Comparative techno-economic assessment of a large-scale hydrogen transport via liquid transport media

A large-scale point to point hydrogen transport is one strategy for a prospective energy import scenario for certain countries. The case for a hydrogen transport from Australia to Japan has been addressed in several studies. However, most studies lack transparency and detailed insights into the made assumptions thus a fair evaluation of different transport pathways is challenging. To address this issue, we developed a model where a large-scale point to point hydrogen transport of liquid hydrogen is compared with the transport via liquid organic hydrogen carrier (LOHC), namely via methyl cyclohexane and hydrogenated dibenzyl toluene. We analyzed, where energy is required along the different pathways, where hydrogen losses do occur and how the costs are put together. Furthermore, the influence of hydrogen feed costs is also considered. For hydrogen production costs of 5 €₂₀₁₈/kg_H2 the total delivery costs are in the range of 6.40– 8.10 €₂₀₁₈/kg_H2.     

Evaluation of the pore morphology formation of the Freeze Foaming process by in situ computed tomography

The so-called Freeze Foaming method aims at manufacturing ceramic cellular scaffolds for diverse applications. One application is dedicated to potential bone replacement material featuring open, micro and interconnected porosity. However, the main challenges of this foaming method is to achieve a homogeneous pore morphology. In a current project, the authors throw light on the bubble/pore and strut formation of this process by in situ computed tomography. This allows for evaluating varying process parameter’s effects on the growth of the ceramic foam during the foaming process. As first result and basis for CT analysis, a stable and reproducible model suspension was developed which resulted in reproducible foam structures. In dependence of selected process parameters like pressure reduction rate or air content in the ceramic suspension resulting Freeze Foams became adjustable with regard to their pore morphology. Pore size and distribution data as well as the porosity were characterized and evaluated accordingly.     

Improvement of Aglycone π-Stacking Yields Nanomolar to Sub-nanomolar FimH Antagonists

Antimicrobial resistance has become a serious concern for the treatment of urinary tract infections. In this context, an anti-adhesive approach targeting FimH, a bacterial lectin enabling the attachment of E. coli to host cells, has attracted considerable interest. FimH can adopt a low/medium-affinity state in the absence and a high-affinity state in the presence of shear forces. Until recently, mostly the high-affinity state has been investigated, despite the fact that a therapeutic antagonist should bind predominantly to the low-affinity state. In this communication, we demonstrate that fluorination of biphenyl α-d -mannosides leads to compounds with perfect π–π stacking interactions with the tyrosine gate of FimH, yielding low nanomolar to sub-nanomolar K_D values for the low- and high-affinity states, respectively. The face-to-face alignment of the perfluorinated biphenyl group of FimH ligands and Tyr48 was confirmed by crystal structures as well as ¹H,¹⁵N-HSQC NMR analysis. Finally, fluorination improves pharmacokinetic parameters predictive for oral availability.     

Model-based test case generation and prioritization: a systematic literature review

Software and Systems Modeling - Model-based test case generation (MB-TCG) and prioritization (MB-TCP) utilize models that represent the system under test (SUT) for test generation and...     

Effect of Constant‐Rate Reduction on the Performance of a Ternary Cu/ZnO/Al2O3 Catalyst in Methanol Synthesis

A multi‐functional flow set‐up was developed for the rate‐ and temperature‐controlled reduction of copper catalysts, their application in high‐pressure methanol synthesis and the determination of the copper surface area by N₂O frontal chromatography. The influence of constant‐rate reduction on the catalytic properties of a ternary Cu/ZnO/Al₂O₃ catalyst was investigated. The temperature during the constant‐rate reduction was found to decrease, indicating autocatalytic kinetics, but no significant catalytic effect of the milder reduction conditions was observed compared with a slow linear heating ramp.     

A quadratic complementarity problem related to the optimal design of a pipeline freely resting on a rough sea bottom

A submarine pipeline resting on a rigid, frictionless seabed assumes an equilibrium configuration which can be determined by solving a unilateral contact problem, i.e. a quadratic program or a variational inequality.When the sea bed is irregular, its profile along the sealine can be modified, in present offshore technology, by means of costly trench excavations or artificial supports, in order to avoid excessive bending moments in the pipe. Thus, the problem arises of determining the minimum cost of the profile changes provided that a specified curvature is nowhere exceeded.This optimal design problem is solved by assuming a quadratic cost function and reducing it to a sequence of convex quadratic programming problems leading to the optimal design situation. Upper and lower bounds on the absolute minimum cost and optimality conditions are established. A suitable algorithm for solving the problem in a finite number of steps is developed. Its generalization to allow for ‘fixed charges’ due to the equipment mobilization cost is presented. Two numerical examples illustrate the optimum (minimum cost) proposed design method.     

Determining Inputs for Neural Network Models of Multivariate Time Series

In recent years, artificial neural networks have been used successfully to model multivariate water resources time series. By using analytical approaches to determine appropriate model inputs, network size and training time can be reduced. In this paper, it is proposed that the method of Haugh and Box and a new neural network–based approach can be used to identify the inputs for multivariate artificial neural network models. Both methods were used to obtain the inputs for a multivariate artificial neural network model used for forecasting salinity in the River Murray at Murray Bridge, South Australia. The methods were compared with a third method that uses knowledge of travel times in the river to identify a reasonable set of inputs. The results obtained indicate that all three methods are suitable for determining the inputs for multivariate time series models. However, the neural network–based method is preferable because it is quicker and simpler to use. Any prior knowledge of the underlying processes should be used in conjunction with the neural network method.