bluuebunny/crossref_metadata_2025 huggingface dataset:
CNT and H2 Production during CH4 Decomposition over Ni/CeZrO2. II. Catalyst Performance and Its Regeneration in a Fluidized Bed
Authors: Łamacz Agata , Łabojko Grzegorz
Published: March 2019

In this work, a ceria-zirconia supported nickel catalyst (Ni/CeZrO2) was for the first time used in a fluidized bed reactor in order to obtain carbon nanotubes (CNTs) and H2 in the reaction of the decomposition of CH4. The same catalyst was afterward regenerated with H2O, which was accompanied with the production of H2. The impact of catalyst granulation, temperature, and gas hourly space velocity (GHSV) on the amount and type of carbon deposits was determined using thermogravimetric analysis (TGA) and scanning and transmission electron microscopy (SEM and TEM). The presence of randomly oriented and curved CNTs with an outer diameter of up to 64 nm was proved. The Ni/CeZrO2 particles were loosely covered with CNTs, freely dispersed over CNTs, and strongly attached to the external CNT walls. TEM proved the presence of a Ni/CeZrO2@CNT hybrid material that can be further used as catalyst, e.g., in WGS or DRM reactions. The impact of GHSV on hydrogen production during catalyst regeneration was determined. The catalyst was subjected to cyclic tests of CH4 decomposition and regeneration. According to the obtained results, Ni/CeZrO2 can be used in CH4 conversion to CNTs and H2 (instead of CH4 combustion), e.g., in the vicinity of installations that require methane utilization.
Diammonium Hydrogenphosphate Treatment on Dolostone: The Role of Mg in the Crystallization Process
Authors: Possenti Elena , Conti Claudia , Gatta G. Diego , Realini Marco , Colombo Chiara
Published: March 2019

The diammonium hydrogenphosphate (DAP, (NH4)2HPO4) reaction with calcite has been extensively investigated. The availability of free calcium ions in the reaction environment has been acknowledged as a crucial factor in the crystallization of calcium phosphates with a high (hydroxyapatite, Ca/P 1.67) or low Ca/P molar ratio (dicalcium phosphate dihydrate, Ca/P 1.00; octacalcium phosphate, Ca/P 1.33). On the contrary, no data are available on the DAP interaction at room temperature with dolomite in terms of reaction mechanism and composition of the reaction products. Here, a multi-analytical approach based on scanning electron microscopy (SEM) coupled with energy dispersive X-ray spectrometry (EDS) and X-ray powder diffraction before and after heating treatments is proposed to explore how the formation of calcium phosphates occur on Mg-enriched substrates and if the presence of magnesium ions during the reaction influences the crystallization process of calcium phosphates. The DAP reaction with polycrystalline dolomite gives rise to the formation of struvite and of poorly crystalline hydroxyapatite. Calcium and magnesium ions mutually interfered in the crystallization of magnesium and calcium phosphates, respectively, whose effects influenced the properties (size, micro-morphology, composition and crystallinity) of the newly-formed phases.
Synergistic Effect of Mica, Glass Frit, and Melamine Cyanurate for Improving Fire Resistance of Styrene-Butadiene Rubber Composites Destined for Ceramizable Coatings
Authors: Imiela Mateusz , Anyszka Rafał , Bieliński Dariusz M. , Lipińska Magdalena , Rybiński Przemysław , Syrek Bartłomiej
Published: March 2019

Synergistic effects of different fillers are widely utilized in polymer technology. The combination of various types of fillers is used to improve various properties of polymer composites. In this paper, a synergistic effect of flame retardants was tested to improve the performance of ceramizable composites. The composites were based of styrene-butadiene rubber (SBR) used as polymer matrix. Three different types of flame retardants were tested for synergistic effect: Mica (phlogopite) high aspect-ratio platelets, along with low softening point temperature glass frit (featuring ceramization effect), and melamine cyanurate, a commonly used flame retardant promoting carbonaceous char. In order to characterize the properties of the composites, combustibility, thermal stability, viscoelastic properties, micromorphology, and mechanical properties were tested before and after ceramization. The results obtained show that the synergistic effect of ceramization promoting fillers and melamine cyanurate was especially visible with respect to the flame retardant properties resulting in a significant improvement of fire resistance of the composites.
Property Improvement of GaAs Surface by 1-Octadecanethiol Passivation
Authors: Zhou Lu , Chu Xuefeng , Chi Yaodan , Yang Xiaotian
Published: March 2019

In this study the effects of 1-Octadecanethiol (ODT, 1-CH3 [CH2]17SH) passivation on GaAs (100) surface and GaAs/Al2O3 MOS capacitors are investigated. The results measured by X-ray photoelectric spectroscopy (XPS), Raman spectroscopy and scan electron microscopy (SEM) show that the ODT passivation can obviously suppress the formation of As-O bonds and Ga-O bonds on the GaAs surface and produce good surface morphology at the same time, and especially provide better protection against environmental degradation for at least 24 h. The passivation time is optimized by photoluminescence (PL), and the maximum enhancement of PL intensity was 116%. Finally, electrical property of a lower leakage current was measured using the metal-oxide-semiconductor capacitor (MOSCAP) method. The results confirm the effectiveness of ODT passivation on GaAs (100) surface.
The Malocclusion Impact Questionnaire (MIQ): Cross-Sectional Validation in a Group of Young People Seeking Orthodontic Treatment in New Zealand
Authors: Benson Philip E. , Gilchrist Fiona , Farella Mauro
Published: March 2019

