Full-duplex overlay cognitive wireless powered communication network using RF energy harvesting

In this paper, a novel full-duplex overlay cognitive wireless powered communication network (FD-OCWPCN) is proposed where a full-duplex (FD) hybrid-access point (H-AP) supports the full access of all battery-free secondary users (SUs). The H-AP broadcasts wireless power to empower the nearby SUs in the downlink (DL) phase while decoding the information transmitted uplink (UL) phase by the SUs, simultaneously. To overcome the self-interference (SI) phenomenon in FD-OCWPCN, the problem of maximizing the system sum-throughput with optimal UL-DL transmission/reception time and H-AP’s transmit power allocation is considered. This problem is non-convex under perfect/imperfect SI cancelation (SIC), so we employ the active interference temperature control and the gradient projection techniques to effectively reduce it into a convex problem. Closed-form expressions for the perfect/imperfect SIC cases are also derived. To assess the performance of the FD-OCWPCN, a comparison with a half-duplex OCWPCN (HD-OCWPCN) is provided. The achievable average sum-throughput for different FD/HD-OCWPCN is compared in the context of the average and peak transmit power at the H-AP, the number of SUs, path loss exponent and fairness metric. The simulation results depict the superiority of the FD-OCWPCN over the HD-OCWPCN for the perfect SIC and the effective imperfect SIC.

     

3D seismic and formation micro-imager (FMI) integrated study to delineate depositional pattern of Abu Madi (Upper Miocene) clastic reservoir rocks in El-Wastani gas field,onshore Nile Delta,Egypt

Ten wells (EW-4, EW-5, EW-6, EW-7, EW-8, EW-9, EW-10, EW-12, EW-13 and EW-15) were interpreted using the composite well logs, data of core analysis, gamma-ray logs, formation micro-imager logs (FMI), and 3D seismic data in SEGY format to understand the stratigraphy of the onshore, Nile Delta, Egypt.The amplitude analysis of 3-D seismic horizon slice of Lower Abu Madi rock unit together with the lithostratigraphic correlation through the study area depending on the gamma-ray log “HSGR” (left to right increasing), and the identification of type of bed geometry, nature of bed contacts, type of the sedimentary structures and the dominant formative paleocurrents by using some available borehole micro-resistivity images (FMI) and core photos. All of these techniques are used together to define the different depositional facies and depositional environment of the Messinian clastics (Lower Abu Madi rock unit), which is considered to be the main reservoir in the El-Wastani gas field, onshore Nile Delta, Egypt.The present study of depositional pattern of the Upper Miocene clastics reservoir (Lower Abu Madi rock unit) revealed that it is represented by high sinuous meandering channels or paleo-valley and three types of fluvial facies were defined; channel fill, channel margin, and floodplain basin.     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

An E-Business Event Stream Mechanism for Improving User Tracing Processes

With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets; namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets.     

Introducing a proper hydrogen liquefaction concept for using wasted heat of thermal power plants-case study: Parand gas power plant

A hydrogen liquefaction concept with an innovative configuration and a capacity of 4 kg·s^-1 (345.6 t·d^-1) is developed. The concept involves an ammonia absorption refrigeration system for the pre-cooling of hydrogen and MR streams from 25 ℃ to -30 ℃. The ammonia absorption refrigeration system is fed by exhaust gases of the Parand gas power plant that are normally dissipated to the environment with a temperature of 546 ℃. The simulation is performed by Aspen HYSYS V9.0, using two separate equations of state for simulating hydrogen and MR streams to gain more accurate results especially for ortho-para conversion. Results show that conversion enthalpy estimated by Aspen HYSYS, fits very well to the experimental data. Determining the important independent variables and composition of MRs are done using trial and error procedure, a functional and straightforward method for complicated systems. The minimum temperature limit in the cooling section is lowered, and an ortho-para converter is implemented in this section. The proposed concept performs well from energy aspects and leads to COP and SEC equal to 0.271 and 4.54 kW·h·kg^-1, respectively. The main advantage of this study is in the low SEC, eliminating the losses of the distribution network, and improving the ability of the hydrogen liquefaction for energy storage in off-peak times.     

Barium promoted manganese oxide catalysts in low-temperature methane catalytic combustion

In this study, a series of BaO-MnO_x mixed oxide catalysts were synthesized by the mechanochemical method and employed in lean methane catalytic combustion (MCC) at low temperatures. The synthesized catalysts were characterized by XRD, BET, TGA, FT-IR, H₂-TPR, O₂-TPD, and FESEM analyses. The results indicated that the 10 wt% BaO-MnO_x catalyst with a BET surface area of 25 m² g^?1 possessed the best catalytic performance. The higher activity of the 10 wt% BaO-MnO_x catalyst was due to the higher ability to supply oxygen through the components during the MCC process. The light-off temperature corresponding to 50% of the methane conversion was about 330 °C, which was about 50 °C lower than the pure MnO_x. Moreover, for the BaO(10)-MnO_x catalyst, the 10 and 90% of methane conversion temperatures were about 305 and 427 °C, respectively. Also, the 10 wt% BaO-MnO_x catalyst exhibited high catalytic stability under dry feed condition at 450 °C for 50 h. Furthermore, the influence of various parameters such as calcination temperature, feed ratio, GHSV, pretreatment condition, and presence of water vapor in the feedstock was studied on the catalytic performance.     

