Advanced Schemes Research Articles

Blue, green, and turquoise pathways for minimizing hydrogen production costs from steam methane reforming with CO2 capture

Rising climate change ambitions require large-scale clean hydrogen production in the near term. “Blue” hydrogen from conventional steam methane reforming (SMR) with pre-combustion CO2 capture can fulfil this role. This study therefore presents techno-economic assessments of a range of SMR process configurations to minimize hydrogen production costs. Results showed that pre-combustion capture can avoid up to 80% of CO2 emissions cheaply at 35 €/ton, but the final 20% of CO2 capture is much more expensive at a marginal CO2 avoidance cost around 150 €/ton. Thus, post-combustion CO2 capture should be a better solution for avoiding the final 20% of CO2. Furthermore, an advanced heat integration scheme that recovers most of the steam condensation enthalpy before the CO2 capture unit can reduce hydrogen production costs by about 6%. Two hybrid hydrogen production options were also assessed. First, a “blue-green” hydrogen plant that uses clean electricity to heat the reformer achieved similar hydrogen production costs to the pure blue configuration. Second, a “blue-turquoise” configuration that replaces the pre-reformer with molten salt pyrolysis for converting higher hydrocarbons to a pure carbon product can significantly reduce costs if carbon has a similar value to hydrogen. In conclusion, conventional pre-combustion CO2 capture from SMR is confirmed as a good solution for kickstarting the hydrogen economy, and it can be tailored to various market conditions with respect to CO2, electricity, and pure carbon prices.

A HEURISTIC COMPUTING APPROACH USING SEQUENTIAL QUADRATIC PROGRAMMING TO SOLVE THE FIFTH KIND OF INDUCTION MOTOR MODEL

The purpose of the current investigation is to solve the fifth kind of induction motor model using an advanced computational scheme by operating the artificial neural networks (ANNs), global scheme as genetic algorithm (GA) along with the rapid local search sequential quadratic programming technique (SQPT), i.e. ANN-GA-SQPT. ANNs are implemented to discretize the fifth kind of induction motor model to express the merit function based on the mean square error. The numerical presentation of the proposed ANN-GA-SQPT is pragmatic for three different problems based on the fifth kind of induction motor model to authenticate the efficacy, consistency and importance of the proposed ANN-GA-SQPT. Moreover, statistical representations are provided in order to check the precision, convergence and accuracy of the present ANN-GA-SQPT.

Analysis of Peak to Average Power in the 5G NOMA-FBMC Waveform

Objectives: In this work, we investigate suitable techniques to reduce the Peak to Average Power Ratio (PAPR) for advanced modulation schemes in order to obtain better performance than current or commonly used modulation schemes for Fourth Generation (4G) and Fifth Generation (5G). Methods: The proposed scheme incorporates a combination of Selective Mapping (SLM) and Partial Transmission Scheme (PTS) and thereby efficiently minimizes the PAPR and the complexity of the framework. Further, it is seen that the proposed algorithm is crucial to achieving better spectral and power characteristics compared with the existing waveforms. Findings: The comparative results of the bit error rate (BER) and PAPR of the advanced SLM-PTS when applied to the OFDM, FBMC, NOMA, and NOMA-FBMC structures are shown, and it is found that the power and complexity are significantly decreased in the advanced waveforms, which makes the proposed algorithm efficient for the advanced waveforms. Novelty: A natural motivation for future modulation schemes is to harmoniously merge the newer modulation technique, Filter Bank Multi Carrier (FBMC), with the Non-Orthogonal Multiple Access (NOMA) framework. This has led to a recent modulation paradigm called FBMC-NOMA, wherein the NOMA power domain principle is applied to a group of FMBC modulated signals. The proposed SLM-PTS-based NOMA-FBMC structure efficiently enhances the throughput and PAPR performance for 5G and beyond 5G systems. Keywords: PAPR; FBMC; SLM; PTS; NOMA

Characterization of the Thermal Time Constants of HAMR Media

Here we describe experimental spin stand measurements as well as simulations of the time constants associated with thermal transients in heat-assisted magnetic recording (HAMR) media. The thermal transients associated with both increasing and decreasing steps in laser power contain at least two time constants each, one on the order of 1 ns and another on the order of 10 ns. These time constants are associated with the central thermal peak in the media and the diffuse thermal background, respectively. In addition, the measured thermal fall time is longer than the thermal rise time due to the disk’s motion relative to the head. Whereas in conventional HAMR a constant laser power is applied during writes, a number of proposed advanced recording schemes use pulsed laser power to improve either recording performance or reliability. The thermal time constants reported here suggest care must be taken when implementing such schemes so that transition locations are not shifted to the detriment of areal density capability.

