Transmission Congestion Management with Generalized Generation Shift Distribution Factors

A major concern in modern power systems is that the popularity and fluctuating characteristics of renewable energy may cause more and more transmission congestion events. Traditional congestion management modeling involves AC or DC power flow equations, while the former equation always accompanies great amount of computation, and the latter cannot consider voltage amplitude and reactive power. Therefore, this paper proposes a congestion management approach incorporating a specially-designed generalized generator shift distribution factor (GSDF) to derive a computationally-efficient and accurate management strategies. This congestion management strategy involves multiple balancing generators for generation shift operation. The proposed model is superior in a low computational complexity (linear equation) and versatile modeling representation with full consideration of voltage amplitude and reactive power.Comment: 5 pages, 4 figures. Accepted by conference: ICPES 202

Differences in structural connectivity between diabetic and psychological erectile dysfunction revealed by network-based statistic: A diffusion tensor imaging study

IntroductionType 2 diabetes mellitus (T2DM) has been found to be associated with abnormalities of the central and peripheral vascular nervous system, which were considered to be involved in the development of cognitive impairments and erectile dysfunction (ED). In addition, altered brain function and structure were identified in patients with ED, especially psychological ED (pED). However, the similarities and the differences of the central neural mechanisms underlying pED and T2DM with ED (DM-ED) remained unclear.MethodsDiffusion tensor imaging data were acquired from 30 T2DM, 32 ED, and 31 DM-ED patients and 47 healthy controls (HCs). Then, whole-brain structural networks were constructed, which were mapped by connectivity matrices (90 × 90) representing the white matter between 90 brain regions parcellated by the anatomical automatic labeling template. Finally, the method of network-based statistic (NBS) was applied to assess the group differences of the structural connectivity.ResultsOur NBS analysis demonstrated three subnetworks with reduced structural connectivity in DM, pED, and DM-ED patients when compared to HCs, which were predominantly located in the prefrontal and subcortical areas. Compared with DM patients, DM-ED patients had an impaired subnetwork with increased structural connectivity, which were primarily located in the parietal regions. Compared with pED patients, an altered subnetwork with increased structural connectivity was identified in DM-ED patients, which were mainly located in the prefrontal and cingulate areas.ConclusionThese findings highlighted that the reduced structural connections in the prefrontal and subcortical areas were similar mechanisms to those associated with pED and DM-ED. However, different connectivity patterns were found between pED and DM-ED, and the increased connectivity in the frontal–parietal network might be due to the compensation mechanisms that were devoted to improving erectile function

Sound wave neural network based on partial differential equation

Applications of neural network algorithms in rock physics have developed rapidly developed, mainly due to the neural network's powerful abilities in data modeling, signal processing, and image recognition. However, mathematical and physical explanations of neural networks remain limited, which makes it difficult to understand the behavior and mechanism of neural networks and limits their further development. Using mathematical and physical methods to explain the behavior of neural networks remains a challenging task. The goal of this study was to design a sound wave neural network (SWNN) structure based on sound wave partial differential equations and finite difference methods. The method transforms the first-order sound equations into the frequency domain and discretizes them using a central difference scheme. The differential formula takes the same form as the propagation function of a neural network, enabling the construction of a sound wave neural network. The main features of the SWNN are (1) a neural network with explicitly coupled pressure-velocity streams and inter-layer connections and (2) an adjoint variable method to improve the vanishing gradient problem in network training. The sound wave neural network established from the sound wave partial differential equation and finite difference algorithm has a solid mathematical modeling process and a clear physical explanation. This makes improving network performance within the framework of the mathematical and physical methods feasible. The numerical results showed that SWNN outperforms residual neural networks in image classification on CIFAR-10 and CIFAR-100 datasets. The partial differential equation neural network modeling method proposed in this paper can be applied to many other types of mathematical physics equations, providing a deep mathematical explanation for neural networks

Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning

The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural networks have started to gain momentum. Convolutional neural networks are particularly efficient at pattern recognition within images, and this is why they are suitable for seismic facies classification and interpretation tasks. We experimented with three different architectures based on convolutional layers and compared them with different synthetic and field datasets in terms of quality of the seismic interpretation results and computational efficiency. The architectures used in our study were three deep fully convolutional architectures: a 3D convolutional network with a fully connected head; a 2D fully convolutional network, and U-Net. We found the U-Net architecture to be both robust and the fastest when performing classification at the prediction stage. The 3D convolutional model with a fully connected head was the slowest, while a fully convolutional model was unstable in its predictions

Tight gas detection based on the reflectivity dispersion technology

The study focuses on the reflectivity dispersion caused by velocity dispersion related to pore fluid and its application to identify tight gas reservoirs. Seismic attenuation in viscoelastic media has two effects: (1) the change in frequency content and amplitude of a pulse propagating through attenuating media; (2) reflection coefficients are frequency dependent. Based on linear viscoelastic theory of velocity dispersion and the linearization of Zoeppritz equations, a new approximate reflection coefficient is presented considering P & S wave velocity dispersion, which adds the effects of frequencies and quality factors in reflection coefficient formula, representing the characteristics of reflection coefficient dependent by frequency, incident angle, quality factor etc. Based on the relationship between P-wave velocity and gas saturation, the reflected energy changes induced by reflectivity dispersion can be calculated and used to directly detect gas reservoirs, combining with seismic velocity, amplitude and energy attenuation mechanism caused by gas, such as squirt flow mechanism. This method has been successfully applied to characterize the distribution of tight gas sands in the GA101 well area of Sichuan Basin, and the results offer reliable foundations for seismic prospecting and well designing. Key words: reflectivity dispersion, seismic attenuation, velocity dispersion, tight gas reservoir, seismic gas detection, prestac

Electrical-Elastic Joint Inversion Method for Fracture Characterization in Anisotropic Media

Fracture networks are omnipresent in unconventional energy reservoirs. The inversion of fractures is of vital importance to oil and gas exploration and production. Most of the existing inversion methods are developed based on homogeneous media theory and rely on a solitary physical descriptor. For instance, one commonly employed single-property inversion approach is the determination of water saturation through the use of the media's electrical conductivity. With the fast development of multiphysics geological survey, a joint inversion framework that is suitable for anisotropic fractured media is needed. In this article, we propose an electrical-elastic joint inversion method involving both electrical tensor and elastic tensor to invert the fracture characteristics (e.g., fracture shape, inclination angle, and porosity). We conduct numerical experiments with two-phase geometries containing idealized ellipsoidal fractures. The resistivity tensor and Young's moduli of different directions are calculated and used to construct an anisotropy diagram and a joint inversion chart. The method is validated by comparing the predicted fracture geometry with the actual geometry of the fracture embedded in media. Both ideal homogeneous media and digital rock samples are used to test the inversion framework. A comparison between the single- and the joint-property inversion is also presented, and the joint-property inversion shows a higher accuracy in predicting fracture volume and tilting angle. This work indicates that the proposed electrical-elastic joint method can capture the anisotropy of the formation rock, and the multiphysics inversion framework exhibits the potential to recover fracture features with high fidelity

Electrospun Nanofiber-Based Membranes for Water Treatment

Water purification and water desalination via membrane technology are generally deemed as reliable supplementaries for abundant potable water. Electrospun nanofiber-based membranes (ENMs), benefitting from characteristics such as a higher specific surface area, higher porosity, lower thickness, and possession of attracted broad attention, has allowed it to evolve into a promising candidate rapidly. Here, great attention is placed on the current status of ENMs with two categories according to the roles of electrospun nanofiber layers: (i) nanofiber layer serving as a selective layer, (ii) nanofiber layer serving as supporting substrate. For the nanofiber layer’s role as a selective layer, this work presents the structures and properties of conventional ENMs and mixed matrix ENMs. Fabricating parameters and adjusting approaches such as polymer and cosolvent, inorganic and organic incorporation and surface modification are demonstrated in detail. It is crucial to have a matched selective layer for nanofiber layers acting as a supporting layer. The various selective layers fabricated on the nanofiber layer are put forward in this paper. The fabrication approaches include inorganic deposition, polymer coating, and interfacial polymerization. Lastly, future perspectives and the main challenges in the field concerning the use of ENMs for water treatment are discussed. It is expected that the progress of ENMs will promote the prosperity and utilization of various industries such as water treatment, environmental protection, healthcare, and energy storage

Highly Enantioselective Organocatalytic α‑Sulfenylation of Azlactones

The first asymmetric α-sulfenylation of azlactones with <i>N</i>-(sulfanyl)­succinimides has been developed by using <i>cinchona</i> alkaloid-derived squaramide as a catalyst and 4 Å molecular sieves as an additive. The reaction conditions were suitable to 4-alkyl and benzyl-substituted azlactones as well as <i>N</i>-(benzyl/alkyl/arylthio)­succinimides, affording adducts with high enantioselectivities (81–94% ee)

Chemical Looping Gasification of Wood Waste Using NiO-Modified Hematite as an Oxygen Carrier

Chemical looping gasification (CLG) technology is an effective approach to converting wood waste into high-quality syngas. In the present work, the reactivity of natural hematite is enhanced by doping with nickel oxide (NiO), and the effects of various operating parameters upon the CLG of wood waste are investigated using the NiO-modified hematite as an oxygen carrier. The NiO-modified hematite gives a significantly increased carbon conversion of 79.74%, and a valid gas yield of 0.69 m3/kg, compared to 68.13% and 0.59 m3/kg, respectively, for the pristine (natural) hematite, and 54.62% and 0.55 m3/kg, respectively, for the Al2O3, thereby indicating that the modification with NiO improves reactivity of natural hematite towards the CLG of wood waste. In addition, a suitable mass ratio of oxygen carrier to wood waste (O/W) is shown to be beneficial for the production of high-quality syngas, with a maximum valid gas yield of 0.69 m3/kg at an O/W ratio of 1. Further, an increase in reaction temperature is shown to promote the conversion of wood waste, giving a maximum conversion of 86.14% at reaction temperature of 900 °C. In addition, the introduction of an appropriate amount of steam improves both the conversion of wood waste and the quality of the syngas, although excessive steam leads to decreases in the reaction temperature and gas residence time. Therefore, the optimum S/B (mass ratio of steam to biomass) is determined to be 0.4, giving a carbon conversion and valid gas yield of 86.63% and 0.94 m3/kg, respectively. Moreover, the reactivity of the NiO-modified hematite is well-maintained during 20 cycles, with a carbon conversion and valid gas yield of around 79% and 0.69 m3/kg, respectively. Additionally, the XRD and SEM-EDS analyses indicate no measurable change in the crystal phase of the re-oxidized oxygen carrier