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This review presents recent advancements in high-voltage rechargeable aqueous batteries employing water-in-salt and modified water-in-salt electrolytes.

Abstract Biodiesel has gained the forefront of our focus on renewable transportation fuels. This article provides a comprehensive review on the sources used as feedstock and their classification based on generation or type (edible, non-edible, waste resources and animal fats) along with a variety of classical and modern oil extraction techniques. The technical aspects of the various biodiesel production methods currently implemented to the best of our knowledge are discussed here, which include in-situ biodiesel production, both catalysed (homogeneous and heterogeneous systems) and uncatalysed classical production approaches, with emphasis on how each of these approaches are affected by their reaction parameters. The review also highlights the observed drawbacks of each process with a view to assessing the implementation of supercritical and superheated technologies as an alternative, economically feasible advancement. Supercritical process (SCP) has shown great prospect in the obtainment of high quality biodiesel from a wide range of high to low grade feedstock with minimal impacts on the presence of water or FFAs (free fatty acids). From available literature it is shown that these do not affect the process significantly, and various other supercritical fluids such as methyl acetate, tert-butyl methyl ether (MTBE) and dimethyl carbonate can also be used to avoid glycerol formation. The process however, suffers from high initial implementation cost being the most prominent drawback, among others like thermal degradation of the fuel. Another promising technique, the superheated vapour technology (SHV) has emerged as an alternative, with limited literature proving the superiority of either of these processes to be inconclusive. In future works, researchers need to look into various aspects such as developing a spiral reactor for heat recovery, using software based optimization for eliminating redundant experiments analysing production cost for industrial scale-up and improving the fuel’s oxidative stability by adding antioxidants for convenient long-term storage and use.

Abstract Heat exchanger is one the most essential heat transfer equipment in thermal area. On that heat transfer and effectiveness enhancement by using nano particles is recently developing method. In this study enhancement of effectiveness focused by using nano particles mixed with cold fluid used in the heat exchanger. Hot fluid is water with 200 °C flow into Counter flow heat exchanger and the cold fluid is mixed with 5% of nano particles which is combination of Aluminium oxide and Magnesium oxides with equal proportion. The inlet temperature of the cold fluid is maintained at 30 °C throughout the experiments. The temperatures of each place need to measure are obtained by the thermocouples connected on the particular portion of the setup. There is different mass flow rate of cold fluid passed in each successive experiment for the same mass flow rate and inlet temperature of the hot fluid. The consequent exit temperatures of hot and cold fluids were measured and considered for the mathematical calculations of the outcome of the experiments.

In this study, the freshwater microalgae Selenastrum sp. was assessed for the effective degradation of pyrene and simultaneous production of biodiesel from pyrene-tolerant biomass. The growth of algae was determined based on the cell dry weight, cell density, chlorophyll content, and biomass productivity under different pyrene concentrations. Further, lipids from pyrene tolerant culture were converted into biodiesel by acid-catalyzed transesterification, which was characterized for the total fatty acid profile by gas chromatography. Increased pyrene concentration revealed less biomass yield and productivity after 20 days of treatment, indicating potent pyrene biodegradation by Selenastrum sp. Biomass yield was unaffected till the 20 mg/L pyrene. A 95% of pyrene bioremediation was observed at 20 days of culturing. Lipid accumulation of 22.14%, as evident from the estimation of the total lipid content, indicated a marginal increase in corroborating pyrene stress in the culture. Fatty acid methyl esters yield of 63.06% (% per 100 g lipids) was noticed from the pyrene tolerant culture. Moreover, fatty acid profile analysis of biodiesel produced under 10 mg/L and 20 mg/L pyrene condition showed escalated levels of desirable fatty acids in Selenastrum sp., compared to the control. Further, Selenastrum sp. and other freshwater microalgae are catalogued for sustainable development goals attainment by 2030, as per the UNSDG (United Nations Sustainable Development Goals) agenda. Critical applications for the Selenastrum sp. in bioremediation of pyrene, along with biodiesel production, are enumerated for sustainable and renewable energy production and resource management.

Abstract Globally, three-quarter of a billion people live without electricity. Besides, hundreds of million use a solar lantern for less than 4 h a day. Most of the access-deprived are in the Global South, predominantly in Sub-Saharan Africa and South Asia. The United Nations framed the seventh Sustainable Development Goal to improve access. Solar photovoltaic powered mini-grids are increasingly extending better service to deprived regions. However, poor load-factor and expensive storage adversely affect viability. Also, these mini-grids do not support infrequent large loads to avoid further loss of load-factor. Electric cooking is efficient and non-polluting; water treatment facilities can save millions from contaminant and pathogen by providing clean water. Besides, both electric cooking and water treatment are less expensive than alternatives. But mini-grids frequently do not support these. Indeed, the presence of sustained productive loads favourably influences the mini-grid economy. This study investigates the role of critical household loads to deliver similar bearing on the mini-grid economy. Results underscore realisation of desirable impact with household collaboration under a demand-response program. Collaborative consumption can lower initial investment by 62% and reduce the unit energy cost to $0.23. Also, cooperation improves the mini-grid load factor and promotes viability. Additionally, fast deployment needs during and after Covid-19 remains inherently supported while mitigating the pandemic induced financial stress of both consumer and mini-grid operator. This study of 88 nation-states underscores that demand response in a mini-grid can not only improve affordability for all consumers, but it can also bring 186 million people within affordable access.

The article contains laboratory data comparing the rate of gasification of five types of woody plants—beech, oak, willow, poplar and rose. The gasification rate was determined thermogravimetrically. Carbon dioxide and steam were used as gasification gases. Willow wood was the most gasifiable, whereas rose wood the least.

Abstract Time series data is a sequence of values recorded systematically over a period which are mostly used for prediction, clustering, and analysis. The two essential features of a time series data are trend and seasonality. Preprocessing of the time series data is necessary for performing prediction tasks. In most of the cases, the trend and the seasonality are removed before applying the regression algorithms. The accuracy of such algorithms depends upon the functions used for the removal of trend and seasonality. Clustering of an unlabeled time series data with the presence of trend and seasonality is challenging. In this paper, we propose a probabilistic representational learning method for grouping the time series data. We introduce five terminologies in our method of clustering namely the trendlets, uplets, downlets, equalets and trendlet string. These elements are the representational building blocks of our proposed method. Experiments on the proposed algorithm are performed with the renewable energy data on the electricity supply system of continental Europe which includes the demand and inflow of renewable energy for the term 2012 to 2014 and UCR-2018 time series archive containing 128 datasets. We compared our proposed representational method with various clustering algorithms using the silhouette score. Mini-batch k-means and agglomerative hierarchical clustering algorithms show better performance in terms of quality, logical accordance with data and time taken for clustering.