The aim of the study was to test the validity of the Malocclusion Impact Questionnaire (MIQ) in a NZ sample and to evaluate possible cross-cultural differences in MIQ data between a NZ and a UK sample. A cross-sectional, non-random sample of young people, aged 10–16 years, attending their first appointment at the orthodontic clinic of New Zealand’s National Centre for Dentistry were asked to complete a questionnaire. This consisted of the 17 item MIQ, the short form CPQ11-14-ISF16 and two global questions. Some basic demographic and clinical data were collected. Sixty-six participants completed the questionnaire; however, the data for 2 were excluded due to the number of incomplete responses. MIQ was found to have excellent internal consistency (Cronbach’s alpha 0.924), good construct validity (Spearman’s rho, 0.661 global Q1 ‘Overall, how much do your teeth bother you?’; 0.583 global Q2 ‘Overall, how much do your teeth affect your life?’). MIQ also demonstrated good criterion validity with CPQ11-14-ISF16 (Pearson rho, 0.625). The Rasch analysis confirmed that the questionnaire performed similarly and there was no differential item functioning between the two populations. The main differences between the samples were that the young people in NZ were less concerned about their malocclusion and reported lower item-impact scores compared with the young people in the UK.
A Novel Construction of Efficient Substitution-Boxes Using Cubic Fractional Transformation
Authors: Zahid Amjad Hussain , Arshad Muhammad Junaid , Ahmad Musheer
Published: March 2019

A symmetric block cipher employing a substitution–permutation duo is an effective technique for the provision of information security. For substitution, modern block ciphers use one or more substitution boxes (S-Boxes). Certain criteria and design principles are fulfilled and followed for the construction of a good S-Box. In this paper, an innovative technique to construct substitution-boxes using our cubic fractional transformation (CFT) is presented. The cryptographic strength of the proposed S-box is critically evaluated against the state of the art performance criteria of strong S-boxes, including bijection, nonlinearity, bit independence criterion, strict avalanche effect, and linear and differential approximation probabilities. The performance results of the proposed S-Box are compared with recently investigated S-Boxes to prove its cryptographic strength. The simulation and comparison analyses validate that the proposed S-Box construction method has adequate efficacy to generate efficient candidate S-Boxes for usage in block ciphers.
Breaking an Image Encryption Algorithm Based on DNA Encoding and Spatiotemporal Chaos
Authors: Wen Heping , Yu Simin , Lü Jinhu
Published: March 2019

Recently, an image encryption algorithm based on DNA encoding and spatiotemporal chaos (IEA-DESC) was proposed. In IEA-DESC, pixel diffusion, DNA encoding, DNA-base permutation and DNA decoding are performed successively to generate cipher-images from the plain-images. Some security analyses and simulation results are given to prove that it can withstand various common attacks. However, in this paper, it is found that IEA-DESC has some inherent security defects as follows: (1) the pixel diffusion is invalid for attackers from the perspective of cryptanalysis; (2) the combination of DNA encoding and DNA decoding is equivalent to bitwise complement; (3) the DNA-base permutation is actually a fixed position shuffling operation for quaternary elements, which has been proved to be insecure. In summary, IEA-DESC is essentially a combination of a fixed DNA-base position permutation and bitwise complement. Therefore, IEA-DESC can be equivalently represented as simplified form, and its security solely depends on the equivalent secret key. So the equivalent secret key of IEA-DESC can be recovered using chosen-plaintext attack and chosen-ciphertext attack, respectively. Theoretical analysis and experimental results show that the two attack methods are both effective and efficient.
Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns
Authors: Shekaramiz Mohammad , Moon Todd , Gunther Jacob
Published: March 2019

We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of clustered sparsity pattern, or clumpy behavior, along each column, as well as joint sparsity across the columns. In this paper, we propose a new sparse Bayesian learning (SBL) method that incorporates a total variation-like prior as a measure of the overall clustering pattern in the solution. We further incorporate a parameter in this prior to account for the emphasis on the amount of clumpiness in the supports of the solution to improve the recovery performance of sparse signals with an unknown clustering pattern. This parameter does not exist in the other existing algorithms and is learned via our hierarchical SBL algorithm. While the proposed algorithm is constructed for the MMVs, it can also be applied to the single measurement vector (SMV) problems. Simulation results show the effectiveness of our algorithm compared to other algorithms for both SMV and MMVs.
Temporal Variation and Statistical Assessment of the b Value off the Pacific Coast of Tokachi, Hokkaido, Japan
Authors: Xie Weiyun , Hattori Katsumi , Han Peng
Published: March 2019

