Faculty of Chemical and Biomedical Engineering

Dr. Sridhar Ungarala

Dr. Orhan Talu Associate Professor Post Doctoral Research, 1999
The Ohio State University

Ph.D., Chemical Engineering, 1998
Michigan Technological University

M.Chem.Eng.,Chemical Engineering, 1994
University of Bombay

B.Tech., Chemical Engineering, 1991
Andhra University

Process Systems Engineering

Research Projects


Process Modeling

Process models are considered in multiscale cell-space by representing the evolution of process as a set of stochastic or deterministic cell mappings with temporal and scale dependent parameters on multiscale cell networks. Data are considered as collections of cells wrapped in observational and modeling uncertainty according to the scale of observation. Processes with wide ranging time constants and multirate sampled systems can be treated in this general framework. We are developing multiscale online system identification methods for nonstationary nonlinear models based on spectral analysis and sinusoidal modulation. Time-frequency localized modulation is implemented via Fourier/wavelet transforms. Efforts are underway to extend these approaches to wider classes of models with trancendental nonlinearities, discrete time and stochastic systems. These developments are also geared towards fault detection and diagnosis. Several industrial applications are under investigation including, tracking of microbial growth parameters, catalyst deactivation rates and heat exchanger fouling coefficients as well as modeling kinetics in polymerization reactors. Investigations are in progress for multiscale rectification and cerebral response modeling in functional magnetic resonance (fMR) imaging, a technique to identify patterns of cerebral activity in series of MR images. The image analysis is a combination of denoising, establishing model structures for pixel dynamics and estimating the parameters.

Process Data Analysis

Current research is focused on the development of data rectification methods by means of Bayesian inference in a multiresolution framework for linear and nonlinear systems. Spectral decomposition of noisy data using orthonormal wavelets separates the deterministic components into a scaled signal and captures the stochastic features into approximately decorrelated wavelet coefficients at multiple scales. Data is rectified according to the statistical properties of the features isolated at each scale. Dynamic systems are characterized by temporal and scale evolution of probability distributions which necessitates simultaneous rectification and modeling. The generalized multiscale data rectification approach incorporates previously untapped issues (a) applications to nonstationary nonlinear/non-Gaussian processes with random and gross errors, (b) time-scale recursive moving horizon and error-in-variables algorithms and (c) reconciliation with process constraints and dynamic models. Industrial applications include rectification and fusion of multirate or irregularly sampled data, for example, in sheet manufacturing processes and the development of "software sensors" for the estimation of unmeasured variables in bioprocess. Investigations are also in progress for the removal of non-Gaussian signal dependent noise and Gibbs artifacts in magnetic resonance (MR) images.

Process Monitoring

The theory of process integrity combines estimation, historical data and global analysis to define dynamic safety levels. This information is vital to assess the strength of failure conditions and the sphere of their influence, which can be used to design feedback integrity control systems. For a given set of parameters, nonlinear systems can exhibit several attractors - equilibrium points, periodic motions and chaotic behavior. A complete understanding of the behavior of nonlinear processes must include knowledge of the attractors, the domains of attraction (DOA) and the effects of external perturbations and parameter changes on the nature of the boundaries. The cell to cell mapping analysis is recursively refined in a multiscale framework to locate attractors and DOA's. Coarse descriptions are generated with large cells and finer cell spaces are defined for analyzing cell clusters capturing long term dynamics. The approach shows parallels with multigrid and wavelet methods for solving differential equations. By augmenting cell space with parameter space, parametric sensitivity and coagulation of domains are investigated. The broader aims of the theory of process integrity are the safe design and retrofit of chemical process equipment to guarantee safe and economic operation. Investigations are underway to analyze reactor thermal runaway and identify regions of safe and runaway conditions, and flooding in distillation columns.


