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Ammonia production through more efficient technologies can be achieved using exergy analysis. Ammonia production is one of the most important but also one of most energy consuming processes in the chemical industry. Based on a panel of solutions previously developed, this study helps to identify potential areas of improvement using an exergy analysis that covers all aspects of conventional ammonia synthesis and separation. The total internal and external exergy losses are calculated as 3,152 and 6,364 kJ/kg, respectively. The process is then divided into five main functional blocks based on their exergy losses. The reforming block contains the largest exergy loss (3,098 kJ/kg) and thus the largest potential for improvement including preheating cold feed through an economizer, developing technology towards isobaric mixing, and pressure drop reduction in the secondary reformer as the main contributors to the irreversibility (1,302 kJ/kg) in this block. The second largest exergy loss resides in the ammonia synthesis block (3,075 kJ/kg) where solutions such as reduced temperature rise across the compressor, proper compressor isolation, reducing undesired components such as argon in the reactor feed, and using lower temperatures for reactor outlet streams, are proposed to decrease the exergy losses. Throttling process in the syngas separator is the key contributing mechanism for the irreversibility (1,635 kJ/kg exergy losses) in the gas upgrading block. The exergy losses in the residual ammonia removal block (833 kJ/kg exergy losses) are mainly due to the stripper and the absorber column where a modified column design might be helpful. The highest exergy loss in the preheating block belongs to the compressors (518 kJ/kg exergy losses) where a lower inlet temperature and better system isolation could help to reduce losses.

Le Meillour & Sinet-Mathiot et al. 2024 Increasing sustainability in palaeoproteomics by optimizing digestion times for large-scale archaeological bone analyses DOI: 10.1016/j.isci.2024.109432 MALDI-ToF MS data (raw data: mzML files, merged spectra: msd files) used for the ZooMS analysis of the bone material from Baishiya Karst Cave (China) and La Draga (Spain), along with the R codes for merging triplicates into one msd file. 

This paper presents results of an online stated choice experiment on preferences of Dutch private car owners for alternative fuel vehicles (AFVs) and their characteristics. Results show that negative preferences for alternative fuel vehicles are large, especially for the electric and fuel cell car, mostly as a result of their limited driving range and considerable refueling times. Preference for AFVs increases considerably with improvements on driving range, refueling time and fuel availability. Negative AFV preferences remain, however, also with substantial improvements in AFV characteristics; the remaining willingness to accept is on average € 10,000-€ 20,000 per AFV. Results from a mixed logit model show that consumer preferences for AFVs and AFV characteristics are heterogeneous to a large extent, in particular for the electric car, additional detour time and fuel time for the electric and fuel cell car. An interaction model reveals that annual mileage is by far the most important factor that determines heterogeneity in preferences for the electric and fuel cell car. When annual mileage increases, the preference for electric and fuel cell cars decreases substantially, whilst the willingness to pay for driving range increases substantially. Other variables such as using the car for holidays abroad and the daily commute also appear to be relevant for car choice. © 2014 Elsevier Ltd.

Climaps.eu is an online atlas providing data, visualizations and commentaries about climate adaptation debate. It contains 33 issue-maps and 5 issue-stories. Each of the maps focuses on one issue in the adaptation debate and provides.The atlas is addressed to climate experts (negotiators, NGOs and companies concerned by global warming, journalists…) and to citizens willing to engage with the issues of climate adaptation.It employs advanced digital methods to deploy the complexity of the issues related to climate adaptation and information design to make this complexity legible.

In ecological risk assessment, exposure is generally modelled assuming static conditions, herewith neglecting the potential role of emission, environmental and biomass dynamics in affecting bioavailable concentrations. In order to investigate the influence of such dynamics on predicted bioavailable concentrations, the spatially-resolved dynamic model "ChimERA fate" was developed, incorporating macrophyte and particulate/dissolved organic carbon (POC/DOC) dynamics into a water-sediment system. An evaluation against three case studies revealed a satisfying model performance. Illustrative simulations then highlighted the potential spatio-temporal variability of bioavailable concentrations after a pulsed emission of four chemicals in a system composed of a pond connected to its inflow and outflow streams. Changes in macrophyte biomass and POC/DOC levels caused exposure variations which were up to a factor of 4.5 in time and even more significant (several orders of magnitude) in space, especially for highly hydrophobic chemicals. ChimERA fate thus revealed to be a useful tool to investigate such variations and to identify those environmental and ecological conditions in which risk is expected to be highest.

The present paper exhibits a real time assessment of a robust Energy Management Strategy (EMS) for battery-super capacitor (SC) Hybrid Energy Storage System (HESS). The proposed algorithm, dedicated to an electric vehicular application, is based on a self-gain scheduled controller, which guarantees the H∞ performance for a class of linear parameter varying (LPV) systems. Assuming that the duty cycle of the involved DC-DC converters are considered as the variable parameters, that can be captured in real time, and forwarded to the controller to ensure both; the performance and robustness of the closed-loop system. The subsequent controller is therefore time-varying and it is automatically scheduled according to each parameter variation. This algorithm has been validated through experimental results provided by a tailor-made test bench including both the HESS and the vehicle traction emulation system. The experimental results demonstrate the overall stability of the system, where the proposed LPV supervisor successfully accomplishes a power frequency splitting in an adequate way, respecting the dynamic of the sources. The proposed solution provides significant performances for different speed levels.

Energy companies trading in derivatives which have a value based on an energy product ( “Energy Trading”) are subjected to a fragmented regulatory framework. They are supervised by a multitude of authorities on a national and European level. These authorities cooperate horizontally (between EU agencies or between national regulatory authorities) or vertically – between EU agencies and NRA’s) and in a formal and informal way. They share information on Energy Trading to detect market abuse. This article aims to provide insight in the effectiveness of supervisory activities within the Energy Trading landscape. In doing so, the obligation for energy companies to report fundamental data on the derivatives they trade in, is used for illustrative purposes and explained in light of the European Market Infrastructure Regulation (‘EMIR’), the Markets in Financial Instruments Directive and Regulation (‘MiFID II and MiFIR’) and the regulation on wholesale energy market integrity and transparency (‘REMIT’). The article finishes with a conclusion which advocates the necessity of information sharing and professional secrecy in the current regulatory landscape and intends to contribute to a discussion on the effectiveness of information sharing within an international context.

This work analyzes the synergy between two complementary unit operations - adsorbent dehumidification and drying - and presents a mixed integer nonlinear programming approach to optimize energy performance in a two-stage system. Combined with active constraint analysis, the adsorbent properties that promote energy performance are derived. Microporous adsorbents with higher sorption capacities at low vapor pressures and requiring higher regeneration temperatures are preferred for ambient air dehumidification in the first stage. For exhaust air dehumidification, mesoporous adsorbents with lower regeneration temperatures are preferred such that the exhaust air from the first regeneration stage can sufficiently regenerate them. For drying below 50 C, energy consumption reductions of about 70% are achieved compared to conventional dryers without adsorbent dehumidification depending on adsorbent properties. The results demonstrate the usefulness of superstructure optimization in matching the drying process with the capabilities of the adsorbents to enhance process synergy for improved energy efficiency. © 2013 American Chemical Society.