The Gutenberg-Richter Law describes the frequency-magnitude distribution of earthquakes. A number of studies have shown that the slope (b value) of the relationship between frequency and magnitude decreased before large earthquakes. In this paper, we investigate the temporal variation of the b value off the Pacific coast of Tokachi, Hokkaido, Japan, during 1990–2014. The magnitude of completeness (Mc) in the catalog is evaluated by combining the maximum curvature (MAXC) technique and the bootstrap approach. Then, the b value, and its uncertainty, is computed by using the maximum likelihood estimation. The Akaike Information Criterion (AIC) with the bootstrap approach is introduced to statistically assess the temporal variation of b values and quantify the significance level. The results show a decrease in trends of the b value prior to two large earthquakes (26 September 2003 (M8.0) and 11 September 2008 (M7.1)) in the analyzed area. In addition, the decrease of b values shows certain statistical significance three months before the 2003 Earthquake off the Pacific coast of Tokachi (M8.0). It is concluded that the b value with statistical assessment may contain potential information for future large earthquake preparation off the Pacific coast of Tokachi, Hokkaido, Japan.
Analysis of the Sensitivity of Algorithms for Assessing the Harmful Information Indicators in the Interests of Cyber-Physical Security
Authors: Kotenko Igor , Parashchuk Igor
Published: March 2019

The secure functioning of cyber-physical systems depends on the presence and amount of harmful (unwanted and malicious) information in its digital network content. The functioning of cyber-physical systems is carried out in non-stationary conditions and in conditions of continuous exposures. This leads to the uncertainty of indicators (parameters, features) of harmful information that must be assessed in the analytical processing of digital network content. The paper proposes an approach to analyse the sensitivity of algorithms for estimating the status of indicators of harmful information observed in noise. This approach allows one to consider possible errors in the estimation accuracy. It gives the possibility to identify the allowable range of changes in the parameters of the digital network content of cyber-physical systems, within which the requirements for the assessment reliability are met. This, in turn, makes a significant contribution to the effectiveness of harmful information detection and counteraction against it. Accounting for a priori uncertainty of the indicators under various influences is advisable to carry out on the basis of expressions for the sensitivity coefficients (functions) described in the paper.
Design and Implementation of Decoupled IoT Application Store: A Novel Prototype for Virtual Objects Sharing and Discovery
Authors: Ahmad Shabir , Mehmood Faisal , Mehmood Asif , Kim DoHyeun
Published: March 2019

The internet of things (IoT) has been growing towards the conception of a cyber-physical space in which physical things could be discovered, examined, activated, interlinked, and updated, for the realization of possible interactions in both virtual and physical space. The fundamental idea of IoT is the concept of a virtual object, which is regarded as the digital equivalent of a physical entity. Currently, in every IoT platform, the use of virtual objects has become a vital component. These virtual objects form building blocks of IoT applications, support the discovery of services, nurture the production of complex applications, foster the objects’ energy and power management efficiency, as well as address heterogeneity and scalability issues. Idea sharing and reusing are widesplread, and different solutions are built for this purpose to share applications or their components with similar domains to avoid duplication of efforts. In this paper, we design and implement a cloud-centric IoT application store that serves a purpose for hosting virtual objects of different IoT domains so that technology tinkerers can consume them and integrate them to build IoT applications. The proposed system is different than existing IoT marketplaces in the sense that they provide full-fledged IoT applications which include software and hardware that users can plug and play. The application store is aimed to be decoupled and can expose virtual objects of different IoT domains so that similar areas can use them with little or no modification. Moreover, it is also aimed to be modular, scalable, secure and support heterogeneity, which are considered vital attributes of IoT applications. An IoT testbed client application is developed to reuse some of the virtual objects from the proposed application store and to share specialized virtual objects to the proposed system for the use of other clients applications with similar goals. The performance and load of the platform are tested and found to be within an acceptable response time for up to 40 simultaneous users.
An Efficient 3-D Ray Tracing Method: Prediction of Indoor Radio Propagation at 28 GHz in 5G Network
Authors: Hossain Ferdous , Geok Tan Kim , Rahman Tharek Abd , Hindia Mohammad Nour , Dimyati Kaharudin , Ahmed Sharif , Tso Chih P. , Abd Rahman Noor Ziela
Published: March 2019

Millimeter wave technology will be dominating the fifth-generation networks due to the clear advantage of higher frequency bands and hence wider spectrum. In this paper, the indoor radio wave propagation at 28 GHz is studied by developing an efficient three-dimensional ray tracing (ETRT) method. The simulation software based on the ETRT model has been verified by measurement data. The received signal strength indication and path loss have shown significant agreement between simulation and measurement. Compared with the conventional shooting bouncing ray tracing method, the proposed ETRT method has better agreement with measurement data.
Coherent Integration for Radar High-Speed Maneuvering Target Based on Frequency-Domain Second-Order Phase Difference
Authors: Jin Ke , Lai Tao , Wang Yubing , Li Gongquan , Zhao Yongjun
Published: March 2019

