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This paper proposes an intelligent faculty evaluation and ranking system in a fuzzy environment, with a focus on semester-wise evaluation rather than annual evaluation. This approach is warranted because a university’s academic goals may vary across semesters, affecting the selection and weights of performance indicators related to teaching effectiveness, research output, and official responsibilities. To achieve this objective, the authors introduce the concept of N-framed plithogenic hypersoft set (PHSS), where N represents the number of frames or semesters. Three types of N-framed PHSS are introduced, and an efficient rank reversal-free multi-criteria decision-making technique, namely NR-TOPSIS, is extended by embedding N-framed PHSS in the algorithm of NR-TOPSIS, termed as ENR-TOPSIS. The modified algorithm is capable of extracting data from a source file and producing the desired results. The procedure is implemented for faculty evaluation in a double-framed fuzzy environment, and sensitivity analysis is performed for the proposed ENR-TOPSIS. The developed framework combines intelligent systems that are adaptable and contain additional characteristics, making it more versatile and increasing its accuracy and transparency, in line with SDG-4, which focuses on quality education.

This study aims to develop an environmentally friendly multi-energy system for sustainable production of electricity, cooling and hydrogen. The study introduces a pioneering geothermal-based system, integrating an ejector refrigeration cycle, a dual-loop organic Rankine cycle, and a hydrogen production unit with proton exchange membrane electrolyzers. The study provides a thorough analysis of the system's energy and exergy performance, as well as its economic feasibility. Through sensitivity and parametric analyses, the research identifies key parameters that significantly influence system performance. The system's innovative design promises minimal environmental impact while delivering multifaceted performance: generating 1.38 MW of electricity, supplying 436 kW of cooling load, and producing 5.39 kg/h of hydrogen. In the exergy analysis, Evaporator1 is identified as the primary contributor to exergy loss, representing 34 % of the total exergy destruction. This is followed by the electrolysis unit, the condenser, and the ejector refrigeration cycle, which contribute 18 %, 14 %, and 12 %, respectively. The system achieves optimal efficiency at an organic Rankine cycle turbine1 inlet temperature of 387 K, yielding a power generation of 885.4 kW and an exergy efficiency of 26.7 %. Beyond this temperature, any further increase leads to a decline in power output due to operational disturbances. A multi-criteria optimization using genetic algorithm is applied, resulting in an optimized system with a cost rate of 18.13 $/h and an exergy efficiency of 38.96 %.

Optimum operation of power system network becomes of great importance to handle the problem of power shortage. Connecting renewable energy (RE) based distributed generators (DG) into electrical networks (EN) are spreading rapidly to earn more benefits environmentally and economically. This paper aims to optimally operate DGs in order to minimize daily power loss in case of uncertainty of wind and PV system. Three different types of DGs, photovoltaic DG (PVDG), wind DG (WDG) and conventional DG (CDG) are connected to IEEE 33-bus system. Monte Carlo simulation (MCS) is used to generate probable scenarios for wind and photovoltaic power plants based on actual data for wind speed and solar radiation for one day. Fast forward scenario reduction (FFSR) is used to reduce the number of scenarios generated by MCS. Particle swarm optimization (PSO) is used to minimize daily power losses through determining the optimum operating point (operating power and power factor (PF)) for CDG to cope with PVDG and WDG uncertainties.

This study addresses the pressing need to enhance food production and quality by tackling the challenge of crop pest recognition. Traditional methods for identifying crop pests are time-consuming and reliant on highly trained experts. To overcome these limitations, this research introduces the Enhanced Metaheuristics with Deep Transfer Learning-based Robust Crop Pest Recognition and Classification (EMDTL-CPRC) model. The EMDTL-CPRC model incorporates several innovative elements, including contrast enhancement to improve image quality, the integration of the Whale Optimization Algorithm (WOA) with the Residual Network (ResNet) approach as a feature extractor, and the utilization of the Long Short-Term Memory (LSTM) method for pest recognition. Furthermore, to enhance the efficacy of the LSTM approach, a Chaotic Salp Swarm Algorithm (CSSA) is incorporated. The simulation results, based on a comprehensive crop pest dataset, demonstrate the superior performance of the EMDTL-CPRC model in crop pest classification. By enhancing pest recognition accuracy and streamlining the process, this research contributes to the overall goal of improving agricultural crop productivity and ensuring the supply of healthy food.