Risk-aware learning for scalable voltage optimization in distribution grids

Real-time coordination of distributed energy resources (DERs) is crucial for regulating the voltage profile in distribution grids. By capitalizing on a scalable neural network (NN) architecture, one can attain decentralized DER decisions to address the lack of real-time communications. This paper develops an advanced learning-enabled DER coordination scheme by accounting for the potential risks associated with reactive power prediction and voltage deviation. Such risks are quantified by the conditional value-at-risk (CVaR) using the worst-case samples only, and we propose a mini-batch selection algorithm to address the training speed issue in minimizing the CVaR-regularized loss. Numerical tests using real-world data on the IEEE 123-bus test case have demonstrated the computation and safety improvements of the proposed risk-aware learning algorithm for decentralized DER decision making, especially in terms of reducing feeder voltage violations. • Scalable NN architecture for decentralized DER operations in real-time. • Risk-aware learning by using CVaR to systematically improve worst-case performance. • Improving the safety of DER decision rules by using the CVaR regularization. • Accelerating the training speed by proposing a statistical based mini-batch selection. • Numeric tests demonstrate computation and safety improvements over MSE based learning.

A New Halocarbon Absorption Model Based on HITRAN Cross‐Section Data and New Estimates of Halocarbon Instantaneous Clear‐Sky Radiative Forcing

AbstractThe article describes a new practical model for the infrared absorption of chlorofluorocarbons and other gases with dense spectra, based on high‐resolution transmission molecular absorption database (HITRAN) absorption cross‐sections. The model is very simple, consisting of frequency‐dependent polynomial coefficients describing the pressure and temperature dependence of absorption. Currently it is implemented for the halocarbon species required by the Radiative Forcing Model Intercomparison Project. In cases where cross‐section data is available at a range of different temperatures and pressures, this approach offers practical advantages compared to previously available options, and is traceable, since the polynomial coefficients follow directly from the laboratory spectra. The new model is freely available and has several important applications, notably in remote sensing and in developing advanced radiation schemes for global circulation models that include halocarbon absorption. For demonstration, the model is applied to the problem of computing instantaneous clear‐sky halocarbon radiative efficiencies and present day radiative forcing. Results are in reasonable agreement with earlier assessments that were carried out with the less explicit Pinnock method, and thus broadly validate that method.

Real-Time Grid and DER Co-Simulation Platform for Testing Large-Scale DER Coordination Schemes

Distributed energy resources (DERs), such as responsive loads and energy storage systems, can help grid operators to more effectively balance supply and demand and manage network constraints. However, consumer acceptance of load coordination schemes depends on ensuring customer quality of service (QoS), thus requiring the careful management of device-level constraints. Simultaneously managing both device-level and system-level objectives is a difficult problem. Most potential solutions to the DER coordination problem are in some way distributed, with decision logic running both at the device (such as a thermostat) and the coordinator, with potentially complex communications between devices and one or more coordinators. It is difficult to effectively test distributed DER control schemes, particularly when there are thousands of devices with both device-level and grid-level constraints and asynchronous communications. This paper addresses this challenge by presenting a novel real-time grid-and-DER cyber-physical co-simulation platform for testing advanced DER coordination schemes and characterizing the behaviors of a DER fleet. The co-simulation platform is particularly suitable for: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> ) testing the real-time performance of a large fleet of DERs delivering advanced grid services; <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula> ) characterizing the distribution of states for a large fleet of managed DERs; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$iii$ </tex-math></inline-formula> ) incorporating practical limitations of DERs and communications and analyzing the effects on fleet-wide performance. To illustrate these benefits, we demonstrate how the platform can be used to test a fleet with thousands of DERs coordinated using Packetized Energy Management (PEM) and interacting with a realistic transmission system model.

Wavefield focusing using a generalised, potentially asymmetric homogeneous Green’s function

Marchenko-type integrals typically relate so-called focusing functions and Green’s functions via the reflection response measured on the open surface of a volume of interest. Originating from one dimensional inverse scattering theory, the extension to two and three dimensions set in motion various new developments regarding imaging in complex materials. This extension, however, is based on wavefield decomposition inside the volume and a truncated medium state, i.e. a version of the medium that is reflection-free underneath the focusing location, suggesting that evanescent, refracted and diving waves cannot be included in the representation. We elaborate on a new derivation for Marchenko-like integrals that (i) extends the concept of wavefield focusing by using a generalised homogeneous Green’s function, (ii) is based on partial differential equations and thereby allows for additional insights and a new physical intuition for Marchenko equations, (iii) unifies wavefield focusing for open and closed boundary systems, (iv) does not require wavefield decomposition or a truncated medium state, thus including the full wavefield Green’s function, (v) enables using forward modelling to obtain, e.g., Marchenko-type, time-compact focusing functions. We place a particular focus on the latter point, illustrating and investigating how to solve the underlying partial differential equations for various types of focusing functions. This paves the way for a deeper understanding of focusing functions as well as advanced full wavefield Marchenko schemes. While the derivations are generally presented for the 3D case, we show numerical examples in 1D.

A review of energy saving routing schemes for WSN assisted IoT network

Abstract In WSN assisted IoT networks, the devices have to search the path up to the IoT hub for data transmission. Although the device’s hardware capabilities are increasing day by day, the device’s battery capacity is not increasing at the same rate. IoT devices have limited-power batteries that are not rechargeable. It is also challenging to replace their battery while the devices are in use. Moreover, we need advanced power-saving schemes to make these IoT technologies more adaptable. Due to the energy constraints and limited memory capacity of these IoT devices, we require more energy-efficient routing protocols to route data to the IoT hub. This paper has surveyed the most recent state-of-the-art routing protocol and highlighted their limits and path for future research.

Uncovering Molecular Structure – Redox Potential Relationships for Organic Electrode Materials: A Hybrid DFT – Machine Learning Approach

Density functional theory and machine learning are used to investigate the structure-electrochemical performance relationships of organic moieties for use in Li-ion batteries. Namely, DFT calculations are performed to predict the redox potential of several novel organic molecules with an accuracy within ~0.1 V of experimental measurements. However, despite its ability to provide valuable insight regarding the electrochemical properties of novel organic molecules, our high efficacy DFT modeling protocol demands significant computational time and is therefore impractical for the vast screening of novel material candidate. As a result, we explore machine learning as a strategy for the accelerated discovery of novel organic materials. More critically, we use machine learning as a method for assessing the various structure-electrochemical relationships which can provide a more general guideline for the design of organic electrode materials. We are employing different learning models, including artificial neural networks, gradient-boosting regression, and kernel methods (such as kernel ridge regression and Gaussian process regression), via three different pipelines with varying sophistication with the aim of generating an advanced ML scheme for the accurate prediction and analysis of electrochemical activity. In addition to incorporating structural fingerprints, we are exploring an active learning framework, namely using the efficient global optimization scheme, to explore the materials space strategically using publicly available datasets. Through this approach, we can discover new electrode materials that could 1) have a higher probability for achieving enhanced electrochemical properties and 2) increase our learning model’s performance by increasing our dataset’s representation of the material space. Additionally, we are implementing a high-throughput virtual screening (HTVS) pipeline which consists of several surrogate learning models with increasing levels of fidelity. The material candidates are pruned at each stage to discard samples that are unlikely to possess a desirable redox potential for cathodic application, and the remaining samples move on to the next model. In this way, only a subset of the original dataset which has a higher probability for lying within the desired redox potential range is pursued using the most computationally expensive model.

