model based algorithms via simulations involving a distributed along-the-channel model of the PEMFC, and also present experimental validation on a PEMFC setup.

Online fault diagnostics and impedance signature mapping of High Temperature PEM fuel cells using rapid small signal injection

Abstract: The recent development of electric vehicles and electronic equipment has resulted in a demand for portable power supply systems with high power density and life expectancy. High Temperature (HT) Proton Exchange Membrane (PEM) fuel cells have emerged as a possible solution to this requirement. Recent advances in the materials allow the cells to operate at higher temperatures resulting in a number of advantages. These systems still suffer from degradation mechanisms that can degrade performance and decrease the life expectancy. In order to diagnose the fuel cell performance and degradation mechanisms, impedance analysis can be performed. This paper presents a design for the power electronic hardware that can perform these analyses online for single cell applications, where the output voltage is very low. Special consideration must be given to the hardware selection and small signal injection capability. The Nyquist plots and small amplitude current step voltage response of the HTPEM fuel cell is presented along with those captured after catalyst degradation has occurred. It is shown how the online system can capture these changes by using equivalent circuits.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.