Chemical & Biomolecular Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/2219
Formerly known as the Department of Chemical Engineering.
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Item COMPUTATIONAL ANALYSIS OF METABOLIC NETWORKS AND ISOTOPE TRACER EXPERIMENTS FOR METABOLIC FLUX EVALUATION IN COMPLEX SYSTEMS(2021) Lugar, Daniel James; Sriram, Ganesh; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Metabolic engineering endeavors seek to develop microorganisms as feedstocks for biofuels and commodity chemicals. Towards this, quantifying metabolic fluxes is an important step for characterizing an organism’s metabolism and designing effective engineering strategies. Metabolic fluxes are quantified using sophisticated techniques, namely flux balance analysis (FBA), an in silico technique, and isotope-assisted metabolic flux analysis (MFA), a hybrid experimental and computational technique. FBA uses a network’s stoichiometry with linear programming techniques to generate in silico flux predictions for genome-scale networks. MFA uses measurements from stable isotope (typically 13C) tracer experiments to estimate fluxes of central carbon metabolism. In MFA, fluxes are parameters to a model developed from the network’s carbon atom rearrangements, which is fit to isotope labeling data, typically acquired using mass spectrometry.We developed novel mathematical and computational techniques for quantifying and analyzing flux predictions obtained using MFA and FBA. FBA applications typically generate flux predictions for networks with on the order of 1000 [O(1000)] reactions and metabolites. We developed a network reduction algorithm that uses matrix algebra to reduce a large network and flux prediction to a smaller representation. From this reduced representation, a researcher may quickly gain holistic insights from the FBA model. In isotopically nonstationary MFA, time-series labeling measurements are acquired on the approach to steady state. A model consisting of a large system of typically O(1000) ordinary differential equations is fit to the measurements to estimate fluxes and pool sizes. For detailed networks, the number of parameters may be large. We developed a computationally effective framework for solving this problem having robust convergence and efficient scalability to large networks. In this approach, we formulate the problem as an equality-constrained nonlinear program (NLP), solved efficiently using a solver implemented on an algebraic modeling language. Finally, we apply this approach to a detailed model of Phaeodactylum tricornutum photoautotrophic and mixotrophic (on acetate) metabolism. Using the flux estimates, we characterized this organism’s metabolism under disparate growth conditions, which may inform future endeavors to engineer P. tricornutum as a chemical feedstock.Item MODELING AND OPTIMIZATION OF A PHOTOVOLTAIC-ELECTROLYSIS SYSTEM FOR HYDROGEN GENERATION(2019) Al-Obaid, Aisha; Adomaitis, Raymond A; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Hydrogen production from water electrolysis is widely accepted to be the most sustainable source of hydrogen production, especially when integrated with renewable energy sources such as solar or wind energy. Nevertheless, considerable improvements still are needed for renewable hydrogen to be fully competitive with the current fossil fuel energy resources. In this work, we develop and apply computational tools to model hydrogen production from solar powered water electrolysis systems. A kinetic and mechanistic investigation of the oxygen evolution (OER) and hydrogen evolution (HER) reactions on the active nickel-iron layered double hydroxide (NiFe LDH) electrode is presented. Both linear sweep voltammetry and electrochemical impedance spectroscopy measurements were combined with theoretical models describing the electrode kinetics to evaluate the OER and HER rate constants. The rate determining step and reaction mechanism of the OER and HER were identified as a result of this analysis. A computational algorithm to model an integrated PV-electrolysis-battery system is presented with the goals of identifying the system’s optimal design that maximizes the hydrogen production rate, minimizes the levelized cost of energy and total system’s cost, while targeting a net-zero grid energy operation. The coupled system is connected to the electric grid to ensure uninterrupted operation of the electrolyzer. The model is simulated over one year to include both diurnal and seasonal weather variations. Over 2 million different design configurations were evaluated, 13 of which were chosen as the Pareto Front for this optimization problem. Finally, we develop and apply computational tools to identify optimal design configurations when integrating a large number of PV cells under shady or faulty conditions. Monte Carlo simulation is used to introduce the stochastic effect and uncertainty generated by shading, and the model is simulated under different shading intensities. With the inclusion of a cost attribute, a multi-objective optimization problem is developed. Information entropy weight and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods were combined to identify the optimal number of bypass diodes for each shading case. This computational platform can be extended towards developing simulation-based design tools for the integration of nano-scale energy devices.Item Modeling and Optimization of a Photoelectrochemical Solar Hydrogen Cell with TiO2 as a Photo-anode(2014) Alobaid, Aisha A.; Adomaitis, Raymond; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A photoelectrochemical (PEC) cell model for solar hydrogen production with titanium dioxide (TiO2) as a photo anode and platinum (Pt) as a cathode is developed. Despite the wide bandgap of TiO2 resulting in limited photon absorption from the sun, it is still a good candidate due to its stability in liquid electrolytes and reasonable cost. In this model, Beer-Lambert law is used in conjunction with the empirical diode equation to calculate the electron/hole pair generation rate in the photo-anode, and the external current reaching the cathode to estimate and optimize the hydrogen generation rate evolving at the cathode with TiO2 and ITO thicknesses as optimization variables. The model revealed an optimal solution of TiO2 thickness of 3230 nm at 400 nm ITO thickness, with optimal external current value of 26.9 A/m2, hydrogen generation rate of 1.394x10-4 mol/(m2s), and an overall cell efficiency of 3.4 %.