•Choosing a suitable value for the power and the energy capacity of a flow battery in the hybrid system.•Consider the effect of number of solar panels on the operation of the hybrid system.•Consider ...the effect of the power of fuel cell on the operation of the hybrid system.•Consider the effect of the current density of the fuel cell on the operation of the hybrid system.
Hybrid power generation systems are a promised solution for the recent environmental problems. Energy storage systems are an inseparable part of the hybrid systems and flow batteries are one of the newest storage systems. One of the advantages of the flow batteries is the uncoupled power and energy capacity. But this study shows that in combination with solar panels and solid oxide fuel cell (SOFC), and due to economic, environmental, and reliability point of view, the power and the energy capacity of the flow battery must be chosen relatively to obtain a suitable solution. In addition the effect of the number of solar panels, the power of SOFC and the current density of SOFC on the economic, environmental and reliability operation of the hybrid system is considered. And suitable values for these parameters are recommended.
Abstract
Recurrent neural networks (RNNs) are a class of artificial neural networks capable of learning complicated nonlinear relationships and functions from a set of data. Catchment scale daily ...rainfall–runoff relationship is a nonlinear and sequential process that can potentially benefit from these intelligent algorithms. However, RNNs are perceived as being difficult to parameterize, thus translating into significant epistemic (lack of knowledge about a physical system) and aleatory (inherent randomness in a physical system) uncertainties in modeling. The current study investigates a variational Bayesian dropout (or Monte Carlo dropout (MC-dropout)) as a diagnostic approach to the RNNs evaluation that is able to learn a mapping function and account for data and model uncertainty. MC-dropout uncertainty technique is coupled with three different RNN networks, i.e. vanilla RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) to approximate Bayesian inference in a deep Gaussian noise process and quantify both epistemic and aleatory uncertainties in daily rainfall–runoff simulation across a mixed urban and rural coastal catchment in North Carolina, USA. The variational Bayesian outcomes were then compared with the observed data as well as with a well-known Sacramento soil moisture accounting (SAC-SMA) model simulation results. Analysis suggested a considerable improvement in predictive log-likelihood using the MC-dropout technique with an inherent input data Gaussian noise term applied to the RNN layers to implicitly mitigate overfitting and simulate daily streamflow records. Our experiments on the three different RNN models across a broad range of simulation strategies demonstrated the superiority of LSTM and GRU approaches relative to the SAC-SMA conceptual hydrologic model.
We study the generation of tunable gain without inversion in semiconductor quantum dots using plasmonic effects. For this we investigate the impact of localized surface plasmons on coherent nonlinear ...exciton effects in a quantum dot when it is located in the vicinity of a metallic nanoparticle. It is shown that when such a system is exposed to a laser field and the distance between the quantum dot and the metallic nanoparticle is reduced the initial impact of plasmons is enhancement of the ac-Stark shift in the quantum dot. When this distance is reduced beyond a critical value, the results show abrupt formation of a significant of amount of gain without inversion in the quantum dot. We show that such a 'molecular' gain is associated with the plasmonic metaresonance (PMR) formed via combined effects of laser-induced coherence in the quantum dot and plasmons.
•Response of hydrological drought to large-scale climate index was revealed.•The drought character was greatly influenced by the reservoir operation.•The delayed effect of hydrological drought on ...vegetation cover was investigated.
The Xijiang River is known as the Golden Watercourse because of its role in the development of the Pearl River Delta Regional Economic System in China, which was made possible by its abundant water resources. At present, the hydrological regime of the Xijiang River has now become complicated, the water shortages and successive droughts pose a threat to regional economic development. However, the complexity of hydroclimatological processes with emphasizes on drought has not been comprehended. In order to effectively predict and develop the adaptation strategies to cope with the water scarcity damage caused by hydrological droughts, it is essential to thoroughly analyze the relationship between hydrological droughts and pre/post-dependent hydroclimatological factors. To accomplish this, the extreme-point symmetric mode decomposition method (ESMD) was utilized to reveal the periodic variation in hydrological droughts that is characterized by the Standardized Drought Index (SDI). In addition, the cross-wavelet transform method was applied to investigate the correlation between large-scale climate indices and drought. The results showed that hydrological drought had the most significant response to spring ENSO (El Niño-Southern Oscillation), and the response lags in sub-basins were mostly 8–9months except that in Yujiang River were mainly 5 or 8months. Signal reservoir operation in the Yujiang River reduced drought severity by 52–95.8% from January to April over the 2003–2014 time period. Similarly, the cascade reservoir alleviated winter and spring droughts in the Hongshuihe River Basin. However, autumn drought was aggravated with severity increased by 41.9% in September and by 160.9% in October, so that the land surface models without considering human intervention must be used with caution in the hydrological simulation. The response lags of the VCI (Vegetation Condition Index) to hydrological drought were different in the sub-basins. The response lag for the Hongshuihe, Yujiang, and Liujiang River Basins were mostly 0–4months, 0–1months, and 2–3months, respectively, but there was no obvious regular change pattern in the Guijiang River Basin.
