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Abstract This paper describes the point of view of a gas utility regarding systems for cooling and heating by solid sorption. Indeed, the context is favorable to the emergence of such systems first, of course, due to the regulatory constraints on certain refrigerant fluids, but also for their specific technical characteristics (the storage capacity, for example). The gas utilities have in fact shown an increasingly keen interest in all types of sorption systems, as a way of diversifying natural gas utilization, particularly in the summer. After a review of that context, this paper highlights the advantages of solid sorption and describes the criteria for choosing an installation in air conditioning and cooling production applications. Finally, it discusses the difficulties likely to be encountered in developing sorption systems, as well as the prospects for the future.

Considerable interest is being shown in using biochar production from waste biomass with a variety of disciplines to address the most pressing environmental challenges. Biochar produced by the thermal decomposition of biomass under oxygen-limited conditions is gaining popularity as a low-cost amendment for agro-ecosystems. The efficiency of biochar formation is affected by temperature, heating rate, feedstock type, particle size and reactor conditions. Properties such as pH, surface area and ash content of produced biochar increases with increasing temperatures. Biochar produced at lower heating rates may have high porosity and be beneficial for morphological changes in the soil. Biochar can help to enhance soil health and fertility as well as improve agricultural yield. As a result, biochar can assist in increasing food security by promoting sustainable agricultural systems and preserving an eco-friendly environment. Biochar is also widely being used as a sorbent for organic and inorganic pollutants, owing to its large surface area, allowing it to be immobilized from soil with ease. The functional groups and charges present on the surface of biochar play an important role in pollutants removal. This review focuses on the mechanisms of biochar production using different waste materials as a feed stock, factors that influence biochar quality as well as application of biochar in agricultural soil and their reclamation as well. This article also discusses knowledge gaps and future perspectives in the field of biochar-based toxic-pollution remediation.

The growing population and increasing urbanization have led to a surge in domestic wastewater generation, posing significant challenges for effective and sustainable treatment. The present study demonstrates a novel and sustainable approach for the onsite treatment of domestic wastewater using an integrated settler-based biofilm reactor (ISBR) with efficient biogas generation. The ISBR provides an optimized environment for the growth of biofilm, facilitating the removal of organic pollutants and pathogens. Moreover, the ISBR enables the recovery of a valuable resource in the form of biogas, thus enhancing the overall utility of the treatment process. The performance of the ISBR was comprehensively evaluated at laboratory scale through treating the actual domestic wastewater generated from the hostel of Manipal University Jaipur. The ISBR system was operated under an ambient environment at a hydraulic retention time (HRT) of 24 h. The results demonstrated remarkable efficiency in terms of chemical oxygen demand (COD), total suspended solids (TSS), and coliforms removal, with average removal efficiency being more than 90%. According to the COD mass balance analysis, 48.2% of the influent COD was recovered as bioenergy. The chromatogram revealed a high percentage of methane gas in the collected biogas sample. The field emission scanning electron microscope (FESEM) analysis of the accumulated sludge in the ISBR system depicted the morphology of methanogenic bacteria. Both the experimental and theoretical results confirmed the feasibility and sustainability of the ISBR system at the onsite level.

Abstract To ensure long-term availability of mobility and to keep the effects of humanity on global climate limited, politics, research, and industry currently search for ways to reduce greenhouse gas (GHG) emissions caused by it. A potential reduction could be achieved by transitioning towards more efficient transport modes, optimally powered by renewable energy. Based on mobility (commutes), population, and weather data from Switzerland, we present a model to compute energy savings due to a (hypothetical) widespread deployment of electric bicycles. In different scenarios, we analyze the dependence of the commuting energy demand on users’ preferences about when to take the bike, such as an aversion to biking on cold and rainy days. Our study shows that GHG emission reductions of up to approx. 10% of the total emissions from diesel and gasoline are possible. In combination with a widespread deployment of electric vehicles, further savings of up to 17.5% could be achieved. In particular, the willingness to drive longer commutes by e-bike influences the potential GHG emission reductions, followed by the willingness to use the bicycle at temperatures below 10°C. Using these results, we identify the spatial energy and greenhouse gas savings potential and thus regions that are particularly suited for e-bike use. The identification of regions with high saving potentials allows for targeted marketing, transition-supporting incentives, or infrastructural changes to maximize the reduction of emissions.

AbstractRecently, modern power systems depend heavily on MicroGrids (MGs), which can accommodate Distributed Energy Resources (DERs) economically and with high flexibility. MGs integrated with DERs can assist in enhancing energy security, significant cost savings, and reduction in emission of greenhouse gases. In this paper, the assessment of operating performance of proposed MG system with DERs is employed to investigate the multi‐objective problems of cost optimization and economic scheduling. A grid‐connected Micro‐grid (MG) combined with solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), and Battery Energy Storage System (BESS) is implemented to model the problem. This proposed model is considered as a test system for cost optimization and battery charging/discharging optimization. The developed framework is presented as multi‐objective function with constraints that can be tackled using an effective optimization technique. The above stochastic multi‐objective problem is optimized using various commonly used Physics based Meta‐heuristic techniques such as Simulated Annealing (SA), Harmony Search (HS), Slime Mold Algorithm (SMA), Gravitational Search Algorithm (GSA), Black Hole Optimization (BHO), Sine Cosine Algorithm (SCA), Multiverse optimization (MVO) and Lightning Search Algorithm (LSA). The assessment of the aforementioned physics‐based optimization techniques used on the proposed MG test system is compared using the results. According to the analysis, Black Hole Optimization (BHO) and Lightning Search Algorithm (LSA) both provide greater cost savings overall and for battery charging, respectively. The suggested optimization methods will take the BESS charging/discharging pattern and total cost savings into account.

Heat stress and cold stress have a negative influence on cattle welfare and productivity. There have been some studies investigating the influence of cold stress on cattle, however the emphasis within this review is the influence of heat stress on cattle. The impact of hot weather on cattle is of increasing importance due to the changing global environment. Heat stress is a worldwide phenomenon that is associated with reduced animal productivity and welfare, particularly during the summer months. Animal responses to their thermal environment are extremely varied, however, it is clear that the thermal environment influences the health, productivity, and welfare of cattle. Whilst knowledge continues to be developed, managing livestock to reduce the negative impact of hot climatic conditions remains somewhat challenging. This review provides an overview of the impact of heat stress on production and reproduction in bovines.

AbstractIn the present study, we analyze a field-based seven-year data series of surface mass-balance measurements collected during 2011/12 to 2017/18 on Naradu Glacier, western Himalaya, India. The average annual specific mass balance for the said period is − 0.85 m w.e. with the maximum ablation of − 1.15 m w.e. The analysis shows that the topographic features, south and southeast aspects and slopes between 7 to 24 degrees are the reasons behind the maximum ablation from a particular zone. The causes of surface mass balance variability have been analyzed through multiple linear regression analyses (MLRA) by taking temperature and precipitation as predictors. The MLRA demonstrates that 71% of the observed surface mass balance variance can be explained by temperature and precipitation. It clearly illustrates the importance of summer temperature, which alone explains 64% variance of surface mass balance. The seasonal analysis shows that most of the surface mass balance variability is described by summer temperature and winter precipitation as two predictor variables. Among monthly combinations, surface mass balance variance is best characterized by June temperature and September precipitation.

Abstract The need to have access to accurate short term forecasts is essential in order to anticipate the energy production from intermittent renewable sources, notably wind energy. For hourly and sub-hourly forecasts, benchmarks are based on statistical approaches such as time series based methods or neural networks, which are always tested against persistence. Here we discuss the performances of downscaling approaches using information from Numerical Weather Prediction (NWP) models, rarely used at those time scales, and compare them with the statistical approaches for the wind speed forecasting at hub height. The aim is to determine the added value of Model Output Statistics for sub-hourly forecasts of wind speed, compared to the classical time series based methods. Two downscaling approaches are tested: one using explanatory variables from NWP model outputs only and another which additionally includes local wind speed measurements. Results of both approaches and of the classical time series based methods, tested against persistence on a specific wind farm, are considered. For both hourly and sub-hourly forecasts, adding explanatory variables derived from observations in the downscaling models gives higher improvements over persistence than the benchmark methods and than the downscaling models using only the NWP model outputs.