Selected Publications/Proceedings
  • Ungarala S. and Z. Z. Chen, Bayesian data rectification of nonlinear systems with markov chains in cell space, Proceedings of the American Control Conference (ACC2003), Accepted, (2003).
  • Ungarala S., Z.-Z. Chen and T. B. Co, The theory of process integrity and global analysis for process monitoring and diagnosis, Proceedings of the  4th IFAC Workshop on On-Line Fault Detection and Supervision in the Chemical Process Industries (CHEMFAS-4), G. Stephanopoulos, J. Romognoli and E. S. Yoon, eds., Elsevier, pp. 213-218, (2001).
  • Ungarala S. and B. R. Bakshi, Bayesian inference with constraints - A unified approach for data rectification of linear dynamic systems, Proceedings of the 6th IFAC Symposium on Dynamics and Control of Process Systems (DYCOPS-6), G. Stephanopoulos, J. H Lee and E. S. Yoon, eds., Elsevier, pp. 405-410, (2001).
  • Ungarala S. and T. B. Co, Time-varying system identification using modulating functions and spline models with application to bioprocesses, Computers and Chemical Engineering, 24, 2739-2753, (2000).
  • Ungarala S. and B. R. Bakshi, A multiscale, Bayesian and error-in-variables approach for linear dynamic data rectification, Computers and Chemical Engineering, 24, 445-451, (2000).
  • Ungarala S. and T. B. Co, Model parameter tracking in microbial growth processes, American Institute of Chemical Engineers Journal, 44, 2129, (1998).
  • Co T. B. and S. Ungarala, Batch scheme recursive identification of continuous-time systems using modulating functions method, Automatica, 33, 1185, (1997).
  • sCo T. B. and S. Ungarala, Recursive implementation of modulating functions methods, Identification in Engineering Systems, M. I. Friswell and J. E. Mottershead, eds., Cromwell Press, Wiltshire, UK, pp. 604-613, (1996).
Recent Presentations
  • Ungarala S. and Z. Z. Chen, Bayesian data rectification of nonlinear systems with markov chains in cell space, American Control Conference ACC2003, Denver, CO, Accepted, (Jun. 2003).
  • Ungarala S. and Z. Z. Chen, Dynamic data rectification with Markov chains and Monte Carlo Bayesian Inference, Annual Meeting of the American Institute of Chemical Engineers, Indianapolis, IN, (Nov. 2002).
  • Chen W. -S., B. R. Bakshi, P. Goel and S. Ungarala, Bayesian Estimation of nonlinear dynamic dystems - Dealing with constraints and non-Gaussian errors, Annual Meeting of the American Institute of Chemical Engineers, Indianapolis, IN, (Nov. 2002).
  • Chen Z. Z., M. Bartels, S. Ungarala, J. E. Gatica, M. N. Pedernera and N. S. Schbib, Control and stability analysis of autothermal radial flow reactors using reduced-order models, Annual Meeting of the American Institute of Chemical Engineers, Indianapolis, IN, (Nov. 2002).
  • Chen W. -S., S. Ungarala and B. R. Bakshi, Bayesian rectification of nonlinear dynamic processes by the weighted bootstrap, Annual Meeting of the American Institute of Chemical Engineers, Reno, NV, (Nov. 2001).
  • Ungarala S. and Z. Z. Chen, Global analysis of controlled processes using cell-to-cell mapping, Annual Meeting of the American Institute of Chemical Engineers, Reno, NV, (Nov. 2001).
  • Ungarala S. and T. B. Co, The theory of process integrity and global analysis for process monitoring and diagnosis, The 4th IFAC Workshop on On-Line Fault Detection and Supervision in the Chemical Process Industries (CHEMFAS-4), Jejudo Island, Korea, (Jun. 2001).
  • Ungarala S. and B. R. Bakshi, Bayesian inference with constraints - A unified approach for data rectification of linear dynamic systems, The 6th IFAC Symposium on Dynamics and Control of Process Systems (DYCOPS-6), Jejudo Island, Korea, (Jun. 2001).
  • Ungarala S. and T. B. Co, Nonlinear system identification via orthogonal functions and FFT based modulation, Annual Meeting of the American Institute of Chemical Engineers, Los Angeles, CA, (Nov. 2000).
  • Ungarala S. and Z.-Z. Chen, Analysis of nonlinear CSTR dynamics using cell mapping in state-cell space, Annual Meeting of the American Institute of Chemical Engineers, Los Angeles, CA, (Nov. 2000).
  • Ungarala S. and B. R. Bakshi, Data rectification of nonlinear systems using Bayesian inference and multiresolution analysis, Annual Meeting of the American Institute of Chemical Engineers, Los Angeles, CA, (Nov. 2000).
  • Ungarala S. and B. R. Bakshi, A multiscale, Bayesian and error-in-variables approach for linear dynamic data rectification, The 7th International Symposium on Process Systems Engineering (PSE-2000), Keystone, CO (Jul. 2000).
  • Ungarala S. W. -S. Chen and B. R. Bakshi, Multiscale Bayesian EIV data rectification of linear and nonlinear dynamic systems, Annual Meeting of the American Institute of Chemical Engineers, Dallas, TX (Nov. 1999).
  • Ungarala S. and T. B. Co, Direct estimation of microbial specific growth rates, Annual Meeting of the American Institute of Chemical Engineers, Miami, FL (Nov. 1998).
  • Ungarala S. and T. B. Co, Modeling and control for process integrity, Annual Meeting of the American Institute of Chemical Engineers, Los Angeles, CA (Nov. 1997).
  • Ungarala S. and T. B. Co, Recursive parameter estimation of time-varying nonlinear systems, Annual Meeting of the American Institute of Chemical Engineers, Chicago, IL (Nov. 1996).
  • Co T. B. and S. Ungarala, Recursive implementation of modulating functions methods, International Conference on Identification in Engineering Systems, Swansea, UK (Mar. 1996).
Book Chapters
  • Ungarala S. and B. R. Bakshi, Multiscsale Bayesian Estimation and Data Rectification, Wavelets in Signal and Image Analysis: From Theory to Practice, A. Petrosian and F. Meyer, eds., Kluwer Academic Publishers, New York, pp. 69-110, (2001).
engaged learning
Mailing Address
Chemical and Biomedical Engineering Department
Fenn College of Engineering
Cleveland State University
2121 Euclid Ave., SH 455
Cleveland, Ohio 44115-2214
Campus Location
Stilwell Hall Room 455
1960 East 24th Street
Phone: 216.687.2569
Fax: 216.687.9220
ChE@csuohio.edu
Contact
Jorge E. Gatica
j.gatica@csuohio.edu
Phone: 216.523.7274


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