In recent years, target detection has drawn increasing attention in the field of radar signal processing. In this paper, we address the problem of coherent integration for detecting high-speed maneuvering targets, involving range migration (RM), quadratic RM (QRM), and Doppler frequency migration (DFM) within the coherent processing interval. We propose a novel coherent integration algorithm based on the frequency-domain second-order phase difference (FD-SoPD) approach. First, we use the FD-SoPD operation to reduce the signal from three to two dimensions and simultaneously eliminate the effects of QRM and DFM, which leads to signal-to-noise ratio improvement in the velocity-acceleration domain. Next, we estimate the target motion parameters from the peak position without the need for a search procedure. We show that this algorithm can be easily implemented by using complex multiplications combined with fast Fourier transform (FFT) and inverse FFT (IFFT) operations. We perform comparisons with several representative algorithms and show that the proposed technique can be used to achieve a good trade-off between computational complexity and detection performance. We present both simulated and experimental data to demonstrate the effectiveness of the proposed method.
Application of Lagrange Relaxation to Decentralized Optimization of Dispatching a Charging Station for Electric Vehicles
Authors: Cheng Shan , Feng Yichen , Wang Xianning
Published: March 2019

To improve the computation efficiency of optimally dispatching large-scale cluster electric vehicles (EVs) and to enhance the profit of a charging station (CS) for EVs, this study investigates the optimal dispatch of the CS based on a decentralized optimization method and a time-of-use (TOU) price strategy. With the application of the Lagrange relaxation method (LRM), a decentralized optimization model with its solution is proposed that converts the traditional centralized optimization model into certain sub-problems. The optimization model aims to maximize the profit of CS, but it comprehensively considers the charging preference of EV users, the operation constraints of the distribution network, and the TOU strategy adopted by the CS. To validate the proposed decentralized optimal dispatching method, a series of numerical simulations were conducted to demonstrate its effect on the computation efficiency and stability, the profit of the CS, and the peak-load shifting. The result indicates that the TOU strategy markedly increases the profit of the CS in comparison with the fixed electricity price mechanism, and the computation efficiency and stability are much better than those of the centralized optimization method. Although it does not compensate the load fluctuation completely, the proposed method with the TOU strategy is helpful for filling the valley of power use.
Strategies Comparison for Voltage Unbalance Mitigation in LV Distribution Networks Using EV Chargers
Authors: Nájera Jorge , Mendonça Hugo , de Castro Rosa M. , Arribas Jaime R.
Published: March 2019

The increasing penetration of Electric Vehicles (EVs) in LV distribution networks can potentially cause voltage quality issues such as voltage unbalance and under-voltage conditions. According to the EV charger characteristics, some strategies can be adopted to mitigate the aforementioned effects. Smart decentralized charging controls seem to be a more practical solution than centralized controls, since there is no need for communication because they rely only on local measurements. The four most relevant decentralized charging strategies, two for single-phase and two for three-phase EV chargers, have been implemented in a typical three-phase four-wire European LV distribution network. Simulations have been carried out for scenarios with single-phase EV chargers, three-phase EV chargers, and a combination of both. Single-phase controls are aimed at under-voltage regulation, while three-phase controls are focused on mitigating voltage unbalance. Results show that the implementation of a decentralized EV charging control is an adequate solution for Distribution System Operators (DSOs) since it improves the reliability and security of the network. Moreover, even though decentralized charging control does not use any communication, the combination of three-phase and single-phase controls is able to mitigate voltage unbalance while preventing the under-voltage condition.
Common Switch Fault Diagnosis for Two-Stage DC-DC Converters Used in Energy Harvesting Applications
Authors: Jamshidpour Ehsan , Poure Philippe , Saadate Shahrokh
Published: March 2019

This paper proposes a new Unified Switch Fault Diagnosis (UFD) approach for two-stage non-isolated DC-DC converters used in energy harvesting applications. The proposed UFD is compared with a switch fault diagnosis consisting of two separate fault detection algorithms, working in parallel for each converter. The proposed UFD is simpler than the two parallel fault diagnosis methods in realization. Moreover, it can detect both types of switch failures, open circuit and short circuit switch faults. It can also be used for any two-stage non-isolated DC-DC converters based on two single switch converters, no matter the converter circuits in each stage. Some selected simulation and Hardware-in-the-Loop (HIL) experimentation results confirm the validity and efficiency of the proposed UFD. Also, the proposed UFD is applied successfully for fault-tolerant operation of a buck/buck–boost two-stage converter with synchronous control and a redundant switch.
Compressed Sensing-Based DOA Estimation with Antenna Phase Errors
Authors: Liu Linxi , Zhang Xuan , Chen Peng
Published: March 2019

In array signal processing, the direction of arrivals (DOAs) of the received signals are estimated by measuring the relative phases among antennas; hence, the estimation performance is reduced by the inconsistency among antennas. In this paper, the DOA estimation problem of the uniform linear array (ULA) is investigated in the scenario with phase errors among the antennas, and a diagonal matrix composed of phase errors is used to formulate the system model. Then, by using the compressed sensing (CS) theory, we convert the DOA estimation problem into a sparse reconstruction problem. A novel reconstruction method is proposed to estimate both the DOA and the unknown phase errors, iteratively. The phase errors are calculated by a gradient descent method with the theoretical expressions. Simulation results show that the proposed method is cost-efficient and outperforms state-of-the-art methods regarding the DOA estimation with unknown phase errors.
Smart Inverters for Microgrid Applications: A Review
Authors: Arbab-Zavar Babak , Palacios-Garcia Emilio , Vasquez Juan , Guerrero Josep
Published: March 2019

In a microgrid, with several distributed generators (DGs), energy storage units and loads, one of the most important considerations is the control of power converters. These converters implement interfaces between the DGs and the microgrid bus. In order to achieve higher functionality, efficiency and reliability, in addition to improving the control algorithms it is beneficial to equip the inverters with “smart” features. One interpretation of “smartness” refers to minimizing the requirement of communication and therefore switching from centralized to decentralized control. At the same time, being equipped with efficient and state of the art communication protocols also indicates “smartness” since the requirement of communication cannot be completely omitted. A “smart inverter” should offer some features such as plug and play, self-awareness, adaptability, autonomy and cooperativeness. These features are introduced and comprehensively explained in this article. One contribution discussed here is the possibility of achieving long-range wireless communication between inverters to empower various control schemes. Although current efforts aim to modify and improve power converters in a way that they can operate communication free, if a suitable and functional communication protocol is available, it will improve the accuracy, speed and robustness of them.
Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests
Authors: Lee Gye-Seong , Kim Dong-Hyun , Han Jong-Ho , Hwang Myeong-Hwan , Cha Hyun-Rok
Published: March 2019

In this study, a method to determine the optimal generator operating point is proposed to enhance the utilization of power resources in a range-extended electric vehicle (Re-EV). Currently, the Re-EV is being developed as one of the solutions to the short driving range and charge problem of electric vehicles (EVs). In particular, we present a method for flexibly determining the operating point of the generators mounted on Re-EVs based on the power consumption trends of the users. Our proposed method can address the problem in existing algorithms wherein all the available resources are not utilized, even though there is fuel remaining in the EV because the battery is not completely discharged. The proposed algorithm was developed based on data acquired through actual driving tests using an agricultural utility vehicle; these data can be applied to various power consumption patterns, including nonlinear consumption patterns. In addition, this algorithm can be applied to other types of Re-EV with different battery and generator specifications. We perform simulations and experiments to verify the proposed algorithm and the results demonstrate the effectiveness of the proposed approach compared with other existing methods.
Research on a Faulty Line Selection Method Based on the Zero-Sequence Disturbance Power of Resonant Grounded Distribution Networks
Authors: Shao Wenquan , Bai Jie , Cheng Yuan , Zhang Zhihua , Li Ning
Published: March 2019

For fast and accurate faulty line selection (FLS) in single phase-to-ground (SPG) faults for resonant grounded distribution networks (RGDNs), this paper proposes a FLS method based on zero-sequence power characteristics. Through analysis of the zero-sequence current of SPG faults, it is found that the transient zero-sequence current in the faulty line contains abundant attenuated DC components, while that in the sound line is weak, which could be utilized as a technically practical way of judging faulty lines. Thus a FLS scheme using a transient zero-sequence DC component energy integral is put forward, which highlights the difference of the DC components in the faulty line and the sound line. A series of simulation test results indicate that the proposed scheme shows favorable performance under various SPG fault conditions, and should be helpful to improve the success rate of FLS significantly for RGDN in the future.
Power Quality in DC Distribution Networks
Authors: Barros Julio , de Apráiz Matilde , Diego Ramón I.
Published: March 2019

This paper presents an overview of power quality in low-voltage DC distribution networks. We study which of the power quality disturbances in AC networks are also relevant in DC networks, as well as other disturbances specific to DC networks. The paper reviews the current status of international regulations in this topic and proposes different indices for the detection and characterization of the main types of power quality disturbances, presenting some results obtained in different laboratory tests in DC networks using different DC voltage shapes delivered by different DC power source types.
Energy and Environmental Analysis of Membrane-Based CH4-CO2 Replacement Processes in Natural Gas Hydrates
Authors: Castellani Beatrice , Gambelli Alberto Maria , Nicolini Andrea , Rossi Federico
Published: March 2019

Natural gas hydrates are the largest reservoir of natural gas worldwide. This paper proposes and analyzes the CH4-CO2 replacement in the hydrate phase and pure methane collection through the use of membrane-based separation. The investigation uses a 1 L lab reactor, in which the CH4 hydrates are formed in a quartz sand matrix partially saturated with water. CH4 is subsequently dissociated with a CO2 stream supplied within the sediment inside the reactor. An energy and environmental analysis was carried out to prove the sustainability of the process. Results show that the process energy consumption constitutes 4.75% of the energy stored in the recovered methane. The carbon footprint of the CH4-CO2 exchange process is calculated as a balance of the CO2 produced in the process and the CO2 stored in system. Results provide an estimated negative value, equal to 0.004 moles sequestrated, thus proving the environmental benefit of the exchange process.
Economic Analysis and Environmental Impact Assessment of Heat Pump-Assisted Distillation in a Gas Fractionation Unit
Authors: Lee Jisook , Son Yongho , Lee Kwang , Won Wangyun
Published: March 2019

