Sharad Bhartiya's photograph

Sharad Bhartiya

B.E. R.E.C., Durgapur, 1991
M.Tech.., IIT Madras, 1993
Ph.D., Oklahoma State University, 2000
+91 (22) 2576 7225
+91 (22) 2576 8225
+91 (22) 2572 6895

Link To Detailed Information

Research Areas

  1. Systems & Modeling and control of distributed parameter systems; Grade transition control; Estimation theory; Optimization; Soft sensing
  2. Applications: Paper and pulp industry; Pharmaceutical industry; Petrochemical
  3. Systems Biology: Dynamic modeling and analysis of feedback structure in trp system in E. coli and glu-gal gene regulatory network

Awards and Affiliations

Current Research

  • Profile Control of Distributed Parameter Systems
    The spatial distribution of system properties offers additional challenges for control of the same. A common strategy is explicit control of the endpoint property while ignoring the history of processing along the reaction path. We are exploring the idea of exploring the control of the entire property profile along the spatial direction. It is expected that such an approach will enable a tight regulation of history-dependent properties (such as fiber length in a pulp digester, particle size distribution in polymerization reactor). Further, upstream disturbances may be rejected prior to their effects being felt on the endpoint property. Currently, we are working on observer design for a pulp digester for building inferential measurements of the property at various locations of the digester. These soft measurements will consitute the property profile, which will be controlled using NMPC.

  • Optimization and Grade Transition Control (Collaborator: Prof. R.D. Gudi, IIT Bombay)
    Grade transition is a frequent operation in the process industry (polymer, pulp and paper). Optimal grade transition recipes typically target minimization of transition time and production of off-specification material. Generation of the optimal recipes requires solution to a dynamic optimization problem, which has been approached using gradient-based methods such as SQP (Wang et al., 2000) and differential evolution (DE) methods (Mandal et al., 2003). While gradient-based methods are well known for computational efficiency, they typically fail to provide global optimum (unless the convexity assumption is invoked). Further, they cannot handle discontinuous derivatives of objective functions and constraints. On the other hand, DE can potentially provide the global optimum and is not susceptible to non-smoothness of the objective function and constraints. However, DE methods require numerous function evaluations thereby making the algorithm computationally expensive. We are currently working on a hybrid approach that can combine the global optimum properties of DE and faster convergence rates of SQP.

  • Systems Biology (Collaborator: Prof. K.V. Venkatesh, IIT Bombay)
    Living systems must adapt quickly and stably to uncertain environments. A common theme in cellular regulation is presence of multiple feedback loops in the network. An example of such a feedback structure is regulation of tryptophan concentration in Escherichia coli. A pertinent question is whether such multiple feedback loops is a case of regulatory overkill, or do these different feedback regulators have distinct functions? (Freeman, Nature, 295, 313-319, 2000). Another moot question is how can robustness to uncertainties be achieved structurally through biological interactions. We are currently establishing correlations between the feedback structure and robustness using feedback theory. We are also focussing on how feedback designs in nature cope with intrinsic noise. 

Selected Publications

  1. N.U. Padhiyar, and S. Bhartiya, “Profile Control in Distributed Parameter Systems Using Lexicographic Optimization Based MPC”, Journal of Process Control (In Press) .

  2. N. Nandola and S. Bhartiya , “A multi-model framework for control of hybrid systems”, Journal of Process Control, 18, 131-148 (2008).

  3. N. U. Padhiyar, A. Gupta, A. Gautam, S. Bhartiya, F.J. Doyle III, S. Gaikwad and S. Dash, “Nonlinear Inferential Multi-Rate Control Of Kappa Number At Multiple Locations In A Continuous Pulp Digester” Journal of Process Control, 16, 1037-1053 (2006).

  4. S. Bhartiya, N. Chaudhary, K.V. Venkatesh, and F.J. Doyle, “Multiple feedback loop design in the tryptophan regulatory network of Escherichia coli suggests a paradigm for robust regulation of processes in series”, Journal of the Royal Society Interface, 3, 383-391, (2006)

  5. A. Ruhela, M. Verma, J.S. Edwards, P.J. Bhat, S. Bhartiya, K.V. Venkatesh, “Autoregulation of regulatory proteins is key for dynamic operation of GAL switch in Saccharomyces cerevisiae”, FEBS Letters, 576, 119-126, (2004)

  6. K.V. Venkatesh, S. Bhartiya, and A. Ruhela, “Multiple feedback loops are key to a robust dynamic performance of tryptophan regulation in Escherichia coli”, FEBS Letters, 563, 234-240, (2004).

  7. S. Bhartiya, P. Dufour and F.J. Doyle III, "Fundamental thermal-hydraulic continuous pulp digester model with grade transition", AIChE Journal, 49, 411-425 (2003).

  8. S. Bhartiya and J.R. Whiteley, "Factorized approach to nonlinear MPC using a radial basis function model", AIChE Journal, 47, 358-368 (2001).

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