Renewable energy technologies and resources, particularly solar photovoltaic systems, provide cost-effective and environmentally friendly solutions for meeting the demand for electricity. The design of such systems is a critical task, as it has a significant impact on the overall cost of the system. In this paper, a mixed-integer linear programming-based model is proposed for designing an integrated photovoltaic-hydrogen renewable energy system to minimize total life costs for one of Saudi Arabia’s most important fields, a greenhouse farm. The aim of the proposed system is to determine the number of photovoltaic (PV) modules, the amount of hydrogen accumulated over time, and the number of hydrogen tanks. In addition, binary decision variables are used to describe either-or decisions on hydrogen tank charging and discharging. To solve the developed model, an exact approach embedded in the general algebraic modeling System (GAMS) software was utilized. The model was validated using a far... [more]

The optimal economic power dispatching of a microgrid is an important part of the new power system optimization, which is of great significance to reduce energy consumption and environmental pollution. The microgrid should not only meet the basic demand of power supply but also improve the economic benefit. Considering the generation cost, the discharge cost, the power purchase cost, the electricity sales revenue, the battery charging and discharging power constraints, and the charging and discharging time constraints, a joint optimization model for a multi-scenario microgrid with wind−photovoltaic-load storage is proposed in our study. Additionally, the corresponding model solving algorithm based on particle swarm optimization is also given. In addition, taking the Wangjiazhai project in Baiyangdian region as a case study, the effectiveness of the proposed model and algorithm is verified. The joint optimization model for a microgrid with wind−photovoltaic-load storage in multiple scen... [more]

As a promising new power source, the proton exchange membrane fuel cell (PEMFC) has attracted extensive attention. The PEMFC engine produces a large amount of waste heat during operation. The excessive temperature will reduce the efficiency and lifespan of PEMFC engine and even cause irreversible damage if not taken away in time. The thermal management system of the PEMFC plays a critical role in efficiency optimization, longevity and operational safety. To solve the problem of high heat production in the operation of the PEMFC, two approaches are proposed to improve the heat dissipation performance of the radiators in thermal management systems. Three kinds of nanofluids with excellent electrical and thermal conductivity−Al2O3, SiO2 and ZnO− are employed as the cooling medium. The radiator parameters are optimized to improve the heat transfer capability. A typical 1D thermal management system and an isotropic 3D porous medium model replacing the wavy fin are constructed to reveal the... [more]

The following paper examines the practicality of a methodical approach for energy-flexible and energy-optimal operation in the field of metal-cutting production. The analysis is based on the example of a grinding machine and its central cooling-supply system. In the first step, an energy-flexibility data model is built for each subsystem, which describes energy flexibility potentials generically. This is then extended to enable combined energy cost-optimal production planning. As a basis for the links between the data model representations, the cold flows between the subsystems are modeled using parameter-estimation methods, which have a mean absolute error of only 2.3 percent, making the subsequent installation of heat meters unnecessary. Based on the presented approach, the results successfully validate the possibility of energy-flexible cost-optimal and sensor-reduced production planning by reducing energy costs by 6.6 percent overall and 1.9 percent per workpiece produced.

The transition towards climate neutrality will result in an increase in electrical vehicles, as well as other electric loads, leading to higher loads on electrical distribution grids. This paper presents an optimisation algorithm that enables the integration of more loads into distribution grid infrastructure using information from smart meters and/or smart meter gateways. To achieve this, a mathematical programming formulation was developed and implemented. The algorithm determines the optimal charging schedule for all electric vehicles connected to the distribution grid, taking into account various criteria to avoid violating physical grid limitations and ensuring non-discriminatory charging of all electric vehicles on the grid while also optimising grid operation. Additionally, the expandability of the infrastructure and fail-safe operation are considered through the decentralisation of all components. Various scenarios are modelled and evaluated in a simulation environment. The res... [more]

