Showing papers by "Raghunathan Rengaswamy published in 2009"

A Review of Solid Oxide Fuel Cell (SOFC) Dynamic Models

Abstract: In this paper, state-of-the-art dynamic models for solid oxide fuel cells (SOFCs) in the open literature are reviewed. The review also includes the transient modeling of SOFC systems with reformers. In the transients of a SOFC, three characteristic time constants are observed. One of the challenges in transient modeling is to capture these characteristic times. The first characteristic time is on the order of milliseconds and is mostly neglected, because it is too small, from the viewpoint of practical applications. The second time constant is on the order of seconds and arises mainly because of the mass-transport dynamics. The third characteristic time is on the order of minutes or hours and is dependent on the energy transport characteristics of the system. These characteristic times are extremely system-specific and, therefore, must be identified on a case-to-case basis. In this paper, the existing literature on dynamic studies are reviewed, focusing mainly on the fidelity of the model that is required...

Structural Properties of Gene Regulatory Networks: Definitions and Connections

Abstract: The study of gene regulatory networks is a significant problem in systems biology. Of particular interest is the problem of determining the unknown or hidden higher level regulatory signals by using gene expression data from DNA microarray experiments. Several studies in this area have demonstrated the critical aspect of the network structure in tackling the network modelling problem. Structural analysis of systems has proved useful in a number of contexts, viz., observability, controllability, fault diagnosis, sparse matrix computations etc. In this contribution, we formally define structural properties that are relevant to gene regulatory networks. We explore the structural implications of certain quantitative methods and explain completely the connections between the identifiability conditions and structural criteria of observability and distinguishability. We illustrate these concepts in case studies using representative biologically motivated network examples. The present work bridges the quantitative modelling methods with those based on the structural analysis.

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.

Experimental evaluation of linear model based control strategies for PEMFCs

Abstract: In this work, we investigate linear model based multivariable control schemes for proton exchange membrane fuel cells (PEMFCs). Much of the literature relies on a mechanistic model to design model predictive controllers; however, this can be a difficult and time-consuming exercise for a PEMFC. An effective approach for developing models for control purposes is to use time series analysis and develop control oriented state space models directly from input-output data. In the present work, we develop an innovation form of state space model from input-output perturbation data obtained from a PEMFC. We then demonstrate the development of infinite horizon unconstrained linear model predictive controllers (LMPC) using these models, and compare their performance to IMC based PI control. We conduct servo and regulatory control studies on an experimental single cell PEMFC system, and demonstrate that the proposed control schemes regulate the power obtained from the fuel cell as desired even in the presence of disturbances.