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SummaryThe German government has adopted a law that requires sewage plants to go beyond the recovery of phosphorus from wastewater and to promote recycling. We argue that there is no physical global short‐ or mid‐term phosphorus scarcity. However, we also argue that there are legitimate reasons for policies such as those of Germany, including: precaution as a way to ensure future generations’ long‐term supply security, promotion of technologies for closed‐loop economics in a promising stage of technology development, and decrease in the current supply risk with a new resource pool.

<p>High temperature aquifer thermal energy storage (HT-ATES) can play a key role for a sustainable interplay between different energy sources and in the overall reduction of CO<sub>2</sub>emission. In this study, we numerically investigate the thermo-hydraulic processes of an HT-ATES in the Greater Geneva Basin (Switzerland). The main objective is to investigate how to handle the yearly excess of heat produced by a nearby waste-to-energy plant. We consider potential aquifers located in different stratigraphic units and design the model from available geological and geophysical data. Aquifer properties, flow conditions and well strategies are successively tested to evaluate their influence on the HT-ATES economic performance and environmental impact. This was achieved using a new open-access, user-friendly and efficient code that we also introduce here as a possible tool for geothermal applications.</p><p> </p><p>The results highlight the importance of thorough numerical simulations based on more realistic exploitation when designing HT-ATES systems. We show that relations between thermal performance and the shape of the injected thermal volume are generally hard to derive when complex well schedules are imposed because the injected/produced volumes may not be equal. Despite more complex storage strategies to comply with legal regulations, the shallower group of investigated aquifers in this study remains economically more suitable for storage up to 90ºC. In average four well doublets will be required to store the yearly excess of energy. The deeper group of investigated aquifers, however, become interesting for storage at higher temperatures.</p>

Abstract Gate-to-gate process energy consumption is an important metric for sustainability as it affects both costs and environmental impact. As only little process information is available in early phases of chemical process design, a detailed energy consumption calculation is substantially restrained. Therefore, a reliable estimation of energy consumption in early phases of process design is an important alternative. In this work, an index representing process energy consumption was evaluated and tested for 14 organic solvent case studies. By using simplified process models the indices were calculated and compared to literature values for gate-to-gate energy consumption. The predictability of the process energy consumption on the basis of this indicator, including possible modifications in its original definition, was evaluated with the Pearson's and Spearman's correlation coefficients. The results further validated the use of the EI (energy index) in its original form as a proxy indicator of the process energy consumption for decision making in early stages of process design. For assessing the production of new classes of chemicals the EI should be evaluated as shown in this paper in order to establish its practicability. In certain cases an adjustment of the indicator categories may be necessary.

Most commercially used electrode materials contract and expand upon cycling. This change in volume influences the microstructure of the cell stack, which in turn impacts a range of performance parameters. Since direct observation of these microstructural changes with operando experiments is challenging and time intensive, a simulation tool that takes a real or artificially generated 3D microstructure and captures the volumetric changes in a cell during cycling would be valuable to enable rapid understanding of the impact of material choice, electrode and cell design, and operating conditions on the microstructural changes and identification of sources of mechanically-driven cell aging. Here, we report the development and verification of such a 3D electrochemical-mechanical tool, and provide an example use-case. We validate the tool by simulating the microstructural evolution of a graphite anode and a Li(Ni,Mn,Co)O2 cathode during cycling and comparing the results to X-ray tomography datasets of these electrodes taken during cycling. As an example use case for such a simulation tool, we explore how different volumetric expansion behaviors of the cathode material impact strain in the cell stack, illustrating how the material selection and its operation impact the mechanical behavior inside a cell.

Medium and heavy-duty battery electric trucks (BETs) may play a key role in mitigating greenhouse gas (GHG) emissions from road freight transport. However, technological challenges such as limited range and cargo carrying capacity as well as the required charging time need to be efficiently addressed before the large-scale adoption of BETs. In this study, we apply a geospatial data analysis approach by using a battery electric vehicle potential (BEVPO) model with the datasets of road freight transport surveys for analyzing the potential of large-scale BET adoption in Finland and Switzerland for trucks with gross vehicle weight (GVW) of over 3.5 t. Our results show that trucks with payload capacities up to 30 t have the most potential for electrification by relying on the currently available battery and plug-in charging technology, with 93% (55% tkm) and 89% (84% tkm) trip coverage in Finland and Switzerland, respectively. Electric road systems (ERSs) would be essential for covering 51% trips (41% tkm) of heavy-duty trucks heavier than 30 t in Finland. Furthermore, range-extender technology could improve the trip electrification potential by 3–10 percentage points (4–12 percentage points of tkm).

It has been shown recently that the overpotential originating from ionic conduction of alkali-ions through the inner dense solid-electrolyte interphase (SEI) is strongly non-linear. An empirical equation was proposed to merge the measured resistances from both galvanostatic cycling (GS) and electrochemical impedance spectroscopy (EIS) at 25$^{\circ}$C. Here, this analysis is extended to the full temperature range of batteries from -40$^{\circ}$C to +80$^{\circ}$C for Li, Na, K and Rb-metal electrodes in carbonate electrolytes. Two different transport mechanisms are found. The first one conducts alkali-ions at all measured temperatures. The second transport mechanism conducts ions for all seven measured Li-ion electrolytes and one out of four Na-ion electrolytes, however, only above a certain critical temperature $T_C$. At $T_C$ a phase transition is observed switching-off the more efficient transport mechanism and leaving only the general ion conduction mechanism. The associated overpotentials increase rapidly below $T_C$ depending on alkali-ion, salt and solvent and become a limiting factor during galvanostatic operation of all Li-ion electrolytes at low temperature. In general, the current analysis merges the SEI resistances measured by EIS ranging from 26 $��$cm$^2$ for the best Li up to 292 M$��$cm$^2$ for Rb electrodes to its galvanostatic response over seven orders of magnitude. The determined critical temperatures are between 0-25$^{\circ}$C for the tested Li and above 50$^{\circ}$C for Na electrolytes. 10 pages, 7 figures, file includes Suppl Info, http://jes.ecsdl.org/content/165/2/A323

This paper presents a simplified, yet realistic, model of a hybrid electric powertrain and derives the explicit solution of the optimal energy management. The explicit solution of this optimal control problem consists of simple rules that rely on powertrain parameters only. The simplified model is validated on a more complex model relying on measured data. Finally, a causal, real-time control strategy including anti-windup is presented. This strategy relies on the optimal control of the simplified model and is successfully evaluated on the complex model that relies on measured data.

Energy systems modellers often resort to simplified system representations and deterministic model formulations (i.e., not considering uncertainty) to preserve computational tractability. However, reduced levels of detail and neglected uncertainties can both lead to sub-optimal system designs. In this paper, we present a novel method that quantitatively compares the impact of detail and uncertainty to guide model development and help prioritisation of the limited computational resources. By considering modelling choices as an additional 'uncertain' parameter in a global sensitivity analysis, the method determines their qualitative ranking against conventional input parameters. As a case study, the method is applied to a peer-reviewed heat decarbonisation model for the United Kingdom with the objective of assessing the importance of spatial resolution. The results show that while for the optimal total system cost the impact of spatial resolution is negligible, it is the most important factor determining the capacities of electricity, gas and heat networks. 68 pages, 16 figures, 4 tables, submitted to Computers & Chemical Engineering