We study the inhibition of optical excitation and enhancement of Rabi flopping and frequency in semiconductor quantum dots via plasmonic effects. This is done by demonstrating that the interaction of ...a quantum dot with a laser field in the vicinity of a metallic nanoparticle can be described in terms of optical Bloch equations with a plasmically normalized Rabi frequency. We show that in the weak-field regime plasmonic effects can suppress the interband transitions, inhibiting exciton generation. In the strong-field regime these effects delay the response of the quantum dot to the laser field and enhance Rabi flopping. We relate these to the conversion of Rabi frequency from a real quantity into a complex and strongly frequency-dependent quantity as plasmonic effects become significant. We show that, within the strong-field regime, in the wavelength range where real and imaginary parts of this frequency reach their maxima, a strongly frequency-dependent enhancement of carrier excitation can happen.
With the rapid development of the Internet of Things (IoT) and Big Data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood ...emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big Data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management. FAIS, a national scale prototype, combines flood peak rates and river level information with geotagged tweets to identify a dynamic set of at-risk locations to flooding. The prototype was successfully tested in real-time during Hurricane Dorian flooding as well as for historical event (Hurricanes Florence) across the Carolinas, USA where the storm made extensive disruption to infrastructure and communities.
•FAIS is a national scale Big data gathering and engineering pipeline.•The pipeline integrates crowd intelligence and machine learning to identify a dynamic set of at-risk areas to flooding.•FAIS tested operationally during Hurricane Dorian Flooding in the Carolinas.
Murexide was chemically bonded to silica gel surface immobilized 3-aminopropyl trimethoxysilane (APMS) to produce the new sorbent. A solid phase extraction method using the new sorbent has been ...developed to separate and concentrate trace amount of uranium (VI) from aqueous samples for the measurement by spectrophotometry method using Arsenazo III reagent. The influences of some analytical parameters on the quantitative recoveries of the analyte were investigated both in batch and column methods. Quantitative recovery of U(VI) was achieved by stripping with 0.1
mol
L
−1 HCl. The maximum sorption capacity of the modified silica gel was 1.13
mmol
g
−1 U(VI). A high preconcentration factor value of 400 with a lower limit of detection of 1
μg
L
−1 was obtained for U(VI). The practical applicability of the developed sorbent was examined using synthetic and real samples such as sea/ground water samples.
Intraoperative Thermal Imaging (ITI) is a new minimally invasive diagnosis technique that can potentially locate margins of brain tumor in order to achieve maximum tumor resection with least ...morbidity. This study introduces a new approach to ITI based on artificial tactile sensing (ATS) technology in conjunction with artificial neural networks (ANN) and feasibility and applicability of this method in diagnosis and localization of brain tumors is investigated. In order to analyze validity and reliability of the proposed method, two simulations were performed. (i) An in vitro experimental setup was designed and fabricated using a resistance heater embedded in agar tissue phantom in order to simulate heat generation by a tumor in the brain tissue; and (ii) A case report patient with parafalcine meningioma was presented to simulate ITI in the neurosurgical procedure. In the case report, both brain and tumor geometries were constructed from MRI data and tumor temperature and depth of location were estimated. For experimental tests, a novel assisted surgery robot was developed to palpate the tissue phantom surface to measure temperature variations and ANN was trained to estimate the simulated tumor's power and depth. Results affirm that ITI based ATS is a non-invasive method which can be useful to detect, localize and characterize brain tumors.
Amendments can control the runoff and soil loss by protecting the soil surface. However, scale effects on runoff and soil loss control have not been considered yet. The present study has been ...formulated to determine the efficiency of two plot sizes of 6 and 0.25 m super(2) covered by 0.5 kg m super(-2) of straw mulch with regard to changing the time to runoff, runoff coefficient, sediment concentration and soil loss under laboratory conditions. The study used a sandyloam soil taken from summer rangeland, Alborz Mountains, northern Iran, and was conducted under simulated rainfall intensities of 50 and 90 mm h super(-1) and in three replicates. The maximum change in soil loss at the intensities of both 50 and 90 mm h super(-1) occurred in the 0.25 m super(2) plot, with 100%, whereas in the 6 m super(2) plot, decreasing rates of soil loss for the intensities of both 50 and 90 mm h super(-1) were 46.74 and 63.24%, respectively.
•Revision of Zn and Mn hydrometallurgical recovery processes from Zn-based batteries.•Revision of literature between 2007 and 2019.•Studies dealing with leaching and/or metals separation are ...critically reviewed.•Alkaline-, complexation-, acid- and reductive-acid-assisted leaching strategies.•Metals purification by solvent extraction and chemical precipitation.
This review paper aims to present and analyse data from the most recent literature (between 2007 and 2019) published on the topic of manganese (Mn) and zinc (Zn) recovery from zinc-based spent batteries through hydrometallurgical methods. In a first attempt, a detailed comparative assessment of the metals leaching performance (as well as the experimental variables that influence its performance) reported in the various studies with strong acid or bases, potentially supplemented by complexing or reducing agents, as well as the reactions involved, are reviewed and discussed. All data point out that the use of a reductant is needed to fully solubilize Mn from spent batteries during the leaching process. Comparison of the data seem to indicate that most reductants have similar performance and, therefore, the choice of a reductant should be focused on low cost or even waste materials. In a second attempt, the separative processes mostly described in the literature to recover Mn and Zn from leachates are reviewed emphasizing the strengths and weaknesses of each technique. Solvent extraction is the most widely tested process for this aim. A thorough comparison of existing data indicates that, in general, neutral extractants have higher potential for selective separation of Zn and Mn. Furthermore, although chemical precipitation is a simple process, low pure final metal hydroxide products are expected to be achieved when alkaline precipitation is implemented comparatively to the Mn oxidative precipitation where Mn can be recovered selectively as a solid of manganese (IV) oxide.
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