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Publications (3)3.56 Total impact

  • J. Modak, K. Konde
    New Biotechnology 01/2009; 25. · 1.71 Impact Factor
  • Kakasaheb S Konde, Jayant M Modak
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    ABSTRACT: A new methodology based on a metabolic control analysis (MCA) approach is developed for the optimization of continuous cascade bioreactor system. A general framework for representation of a cascade bioreactor system consisting of a large number of reactors as a single network is proposed. The kinetic and transport processes occurring in the system are represented as a reaction network with appropriate stoichiometry. Such representation of the bioreactor systems makes it amenable to the direct application of the MCA approach. The process sensitivity information is extracted using MCA methodology in the form of flux and concentration control coefficients. The process sensitivity information is shown to be a useful guide for determining the choice of decision variables for the purpose of optimization. A generalized problem of optimization of the bioreactor is formulated in which the decision variables are the operating conditions and kinetic parameters. The gradient of the objective function to be maximized with respect to all decision variables is obtained in the form of response coefficients. This gradient information can be used in any gradient-based optimization algorithm. The efficiency of the proposed technique is demonstrated with two examples taken from literature: biotransformation of crotonobetaine and alcohol fermentation in cascade bioreactor system.
    Biotechnology Progress 01/2007; 23(2):370-80. · 1.85 Impact Factor
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    ABSTRACT: Performance control of batch processes using model based approaches requires the deployment of high fidelity models. However, due to a variety of reasons that stem from short process development cycles and the consequent poor first principles knowledge about the process, such accurate models are difficult to develop. Another daunting aspect of any modeling activity is related to a lack of structural knowledge about the parameter-state dependencies. Furthermore, the changing dynamics resulting from scale-up in manufacturing could render the models developed at the lab scale to be obsolete. All of the above pose a requirement to update the model on a timely basis so as to achieve tight control of the batch processes. In this paper, we propose the use of (i) an iterative learning based model refinement approach that is based on the principles of continuous improvement paradigm of GMP, and (ii) grey box models to represent the cause-effect relationships, to address the above challenges. The proposed iterative learning based estimation approach has been validated on simulations and experimental data from representative batch processes.