Marine transportation is a significant contributor to overall energy consumption among transportation sectors and is responsible for producing a considerable amount of greenhouse gases. A potential solution for alternative applications involves implementing combined heat integration, thereby enabling the production of additional utilities and mitigating emissions. The current work introduces an innovative heat integration process for a marine engine, focusing on implementing an optimal thermal matching technique to minimize overall irreversibility in producing liquefied hydrogen and coolant. The process incorporates an organic flash-bi-evaporator cooling cycle, a humidification dehumidification desalination, a polymer electrolyte membrane water electrolysis process, and a Claude cycle. The generated freshwater is delivered to the electrolyzer to produce gaseous hydrogen. This product and the cooling for freezing are utilized in the Claude cycle for hydrogen liquefaction. The study utilizes an advanced thermo-environmental multi-criteria investigation and optimization, considering sensitivity analysis and optimization based on artificial intelligence method. The optimization process incorporates the training and testing of artificial neural networks, NSGA-II method, and TOPSIS decision-making. The primary objective functions include exergetic efficiency and carbon dioxide emission. The findings demonstrate that the specified objectives are computed to be 0.121 and 2.67 kg/MWh, correspondingly. Besides, this condition exhibits a liquefied hydrogen flow rate of 6.44 L/h and a cooling output of 43.61 kW, showing an energy efficiency of 0.1145. Also, the total exergy destruction rate associated with the arranged structure is 124.5 kW. Furthermore, the optimum state reveals an exergoenvironmental index of 0.840 and an exergetic stability factor of 0.869.

The proposed system is designed to address the growing demand for sustainable and efficient energy solutions that can minimize greenhouse gas emissions while meeting the increasing energy needs of a modern world. The integrated approach of the system allows it to produce not only electricity but also hydrogen and fresh water from seawater, making it a comprehensive solution to multiple challenges. The system comprises several components that work together including a gas turbine unit, Rankine and organic Rankine cycles, a multi-effect desalination (MED) system, and a proton exchange membrane (PEM) electrolyzer. One significant characteristic of this system is its ability to generate hydrogen, which is later combined with the inlet fuel of the gas turbine. This novel methodology presents a more environmentally friendly fuel alternative, showcasing its potential to enhance combustion efficiency in a sustainable manner. To ensure the viability and feasibility of the system, a MATLAB function was utilized for calculating the base case scenario, followed by a GA optimization technique with the aid of a machine learning model to minimize run-time. In the given scenario, the total cost rate is measured at 3726 $/h, with an exergy efficiency of 54% and a normalized CO2 emission of 305 kg/GJ. Following the optimization process, significant enhancements can be observed. The cost rate decreases by 2202 $/h, the exergy efficiency improves from 54% to 55.61%, and the annual CO2 emissions decrease from 133,760 metric tons to 50,320 metric tons. Moreover, the implementation of blended fuel leads to a 10% decrease in normalized CO2 emissions.

Temperature plays a crucial role in various industries, equipment, and human life, and heat exchangers emerge as fundamental devices in managing this essential parameter. Heat exchangers come in various types, each tailored for specific applications. One commonly utilized variant is the finned-tube heat exchanger, which excels in transferring heat between liquid and gas. This study involved a numerical investigation of a finned-tube heat exchanger equipped with four bow-shaped vortex generators. The primary objective of this study was to assess the influence of three distinct geometric parameters associated with vortex generators on the performance of the heat exchanger. The key performance indicators investigated in this research were the Nusselt number and friction factor, which respectively represent the thermal and hydraulic performance of the device. Also, the relative efficiency index parameter was investigated to evaluate thermal and hydraulic performances simultaneously. Artificial Neural Network and Genetic Algorithm methods were employed to develop response models to simplify and increase the analysis accuracy and determine the optimal geometry. The artificial neural network's prediction models demonstrated high accuracy and effectively forecasted the desired outcomes. It was determined that the relative efficiency index of the finned-tube heat exchanger with vortex generators, even in its weakest-performing design, reached 1.0467. This means that by adding the bow-shaped vortex generators to the heat exchanger, there is a 4.67% increase in the overall performance in the worst case. Also, after predicting the optimal design for the heat exchanger, it was observed that this device has a 13.12% improvement in overall performance compared to the case without vortex generators. To adjust the Nusselt number of the finned-tube heat exchanger lacking vortex generators with that of the finned-tube heat exchanger featuring the optimal design, it is necessary to increase the inlet flow rate of the vortex generators-lacking heat exchanger by 53.20%. This increase in flow rate leads to a significant escalation of 108.73% in the heat exchanger's pumping power compared to the pumping power of the optimal design heat exchanger.

Sentiment analysis (SA) is a technique used in natural language processing (NLP), which determines the sentiment or emotion expressed in a piece of text. SA technique is frequently implemented on text datasets to assist in understanding customer needs, product sentiment in customer feedback, and businesses monitoring brands. Accordingly, the deep learning (DL) model is evolved as a promising technique that has been widely employed and attained considerable outcomes. Because DL models automatically extract features from the dataset, it can be claimed that intermediate representation extracted from the network is utilized as a relevant feature. Therefore, this article focuses on the design of automated sentiment analysis in social media using Harris Hawks Optimization with deep learning (ASASM-HHODL) model. The presented ASASM-HHODL model primarily processes the raw social media text into a useful format. Besides, the proposed ASASM-HHODL model uses fastText-based word embedding and skip-gram to examine the decrease of language processing dependency on data-pre-processing. Moreover, the attention-based bidirectional long-short-term model (ABiLSTM) is exploited for the classification of sentiments. Furthermore, the primary hyperparameters of the ABiLSTM model are adjusted by the use of the HHO algorithm to improve the classification performance. The performance validation of the proposed ASASM-HHODL model is carried out using a benchmark dataset and the results highlighted the promising performance over recent state of art approaches.

Remote sensing (RS) has developed significantly with the progress of the Internet of Things (IoT) which is allowed the cheap and fast acquisition of data in millions and billions of interrelated devices utilized throughout the whole world. RS scene classifier that purposes for classifying scene types for RS images has wide applications in several domains like urban planning, national defence security, environmental monitoring, and natural hazard detection. State-of-the-art deep learning (DL) successes are performed in a novel wave of RS scene classification applications, but it is the absence of explainability and trustworthiness. An intrusion detection system (IDS) plays a vital role to ensure security in the RS-based IoT environment. In this aspect, this study presents an ebola optimization algorithm with deep learning-based scene classification and intrusion detection (EOADL-SCID) technique on IoT-enabled remote sensing images. The aim of the EOADL-SCID system lies in the effectual scene classification of remote sensing images and intrusion detection. It involves a two-stage procedure. In the initial stage, the EOADL-SCID algorithm involves a modified DarkNet-53 feature extractor, EOA-based hyperparameter tuning, and graph convolution network (GCN) based classification. Next, in the second stage, the intrusion detection process takes place via two subprocesses namely variational autoencoder (VAE) based intrusion detection and skill optimization algorithm (SOA) based parameter tuning. The simulation outcomes of the EOADL-SCID approach are tested utilizing two benchmark databases and the experimental outcomes highlighted the improved performance of the EOADL-SCID algorithm on scene classification and intrusion classification processes.

This research aims to explore the economic and energy efficiency ramifications of combining the supercritical carbon dioxide oxy-fuel cycle with the Goswami cycle in the context of integrated heating and cooling systems, as well as power generation. Oxy-fuel systems, known for their high operating temperatures. In oxy-fuel systems, gas turbine exhaust gases typically preheat incoming flows to the combustion chamber. Nevertheless, even after preheating the combustion chamber inlets, the exhaust gases from the gas turbine retain a relatively high temperature, demonstrating considerable potential for energy production. Through a comprehensive parametric study, the impact of independent parameters on system performance is systematically explored. Subsequently, a three-stage economic analysis, total capital investment cost estimation, total operating cost estimation, and income evaluation, is conducted. The investment return time is assessed based on the selling price of system products. In the final step, a genetic algorithm, combined with artificial intelligence, optimizes the system for rational and optimal economic and energy conditions. The research findings reveal that in the absence of the Goswami cycle, the system yields 13.5 MW of power and 8.5 MW of heating, with an investment return time of approximately 16.32 years. However, with the inclusion of the Goswami cycle, power production increases to 15 MW, accompanied by 5 MW of cooling and 6.7 MW of heating. Despite augmented initial and operational costs, the investment return time is significantly reduced to 9.9 years. The results of a three-objective optimization strategy, focusing on maximizing power, heating, and cooling, highlight the system's versatility. Opting for maximum power production enhances power by 12.53 % and cooling by 27.48 %, compared to the reference mode. However, heating production decreases by 37.01 %. Notably, total capital investment costs show improvement by about 4.54 %, and the pay-back time experiences a substantial reduction of approximately 27.68 %. Moreover, the levelized cost of energy decreases from 111.4 $/MWh in the reference state to 106.9 $/MWh, underscoring the economic efficiency of the integrated system.

Low-energy cases can be designed more efficiently by using analytical optimization. The non-linear thermal performance of buildings has led to the development of optimization techniques based on simulation. In building optimization, it is important to achieve superior solutions while minimizing calculation expenses. This study aims to optimize an Australian office building using the Balanced Tree Growth Optimizer (BTGO). It has resulted that more than 11.7 % of energy can be saved by the optimization process and also some energy-saving measures. A comparison of the utilized algorithm with benchmark algorithms including the Nelder-Mead method, hybrid Particle Swarm Optimization, Hooke-Jeeves, and Ant Colony Optimization for continuous domain showed that the BTGO can achieve better solutions and needs less computational time.

A distribution system's network reconfiguration is the process of altering the open/closed status of sectionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of network reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic Archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage.

