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This paper presents a novel linear hybrid excited flux switching permanent magnet machine (LHEFSPMM) with a crooked tooth modular stator. Conventional stators are made up of a pure iron core, which results in high manufacturing costs and increased iron core losses. Using a modular stator lowers the iron volume by up to 18% compared to a conventional stator, which minimizes the core losses and reduces the machine’s overall cost. A crooked angle is introduced to improve the flux linkage between the stator pole and the mover slot. Ferrite magnets are used with parallel magnetization to reduce the cost of the machine. Two-dimensional FEA is performed to analyze and evaluate various performance parameters of the proposed machine. Geometric optimization is used to optimize the split ratio (S.R) and winding slot area (Slotarea). Genetic algorithm (GA) is applied and is used to optimize stator tooth width (STW), space between the modules (SS), crooked angle (α), and starting angle (θ). The proposed model has a high thrust density (306.61 kN/m3), lower detent force (8.4 N), and a simpler design with higher efficiency (86%). The linear modular structure makes it a good candidate for railway transportation and electric trains. Thermal analysis of the machine is performed by FEA and then the results are validated by an LPMEC model. Overall, a very good agreement is observed between both the analyses, and relative percentage error of less than 3% is achieved, which is considerable since the FEA is in 3D while 2D temperature flow is considered in the LPMEC model.

Energy performance contracts are a very promising area for attracting private investment in the renewable energy sector. The concept of energy performance contracting is a well-established mechanism aimed at increasing the energy efficiency of a facility and reducing annual maintenance costs. This paper presents a hierarchical model of a decentralized energy system with renewable energy sources and a battery energy storage system under an energy service agreement. This model reflects the interaction between the client and the performance company. The model includes the main parameters characterizing the energy service contract, such as net present value, contract duration and levelized cost of energy. As an example, a real decentralized power system is considered, which currently only uses diesel generation. In the case of building a photovoltaic system, the optimal equipment composition consists of a 100 kW solar station and storage batteries with a capacity of 240 kW·h. The optimal contract term is 5 years, and diesel fuel savings are 69%.

The trend towards the decentralization and decarbonization of the energy sector stimulates the adoption of generation facilities based on renewable energy sources (RES) and distributed generation (DG) facilities that utilize secondary energy resources. Operation features of DG facilities, such as a high speed of electromechanical transient processes and significant deviations of power quality indicators from standard values, require improvement and an increase in the speed of automation devices. Modern electroautomatic devices must determine the operating regions (normal and emergency) and adapt the operation algorithms to the conditions of the current mode. The study presented proposes methods developed to use the Wald Sequential Probability Ratio Test (WSPRT) to improve the reliability and efficiency of the power system automation devices. The paper provides examples of using WSPRT in the devices of automatic frequency load shedding, automatic transformer disconnection, and power quality control. The results of mathematical modeling confirm the high performance of WSPRT in power system automation devices owing to an increase in the reliability of operating regions identification and speed of response. For example, in the automatic frequency load shedding (AFLS) algorithm for a network with DG facilities at a sampling rate that meets the requirements of the IEC 61850 (80 samples per period), the acceptance time does not exceed 1 ms. The study substantiates the need to use WSPRT in the logic blocks of automation devices employed in active distribution networks.

In the current research work, palladium (Pd) nanoparticles were electrochemically deposited on a nitrogen doped montmorillonite (CNx-MMT) support using the underpotential deposition (UPD) method. The prepared Pd based composite electrode was studied as an electrocatalyst for methanol fuel oxidation. The catalysts and the supporting materials montmorillonite, acid activated montmorillonite, and nitrogen doped montmorillonite (MMT, HMMT and CNx-HMMT) were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS) and electrochemical characterization by cyclic voltammetry (CV). The results indicated that Pd supported on CNx-HMMT possesses enhanced electrocatalytic activity and stability compared to commercial Pd/C, which was attributed to its higher electrochemical surface area (ECSA) (23.00 m2 g−1). The results demonstrated the potential application of novel Pd/CNx-HMMT composite nanomaterial as electrocatalysts for methanol electrooxidation in direct methanol fuel cells (DMFCs).

Hydrothermal liquefaction (HTL) is an effective technology for bio-crude production. To date, various co-liquefaction studies were performed with contrasted (different composition) biomasses in subcritical water. Therefore, the present study investigated co-hydrothermal liquefaction of similar kinds of lignocellulosic biomasses (wheat straw, eucalyptus, and pinewood) in supercritical water under equal ratios at 400 °C with catalytic medium (K2CO3). The lower bio-crude and higher solid yields were obtained in co-liquefaction experiments, as compared to liquefaction of individual feedstocks. On the other hand, higher carbon recovery and higher HHVs were noticed in co-liquefaction-derived bio-crudes. Gas chromatography with mass spectrometry (GC-MS) results showed that organic compounds were detected in all bio-crudes in the order of phenol derivatives > ketones/aldehydes > aromatics > carboxylic acids/esters. The aqueous phase from all samples contained higher TOC in the range of 19 to 33 g/L, with alkaline pH. In short, the co-liquefaction slightly improved the bio-crude quality with a significant reduction in bio-crude energy recovery. This reflects that co-liquefaction of lignocellulosic feedstock is not favorable for enhancing bio-crude yield and improving the overall process economics of HTL.

Propane-Precooled Mixed Refrigerant (C3MR) and Single Mixed Refrigerant (SMR) processes are considered as optimal choices for onshore and offshore natural gas liquefaction, respectively. However, from thermodynamics point of view, these processes are still far away from their maximum achievable energy efficiency due to nonoptimal execution of the design variables. Therefore, Liquefied Natural Gas (LNG) production is considered as one of the energy-intensive cryogenic industries. In this context, this study examines a single-solution-based Vortex Search (VS) approach to find the optimal design variables corresponding to minimal energy consumption for LNG processes, i.e., C3MR and SMR. The LNG processes are simulated using Aspen Hysys and then linked with VS algorithm, which is coded in MATLAB. The results indicated that the SMR process is a potential process for offshore sites that can liquefy natural gas with 16.1% less energy consumption compared with the published base case. Whereas, for onshore LNG production, the energy consumption for the C3MR process is reduced up to 27.8% when compared with the previously published base case. The optimal designs of the SMR and C3MR processes are also found via distinctive well-established optimization approaches (i.e., genetic algorithm and particle swarm optimization) and their performance is compared with that of the VS methodology. The authors believe this work will greatly help the process engineers overcome the challenges relating to the energy efficiency of LNG industry, as well as other mixed refrigerant-based cryogenic processes.

VANETs have gained much attention from both industry and academia because of their characteristics, such as dynamic topology. There are various applications of VANETs that are classified on the basis of safety, efficiency, commercial usage, and productive areas. This paper presents an IoT-based formal model for vehicle-life integration enabling RSUs with the help of different approaches. We have developed a model that uses vehicle scenarios in smart transportation systems so that quick data transmission is provided between the source and destination vehicles. Further, fog-based RSUs provide a wide range to communicate with hospitals and emergency vehicles to deal with emergency situations. All the appropriate entities are connected to ensure a consistent traffic flow for the arrival of an emergency vehicle in emergency places. The UML, graph theory, and VDM-SL formal technique are used to represent this system. To model the network and discover appropriate paths for V2V communication, graph theory is applied. The system requirements are designed using a UML diagram. The VDM-SL, an object-oriented model-based formal technique, was utilized for this modeling procedure. This approach assures the safety and accuracy of systems by detecting flaws early in the design process. It also gives an exceptionally important answer to an issue and increases trust in the software’s quality.