The depletion of fossil fuels and environmental pollution (e.g., greenhouse gas emissions) through the combustion of fossil fuels have stimulated studies on new technologies able to curtail the energy consumption of existing fractionation units. In this regard, heat pumps have garnered substantial attention due to their potential to improve the process energy efficiency. This study aims to provide extensive economic analysis and environmental impact assessment of the application of heat pumps under different conditions and scenarios. For this purpose, we first selected three important conditions: feed composition, plant capacity, and fuel price. Then, we performed a range of analyses to identify the major costs and environmental drivers. The economics and environmental impact of heat pump-assisted distillation was investigated and compared with those of conventional distillation.
A Study of Fuel Cell Scheduling Effect on Local Energy Markets with Heterogeneous Renewable Sources
Authors: Spasova Borislava , Kawamoto Daisuke , Takefuji Yoshiyasu
Published: March 2019

The study presented in this paper aims to show the impact of introducing a priority-based internal power flow management system on the local energy market of prosumers with solar panels, fuels cells and batteries. The community used for the study is based on the Open Energy Systems (OES), in which 19 autonomous subsystems, equipped with batteries, solar panels and AC grid connection are interconnected via a DC power bus. For the simulation purposes, fuel cell, modeled after Ene-Farm, is added to the original configuration. Each standalone system has internal, priority-based, agent, capable of scheduling the operating hours of the fuels cell to maximize the utilization of the generation and minimize curtailment. The energy market is based on a simplified version of the Zaraba, a continuous double auction algorithm used by the Japanese Stock Exchange, in which prosumer can submit a bid for a future timeslot. The preliminary numerical evaluation is based on the results from several simulations using different versions of the internal management system.
A Synthetic Condition Assessment Model for Power Transformers Using the Fuzzy Evidence Fusion Method
Authors: Tian Fenglan , Jing Zhongzhao , Zhao Huan , Zhang Enze , Liu Jiefeng
Published: March 2019

Condition-based maintenance decision-making of transformers is essential to electric enterprises for avoiding financial losses. However, precise transformer condition assessment was tough to accomplish because of the negligence of the influence of bushing and accessories, the difficulty of fuzzy grade division, and the lack of reasonable fuzzy evidence fusion method. To solve these problems, a transformer assessing model was proposed in the paper. At first, an index assessing system, considering the main body, the bushing and the accessories components, was established on the basis of components division of transformers. Then, a Cauchy membership function was employed for fuzzy grades division. Finally, a fuzzy evidence fusion method was represented to handle the fuzzy evidences fusion processes. Case studies and the comparison analysis with other methods were performed to prove the effectiveness of this model. The research results confirm that the proposed model could be recommendation for condition based maintenance of power transformers for electric enterprises.
Experimental Identification of the Optimal Current Vectors for a Permanent-Magnet Synchronous Machine in Wave Energy Converters
Authors: Krüner Simon , Hackl Christoph M.
Published: March 2019

This paper investigates efficiency enhancements in Permanent-Magnet Synchronous Machines (PMSMs) used as generators in wave energy converters. A method is presented, which allows experimental identification of the optimal current vectors leading to minimal losses and maximum efficiency. Iron and inverter losses are considered. The proposed identification method is generic and is applicable to any PMSM used as a generator. The obtained results are compared to the well-known Maximum Torque per Current (MTPC) strategy. Two MTPC strategies are investigated: (i) MTPC using linearised flux linkages; and (ii) MTPC using measured, nonlinear flux linkages. For both MTPC strategies, the influence of the Voltage Source Inverter (VSI) is taken into account. All measurements are performed on a simple and cheap industrial-like test bench. All three presented methods lead to increased efficiency. The best efficiency is obtained by the proposed maximum efficiency strategy, where iron and inverter losses are taken into account by proper measurement of the optimal current vectors.
Flicker of Modern Lighting Technologies Due to Rapid Voltage Changes
Authors: Lodetti Stefano , Azcarate Izaskun , Gutiérrez José Julio , Leturiondo Luis Alberto , Redondo Koldo , Sáiz Purificación , Melero Julio J. , Bruna Jorge
Published: March 2019

The purpose of the present paper is to evaluate the sensitivity of modern lighting technologies to different types of RVCs. In order to do that, 27 modern lamps—mainly LED—have been subjected to real RVCs and their response has been assessed. The detection of RVCs on the grid has been performed according to the IEC 61000-4-30 detection method, while the response of the lamps has been measured with a light flickermeter and characterized using the instantaneous flicker perception, as defined in IEC 61000-4-15. The obtained results show a high dispersion in the response of the modern lighting technologies and high values of flicker perception, although with a lower sensitivity than the incandescent lamp. The results led the authors to propose the definition of a new immunity test to be added to the lamp immunity protocol IEC TR-61547-1, to ensure that newly produced lamps cause limited irritation to grid users.
Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids
Authors: Naz Aqdas , Javed Muhammad Umar , Javaid Nadeem , Saba Tanzila , Alhussein Musaed , Aurangzeb Khursheed
Published: March 2019

A Smart Grid (SG) is a modernized grid to provide efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world. An efficient energy distribution is required as smart devices are increasing dramatically. The forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of SG. Various learning algorithms have been proposed to solve the forecasting problem. The sole purpose of this work is to predict the price and load efficiently. The first technique is Enhanced Logistic Regression (ELR) and the second technique is Enhanced Recurrent Extreme Learning Machine (ERELM). ELR is an enhanced form of Logistic Regression (LR), whereas, ERELM optimizes weights and biases using a Grey Wolf Optimizer (GWO). Classification and Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Cross validation is done for ERELM using Monte Carlo and K-Fold methods. The simulations are performed on two different datasets. The first dataset, i.e., UMass Electric Dataset is multi-variate while the second dataset, i.e., UCI Dataset is uni-variate. The first proposed model performed better with UMass Electric Dataset than UCI Dataset and the accuracy of second model is better with UCI than UMass. The prediction accuracy is analyzed on the basis of four different performance metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed techniques are then compared with four benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperformed benchmark schemes. The proposed techniques efficiently increased the prediction accuracy of load and price. However, the computational time is increased in both scenarios. ELR achieved almost 5% better results than Convolutional Neural Network (CNN) and almost 3% than LR. While, ERELM achieved almost 6% better results than ELM and almost 5% than RELM. However, the computational time is almost 20% increased with ELR and 50% with ERELM. Scalability is also addressed for the proposed techniques using half-yearly and yearly datasets. Simulation results show that ELR gives 5% better results while, ERELM gives 6% better results when used for yearly dataset.
Insights into the BRT (Boosted Regression Trees) Method in the Study of the Climate-Growth Relationship of Masson Pine in Subtropical China
Authors: Gu Hongliang , Wang Jian , Ma Lijuan , Shang Zhiyuan , Zhang Qipeng
Published: March 2019

Dendroclimatology and dendroecology have entered mainstream dendrochronology research in subtropical and tropical areas. Our study focused on the use of the chronology series of Masson pine (Pinus massoniana Lamb.), the most widely distributed tree species in the subtropical wet monsoon climate regions in China, to understand the tree growth response to ecological and hydroclimatic variability. The boosted regression trees (BRT) model, a nonlinear machine learning method, was used to explore the complex relationship between tree-ring growth and climate factors on a larger spatial scale. The common pattern of an asymptotic growth response to the climate indicated that the climate-growth relationship may be linear until a certain threshold. Once beyond this threshold, tree growth will be insensitive to some climate factors, after which a nonlinear relationship may occur. Spring and autumn climate factors are important controls of tree growth in most study areas. General circulation model (GCM) projections of future climates suggest that warming climates, especially temperatures in excess of those of the optimum growth threshold (as estimated by BRT), will be particularly threatening to the adaptation of Masson pine.
Lessons Learned from Arson Wildfire Incidence in Reforestations and Natural Stands in Spain
Authors: Da Ponte Emmanuel , Costafreda-Aumedes Sergi , Vega-Garcia Cristina
Published: March 2019

Wildfires are currently considered the major threat to forests in Mediterranean countries. It has been implied that a large percentage of arson-caused fires in Spain are connected with the extensive reforestation programs implemented between 1940 and 1970. However, no consistent studies have been conducted to study the relationships between arson-caused fires and stand origin. Therefore, the goal of this study was to analyze occurrences and model the influence of forest stand origin (artificial or not) on the development of wildfires in peninsular Spain. Twenty-one neural network models were trained to estimate fire incidence through fire type (surface or crown fire), burned area and total treed burned area, based on stand age (years), canopy cover (%), natural age class (from seedling to mature stages) and fuel type classification. Models were built for reforested stands and natural stands of Pinus pinaster Ait., the Mediterranean pines Pinus sylvestris L., Pinus nigra Arn., Pinus halepensis Mill. and Eucalyptus sp. L’Hér., or groups of these species, and the resulting models were compared. Reforested stands presented higher fire incidence than natural stands mainly for productive species like Pinus pinaster Ait. According to the fire type models, thickets had a large influence in the development of crown fires in reforested stands in a general model for all species, the model with the Mediterranean group of pines, and the Pinus pinaster Ait. model. Vertical continuity influenced crown fire propagation in natural Mediterranean pines and in Eucalyptus stands. Presence of shrubs, grasslands and wood slash was related to surface fires in models for both reforested and natural stands. The results suggested that stand origin was influential on fire incidence, at least with regard to fire type and commercial species in the northwestern region of Spain.
Origanum syriacum Essential Oil Chemical Polymorphism According to Soil Type
Authors: El-Alam Imad , Zgheib Raviella , Iriti Marcello , El Beyrouthy Marc , Hattouny Paul , Verdin Anthony , Fontaine Joël , Chahine Ramez , Lounès-Hadj Sahraoui Anissa , Makhlouf Hassane
Published: March 2019

Background: Origanum syriacum L. is an aromatic plant growing wild in Lebanon. This species is highly used in Lebanese traditional medicine and is a staple food in Lebanese gastronomy. Due to the over-harvesting, this species has become a cultivated crop rather than being collected from the wild. This study aims to evaluate the chemical polymorphism according to soil type. Methods: Plant samples were cultivated in different soil types including manure, potting mix, professional agriculture mixture, vegetable compost, nursery soils, and natural agricultural soil inoculated with arbuscular mycorrhizal fungi. After 16 weeks of culture, fresh shoot biomass was measured. Root colonization rate was evaluated and foliar biomasses were used for essential oil (EO) extraction. EO yield was calculated and the identification of the main chemical compounds of EO samples was performed by gas chromatography (GC) and gas chromatography–mass spectrometry (GC/MS). Results: Our findings revealed that the soil type affects the O. syriacum chemotype. Indeed, the EO samples could be divided into two groups: thymol chemotype group including manure and vegetable compost soils and non-sterilized non-inoculated EO samples, and the thymol/carvacrol chemotype including potting mix, professional agriculture mixture, nursery mixture, sterilized non-inoculated, non-sterilized inoculated, and sterilized inoculated EO samples. These results showed that manure and vegetable compost soils promoted thymol synthesis, whereas potting mix, professional agriculture mixture, and nursery mixture soils were thymol/carvacrol chemotype. Moreover, mycorrhizal inoculation increased carvacrol and reduced thymol productions in comparison to non-inoculated conditions. Additionally, mycorrhizal inoculation showed significant enhancements in mycorrhizal rates and shoot biomass production with respect to the non-sterilized soil. Conclusions: These variations confirm the influence of the edaphic conditions on the chemical components biosynthesis pathways of oregano plants. The results of this investigation could be used for determining optimal soil type, leading to a good quality herb production.
Headcase is a Repressor of Lamellocyte Fate in Drosophila melanogaster
Authors: Varga Gergely I. B. , Csordás Gábor , Cinege Gyöngyi , Jankovics Ferenc , Sinka Rita , Kurucz Éva , Andó István , Honti Viktor
Published: March 2019

Due to the evolutionary conservation of the regulation of hematopoiesis, Drosophila provides an excellent model organism to study blood cell differentiation and hematopoietic stem cell (HSC) maintenance. The larvae of Drosophila melanogaster respond to immune induction with the production of special effector blood cells, the lamellocytes, which encapsulate and subsequently kill the invader. Lamellocytes differentiate as a result of a concerted action of all three hematopoietic compartments of the larva: the lymph gland, the circulating hemocytes, and the sessile tissue. Within the lymph gland, the communication of the functional zones, the maintenance of HSC fate, and the differentiation of effector blood cells are regulated by a complex network of signaling pathways. Applying gene conversion, mutational analysis, and a candidate based genetic interaction screen, we investigated the role of Headcase (Hdc), the homolog of the tumor suppressor HECA in the hematopoiesis of Drosophila. We found that naive loss-of-function hdc mutant larvae produce lamellocytes, showing that Hdc has a repressive role in effector blood cell differentiation. We demonstrate that hdc genetically interacts with the Hedgehog and the Decapentaplegic pathways in the hematopoietic niche of the lymph gland. By adding further details to the model of blood cell fate regulation in the lymph gland of the larva, our findings contribute to the better understanding of HSC maintenance.
Cytological Evaluations of Advanced Generations of Interspecific Hybrids between Allium cepa and Allium fistulosum Showing Resistance to Stemphylium vesicarium
Authors: Kudryavtseva Natalia , Havey Michael J. , Black Lowell , Hanson Peter , Sokolov Pavel , Odintsov Sergey , Divashuk Mikhail , Khrustaleva Ludmila
Published: March 2019

Interspecific crossing is a promising approach for introgression of valuable traits to develop cultivars with improved characteristics. Allium fistulosum L. possesses numerous pest resistances that are lacking in the bulb onion (Allium cepa L.), including resistance to Stemphylium leaf blight (SLB). Advanced generations were produced by selfing and backcrossing to bulb onions of interspecific hybrids between A. cepa and A. fistulosum that showed resistance to SLB. Molecular classification of the cytoplasm established that all generations possessed normal (N) male−fertile cytoplasm of bulb onions. Genomic in situ hybridization (GISH) was used to study the chromosomal composition of the advanced generations and showed that most plants were allotetraploids possessing the complete diploid sets of both parental species. Because artificial doubling of chromosomes of the interspecific hybrids was not used, spontaneous polyploidization likely resulted from restitution gametes or somatic doubling. Recombinant chromosomes between A. cepa and A. fistulosum were identified, revealing that introgression of disease resistances to bulb onion should be possible.