Performance improvement of microbial fuel cell through artificial intelligence

The current work introduces an enhancement in the performance of the microbial fuel cell through estimating the optimal set of controlling parameters. The maximization of both power density (PD) and the percentage of chemical oxygen demand (COD) removal were considered as the enhancement in the cell's performance. Three main parameters in terms of performance as well as commercialization are the system's inputs; the Pt which takes the range of 0.1‐0.5 mg/cm², the degree of sulphonation in sulfonated‐poly‐ether‐ether‐ketone that changes in the range of 20‐80%, and the rate of aeration of cathode which varies between 10 and 150 mL/min. From the experimental dataset, two robust adaptive neuro‐fuzzy inference system models based on the fuzzy logic technique have been constructed. The comparisons between the models' outputs and the experimental data showed well‐fitting in both training and testing datasets. The mean squared errors of the PD model, for testing and whole datasets, were found 2.575 and 0.909 while for the COD model it showed 19.242 and 6.791, respectively. Then, based on the two fuzzy models, a Particle Swarm Optimization algorithm has been used to determine the best parameters that maximize both of the PD and the COD removal of the cell. The optimization process was utilized for single and multi‐object optimization processes. In the single optimization, the resulting maximums of the PD and the COD removal were found 62.844 (mW/m²) and 99.99 (%), respectively. Whereas, in the multi‐object optimization, the values of 61.787 (mW/m²) and 96.21 (%) were reached as the maximums for the PD and COD, respectively. This implies that, in both cases of optimization processes, the adopted methodology can efficiently enhance the microbial fuel cell performances than the previous work.     

Selective molecularly imprinted polymer nanofiber sorbent for the extraction of bisphenol A in a water sample

Novel molecularly imprinted polymer nanofibers (MIP‐NFs) were prepared for the adsorption of bisphenol A (BPA) in a water sample using the sol–gel process and the electrospinning technique. The effects of a number of synthesis parameters on the adsorption efficiency were investigated. The successful removal of BPA from MIP‐NFs was studied using UV–visible spectroscopy. The prepared MIP‐NFs were characterized by Fourier transform infrared, field emission SEM, TEM and energy dispersive X‐ray analysis. The results showed that the required molar ratio of 3‐aminopropyltriethoxysilane (APTES) to BPA was 15:1, which indicates a good performance in the rebinding test. Likewise, the molar ratio of APTES:acid:water was 1:2:9. The nylon 6 polymer solution, with a concentration of 12 wt%, showed a maximum adsorption capacity for BPA due to a decrease in the nanofiber diameter and an increase in the accessible sites. Furthermore, the maximum adsorption capacity of BPA was achieved at pH 7. Concerning the binding of BPA on MIP‐NFs, the experimental data matched well with the pseudo‐second‐order kinetics data and the Sips isotherm model. The saturated binding capacity for MIP‐NFs was predicted to be 115.1 mg g^?1, which was more than twice as high as that for non‐imprinted polymer nanofibers (46.82 mg g^?1). The results obtained in this study confirmed that the prepared MIP‐NFs showed considerable binding specificity for BPA in comparison with similar structural compounds such as phenol, naphthol and Naphthol AS, in aqueous solution. The binding capacity of MIP‐NFs remained almost constant after five cycles of reuse. The real sample analysis indicated that MIP‐NFs could be utilized as a useful sorbent material for the extraction of BPA from a water sample.     

Novel electrospun polymer electrolytes incorporated with Keggin‐type hetero polyoxometalate fillers as solvent‐free electrolytes for lithium ion batteries

In this study, solvent‐free nanofibrous electrolytes were fabricated through an electrospinning method. Polyethylene oxide (PEO), lithium perchlorate and ethylene carbonate were used as polymer matrix, salt and plasticizer respectively in the electrolyte structures. Keggin‐type hetero polyoxometalate (Cu‐POM@Ru‐rGO, Ni‐POM@Ru‐rGO and Co‐POM@Ru‐rGO (POM, polyoxometalate; rGO, reduced graphene oxide)) nanoparticles were synthesized and inserted into the PEO‐based nanofibrous electrolytes. TEM and SEM analyses were carried out for further evaluation of the synthesized filler structures and the electrospun nanofibre morphologies. The fractions of free ions and crystalline phases of the as‐spun electrolytes were estimated by obtaining Fourier transform infrared and XRD spectra, respectively. The results showed a significant improvement in the ionic conductivity of the nanofibrous electrolytes by increasing filler concentrations. The highest ionic conductivity of 0.28 mS cm^?1 was obtained by the introduction of 0.49 wt% Co‐POM@Ru‐rGO into the electrospun electrolyte at ambient temperature. Compared with solution‐cast polymeric electrolytes, the electrospun electrolytes present superior ionic conductivity. Moreover, the cycle stability of the as‐spun electrolytes was clearly improved by the addition of fillers. Furthermore, the mechanical strength was enhanced with the insertion of 0.07 wt% fillers to the electrospun electrolytes. The results implied that the prepared nanofibres are good candidates as solvent‐free electrolytes for lithium ion batteries. © 2020 Society of Chemical Industry