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Torrefaction is considered as a method for producing biofuels with improved characteristics compared to those of the “raw” biomass (higher calorific value, moisture resistance, better grindability). The torrefaction process is an endothermic process that is usually carried out in a gaseous atmosphere in the absence of oxygen. To reduce the required heat input, it is proposed to employ the oxidative torrefaction and conduct the process in a fluidized bed agitated with flue gases containing less than 6% oxygen. Preliminary studies of the oxidative torrefaction of sunflower husks, including thermogravimetric analysis of the treated material, have shown that the heat treatment time for the biomass should be at least 5 min. A fluidized bed is a reactor with ideal mixing of the treated material where uniform treatment of raw material particles cannot generally be attained. To overcome this disadvantage of the fluidization technique and achieve the required residence time for biomass in a fluidized bed during a continuous torrefaction process, it was proposed to equip a torrefaction reactor with a series of vertical baffles spaced at 50 mm. These baffles induce a loop-like flow of the processed biomass from the inlet to the outlet of the reactor. To investigate the residence time for husk particles in the reactor, a tracer, which was colored to husk particles' color with a water-soluble dye which did not change the weight and size of the particles, was injected into the bed of uncolored particles. Tracer samples were taken every 30 s at the outlet of the reactor and were analyzed using a special procedure to determine the fraction of colored particles in each sample. This enabled us to gauge the time during which the colored particles injected into the fluidized bed reached the point of their discharge from the bed. Studies performed in a “cold” model of the reactor showed that a series of vertical baffles in the bed can provide the required residence time for biomass in a reactor including commercial reactors. Plates can provide the necessary biomass residence time in the reactor.

The moisture origins and associated physical mechanisms for tornadoes of various climatic regions of the United States were investigated. The NOAA Air Resources Laboratory HYSPLIT model and a moisture attribution algorithm were used in conjunction with statistical analyses to explore these relationships on a climate scale (1981–2017). It was found that moisture sources of United States tornadic convection exhibit distinctive regionality. For example, moisture contributions to east coast tornadoes primarily emanate from the Atlantic Ocean as opposed to the Gulf of Mexico. Moisture sources of a class of non-significant severe thunderstorms also show regionality based on the location of storm occurrence with latitudinal differences to that of tornadic storms. Moisture increases for tornadic and non-significant severe storms were shown to be closely related to the respective air parcel's temperature, with underlying sea surface temperatures playing a less important role. Long-term trends of moisture uptake and horizontal advection were also explored using the Mann-Kendall test and linear regressions. Both statistical analyses demonstrate that the magnitude of advective fluxes and the rate of moisture increases have been rising since the 1980s, which can increase the complexity of forecasting downstream convection. The potential ramifications of these trends on storm development and prediction are discussed to conclude the article.

The European Geosciences Union (EGU) brings together geoscientists from all over the world covering all disciplines of the Earth, planetary and space sciences. This geoscientific interdisciplinarity is needed to tackle the challenges of the future. One major challenge for humankind is to provide adequate and reliable supplies of affordable energy and other resources in efficient and environmentally sustainable ways. This Energy Procedia issue provides an overview of the contributions of the Division on Energy, Resources & the Environment (ERE) at the EGU General Assembly 2017.

AbstractNowadays, researchers are focusing on the methods of using recycled industrial/agricultural wastes as raw materials, which are not only economically but also environmentally useful. In the recent years, many scholars have conducted research on the rational use of rice husks (RHs) and found that RHs are rich in inorganic/organic components and used in combination with the certain building materials to produce new green composite building materials. This paper studies the method of using RH processed products and the sodium silicate solution (liquid glass) made from RH ash to produce cheap wood substitute composite materials, which can be improved the insulation performance of the wall greatly. Obtained results are determined by comparing with the properties of standard polymer composite materials such as particleboards, chipboards, and medium‐density fiberboards (MDF). The significance of the work is pointing out by solving the problem of recycling large‐tonnage agricultural waste and reproduce products with the consumer's value.

Abstract This study presents and evaluates the feasibility of a novel hybridization of modified Kalina cycle, reverse osmosis desalination, and low-temperature water electrolysis utilizing geothermal energy to yield power, distilled water, and hydrogen, respectively. The scientific impact of the current work has been improved considering the features of Sabalan flash-binary geothermal wells in Iran as a real model through a case study. In addition to designing a novel setup, the smart use of multi-heat recovery technique, modifying the base cycle, and utilizing a part of generated distilled water to produce hydrogen by the electrolyzer are the other structural originalities, distinguishing the current work from the previous studies. The suggested system is scrutinized via a parametric study and optimized based on a genetic algorithm. The parametric study demonstrated that the highest sensitivity of varying the performance criteria of the whole system is attributed to the change in flash tank pressure. Moreover, the multi-objective optimization led to achieving the exergy efficiency and trigeneration gain output ratio as 51.3% and 1.7 for the system, respectively. Furthermore, the system was able to produce 4795 kW of power, 5.3 kg/h of hydrogen, and 19.9 kg/s of distilled water.

Innovative control method is prosed for the RED WoLF hybrid storage system. The technology is aimed for residential dwellings and allows to reduce the load from the electrical grid during time with high CO emissions. The RED WoLF system consists of a battery, water cylinder, PV array and storage heaters. This technology allows the grid energy to be stored at the “greenest” time, in order to accommodate needs of dwellings with the aid of AI. The original RED WoLF algorithm is considerably improved, following modified progressive threshold approach up to additional 14% savings of CO could be obtained. Intriguingly, savings are only slightly lower than global possible mathematical minimum, for the system barred of predictions errors. However, the computation time of the proposed control method is lower by a few orders of magnitudes, with comparison to standard optimisation techniques. Furthermore, the investigation on 11 months period was performed in order find out if there is significant difference between following a time of use tariff or an environmental signal. Results, suggest that the differences are minor in both cases following any signal improves the used energy quality. Although, the price signal has been affected slightly more to the choice of a target. Finally, the average system composition with 2 kWh battery and 4 kW PV array provides reduction by 55%–60% of both CO emissions and the bill. Such achievement could potentially lead to smooth substitution of carbon intensive residential systems with gas and oil heaters.

Closed-loop supply chain networks are gaining research popularity due to environmental, economic and social concerns. Such networks are primarily designed to overcome carbon footprints and to retrieve end of life products from customers. This study considers a multi echelon closed-loop supply chain in the presence of machine disruption. A multi-objective model is presented to optimize the total cost, the total time and emissions in a closed-loop supply chain network. The aim is to analyze the trade-off between the objectives of cost, time, and emissions and how these decisions are impacted by the selection of different available machines. A number of solution approaches are tested on a case study from the tire industry. The results suggest the improved performance of the hybrid heuristic and the importance of controlling disruption in a closed-loop supply chain network. Furthermore, there is a trade-off between the different objective functions which can help the decision maker to choose a particular solution according to the preference of an organization. Finally, conclusion and future research avenues are provided.

Is it true that a nuclear technology approach to generate electric energy offers a clean, safe, reliable and affordable, i.e., sustainable option? In principle yes, however a technology impact on the environment strongly depends on the actual implementation bearing residual risks due to technical failures, human factors, or natural catastrophes. A full response is thus difficult and can be given first when the wicked multi-disciplinary issues get well formulated and “resolved”. These problems are lying at the interface between: the necessary R&D effort, the industrial deployment and the technology impact in view of the environmental sustainability including the management of produced hazardous waste. As such, this problem is clearly of multi-dimensional nature. This enormous complexity indicates that just a description of the problem might cause a dilemma. The paper proposes a novel holistic approach applying Multi-Criteria Decision Analysis to assess the potential of nuclear energy systems with respect to a sustainable performance. It shows how to establish a multi-level criteria structure tree and examines the trading-off techniques for scoring and ranking of options. The presented framework allows multi-criteria and multi-group treatment. The methodology can be applied to support any pre-decisional process launched in a country to find the best nuclear and/or non-nuclear option according to national preferences and priorities. The approach addresses major aspects of the environmental footprint of nuclear energy systems. As a case study, advanced nuclear fuel cycles are analyzed, which were previously investigated by the Nuclear Energy Agency (NEA/OECD) expert group WASTEMAN. Sustainability facets of waste management, resource utilization and economics are in focus.