In modern technology, anticipating the optimal thermal performance in microchannel heat sinks (MCHSs) is a vital response to overheating challenges in high-performance electronic devices. The Response Surface Methodology (RSM) application presents an innovative way of modeling such equipment's behavior. In the current study, an MCHS with pin fins was chosen to examine the Nusselt number (Nu) and pressure drop (ΔP). Using two RSM models and multi-objective optimization, the study tried to discern the optimal values of the responses in the MCHS. The RSM models examined three key parameters: length, installation angle, and the spacing between pin fins. The interactive impacts of independent parameters and their consequent effect on the responses were also explored. The RSM models attained exceptional predictive accuracy, with the determination coefficient values of 0.9946 and 0.9986 for Nu and ΔP, respectively. The working fluid applied in the present work was water with an inlet temperature of 288 K and a Reynolds number of 300. Besides, the constant heat flux of 100 W/cm2 was exposed to the bottom wall of the MCHS. In pursuit of an optimal MCHS design that maximizes Nu while minimizing ΔP, the study employed the relative efficiency index (η) parameter. Considering heat transfer and ΔP in the pin-fin MCHS, the optimal design showed an impressive improvement of about 64.5 % compared to the fin-free heat sink. Moreover, the study provided Pareto optimal points for Nu, ΔP, and η, adding depth to understanding the complex trade-offs within the investigated MCHS.

In order to achieve a sustainable energy future, it is of utmost importance to harness solar energy for the production of liquid hydrogen. This undertaking is justified by the significant role that liquid hydrogen plays as a clean and highly efficient fuel source, effectively addressing the pressing concerns regarding greenhouse gas emissions and reducing reliance on conventional fossil fuels. An additional advantage lies in the easy storage and transportation capabilities of liquid hydrogen, making it a viable solution for energy storage across diverse sectors. In this regard, the current research presents a novel solar-powered setup consisting of parabolic trough solar collectors (PTSCs), a sequential organic Rankine cycle (ORC), a liquefied natural gas (LNG) regasification unit, and a module dedicated to the production and subsequent liquefaction of hydrogen. A meticulously crafted MATLAB code is utilized to replicate the operations of the proposed system, facilitating a thorough examination of its energy, exergy, environmental, and economic performance. The proposed system exhibits a net output power of 1.13 MW and possesses the capacity to produce hydrogen at a rate of 34.92 kg/h, featuring a levelized cost of hydrogen (LCOH) of 3.59 $/kg. Additionally, the system encompasses a cooling capability of 192 kW. From an environmental perspective, this configuration contributes to a decrease in carbon dioxide emissions by 255.96 kg/h. After conducting a dynamic analysis with meteorological data, it was determined that the system has the potential to generate an annual hydrogen output of 536.84 tons in San Francisco.

The proficient management and supervision of thermal conditions in microelectronic equipment are becoming progressively challenging owing to the imposition of heightened heat fluxes. The exploration of recently developed microchannel heat sinks (MCHSs) is currently underway to effectively tackle this matter. Modifying the geometric characteristics of these gadgets has proven to be highly effective in augmenting their comprehensive performance. In the present study, a novel arc-curved microchannels engraved on segmented circular heat sink and introduced configurations comprising of six discrete segments of spiral microchannels. The study focused on examining the influence of individual partitions on the Nusselt number (Nu) and pressure drop (ΔP) of the MCHS. The obtained results demonstrate that all the proposed configurations exhibit considerably higher values of the Nu in comparison to the reference MCHS consisting of 21 rectangular channels. Nevertheless, the notable augmentation in the value of the parameter Nu occurred simultaneously with an escalated ΔP. Hence, the relative efficiency index was employed as a more precise measure for evaluations. All of the designs suggested in this investigation were found to be effective when subjected to volume flow rates surpassing 47.303 ml/min. In an illustrative case, the segmented circular MCHS encompassing 10 spiral microchannels exhibited an enhanced overall efficiency of 25.9 % when operated at a volume flow rate of 66.225 ml/min, as compared to the benchmark design. In addition, artificial neural networks (ANN) were utilized to comprehensively scrutinize the influence of variables and construct a prognostic model to ascertain the overall effectiveness of the MCHS, obviating the necessity for convoluted, time-consuming, and costly experimental procedures and computations. The presentation of the Pareto optimal points effectively portrayed the optimal and unfavorable outcomes under consideration.

Customizing the configuration of Tesla valves should be adjusted to match the specific goals of the desired purpose. Channels with Tesla design have various applications in hydraulic and thermal fields. If the purpose is hydraulic application, this design of channels is used in the construction of one-way valves. The main focus may not be on thermal efficiency, instead, the emphasis lies on attaining a strong diode characteristic. However, in situations involving thermal applications, and considering that the thermal performance of these channels is suitable, they are used in the construction of heat sinks and heat exchangers. Microchannel heat sinks with Tesla design are used in cooling of CPUs, batteries, etc. In these situations, the goal is to minimize the pressure drop, while maximizing convective heat transfer. This research introduces an innovative structural design for the Tesla valve and presents two artificial neural network (ANN) models for predicting and optimizing its thermal and hydraulic capabilities. The design parameters being studied are the inner radius of the fins (R), the length of the folded segment of the divider (L), and the angle of the folded segment of the divider (θ). The researchers are examining how these variables affect the Nusselt number (Nu) and pressure drop (ΔP) in both forward and reverse flow directions. Based on the findings, by utilizing variables of optimal design 4 (θ = 130°, L = 3 mm, and R = 0.258 mm) and optimal design 5 (θ = 130°, L = 3 mm, and R = 0.586 mm) during the valve manufacturing process and altering the flow direction from forward to reverse, the thermal performance and diodicity increased by about 26.6% and 78.2%, respectively.

