In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and ...user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.
Traditional bulk adsorbents, employed for the removal of dyes and metal ions, often face the drawback of requiring an additional filtration system to separate the filtrate from the adsorbent. In this ...study, we address this limitation by embedding the adsorbent into the polymer matrix through a process involving dissolution–dispersion, spin-casting, and heat-stretching. Selective dissolution and dispersion facilitate the integration of the adsorbent into the polymer matrix. Meanwhile, spin-casting ensures the formation of a uniform and thin film structure, whereas heat-induced stretching produces a porous matrix with a reduced water contact angle. The adsorbent selectively captures dye molecules, while the porous structure contributes to water permeability. We utilized inexpensive and readily available materials, such as waste polyethylene and calcium carbonate, to fabricate membranes for the removal of methylene blue dye. The effects of various parameters, such as polymer-adsorbent ratio, initial dye concentration, and annealing temperature, were investigated. Equilibrium data were fitted to Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich isotherms. The equilibrium data were best represented by the Langmuir isotherm, with maximum adsorption capacity of 35 mg/g and 43 mg/g at 25 °C and 45 °C, respectively. The membranes can be regenerated and recycled with a 97% dye removal efficiency. The study aims to present a template for adsorbent-embedded polymeric membranes for dye removal, in which adsorbent can be tailored to enhance adsorption capacity and efficiency.
Li-ion batteries degrade with time and usage, caused by factors like the growth of solid electrolyte interface (SEI), lithium plating, and several other irreversible electrochemical reactions. These ...failure mechanisms exacerbate degradation and reduce the remaining useful life (RUL). This paper highlights the importance of feature engineering and how a careful presentation of the data can capture the hidden trends in the data. It develops a novel framework of deep neural networks with memory features (DNNwMF) to accurately predict the RUL of Li-ion batteries using features of current and previous
n
cycles. The results demonstrate that introducing memory in this form significantly improves the accuracy of RUL prediction as root mean square error (RMSE) decreases more than twice with memory compared to without memory. The optimal value of
n
, referred to as
n
opt
, is also determined, which minimizes the prediction error. Moreover, the number of optimization parameters reduces by more than an order of magnitude if an autoencoder is used in conjunction with the proposed framework (DNNwMF). The framework in this paper results in a trade-off between accuracy and computational complexity as the accuracy improves with the encoding dimensions. To validate the generalizability of the developed framework, two different datasets, i) from the National Aeronautics and Space Administration’s Prognostic Center of excellence and ii) from the Center for Advanced Life Cycle Engineering, are used to validate the results.
This paper presents a novel sampling scheme on the sphere for obtaining head-related transfer function (HRTF) measurements and accurately computing the spherical harmonic transform (SHT). The scheme ...requires an optimal number of samples, given by the degrees of freedom in the spectral domain, for the accurate representation of the HRTF that is band-limited in the spherical harmonic domain. The proposed scheme allows for the samples to be easily taken over the sphere due to its iso-latitude structure and non-dense sampling near the poles. In addition, the scheme can be used when samples are not taken from the south polar cap region of the sphere as the HRTF measurements are not reliable in south polar cap region due to reflections from the ground. Furthermore, the scheme has a hierarchical structure, which enables the HRTF to be analyzed at different audible frequencies using the same sampling configuration. In comparison to the proposed scheme, none of the other sampling schemes on the sphere simultaneously possess all these properties. We conduct several numerical experiments to determine the accuracy of the SHT associated with the proposed sampling scheme. We show that the SHT attains accuracy on the order of numerical precision (10 -14 ) when samples are taken over the whole sphere, both in the optimal sample placement and hierarchical configurations, and achieves an acceptable level of accuracy (10 -5 ) when samples are not taken over the south polar cap region of the sphere for the band-limits of interest. Simulations are used to show the accurate reconstruction of the HRTF over the whole sphere, including unmeasured locations.