Advanced control scheme and dynamic phasor modelling of grid‐tied droop‐controlled inverters

AbstractThis paper develops an advanced scheme, modelling, and analysis of power flow control intended for grid‐connected droop‐controlled VSIs within a single‐phase microgrid (MG). The proposed control scheme includes a power calculation method based on an enhanced second‐order generalized integrator frequency‐locked loop (ESOGI‐FLL). Contrary to the existing power calculation methods that use low‐pass filter (LPF) with a low cutoff frequency to reject the grid voltage distortion and achieve average power, which may result in reducing the power calculation speed, the involved ESOGI‐FLL can offer a fast transient response and benefits from high filtering capability of sub‐ and low‐order harmonics. In addition, the ESOGI‐FLL provides total rejection of the DC offset, which is another issue that may adversely affect the accuracy of the traditional methods. Thus, this proposal can contribute to improving the speed and accuracy of the power computation and make the power calculation scheme immune to DC disturbance, thereby enhancing the performance of the power control. On the other hand, a dynamic phasor modelling approach is adopted considering the dynamics of the ESOGI‐based power calculation, instead of the LPF transfer function used to describe the power computation dynamic in the related works. The small‐signal model of the grid‐connected VSI power flow considering the line impedance R/X ratio as well as that describing the dynamics of the ESOGI‐based power calculation are derived. Using these models, the closed‐loop model of the grid‐interactive inverter including the power controller dynamics is obtained. The system stability is assessed, which helps to determine properly the controller's gains. A simulation study of a grid‐connected VSI is carried out in MATLAB/Simulink™ and PSIM's processor in the loop (PIL) platforms to assess the effectiveness of the proposed control approach. The results confirm the effectiveness of the proposed control to regulate real and reactive powers with good transient performances when the grid is subject to several working conditions.

A review of multistage solar driven photovoltaic–thermal​ components with cascade energy storage system for tri-generation

Energy demand has exponentially increased in recent times, mainly because of population and economic growth as well as high standards of living in commercial and residential buildings. Conventional power systems have been undergoing great transformation owing to the integration of renewable energy sources (RESs) and demand side management programs. Solar Photovoltaic–Thermal (PV/T) is one of the most growing Renewable Energy Resources (RERs) because of its low carbon emission, high energy efficiency, cost effectiveness, readily available, among other benefits. Several researches have been explored to enhance the performance of different components in the building integrated systems distributed solar energy for tri-generation: heating, cooling and electricity generation. The Photovoltaic–Thermal (PV/T) energy system may be enhanced using various advanced control schemes and cooling technologies. Energy storage systems have shown outstanding benefits by improving the reliability and security of modern power systems. Ice Thermal Energy Storage (ITES) systems have been used for cooling loads, while Heat Storage Water Tank (HSWT) are used for hot water demand. Alternatively, battery banks have been used for storing chemical energy and releasing electricity to the demand side. This reviewed work highlights various components of a solar driven grid-connected PV/T energy system, considering the ITES, HSWT and a battery bank under-price based Demand Response (DR) in terms of the optimal sizing and operational control.

Index Modulation Pattern Design for Non-Orthogonal Multicarrier Signal Waveforms

Spectral efficiency improvement is a key focus in most wireless communication systems and achieved by various means such as using large antenna arrays and/or advanced modulation schemes and signal formats. This work proposes to further improve spectral efficiency through combining non-orthogonal spectrally efficient frequency division multiplexing (SEFDM) systems with index modulation (IM), which can efficiently make use of the indices of activated subcarriers as communication information. Recent research has verified that IM may be used with SEFDM to alleviate inter-carrier interference (ICI) and improve error performance. This work proposes new SEFDM signal formats based on novel activation pattern designs, which limit the locations of activated subcarriers and enable a variable number of activated subcarriers in each SEFDM subblock. SEFDM-IM system designs are developed by jointly considering activation patterns, modulation schemes and signal waveform formats, with a set of solutions evaluated under different spectral efficiency scenarios. Detailed modelling of coded systems and simulation studies reveal that the proposed designs not only lead to better bit error rate (BER) but also lower peak-to-average power ratio (PAPR) and reduced computational complexity relative to other reported index-modulated systems.

Advanced Pressure Management for Sustainable Leakage Reduction and Service Optimization: A Case Study in Central Chile

The central part of Chile has been in severe unprecedented drought for more than ten years, becoming a water-stressed area. ESVAL, one of the main water utilities in the Chilean water sector, is responsible for delivering potable water to the population and securing long-term supply in the vast majority of this area. In order to achieve this goal, ESVAL is implementing different measures, with pressure management being one of them. This paper aims to describe the works executed in La Calera city to implement a new advanced pressure management scheme on an existing pressure-reducing valve with two main targets: reducing leakage and improving the level of service to the customers. The results show that advance pressure management generates a 10.12% reduction in the minimum night flow while, at the same time, helping to improve the level of service to the customers reducing the number of times pressure falls below the minimum target set by ESVAL by 52%. It is concluded that pressure management facilitates quick water savings by optimizing the performance of existing pressure-reducing valves.

ETpathfinder: a cryogenic testbed for interferometric gravitational-wave detectors

The third-generation (3G) of gravitational wave observatories, such as the Einstein Telescope (ET) and Cosmic Explorer, aim for an improvement in sensitivity of at least a factor of ten over a wide frequency range compared to the current advanced detectors. In order to inform the design of the 3G detectors and to develop and qualify their subsystems, dedicated test facilities are required. ETpathfinder prototype uses full interferometer configurations and aims to provide a high sensitivity facility in a similar environment as ET. Along with the interferometry at 1550 nm and silicon test masses, ETpathfinder will focus on cryogenic technologies, lasers and optics at 2090 nm and advanced quantum-noise reduction schemes. This paper analyses the underpinning noise contributions and combines them into full noise budgets of the two initially targeted configurations: (1) operating with 1550 nm laser light and at a temperature of 18 K and (2) operating at 2090 nm wavelength and a temperature of 123 K.

An enhanced master–slave control for accurate load sharing among parallel standalone AC microgrids

SummaryWith the advancement of power electronic converters over the last few decades, the capability to integrate solar photovoltaics, wind, diesel generators, batteries, ultracapacitor, and electric vehicles with standalone networks or utility grids has increased. For the reliable operation of standalone networks, accurate load sharing among these sources is required. As a result, several load sharing schemes with inherent merits and related demerits are presented in the literature. This work intends to address shortcomings of earlier reported control schemes with the inheritance of associated merits using the proposed control scheme. The proposed enhanced Master–Slave control, with advanced droop control scheme, is illustrated for Master voltage source inverter with resistive line impedance and improves the steady‐state response by improving voltage and frequency profile. It reduces the voltage drop caused by load effect and droop effect, allowing the voltage and frequency regulation during load changes and distinct load conditions such as balanced linear load, unbalanced linear load, and balanced nonlinear load in standalone alternating current (AC) microgrids. Furthermore, Slave voltage source inverters independently compensate for local load demand. This paper presents precise current control and voltage control design methodology and a mathematical model for voltage control and their performance analysis in time and frequency domain. Finally, the proposed enhanced Master–Slave control for accurate load sharing among parallel standalone AC microgrids validated using real‐time simulator and experimental results is provided to verify the effectiveness of the proposed control scheme.

An Advanced Approach to Improve Synchronization Phase Accuracy with Compressive Sensing for LT-1 Bistatic Spaceborne SAR

In the bistatic synthetic aperture radar (BiSAR) system, the unavoidable frequency deviation between the oscillators (USOs) will result in additional phase modulation in the demodulated radar signal, which significantly degrades the quality of the SAR image and digital elevation model (DEM) product. The innovative L-band spaceborne BiSAR system LuTan-1 (LT-1) employs a non-interrupted synchronization scheme to acquire the synchronization phase error. This advanced phase synchronization scheme avoids interrupting the normal BiSAR data acquisition and further increases the synchronization frequency. However, some non-ideal factors in the transmission link like attenuation, multipath effect, interference, etc., may cause the synchronization phase to be polluted by noise. A phase denoising approach based on compressive sensing (CS) is proposed to improve the accuracy of synchronization phase. The imaging phase with high signal-to-noise ratio (SNR) is input into the K-SVD algorithm to learn the prior information, and then the noise of the synchronization compensation phase is eliminated by maximum a posteriori (MAP) estimation. The data acquired from the ground validation system of the LT-1 synchronization module are adopted for the validation experiment. The proposed phase denoising method achieves higher phase synchronization accuracy compared with traditional ones. The processing results verify the effectiveness of the proposed method and demonstrate its potential for future on-orbit applications of the LT-1 mission.

Cluster: the Globalization of Economies

Globalization generated advanced competitive schemes that forced human beings and companies to take new initiatives in order to face the new business models that are being developed. A new scheme, model or strategic tool is the Cluster, which helps companies, governments and the community in general to have an opportunity to compete at a global scale. This paper presents a theoretical-conceptual overview. It makes reference of the contributions that have been made to the cluster concept: The globalization of the economy has been consolidating over time and local regions accept it to mold new strategies for competitive development. The constitution of clusters is not a fashion, it is a local development trend, which seeks to penetrate globalized markets, but with the conviction that it cannot be achieved in "solitary", so that the "collectivity" of sectorial business efforts, becomes the alternative for the formation of the cluster and thus be able to effectively meet the exigencies of globalization. Therefore, this paper focuses on conceptual development around clusters and globalization, from different chronological views of development.

Relationship between advanced pharmacy practice experience grading schemes and pharmacy residency match rates.

To determine the relationship of advanced pharmacy practice experience (APPE) grading schemes and other pharmacy program variables (ie, program age and funding) with pharmacy residency match rates. A 12-question survey was disseminated to experiential administrators of pharmacy programs in October 2018. Respondents identified their program's APPE grading scheme (pass/fail, letter grades, or other) and associated pros and cons. Responding programs were categorized by age and funding status. Survey responses were correlated with the American Society of Health-System Pharmacists residency match rates for 2016 through 2018. Data were analyzed using descriptive statistics and logistic regression models as well as by attributes via thematic analysis. Most pharmacy programs (62%) reported using letter grades for APPEs compared to pass/fail (30%) or other (8%) schemes. Pharmacy programs using pass/fail grading were more likely to have students match to postgraduate year 1 (PGY1) (P < 0.001) and postgraduate year 2 (PGY2) (P = 0.0074) residencies. Older pharmacy programs for each grading scheme were more likely to have higher match rates; however, for PGY1 match rates, older programs utilizing letter grades correlated to lower match rates than those utilizing pass/fail grading (P < 0.0001). Likewise, both public and private pharmacy programs using pass/fail grading had higher PGY1 match rates than those using letter grades (P = 0.0006 and P = 0.0014). Pass/fail grading in APPEs does not hinder PGY1 or PGY2 residency placement compared to other grading schemes both overall and in combination with certain pharmacy program variables. Grading scheme strengths and weaknesses should be considered when deciding on optimal assessment strategies for APPEs and when evaluating candidates for residencies.

Adaptive VR Video Data Transmission Method Using Mobile Edge Computing Based on AIoT Cloud VR

Aiming at the high requirements of cloud service-based virtual reality in AIoT for data transmission rate and delay sensitivity, a cloud VR system scheme based on MEC (Mobile Edge Computing) is proposed, which mainly incorporates viewpoint-based VR video data processing and hybrid digital-to-analog (HDA) transmission optimization and can be served for AIoT transmission filed. Firstly, a learning-driven multiaccess MEC offloading strategy is designed, in which the VR terminal automatically selects the optimal MEC server for task offloading, thereby effectively improving network efficiency and reducing service delay. Secondly, the progressive transmission of the VR data is realized through viewpoint-aware dynamic streaming based on RoI (region of interest) and the priorities of different objects. The transmission priority of each object in the scene is determined through the ROI layering, which effectively solves the contradiction between the large data volume in the VR scenes and the network bandwidth limitation when applied in AIoT domain, and further improves the real-time performance of the system. Then, the HDA (hybrid digital-analog) technique is introduced to optimize the transmission. Finally, the base station protocol stack is modified on the basis of the LTE (Long-Term Evolution) system, and the MEC technology is integrated to realize a complete cloud VR system in AIoT. The experimental results show that compared with other advanced schemes, the proposed scheme can achieve more robust and efficient data transmission performance and provide better VR user experience.