As a nuclear power source, gas turbine modular helium reactors (GT-MHR) can play a major role in the energy industry due to their high safety index and low operating costs. In light of the GT-MHR's high heat capacity, this study integrates a GT-MHR cycle with a Stirling engine to achieve a high-efficiency electricity production framework in an eco-friendly framework. Multi-aspect study from energy, exergy, economic and exergoenvironmental viewpoints are conducted. Detailed heat exchanger modeling is performed to accurately evaluate the system's performance conditions as part of the design process. The outcomes of this study reveal that in the optimum operating mode, the turbine's inlet temperature and expansion ratio are 1300 K and 2, respectively. The compressor compression ratio is 2.1, the Stirling engine inlet temperature is 850.6 K, and the regenerative effectiveness is 0.866. Consequently, the system's total electricity becomes 325.57 MW with 54.26 % energetic and 75.13 % exergetic efficiencies. The economic assessment of the system reveals that the unit electricity product cost is 8.9 $/GJ, and the total investment cost is 12737 $/h. Also, the optimal exergoenvironmental results show an exergoenvironmental index of 0.237, an environmental damage effectiveness index of 0.0031, and an exergetic stability factor of 0.24. Compared to GT-MHR, GT-MHR/KC, GT-MHR/APC, and GT-MHR/ORC, the newly designed system produces 44.57, 21.47, 29.74, and 25.17 MW more electricity and is more energetically efficient by 28.3 %, 24.45 %, 25.8 %, and 25.07 %, respectively.

Traditional power system stabilizer (PSS) is an effective controller to damp out low frequency oscillation (LFO) through the excitation system. Adding proportional integral derivative (PID) controller improves the performance of PSS controller. This paper propose Archimedes optimization algorithm (AOA) to improve the performance of series PSS-PID controller through adjusting the tuning parameters to effectively damp out LFO and improve power system transient stability in western system coordinate council (WSCC) power system. AOA is used to optimize the system based on minimizing the integral time absolute error (ITAE) of rotor angular speed. A nonlinear model of WSCC system and PSS-PID controller are simulated under three phase short circuit fault applied at terminals of machines. The system has been tested without PSS-PID controller. Then, PSS-PID controller is optimized using AOA algorithm and whale optimization algorithm (WOA) to compare between their performances. The system under study is simulated in time-domain using MATLAB software.

Over the past few years, the distinctive design and versatile applications of Tesla valves have captured considerable interest across diverse industries. In contrast to conventional check valves, Tesla valves employ interconnected channels, establishing a highly efficient and reliable fluid flow control mechanism. This research delves into an investigation of the optimum geometric parameters that significantly influence the performance of a novel Tesla valve. The study focuses on three key geometric characteristics: the valve angle (α), the distance between consecutive stages (D), and the distance between the divider wall in the second stage of each step group and the wall of the straight channel (H). The authors carried out a numerical study using computational fluid dynamics to acquire the results. Four multi-layer perceptron models, each with a structure of 3-2-2-1, were applied to predict the selected responses of Nusselt numbers in the forward (Nuf) and reverse (Nur) directions, as well as pressure drops in the forward (ΔPf) and reverse (ΔPr) directions. The findings revealed that among all the variables examined, the parameter H exerted the most substantial influence on all measured responses. It was concluded that by incorporating specific values of α = 34.065°, D = 9 mm, and H = 5.624 mm during the manufacturing process of the valve and altering the flow direction from forward to reverse, a remarkable improvement of approximately 271.7% in pressure diodicity was achieved.

Produced CO2 from fossil fuel-based transportation vehicles and energy systems can have a direct impact on people who live in adjacent of the power plants. So, in the present work, we proposed and modeled an integrated system comprising a gas engine, a carbon dioxide capturing unit, an absorption chiller, and a greenhouse. The greenhouse uses directly the CO2 produced in gas engine through the carbon dioxide capturing unit to mitigate the emitted CO2 into the atmosphere. Besides that, the produced heating in gas engine could be exerted in a cycle to render the greenhouse space in cold weather condition. In warm seasons this thermal energy could also be delivered to an absorption chiller for producing required cooling for inside space of the greenhouse. Herein, the generated electricity is sold to grid, and an economic analysis is brought about. To evaluate and estimate the system performance and interest of selling electricity, a mathematical function was applied. The results show that, during cold seasons, the maximum possible net interest is 800,000 $, and for warm seasons is 2,500,000 $. The maximum COP is also achieved by 1.007. Finally, the amount of CO2 emission rate is mitigated at least 50%.