Mixed polyolefin-based waste needs urgent attention to mitigate its negative impact on the environment. The separation of these plastics requires energy-intensive processes due to their similar ...densities. Additionally, these materials cannot be blended without compatibilizers, as they are inherently incompatible and immiscible. Herein, non-wettable microporous sheets from recycled polyethylene (PE) and polypropylene (PP) are presented. The methodology involves the application of phase separation and spin-casting techniques to obtain a bimodal porous structure, facilitating efficient oil-water separation. The resulting sheets have an immediate and equilibrium sorption uptake of 100 and 55 g/g, respectively, due to the presence of micro- and macro-pores, as revealed by SEM. Moreover, sheets possess enhanced crystallinity, as evidenced by XRD; hence, they retain their structure during sorption and desorption and are reusable with 98% efficiency. The anti-wetting properties of the sheets are enhanced by applying a silane coating, ensuring waterless sorption and a contact angle of 140°. These results highlight the importance of implementing sustainable solutions to recycle plastics and mitigate the oil spill problem.
Molecular anthracene has been used as an arene in the Friedel-Crafts (FC) type arylation reaction of anthracenyl-α-hydroxyphosphonate in the presence of acid. A diverse product formation is observed, ...in which anthracene unit is found to be linked through its C1 position with α-C of phosphonate. Interestingly, the molecular conformation (X-ray structure) of this phosphonate reveals one of the bond angles of a tetrahedral carbon as 118° which is close to the C of sp
2
character. Further, molecular anthracene is also recognized to attack at the C10 position of 9-anthracenylphosphonate through C1 or C2 or C9 atoms and the structures of three isomeric phosphonates are established with the help of
1
H and
31
P NMR studies. The bis-anthracenyl compounds with a P-CH
2
unit have been successfully utilized in Horner-Wadsworth-Emmons (HWE) reactions to afford extensive bis-anthracenyl-linked π-conjugates.
Valuable compounds, naphthylarylketones are synthesized via oxy-Wittig (oxygenation of phosphonate carbanions) type reactions of diverse 1-naphthyl(aryl)methylphosphonates at room temperature by ...producing water soluble by-product. Commercially known but synthetically unexposed and expensive naphthylarylketones are easily synthesized using this transition metal-free, operationally simple strategy. As precursors, a range of new naphthyl(aryl)methylphosphonates are obtained in excellent yield and regioselectivity by direct and clean synthetic protocol that involves FeCl3 or triflic acid (TfOH) mediated arylation reactions of easily accessible (hydroxy)-1-naphthylmethylphosphonate with activated as well as unactivated arenes including halogenated anisoles, biphenyl, naphthalene and pyrene (extended π-systems) at room temperature.
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This paper considers the 3-D spatial fading correlation (SFC) resulting from an angle-of-arrival (AoA) distribution that can be modeled by a mixture of Fisher-Bingham distributions (FB-distributions) ...on the sphere. By deriving a closed-form expression for the spherical harmonic transform for the component FB-distributions, with arbitrary parameter values, we obtain a closed-form expression of the 3-D SFC for the mixture case. The 3-D SFC expression is general and can be used in arbitrary multiantenna array geometries and is demonstrated for the cases of a 2-D uniform circular array (UCA) and a 3-D regular dodecahedron array (RDA). In computational aspects, we use recursions to compute the spherical harmonic coefficients and give pragmatic guidelines on the truncation size in the series representations to yield machine precision accuracy results. The results are further corroborated through numerical experiments to demonstrate that the closed-form expressions yield the same results as significantly more computationally expensive numerical integration methods.
The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. ...Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.
Plastic waste comprises 15% of the total municipal solid waste and can be a rich source for producing value-added materials. Among them, polyethylene (PE) and polypropylene (PP) account for 60% of ...the total plastic waste, mainly due to their low-end and one-time-use applications. Herein, we report reusable oil sorbent films made by upcycling waste PE and PP. The as-prepared oil sorbent had an uptake capacity of 55 g/g. SEM analysis revealed a macroporous structure with a pore size range of 1-10 µm, which facilitates oil sorption. Similarly, the contact angle values reflected the oleophilic nature of the sorbent. Moreover, thermal properties and crystallinity were examined using DSC, while mechanical properties were calculated using tensile testing. Lastly, 95% of the sorbed oil could be easily recovered by squeezing mechanically or manually.
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