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- ItemAuger- and X-ray Photoelectron Spectroscopy at Metallic Li Material: Chemical Shifts Related to Sample Preparation, Gas Atmosphere, and Ion and Electron Beam Effects(Basel : MDPI, 2022) Oswald, SteffenLi-based batteries are a key element in reaching a sustainable energy economy in the near future. The understanding of the very complex electrochemical processes is necessary for the optimization of their performance. X-ray photoelectron spectroscopy (XPS) is an accepted method used to improve understanding around the chemical processes at the electrode surfaces. Nevertheless, its application is limited because the surfaces under investigation are mostly rough and inhomogeneous. Local elemental analysis, such as Auger electron spectroscopy (AES), could assist XPS to gain more insight into the chemical processes at the surfaces. In this paper, some challenges in using electron spectroscopy are discussed, such as binding energy (BE) referencing for the quantitative study of chemical shifts, gas atmospheric influences, or beam damage (including both AE and XP spectroscopy). Carefully prepared and surface-modified metallic lithium material is used as model surface, considering that Li is the key element for most battery applications.
- ItemArtificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems(Basel : MDPI, 2021) Asfahan, Hafiz M.; Sajjad, Uzair; Sultan, Muhammad; Hussain, Imtiyaz; Hamid, Khalid; Ali, Mubasher; Wang, Chi-Chuan; Shamshiri, Redmond R.; Khan, Muhammad UsmanThe present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the prediction efficiency. The deep learning algorithm was then optimized to increase the learning rate and predictive accuracy with respect to experimental data by tuning the hyperparameters, such as by manipulating the activation functions, the number of hidden layers, and the neurons in each layer by incorporating optimizers, including Adam and RMsprop. The results confirmed the applicability of the method with an overall value of R2 = 0.987 between the input data and ground-truth data, showing that the most competent model could predict the designated output features (Tdbout, wout, and Eairout). The suggested method is straightforward and was found to be practical in the evaluation of the thermal performance of deployed air conditioning systems under different conditions. The results supported the hypothesis that the proposed deep learning AI algorithm has the potential to explore the feasibility of the three evaporative cooling systems in dynamic ambient conditions for various agricultural and livestock applications.
- ItemComparative Studies on Water- and Vapor-Based Hydrothermal Carbonization: Process Analysis(Basel : MDPI, 2020) Ro, Kyoung S.; Libra, Judy A.; Alvarez-Murillo, AndrésHydrothermal carbonization (HTC) reactor systems used to convert wet organic wastes into value-added hydrochar are generally classified in the literature as liquid water-based (HTC) or vapor-based (VTC). However, the distinction between the two is often ambiguous. In this paper, we present a methodological approach to analyze process conditions for hydrothermal systems. First, we theoretically developed models for predicting reactor pressure, volume fraction of liquid water and water distribution between phases as a function of temperature. The reactor pressure model predicted the measured pressure reasonably well. We also demonstrated the importance of predicting the condition at which the reactor system enters the subcooled compression liquid region to avoid the danger of explosion. To help understand water–feedstock interactions, we defined a new solid content parameter %S(T) based on the liquid water in physical contact with feedstock, which changes with temperature due to changes in the water distribution. Using these models, we then compared the process conditions of seven different HTC/VTC cases reported in the literature. This study illustrates that a large range of conditions need to be considered before applying the label VTC or HTC. These tools can help in designing experiments to compare systems and understand results in future HTC research
- ItemProcess Analysis of Main Organic Compounds Dissolved in Aqueous Phase by Hydrothermal Processing of Açaí (Euterpe oleraceae, Mart.) Seeds: Influence of Process Temperature, Biomass-to-Water Ratio, and Production Scales(Basel : MDPI, 2021) da Silva, Conceição de Maria Sales; de Castro, Douglas Alberto Rocha; Santos, Marcelo Costa; Almeida, Hélio da Silva; Schultze, Maja; Lüder, Ulf; Hoffmann, Thomas; Machado, Nélio TeixeiraThis work aims to systematically investigate the influence of process temperature, biomass-to-water ratio, and production scales (laboratory and pilot) on the chemical composition of aqueous and gaseous phases and mass production of chemicals by hydrothermal processing of Açaí (Euterpe oleraceae, Mart.) seeds. The hydrothermal carbonization was carried out at 175, 200, 225, and 250 °C at 2 °C/min and a biomass-to-water ratio of 1:10; at 250 °C at 2 °C/min and biomass-to-water ratios of 1:10, 1:15, and 1:20 in technical scale; and at 200, 225, and 250 °C at 2 °C/min and a biomass-to-water ratio of 1:10 in laboratory scale. The elemental composition (C, H, N, S) in the solid phase was determined to compute the HHV. The chemical composition of the aqueous phase was determined by GC and HPLC and the volumetric composition of the gaseous phase using an infrared gas analyzer. For the experiments in the pilot test scale with a constant biomass-to-water ratio of 1:10, the yields of