Techno-Economic Analysis of Hybrid Renewable Energy Systems for Electrification of Rustic Area in Egypt

Hybrid Power Systems (HPSs), particularly renewable energy-mix systems, use a wide variety of enabling technologies to overcome the difficulties associated with Renewable Energy (RE) resource variability in both standalone and grid-connected systems. According to the Egyptian national program towards reaching the 2020 objectives together with the continuous declination of the RE generation cost, extensive development and deployment of RE are witnessed from the government, academia, utilities and industry in Egypt. Therefore, this paper mainly aims at highlighting the potential of RE-hydrogen concept application for rural electrification in the small villages in Egypt in comparison with batteries. After introducing the comprehensive literature review that demonstrates the advantages and drawbacks of the RE standalone systems that mostly necessitates an Energy Storage System (ESS) support. The optimal.conomic design of the HPS that feeds the required electric load of the small Mansheat Taher village at Beni-Suef Governorate, Egypt is considered. For this purpose, five different HPS configurations are studied such as: PV-wind-battery, PV-Fuel Cell (FC), wind-FC, PV-wind-FC, and PV-wind-battery-FC systems. The models of various systems are optimally designed, sized based on the daily data for energy availability and the demand using HOMERTM software. From the viability analysis of the simulation results, HPS system of xxxxxxx that provides a total net present cost of $1,233,317 is considered the most economic and feasible option. The cost of energy is 0.1424$/kwh with a required initial capital of $916,728. A case study area, Monshaet Taher village at Beni-Suef Governorate, Egypt with (29° 1' 17.0718"N, 30° 52' 17.04"E) is identified for economic feasibility in this work. HOMER optimization model plan was designed with annual average solar radiation scaled of 5.93 (kWh/m2/day), annual average wind speed for the location is 4.92 m/s. Keywords: Batteries, Energy Storage; Fuel Cells; Hybrid System; Renewable Energy; Rural Electrification

Optimum Number of Grounded Shield Wires underneath Extra High Voltage Direct Current Transmission Lines

This paper is aimed at reducing the electric fields underneath extra high voltage direct current transmission lines using grounded shield wires. Two extra high voltage direct current transmission lines are modeled and analyzed. One line is homopolar and the other is bipolar, both are operating at 400 kV. The electric field calculated at the ground surface for the two transmission lines with and without grounded shield wires. In addition, the right-of-way limits for those transmission lines according to the maximum allowable electric field strength are calculated. The charge simulation method is used for calculating electric fields underneath the lines with and without grounded shield wires. A soft computer technique namely genetic algorithm was used also to determine the best location and number of grounded shield wires. The genetic algorithm was applied to a 400kV monopolar dc transmission; the best location of grounded shield wires is at 15m height above ground level for an optimum number of five grounded shield wires at spacing of 6m between them. Keywords: Charge Simulation Method, Electric Field Calculation, Electric Field Reduction, Shield Wires, Direct Current Transmission Lines, Health Effects, Right of Way

Unlocking Literary Insights: Predicting Book Ratings with Neural Networks

Abstract: This research delves into the utilization of Artificial Neural Networks (ANNs) as a powerful tool for predicting the overall ratings of books by leveraging a diverse set of attributes. To achieve this, we employ a comprehensive dataset sourced from Goodreads, enabling us to thoroughly examine the intricate connections between the different attributes of books and the ratings they receive from readers. In our investigation, we meticulously scrutinize how attributes such as genre, author, page count, publication year, and reader reviews influence a book's overall rating. Through rigorous analysis and experimentation, we construct an advanced ANN model tailored for predictive analysis in the realm of book ratings. The outcomes of our study reveal the remarkable potential of ANNs in this domain. The ANN model exhibits an impressive level of accuracy when it comes to forecasting book ratings, underlining the efficacy and promise of artificial neural networks in enhancing our understanding and prediction of book evaluations. This research opens up new avenues for leveraging machine learning techniques to gain deeper insights into the dynamics of book ratings and reader preferences

A framework for analyzing and testing cyber-physical interactions for smart grid applications

