A mis padres por su amor y apoyo incondicional.A Huguis por ser mi inspiración y el sol de mi vida. I would also like to thank M. Eng. Miguel Ricardo Hernandez-Garcia from University of South California for the opportunity to use high performance computation in some of the simulations. Man muss Optimist sein Max PlanckMi agradecimiento de todo corazón a mis padres por su amor y apoyo incondicional durante toda mi vida. A Huguis por ser mi compañero en esta gran aventura que es la vida, y por ser mi sol especialmente durante los 4 inolvidables inviernos que viví en Berlín. A mis hermanos Liliana y David, por su apoyo y complicidad. A Elsita y José Miguel por su amor y por acogerme cálidamente como parte de su hogar. A todos, gracias por su apoyo, confianza y amor que me han permitido culminar exitosamente una de mis más grandes metas.Señor Dios te agradezco infinitamente y de todo corazón por regalarme cada día, por permitirme disfrutar de mi trabajo, por cada una de las bendiciones que me has regalado durante mi vida, por todas las experiencias vividas en estos cuatro años y especialmente por la culminación con éxito de este proyecto. Berlin, April 2010Plantwide Optimizing Control for the Continuous Bio-Ethanol Production Process v AbstractIn this work, the Plantwide Control (PWC) problem of a continuous bio-ethanol process is investigated from a Plantwide Optimizing Control (PWOC) perspective. A PWOC methodology is proposed which addresses this problem by integrating real-time optimization and control for optimal operation. The PWOC methodology consists of two main tasks. The first is a local control-oriented task related to the identification and design of necessary local control loops required for satisfying the primary objectives of the process (e.g. safe operation, environmental and equipment protection, etc.). The second is a Plantwide control-oriented task in which the available control degrees of freedom are used for maximizing the process profitability. This means that, excluding the local loops, no pre-defined set points will be either regulated or tracked. The core of the PWOC methodology proposed is the formulation

Despite many environmental advantages of using alcohol as a fuel, there are still serious questions about its economical feasibility when compared with oil-based fuels. The bioethanol industry needs to be more competitive, and therefore, all stages of its production process must be simple, inexpensive, efficient, and "easy" to control. In recent years, there have been significant improvements in process design, such as in the purification technologies for ethanol dehydration (molecular sieves, pressure swing adsorption, pervaporation, etc.) and in genetic modifications of microbial strains. However, a lot of research effort is still required in optimization and control, where the first step is the development of suitable models of the process, which can be used as a simulated plant, as a soft sensor or as part of the control algorithm. Thus, toward developing good, reliable, and simple but highly predictive models that can be used in the future for optimization and process control applications, in this paper an unstructured and a cybernetic model are proposed and compared for the simultaneous saccharification-fermentation process (SSF) for the production of ethanol from starch by a recombinant Saccharomyces cerevisiae strain. The cybernetic model proposed is a new one that considers the degradation of starch not only into glucose but also into dextrins (reducing sugars) and takes into account the intracellular reactions occurring inside the cells, giving a more detailed description of the process. Furthermore, an identification procedure based on the Metropolis Monte Carlo optimization method coupled with a sensitivity analysis is proposed for the identification of the model's parameters, employing experimental data reported in the literature.

Streptomyces clavuligerus is a filamentous Gram-positive bacterial producer of the β-lactamase inhibitor clavulanic acid. Antibiotics biosynthesis in the Streptomyces genus is usually triggered by nutritional and environmental perturbations. In this work, a new genome scale metabolic network of Streptomyces clavuligerus was reconstructed and used to study the experimentally observed effect of oxygen and phosphate concentrations on clavulanic acid biosynthesis under high and low shear stress. A flux balance analysis based on experimental evidence revealed that clavulanic acid biosynthetic reaction fluxes are favored in conditions of phosphate limitation, and this is correlated with enhanced activity of central and amino acid metabolism, as well as with enhanced oxygen uptake. In silico and experimental results show a possible slowing down of tricarboxylic acid (TCA) due to reduced oxygen availability in low shear stress conditions. In contrast, high shear stress conditions are connected with high intracellular oxygen availability favoring TCA activity, precursors availability and clavulanic acid (CA) production.

In this work, a methodology for the model-based identifiable parameter determination (MBIPD) is presented. This systematic approach is proposed to be used for structure and parameter identification of nonlinear models of biological reaction networks. Usually, this kind of problems are over-parameterized with large correlations between parameters. Hence, the related inverse problems for parameter determination and analysis are mathematically ill-posed and numerically difficult to solve. The proposed MBIPD methodology comprises several tasks: (i) model selection, (ii) tracking of an adequate initial guess, and (iii) an iterative parameter estimation step which includes an identifiable parameter subset selection (SsS) algorithm and accuracy analysis of the estimated parameters. The SsS algorithm is based on the analysis of the sensitivity matrix by rank revealing factorization methods. Using this, a reduction of the parameter search space to a reasonable subset, which can be reliably and efficiently estimated from available measurements, is achieved. The simultaneous saccharification and fermentation (SSF) process for bio-ethanol production from cellulosic material is used as case study for testing the methodology. The successful application of MBIPD to the SSF process demonstrates a relatively large reduction in the identified parameter space. It is shown by a cross-validation that using the identified parameters (even though the reduction of the search space), the model is still able to predict the experimental data properly. Moreover, it is shown that the model is easily and efficiently adapted to new process conditions by solving reduced and well conditioned problems.

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