solid, liquid, and gaseous phases varied between 53.39 and 37.01% (wt.), 46.61 and 59.19% (wt.), and 0.00 and 3.80% (wt.), respectively. The yield of solids shows a smooth exponential decay with temperature, while that of liquid and gaseous phases showed a smooth growth. By varying the biomass-to-water ratios, the yields of solid, liquid, and gaseous reaction products varied between 53.39 and 32.09% (wt.), 46.61 and 67.28% (wt.), and 0.00 and 0.634% (wt.), respectively. The yield of solids decreased exponentially with increasing water-to-biomass ratio, and that of the liquid phase increased in a sigmoid fashion. For a constant biomass-to-water ratio, the concentrations of furfural and HMF decreased drastically with increasing temperature, reaching a minimum at 250 °C, while that of phenols increased. In addition, the concentrations of CH3COOH and total carboxylic acids increased, reaching a maximum concentration at 250 °C. For constant process temperature, the concentrations of aromatics varied smoothly with temperature. The concentrations of furfural, HMF, and catechol decreased with temperature, while that of phenols increased. The concentrations of CH3COOH and total carboxylic acids decreased exponentially with temperature. Finally, for the experiments with varying water-to-biomass ratios, the productions of chemicals (furfural, HMF, phenols, cathecol, and acetic acid) in the aqueous phase is highly dependent on the biomass-to-water ratio. For the experiments at the laboratory scale with a constant biomass-to-water ratio of 1:10, the yields of solids ranged between 55.9 and 51.1% (wt.), showing not only a linear decay with temperature but also a lower degradation grade. The chemical composition of main organic compounds (furfural, HMF, phenols, catechol, and acetic acid) dissolved in the aqueous phase in laboratory-scale study showed the same behavior as those obtained in the pilot-scale study.
- ItemStructural and Electrochemical Properties of Layered P2-Na0.8Co0.8Ti0.2O2 Cathode in Sodium-Ion Batteries(Basel : MDPI, 2022) Pohle, Björn; Gorbunov, Mikhail V.; Lu, Qiongqiong; Bahrami, Amin; Nielsch, Kornelius; Mikhailova, DariaLayered Na0.8Co0.8Ti0.2O2 oxide crystallizes in the β-RbScO2 structure type (P2 modification) with Co(III) and Ti(IV) cations sharing the same crystallographic site in the metal-oxygen layers. It was synthesized as a single-phase material and characterized as a cathode in Na- and Na-ion batteries. A reversible capacity of about 110 mA h g−1 was obtained during cycling between 4.2 and 1.8 V vs. Na+/Na with a 0.1 C current density. This potential window corresponds to minor structural changes during (de)sodiation, evaluated from operando XRD analysis. This finding is in contrast to Ti-free NaxCoO2 materials showing a multi-step reaction mechanism, thus identifying Ti as a structure stabilizer, similar to other layered O3- and P2-NaxCo1−yTiyO2 oxides. However, charging the battery with the Na0.8Co0.8Ti0.2O2 cathode above 4.2 V results in the reversible formation of a O2-phase, while discharging below 1.5 V leads to the appearance of a second P2-layered phase with a larger unit cell, which disappears completely during subsequent battery charge. Extension of the potential window to higher or lower potentials beyond the 4.2–1.8 V range leads to a faster deterioration of the electrochemical performance. After 100 charging-discharging cycles between 4.2 and 1.8 V, the battery showed a capacity loss of about 20% in a conventional carbonate-based electrolyte. In order to improve the cycling stability, different approaches including protective coatings or layers of the cathodic and anodic surface were applied and compared with each other.
- ItemBalancing Health, Economy and Climate Risk in a Multi-Crisis(Basel : MDPI, 2021) Nathwani, Jatin; Lind, Niels; Renn, Ortwin; Schellnhuber, Hans JoachimIn the presence of a global pandemic (COVID-19), the relentless pressure on global decision-makers is to ensure a balancing of health (reduce mortality impacts), economic goals (income for livelihood sustenance), and environmental sustainability (stabilize GHG emissions long term). The global energy supply system is a dominant contributor to the GHG burden and deeply embedded in the economy with its current share of 85%, use of fossil fuels has remained unchanged over 3 decades. A unique approach is presented to harmonizing the goals of human safety, economic development, and climate risk, respectively, through an operational tool that provides clear guidance to decision-makers in support of policy interventions for decarbonization. Improving climate change performance as an integral part of meeting human development goals allows the achievement of a country’s environmental, social, and economic well-being to be tracked and monitored. A primary contribution of this paper is to allow a transparent accounting of national performance highlighting the goals of enhancing human safety in concert with mitigation of climate risks. A measure of a country’s overall performance, combined as the Development and Climate Change Performance Index (DCI), is derived from two standardized indexes, the development index H and the Climate Change Performance Index CCPI. Data are analyzed for 55 countries comprising 65 percent of the world’s population. Through active management and monitoring, the proposed DCI can illustrate national performance to highlight a country’s current standing, rates of improvement over time, and a historical profile of progress of nations by bringing climate risk mitigation and economic well-being into better alignment.
- ItemInvestigation of Energy Consumption and Associated CO2 Emissions for Wheat–Rice Crop Rotation Farming(Basel : MDPI, 2021) Ashraf, Muhammad N.; Mahmood, Muhammad H.; Sultan, Muhammad; Shamshiri, Redmond R.; Ibrahim, Sobhy M.This study investigates the input–output energy-flow patterns and CO2 emissions from the wheat–rice crop rotation system. In this regard, an arid region of Punjab, Pakistan was selected as the study area, comprising 4150 km2. Farmers were interviewed to collect data and information on input/output sources during the 2020 work season. The total energy from these sources was calculated using appropriate energy equivalents. Three energy indices, including energy use efficiency (ηe), energy productivity (ηp), and net energy (ρ), were defined and calculated to investigate overall energy efficiency. Moreover, the data envelopment analysis (DEA) technique was used to optimize the input energy in wheat and rice production. Finally, CO2 emissions was calculated using emissions equivalents from peer-reviewed published literature. Results showed that the average total energy consumption in rice production was twice the energy consumed in wheat production. However, the values of ηe, ηp, and ρ were higher in wheat production and calculated as 5.68, 202.3 kg/GJ, and 100.12 GJ/ha, respectively. The DEA showed the highest reduction potential in machinery energy for both crops, calculated as −42.97% in rice production and −17.48% in wheat production. The highest CO2 emissions were found in rice production and calculated as 1762.5 kg-CO2/ha. Our conclusion indicates that energy consumption and CO2 emissions from wheat–rice cropping systems can be minimized using optimized energy inputs.
- ItemElectrical Modelling of Switching Arcs in a Low Voltage Relay at Low Currents(Basel : MDPI, 2020) Najam, Ammar; Pieterse, Petrus; Uhrlandt, DirkThe arc behaviour of short, low current switching arcs is not well understood and lacks a reliable model. In this work, the behaviour of an arc in the air is studied during contact separation at low DC currents (0.5 A to 20 A) and for small gap lengths (0 mm to 6 mm). The experiments are performed on a low voltage relay with two different electrode configurations. The arc voltage is measured during the opening of the contacts at constant current. The arc length is determined optically by tracing the mean path of the arc over time from a series of high-speed images. From the synchronised data of voltage vs. distance, first a sudden jump of the voltage at the start of contact opening is observed. Secondly, a sudden change in the voltage gradient occurs as the arc is elongated. Short arcs with a length up to approximately 1.25 mm show an intense radiation in the overall gap region and high voltage gradients. An unexpected behaviour never reported before was observed for longer arcs at low current: Two characteristic regions occur, a region in front of the cathode, with a length of approximately 1.25 mm, having an intense radiation and a high voltage gradient as well as a region of much lower radiation intensity and a comparatively lower voltage gradient in the remaining gap area despite a small anode spot region. The characteristic border of approximately 1.25 mm is almost independent of the current. A generalised arc voltage model is proposed based on the assumption that a constant sheath voltage and two discrete field regions exist, which are modelled as two independent linear functions of voltage vs. length. The data for various currents is combined to yield a general non-linear function for predicting the arc voltage vs. arc length and current.
- ItemResponsible Knowledge Management in Energy Data Ecosystems(Basel : MDPI, 2022) Janev, Valentina; Vidal, Maria-Esther; Pujić, Dea; Popadić, Dušan; Iglesias, Enrique; Sakor, Ahmad; Čampa, AndrejThis paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces.
- ItemInfluence of the Reaction Injection Moulding Process on the Thermomechanical Behaviour of Fast Curing Polyurethane(Basel : MDPI, 2022) Lehmenkühler, Peter; Stommel, MarkusIn this contribution, the influence of the reaction injection moulding process on the thermomechanical material behaviour of aliphatic hexamethylene diisocyanate (HDI) based fast curing polyurethane is demonstrated. Uniaxial tensile tests, temperature-frequency dependent dynamic mechanical thermal analysis (DMTA) and Differential Scanning Calorimetry (DSC) are used to show the differences in properties for ten different sets of process parameters. The mould and resin components temperature, the mass flow during the filling process and the residence time during the reaction process of the polyurethane are varied in several stages. Further experiments to determine the molar mass of the molecular chain between two crosslinking points of the polyurethane are used to explain the process influences on the thermomechanical properties. Thus, a direct correlation between manufacturing and material properties is shown. In addition, the mutual effect of the different parameters and their overall influence on the material behaviour is presented.