Microchannel heat sinks (MCHSs) are widely utilized in various industries, including electronics, power systems, and aerospace, to dissipate heat generated by high-power components effectively. The efficient cooling of these devices is crucial for maintaining their optimal performance, reliability, and longevity. This research aimed to augment the overall efficiency of the MCHS by utilizing 25 baffles with varying geometric parameters. The aim was to identify the optimum geometry for these baffles using an Artificial Neural Network (ANN) model. The ANN model was applied to propose two optimal designs for the baffles, specifically targeting the maximum Nusselt number and overall performance of the MCHS. It was noticed that the vertical pitch of the baffles ''d'' had the most substantial influence on the device's behavior among the investigated geometric parameters. The higher value of ''d'' allowed the fluid to spread over the entire space of the heat sink rather than being confined to a specific area. This spreading effect enabled the fluid to contact all the baffles and channel walls, facilitating efficient heat transfer. The applied ANN model with R2 values of about 0.98 and lower values of MAE and RMSE successfully fitted the data. The findings of this study demonstrated that applying the specified input variables (horizontal pitch of 8.496 mm, vertical pitch of 5 mm, and attack angle of 210. 560°), as defined in the optimal overall design, in manufacturing of the baffles resulted in a remarkable enhancement of about 46% in the overall performance of the MCHS in comparison with the design proposed in the reference paper. The heat transfer improvement of the MCHS with baffles were due to the vortices in the baffles bring about chaotic advection and could greatly enhance the convective fluid mixing.

Model predictive control (MPC) is recently emerging as an efficient and promising technique for the control of power converters. In the conventional MPC algorithm, the control objectives are usually estimated and evaluated for a large/definite number of switching states. Since prediction and evaluation are done for all possible states, massive amounts of estimations are needed, moreover, the computational burden is more challenging with the increase of control objectives. In this paper, a computationally efficient version of the finite control set-MPC (FCS-MPC) is proposed to decrease the calculation effort of the MPC algorithm likewise minimizing its execution time to enforce its vast application for the control of three-phase power converters. The suggested procedure is to eliminate the current predictions as well as reduce the number of available switching states that need to be estimated by the algorithm which reduces considerably the amount of time consumed by these computations. The studied techniques achieved nearly the same performance with an interesting reduction in the algorithm execution time accomplished by the proposed modified FCS-MPC algorithms.

Artificial intelligence (AI), often known as machine learning, is a powerful tool for solving engineering problems. The evaluation of the network reliability of a flow network is a NP-hard problem, with computational effort growing exponentially with the number of nodes and arcs in the network. Also, the components assignment issue is NP-hard, and the computational effort increases with the number of available components. Many candidate solutions are typically examined during optimal components or optimal capacity assignment, each requiring reliability calculation. Consequently, this paper proposes an artificial neural network (ANN) predictive model to evaluate the flow network reliability. The neural network is one of the artificial intelligence tools constructed, trained, and validated using the maximum capacity of each component input and the network reliability as the target. The proposed ANN model provides empirical proof that neural networks can accurately estimate reliability by modeling the connection between the maximum capacities of network components and the reliability value.

A Stable and highly reliable DC link voltage represents an important factor for efficient power transfer in high voltage direct current (HVDC) networks. In this framework, this paper investigates the control and stability analysis of voltage source converter (VSC) for DC link voltage regulation. To separately achieve the independent active and reactive power control, the system voltages and currents are represented in the synchronous reference frame. In order to optimally design the parameters of proportional-integral (PI) controller, the inner and outer loops’ transfer functions are thoroughly derived/developed. In addition,/moreover, in order to attain satisfactory/certain system performance, modulus optimum, symmetrical optimum pole placement control approaches are studied and implemented for the purpose of tuning the voltage/current controller gain parameters. In particular for the symmetrical optimum control method, the gain parameters of the DC-bus voltage are determined under different values of network impedance. Furthermore, the impact/influence of changing gain parameters on the DC-link voltage and poles/zeros movement are investigated. MATLAB/Simulink model is built and simulation studies are carried out to verify the introduced concepts.

Stochastic nature of load demand has a great impact on the performance of electrical power system. As a result, planning of electrical power system considering load uncertainties became inevitable. This paper presents Monte Carlo simulation based different bio-inspired algorithms, grey wolf optimization (GWO), manta ray foraging optimization (MRFO), satin bower bird optimization (SBO) and whale optimization (WOA) to optimize locations of three DG units under load uncertainties considering 500 scenarios. Each scenario includes 50 iterations which means that for each run we have 25,000 iterations and 500 characteristics for different load value. Two objectives are achieved. Firstly, statistically finding the optimal probabilistic location of three DG units under load uncertainties in IEEE 33-bus and IEEE 69-bus radial distribution system based on Monte Carlo simulation integrated with different bio-inspired algorithms. Secondly, comparing between the performances of four different bio-inspired algorithms. Three objective functions are considered, minimizing active power loss, minimizing voltage deviation and maximizing voltage stability index. The active and reactive power demand are normally distributed using normal distribution function. The optimal probabilistic location is investigated considering two cases under load uncertainties, optimizing location of three DG units generally and optimizing location of one DG unit assuming two optimum locations for the other two units extracted from case I. The obtained results (after placing DG units) are compared to the base case (DG units are not connected) and compared to each other according to the optimization technique. The results show that, SBO algorithm superiors other algorithms almost in all cases. Comes next GWO which provide good results generally. However, the good performance obtained by MRFO, it consumes twice the time of other algorithms. WOA however fast convergence, it provides results worse than other algorithms. The system is applied to the well-known IEEE 33-bus and IEEE 69-bus radial distribution system.

A very critical and integral part of the power system is the distribution networks. The final component of the power system, including transmission systems or consumers, is the distribution system (DS). This leads to the highest energy loss that happens. So improving the performance of distribution networks is required not only to provide the reliability of power supply but also to achieve the most economic cost. By optimizing the power flow and simultaneously minimizing the total emission cost and generation cost and taking into account the power losses, these objectives can be achieved. In recent years, heuristic methods are widely employed for solving such complex problems, and The main modern optimization techniques are genetic algorithm(GA). The most important issue in Evolutionary Algorithms is exploration vs. exploitation. Maybe GA is restricted for exploration features, what causes slow convergence and poor robustness Therefore, using the hybridization strategy, the main reason behind this is that such a hybrid approach is expected to create swape between the exploration and exploitation. This work presents performance improvement of a radial distribution networks using a new hybrid optimization technique of Genetic Algorithms (GA) with Equilibrium optimizer (EO) algorithm called Hybrid Genetic Algorithm Equilibrium optimizer (GAEO). It is used for optimum location and size of Renewable Energy Sources (wind energy, photovoltaic, fuel cell) on distribution systems. DG source locations and capacity have strongly influenced the improvement of the distribution network performance by reducing the entire system's power loss, enhancing the voltage profile, reducing fuel costs and emissions of contaminants.

The optimal components assignment problem in a flow network with node failure in the presence of system reliability and total lead-time constraints is studied in this paper. By using fuzzy membership functions, we formulated the problem as fuzzy linear optimization. Genetic optimization algorithm with fuzzy optimization is suggested for solving the presented problem. The suggested approach succeeded in finding an optimal solution that distinguishes with increasing reliability and minimizing total lead-time. The suggested technique is examined on various models, four nodes, six nodes, and eight nodes computer networks.

The endless problem of energy supplies are always floating on the surface. As a result, there are a daily improvement to optimize power generators, networks and system configuration. Renewable distributed generators (RDG) are in the heart of these developments. The size of RDG is increasing daily so, it must be optimized to maximize benefits and eliminate drawbacks. Optimization algorithms are one of the fast growing techniques. In this study the Manta Ray Foraging optimization algorithm (MRFO) is applied to minimize power losses through sizing and allocation of DG type I integrated into radial distribution network (RDN). The proposed technique was tested on three different networks, IEEE 33, 69 and 85 test systems. Also, three cases were assumed to evaluate the effectiveness of MRFO algorithm. The results were compared to recent applied techniques.

This paper proposes an explicit adaptive controller to damp oscillations and to enhance the single machine infinite bus SMIB stability. Owing to the increasing requests for renewable energy and operating conditions, the identification for power systems has been increased recently. Changes in the power system parameters cause to use an explicit self-tuning control. The controller structure consists of combined on-line identifier and a feedback controller as PID and a radial basis function neural network (RBFNN) which acts as an adaptive power system stabilizer for SMIB. An adaptive linear neural network (ADALINE) depending on the input and output of open loop system is employed as on-line model identification to mimic on-line the SMIB output. The difference between SMIB and the identified model responses is used to adjust the ADALANN model weights on-line depending on a recursive least squares principle RLS and a recursive least square with adaptive directional forgetting RLSMadf. The particles swarm optimization (PSO) beside RLS and RLSMadf assess the weights of (RBFNN) and coefficients of PID controllers depending on the on-line ADALINE model weights. The proposed controller is validated with several operating conditions under various disturbances. The simulation results show the proposed controller whose parameters depend on on-line tuning techniques provides better performance than a conventional PID controller.

Recently, an explosive growth in the potential use of natural metaphors in modelling and solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a potent gadget broadly utilized in many data classification tasks. Fundamentally, nature-inspired algorithms have demonstrated their effectiveness and ability over traditional algorithms for generating the optimal ANN parameters, rules and topology that provide the best classification performance with regarding to the quality of the solution, computational cost and avoiding local minima. The literature is vast and growing. This study provides a review on the basic theories and main recent algorithms for optimizing the ANN. Different types of nature-inspired meta-heuristic algorithms are presented; outlining the concepts and components that are used in order to give a summary and ease of the state-of-the-arts to find suitable methods in real world applications for the readers. Additionally, this survey covers the most used type of neural networks, feed-forward neural network (FFNN) in several optimized applications. The performances of FFNNs designed by nature-inspired algorithms have been explored in single and multi-dimensional optimization space; highlighting their models, features, objectives, constraints, etc. to analyse their differences and similarities. A comprehensive survey of the earliest works and recent modified in the last decade in addition to expect approaches has been investigated in details.

Data mining optimization received much attention in the last decades due to introducing new optimization techniques, which were applied successfully to solve such stochastic mining problems. This paper addresses implementation of evolutionary optimization algorithms (EOAs) for mining two famous data sets in machine learning by implementing four different optimization techniques. The selected data sets used for evaluating the proposed optimization algorithms are Iris dataset and Breast Cancer dataset. In the classification problem of this paper, the neural network (NN) is used with four optimization techniques, which are whale optimization algorithm (WOA), dragonfly algorithm (DA), multiverse optimization (MVA), and grey wolf optimization (GWO). Different control parameters were considered for accurate judgments of the suggested optimization techniques. The comparitive study proves that, the GWO, and MVO provide accurate results over both WO, and DA in terms of convergence, runtime, classification rate, and MSE.

Maximizing the classification accuracy and minimizing the number of selected features are the two main incompatible objectives for using feature selection to overcome the curse of dimensionality. “Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy.” This work presents a new meta-heuristic optimization approach, called Parasitism-Predation Algorithm (PPA), which mimics the interaction between the predator (cats), the parasite (cuckoos) and the host (crows) in the crow–cuckoo–cat system model to overcome the problems of low convergence and the curse of dimensionality of large data. The proposed hybrid framework combines the relative advantages of cat swarm optimization (CSO), cuckoo search (CS) and crow search algorithm (CSA) to attain a combinatorial set of features to boost up the classification accuracy. Nesting, parasitism, and predation phases are supposed to help exploration ability and balance in the context of solving classification problems. In addition, Levy flight distribution is applied to help better diversity of conventional CSA and improve ability of exploration. Meanwhile, an effective fitness function is utilized to enable the proposed PPA-based feature selector using K-Nearest Neighbors algorithm (KNN) to attain a combinatorial set of features. The proposed PPA and four standard heuristic search algorithms are looked at to gauge how efficient the proposed option is. Additionally, eighteen classification datasets are deployed to gauges its efficacy. The results highlight that the algorithm proposed is both effective and competitive in terms of performance of classification and dimensionality reduction as opposed to other heuristic options.

Hybrid micro-grid systems (HMGS) are small scale power system where the energy sources are installed to supply local customers. These systems may be considered as promising energy solution to meet the increased in energy demand and traditional sources depletion. Cost of electricity, system reliability, and environmental impacts of the system are three design criteria that must be considered in obtaining the accurate parameters of hybrid renewable energy system components. In this paper, hybrid micro-grid renewable energy system includes photovoltaic system, (PV) wind energy system, (WES) battery bank, (BB) and conventional diesel generator (DG) are proposed to meet the energy requirements in remote area, located in Red Sea called city of Bernice, Egypt, at 23° 54′ 31″ N, 35° 28′ 21″ E. Optimization of Cost of Electricity (COE), Renewable Factor (RF), and Loss of Power Supply Probability (LPSP) are main objective of the designing process of the hybrid system considered as the objective functions. Then, Multi-objective multi-verse optimization (MOMVO) algorithm is used with considering two scenarios, the first one is renewable sources and the second is renewable/diesel energy source. All the possible HMGS configurations namely: PV/battery, wind/battery, PV/wind/battery and PV/battery/diesel, wind/battery/diesel, PV/wind/battery/diesel are studied and analyzed. Moreover, one year hourly meteorological weather data for case study are recorded. Reverse osmosis desalination (ROD) is considered in conjunction with the residential load. The proposed power management strategy is used to manage the system operation when supplying the load. A linear fuzzy membership function is used for purpose of decision making. The simulation results show that MOMVO produces appropriate components size and the PV/wind/battery/diesel is the optimum configuration with values of COE = 0.2720$/KWh, LPSP = 0.1397, and RF = 92.37% at w1 = 0.5, w2 = 0.3, and w3 = 0.2. Sensitivity analysis is performed to show the effect of changing system parameters on the objective functions. It is also shown that the techno-economic feasibility of using HMGS for rural electrification systems and enhance energy access.