The reliable performance of the smart grid is a function of the configuration and cyber- physical nature of its constituting sub-systems. Therefore, the ability to capture the interactions between its cyber and physical domains is necessary to understand the effect that each one has on the other. As such, the work in this paper presents a co-simulation platform that formalizes the understanding of cyber information flow and the dynamic behavior of physical systems, and captures the interactions between them in smart grid applications. Power system simulation software packages, embedded microcontrollers, and a real communication infrastructure are combined together to provide a cohesive smart grid cyber-physical platform. A data-centric communication scheme, with automatic network discovery, was selected to provide an interoperability layer between multi-vendor devices and software packages, and to bridge different protocols. The effectiveness of the proposed framework was verified in three case studies: (1) hierarchical control of electric vehicles charging in microgrids, (2) International Electrotechnical Committee (IEC) 61850 protocol emulation for protection of active distribution networks, and (3) resiliency enhancement against fake data injection attacks. The results showed that the cosimulation platform provided a high-fidelity design, analysis, and testing environment for cyber information flow and their effect on the physical operation of the smart grid, as they were experimentally verified, down to the packet, over a real communication network

Influence of Pyrolysis Temperature and Production Conditions on Switchgrass Biochar for Use as a Soil Amendment

Biochars form recalcitrant carbon and increase water and nutrient retention in soils; however, the magnitude is contingent upon production conditions and thermo-chemical conversion processes. Herein we aim at (i) characterizing switchgrass (Panicum virgatum L.)-biochar morphology, (ii) estimating water-holding capacity under increasing ratios of char: soil; and, (iii) determining nutrient profile variation as a function of pyrolysis conversion methodologies (i.e. continuous, auger pyrolysis system versus batch pyrolysis systems) for terminal use as a soil amendment. Auger system chars produced at 600 °C had the greatest lignin portion by weight among the biochars produced from the continuous system. On the other hand, a batch pyrolysis system (400 °C – 3h) yielded biochar with 73.10% lignin (12 fold increases), indicating higher recalcitrance, whereas lower production temperatures (400 °C) yielded greater hemicellulose (i.e. greater mineralization promoting substrate). Under both pyrolysis methods, increasing biochar soil application rates resulted in linear decreases in bulk density (g cm-3). Increases in auger-char (400 °C) applications increased soil water-holding capacities; however, application rates of \u3e2 Mt ha-1 are required. Pyrolysis batch chars did not influence water-holding abilities (P\u3e0.05). Biochar macro and micronutrients increased, as the pyrolysis temperature increased in the auger system from 400 to 600 °C, and the residence time increased in the batch pyrolysis system from 1 to 3 h. Conversely, nitrogen levels tended to decrease under the two previously mentioned conditions. Consequently, not all chars are inherently equal, in that varying operation systems, residence times, and production conditions greatly affect uses as a soil amendment and overall rate of efficacy

Formulation, evaluation and optimization of miconazole nitrate tablet prepared by foam granulation technique

The aim of our study was to utilize novel foam granulation technique in formulation of miconazole nitrate; a model hydrophobic drug as oral disintegrating tablets "ODT" particularly to enhance its bioavailability. Foam granulation technique has additional advantages over the other traditional granulation technique since; it enhances the granulation process and produce acceptable tablets. Fractional factorial design was used to investigate the effect of formulation and processing variables on the prepared miconazole ODT. The prepared granules were evaluated by measuring their density, flowability, granules size and shape, and granules wetting time. The quality attributes of the prepared tablets; drug content, tablet thickness, uniformity of weight, tablet tensile strength, friability, disintegration, and dissolution were also evaluated. The results indicated that, the prepared granules showed acceptable characteristics and is significantly affected by the disintegrant type, urea concentration, and the lubricant type. The quality attributes of the tablets were not affected by the processing parameters. From the prepared formulas; F20, F19, F12, and F20 displayed 18, 35, 35, and 40 seconds disintegration time respectively and the percent of dissolution after 15 minutes ranged from 94.4-100%. These results ascertained that foam granulation technique fulfill the requirement in preparation of miconazole ODT. Key words: miconazole nitrate, foam granulation, oral disintegrating tablet

Artificial Neural Network for Predicting Car Performance Using JNN

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas