Dynamic and transient modelling of electrolysers by renewable energy sources and cost analysis of electrolytic hydrogen

Hydrogen energy sector has gained significant attention worldwide but one of the key enabling components for its success would be cheaper and sustainable hydrogen production. Hydrogen could be produced directly from natural gas or coal etc; alternatively it could be produced by electrolysis of water powered by renewable energy sources, nuclear energy or fossil fuel. Wind energy is growing rapidly, which can produce cheap hydrogen. Electrolysers can be employed to control the frequency of the electricity grid while also making fuel as a by-product. This thesis concerns the intricacies of hydrogen production by electrolysers from renewable energy sources. A generalised, input-based mathematical model of the electrolyser has been developed for various subsystems, such as current-voltage, Faraday efficiency, gas production, gas purity, differential pressure, temperature subsystem, parasitic losses, gas losses and efficiencies at various stages of operation. Some empirical equations have been developed and some adjusted parameters have been used in the model. The model has been tested and verified against the experimental measurements. A generic method has been developed for modelling the Faraday efficiency. Model simulations have been carried out to investigate the sensitivity of the results to the value of the capacitance and how this affects the dynamic response of the electrolyser. A new sizing method of the electrolyser has been developed for a stand-alone energy system such as the HARI project. The electrolyser model has also been simulated for maximum and efficient hydrogen production in a directly coupled mode of electrolysers with solar PV arrays without the maximum power point (MPP) tracker, which leads to an interesting finding that "electrolysers should not be operated at MPP". It has also been found that the dynamic and intermittent power supply from renewables can damage the stability of electrolysers and reduce the energy capture. This is especially true for pressurised electrolysers, which are favoured by the industry at present. The in-depth theoretical and practical analysis of several aspects confirms - contrary to industry trends - that "Pressurised electrolysers are less energy efficient, less durable, more costly and not adequately compatible for renewable energy powered operation, especially in the stand-alone energy systems, compared to atmospheric electrolysers"

Detection of Long-Range Concerted Motions in Protein by a Distance Covariance

We asses the ability of a distance correlation coefficient (DiCC), calculated from distance covariance, for detecting long-range concerted motion in proteins. We establish a set of criteria for ideal correlation coefficient values based on the coefficient of determination in multidimension, <b>R</b>². We compare in detail DiCC and conventional correlation coefficients against these criteria. We demonstrate that, in contrast to conventional correlation coefficients, which capture long-distance correlation adequately only with certain restrictions in multidimension, DiCC reflects appropriate correlation in both one-dimension and multidimensions. Finally, we demonstrate the usefulness of DiCC for assessing long-distance correlated fluctuation in protein dynamics

Analysis of Multidomain Protein Dynamics

Proteins with a modular architecture of multiple domains connected by linkers often exhibit diversity in the relative positions of domains, while the domain tertiary structure remains unchanged. The biological function of these modular proteins, or the regulation of their activity, depends on the variation in domain orientation and separation. Accordingly, careful characterization of interdomain motion and correlated fluctuations of multidomain systems is relevant for understanding the functional behavior of modular proteins. Molecular dynamics (MD) simulations provides a powerful approach to study these motions in atomic detail. Nevertheless, the common procedure for analyzing fluctuations from MD simulations after rigid-body alignment fails for multidomain proteins; it greatly overestimates correlated positional fluctuations in the presence of relative domain motion. We show here that expressing the atomic motions of a multidomain protein as a combination of displacement within the domain reference frame and motion of the relative domains correctly separates the internal motions to allow a useful description of correlated fluctuations. We illustrate the methodology of separating the domain fluctuations and local fluctuations by application to the tandem SH2 domains of human Syk protein kinase and by characterizing an effect of phosphorylation on the dynamics. Correlated motions are assessed from a distance covariance rather than the more common vector-coordinate covariance. The approach makes it possible to calculate the proper correlations in fluctuations internal to a domain as well as between domains

Tar Reforming in Model Gasifier Effluents: Transition Metal/Rare Earth Oxide Catalysts

The removal of tars from syngas generated in biomass or coal/biomass gasifiers plays an important role in syngas cleanup. Rare earth oxides (REOs, e.g., Ce/LaOx) mixed with transition metals (e.g., Mn, Fe) were synthesized by various methods and in some cases supported on a thermally stable alumina. These catalysts were applied to tar removal in the temperature range <1100 K using synthetic syngas mixtures with C₁₀H₈ as a tar model compound, both with and without H₂S. Some commercial Ni reforming catalyst formulations were examined comparatively. Fresh and used catalysts were characterized by XANES, XAFS, XRD, TPO, and BET. We found that the C₁₀H₈ is reformed to at least methane, although further reforming to CO and H₂ is not always achieved. While CO₂, H₂S, and coke formation all inhibited or deactivated the catalysts at certain temperatures and to different extents, it was determined that Fe- or Mn-doped supported REOs are promising tar cleanup catalysts. They exhibited higher sulfur tolerance, less coking, and less methanation than typical Ni-based high temperature reforming catalysts. This behavior is in part attributed to enhanced generation of oxygen vacancies in the doped REOs

Modeling disordered protein interactions from biophysical principles

<div><p>Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.</p></div

Relative Binding Enthalpies from Molecular Dynamics Simulations Using a Direct Method

The potential for reliably predicting relative binding enthalpies, ΔΔ<i>E</i>, from a direct method utilizing molecular dynamics is examined for a system of three phosphotyrosyl peptides binding to a protein receptor, the Src SH2 domain. The binding enthalpies were calculated from the potential energy differences between the bound and the unbound end-states of each peptide from equilibrium simulations in explicit water. The statistical uncertainties in the ensemble-mean energy values from multiple, independent simulations were obtained using a bootstrap method. Simulations were initiated with different starting coordinates as well as different velocities. Statistical uncertainties in ΔΔ<i>E</i> are 2 to 3 kcal/mol based on calculations from 40, 10 ns trajectories for each system (three SH2–peptide complexes or unbound peptides). Uncertainties in relative component energies, comprising solute–solute, solute–solvent and solvent–solvent interactions, are considerably larger. Energy values were estimated from an unweighted ensemble averaging of multiple trajectories with the a priori assumption that all trajectories are equally likely. Distributions in energy–rmsd space indicate that the trajectories sample the same basin and the difference in mean energy values between trajectories is due to sampling of alternative local regions of this superbasin. The direct estimate of relative binding enthalpies is concluded to be a reasonable approach for well-ordered systems with ΔΔ<i>E</i> values greater than ∼3 kcal/mol, although the approach would benefit from future work to determine properly distributed starting points that would enable efficient sampling of conformational space using multiple trajectories

L-RMSD vs Model Score and IDP RMSD.

<p>Inc: incorrect; Acc: acceptable; Med: medium. PDB ID: 2bzw.</p

Disordered protein complex test set.

<p>Disordered protein complex test set.</p

Examples of successful bound and unbound cases.

<p>Green: native IDP; orange: modeled IDP. a-d: bound cases; e-h: unbound cases. a: Rank 1 model of MDM2 with bound P53 (PDB ID: 1ycr). <i>f</i>_<i>nat</i> 0.42, I-RMSD 1.48 Å, L-RMSD 3.60 Å (medium quality). b: Rank 4 model of PKA C-<i>α</i> with bound protein kinase inhibitor <i>α</i> (2cpk). <i>f</i>_<i>nat</i> 0.56, I-RMSD 1.95 Å, L-RMSD 4.41 Å (medium quality). c: Rank 6 model of RAP1 with bound SIR3 (3owt). <i>f</i>_<i>nat</i> 0.33, I-RMSD 3.30 Å, L-RMSD 6.02 Å. d: Rank 5 model of BoNT/A with bound SNAP-25 (1xtg). <i>f</i>_<i>nat</i> 0.17, I-RMSD 3.79 Å, L-RMSD 9.22 Å. e: Rank 1 model of DRA/DRB5 with unbound myelin basic protein (4ah2). <i>f</i>_<i>nat</i> 0.39, I-RMSD 2.46 Å, L-RMSD 5.83 Å. f: Rank 9 model of <i>α</i>-actin-1 with unbound Cibulot (1ijj). <i>f</i>_<i>nat</i> 0.55, I-RMSD 2.51 Å, L-RMSD 5.15 Å. g: Rank 3 model of Cbp/p300 with unbound CITED2 (1l3e). <i>f</i>_<i>nat</i> 0.25, I-RMSD 6.31 Å, L-RMSD 7.43 Å. h: Rank 2 model of SycE with unbound YopE (1jya). <i>f</i>_<i>nat</i> 0.21, I-RMSD 5.44 Å, L-RMSD 9.97 Å.</p

Summary of modeling performance on training set.

<p>Summary of modeling performance on training set.</p