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Switchgrass (Panicum virgatum L.) is a perennial C4 grass native to North America and successfully adapted to diverse environmental conditions. It offers the potential to reduce soil surface carbon dioxide (CO2) fluxes and mitigate climate change. However, information on how these CO2 fluxes respond to changing climate is still lacking. In this study, CO2 fluxes were monitored continuously from 2011 through 2014 using high frequency measurements from Switchgrass land seeded in 2008 on an experimental site that has been previously used for soybean (Glycine max L.) in South Dakota, USA. DAYCENT, a process-based model, was used to simulate CO2 fluxes. An improved methodology CPTE [Combining Parameter estimation (PEST) with "Trial and Error" method] was used to calibrate DAYCENT. The calibrated DAYCENT model was used for simulating future CO2 emissions based on different climate change scenarios. This study showed that: (i) the measured soil CO2 fluxes from Switchgrass land were higher for 2012 which was a drought year, and these fluxes when simulated using DAYCENT for long-term (2015-2070) provided a pattern of polynomial curve; (ii) the simulated CO2 fluxes provided different patterns with temperature and precipitation changes in a long-term, (iii) the future CO2 fluxes from Switchgrass land under different changing climate scenarios were not significantly different, therefore, it can be concluded that Switchgrass grown for longer durations could reduce changes in CO2 fluxes from soil as a result of temperature and precipitation changes to some extent.

Abstract The reactivity effect of spatial random dispersal of core materials was analyzed in the framework of one-group diffusion theory by the time-eigenvalue method, and in the framework of two-group diffusion theory by both the time- and the reactivity-eigenvalue methods. The analysis based on one-group diffusion theory leads to that spatial random perturbations in the macroscopic cross sections and/or the diffusion coefficient cause the positive reactivity effects under the assumption of zero ensemble-averages of these perturbations. However, the analysis based on two-group diffusion theory shows that the algebraic sign of the reactivity depends upon the reactor types and the buckling. A specific example in which spatial noise causes the negative reactivity was also shown.

In this paper, fault prognosis of aircraft jet engines are considered using computationally intelligent-based methodologies to ensure flight safety and performance. Two different dynamic neural networks namely, the nonlinear autoregressive neural networks with exogenous input (NARX) and the Elman neural networks are developed and designed for this purpose. The proposed dynamic neural networks are designed to capture the dynamics of two main degradations in the jet engine, namely the compressor fouling and the turbine erosion. The health status and condition of the engine is then predicted subject to occurrence of these deteriorations. In both proposed approaches, two scenarios are considered. For each scenario, several neural networks are trained and their performance in predicting multi-flights ahead turbine output temperature are evaluated. Finally, the most suitable neural network for prediction is selected by using the normalized Bayesian information criterion model selection. Simulation results presented demonstrate and illustrate the effective performance of our proposed neural network-based prediction and prognosis strategies.

This paper presents roadmaps for each of the 50 United States to convert their energy infrastructures to 100% wind, water, and sunlight (WWS) for all purposes by 2050.

Abstract CuInS 2 thin films were prepared by sulfurization of electrodeposited Cu–In precursors. Morphological improvement enabled us to fabricate the solar cells using electrodeposited Cu–In precursors. The photovoltaic property of a conversion efficiency of 1.3% was obtained.

Abstract Bioenergy systems are expected to expand over the coming decades due to their potential to address energy security and environmental pollution challenges. Nevertheless, any renewable energy project can only survive if approved environmentally superior to its conventional counterparts. Life cycle assessment (LCA) is an internationally standardized and validated methodology to evaluate and quantify the environmental impacts of bioenergy systems. However, due to its methodological scope, the LCA method measures only the environmental consequences of the target products of energy systems. The LCA approach can neither allocate the environmental impacts at the component level nor measure the environmental impacts of intermediate products. These challenges can be substantially resolved by systematically integrating the LCA approach with the thermodynamically-rooted exergy, offering a powerful environmental sustainability assessment tool known as “exergoenvironmental analysis“. Due to the unique methodological and conceptual characteristics of exergoenvironmental analysis in revealing the possibilities and trends for improvement, it has recently received increasing attention to mitigate the environmental impacts of bioenergy systems. Therefore, this review is aimed to thoroughly summarize and critically discuss the evaluation of sustainability aspects of bioenergy systems based on exergoenvironmental analysis. The pros and cons of using exergoenvironmental analysis in bioenergy research are also outlined to identify possible future directions for the field. Overall, exergoenvironmental analysis can offer more detailed information on the environmental consequences of each flow and component of bioenergy production plants, thereby diagnosing the breakthrough points for additional environmental improvements.

Abstract The United States Department of Defense (DoD) is considering replacing the 2.8 billion gallons of petroleum jet fuel consumed within the continental United States (CONUS) annually with alternative jet fuels to reduce vulnerability to price and supply fluctuations and improve sustainability. We evaluate the feasibility of replacing DoD CONUS jet fuel with alternative jet fuel from domestic feedstocks and assess the cost, greenhouse gas (GHG) emissions, energy balance, land, water, and fertilizer impacts of nine alternative jet fuel pathways. The feedstocks include terrestrial crops (soy, corn, wood, and grass), marine microalgae, and by-products (forestry residue, municipal solid waste, and waste oil) converted by extraction, fermentation, or thermo-chemical conversion, paired with upgrading steps. We found that domestic biomass feedstocks can meet the DoD CONUS jet fuel demand without significantly affecting food supply. Alternative pathways cost more than petroleum jet fuel ($0.78 l-1 and emitting 89 g CO2 equivalent (CO2e) MJ−1), but many lowered GHG emissions. The forestry residue and waste oil pathways yielded the lowest costs ($0.92 and $0.82 l-1, respectively), decreased emissions (23 and 35 g CO2e MJ−1, respectively), and had negligible effects on land, water, and fertilizer resources. Tradeoffs among sustainability metrics are further explored with a sensitivity analysis and by evaluating carbon-cost scenarios.

Abstract The demand for methanol will continue to increase since methanol is an attractive fuel for fuel cells in addition to being an intermediate raw material for hydrogen and dimethyl ether (DME), which are categorized as green energy sources. To produce methanol with a minimum amount of energy, it is necessary to investigate and reconsider a whole methanol synthesis process from energy saving point of view. Recently, we developed an innovative process design technology referred to as self-heat recuperation technology for saving energy. To apply this technology, whole-process heat is recirculated within the process without heat addition leading to large energy savings. In this paper, the feasibility of applying self-heat recuperation technology to the methanol synthesis process is investigated and an innovative process for methanol synthesis is developed from an energy saving point of view. The use of this self-heat recuperation technology in the methanol synthesis process greatly reduces the energy consumption.

Abstract Xylose is a by-product of lignocellulosic biomass processing for production of second-generation biofuels and could be suitable for bioproduct manufacturing. This paper describes an innovative approach that enables the system to achieve high yielding for hydrogen production. The study compared 4 physicochemical pre-treatments performed in an anaerobic mixed culture (acidic, thermal, acidic-thermal and thermal acidic) to achieve an inoculum with a high-efficiency xylose to hydrogen conversion under mesophilic conditions (30 °C). The acidic pre-treatment was the most efficient to select microorganisms able to produce hydrogen and volatile acid from xylose. Kinetics has shown that acidic pre-treatment had a hydrogen/xylose molar yielding factor of 1.57 (molar base) and a hydrogen maximum production rate of 253 mL H2 h−1. Mass balance considered all possible metabolic pathways using xylose as a substrate. Anaerobic degradation of ethanol was the most active pathway for hydrogen production in all experiments, except for the control. Each pre-treatment performed for the original inoculum resulted in different microbiological profiles, but the genus Clostridium was the most abundant in all assays. Acidic pre-treatment stimulated the growth of organisms from the genera Peptostreptococcaceae, Truepera and Kurthia, which could be related to the better results in hydrogen production found in this condition.