A piezoelectric energy harvester (PEH) transduces mechanical energy into electrical energy, which can be utilized as an energy source for self-powered or low-power devices. Therefore, maximizing the power of a PEH is a crucial design objective. It is well known that structural designs are firstly conducted for controlling resonance characteristics, and then circuit designs are pursued through impedance matching for improving power. However, a PEH contains solid mechanics, electrostatics, and even a circuit-coupled multi-physics system. Therefore, this research aims to design a PEH considering a circuit-coupled multi-physics. As a design process, a conceptual design is developed by topology optimization, and a detailed design is developed sequentially by applying size optimization as a post-processing step to refine the conceptual design results for manufacturable design. In the two optimization processes, design optimizations of a structure coupled with circuit resistor are performed t... [more]

As social and environmental issues become increasingly serious, both fuel costs and environmental impacts should be considered in the cogeneration process. In recent years, combined heat and power economic emission dispatch (CHPEED) has become a crucial optimization problem in power system management. In this paper, a novel reinforcement-learning-based multi-objective differential evolution (RLMODE) algorithm is suggested to deal with the CHPEED problem considering large-scale systems. In RLMODE, a Q-learning-based technique is adopted to automatically adjust the control parameters of the multi-objective algorithm. Specifically, the Pareto domination relationship between the offspring solution and the parent solution is used to determine the action reward, and the most-suitable algorithm parameter values for the environment model are adjusted through the Q-learning process. The proposed RLMODE was applied to solve four CHPEED problems: 5, 7, 100, and 140 generating units. The simulatio... [more]

This paper presents a novel approach to tackle the problem of optimal neutral wire grounding in bipolar DC networks including asymmetric loading, which naturally involves mixed-integer nonlinear programming (MINLP) and is challenging to solve. This MINLP model is transformed into a recursive mixed-integer quadratic (MIQ) model by linearizing the hyperbolic relation between voltage and powers in constant power terminals. A recursive algorithm is implemented to eliminate the possible errors generated by linearization. The proposed recursive MIQ model is assessed in two bipolar DC systems and compared against three solvers of the GAMS software. The results obtained validate the performance of the proposed MIQ model, which finds the global optimum of the model while reducing power losses for bipolar DC systems with 21, 33, and 85 buses by 4.08%, 2.75%, and 7.40%, respectively, when three nodes connected to the ground are considered. Furthermore, the model exhibits a superior performance wh... [more]

In order to obtain polyimide-based composite materials for energy storage applications, four synthetic methods towards a polyimide matrix with 2 wt.% pristine or acid-functionalized MWCNTs have been developed. The polyimide is derived from a nitrile aromatic diamine and a fluorene-containing dianhydride which allowed the formation of flexible free-standing nanocomposite films. The films were thoroughly characterized by means of structural identification, morphology, mechanical, thermal and dielectric behavior, as well as the charge storage performance. The obtained data indicated higher homogeneity of the composites loaded with acid-functionalized MWCNTs that enabled significantly increased dielectric properties compared to the matrix. To assess the electrical charge storage capability, cyclic voltammetry and galvanostatic charge−discharge measurements were employed in a three-electrode cell configuration. Due to the higher conductivity of pristine MWCNTs compared to acid-functionalize... [more]

The hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays (DOPR) of the IEEE bus system proposes a new method for coordinating directional overcurrent protection relays in power systems. The method combines the hybrid particle swarm optimization (HPSO) algorithm and a heuristic PSO algorithm to find the minimum total operating time of the directional overcurrent protection relays with speed and accuracy. The proposed method is tested on the IEEE 4-bus, 6-bus, and 8-bus systems, and the results are compared with those obtained using traditional coordination methods. The collected findings suggest that the proposed method may produce better coordination and faster operation of DOPRs than the previous methods, with an increase of up to 74.9% above the traditional technique. The hybridization of the PSO algorithm and heuristic PSO algorithm offers a promising approach to optimize power system protection.

With the wake effect between different wind turbines, a wind farm generally aims to achieve the maximum energy capture by implementing the optimal pitch angle and blade tip speed ratio under different wind speeds. During this process, the balance of fatigue load distribution is easily neglected because it is difficult to be considered, and, thus, a high maintenance cost results. Herein, a novel bi-objective optimal wind farm energy capture (OWFEC) is constructed via simultaneously taking the maximum power output and the balance of fatigue load distribution into account. To rapidly acquire the high-quality Pareto optimal solutions, the decomposition-based multi-classifier-assisted evolutionary algorithm is designed for the presented bi-objective OWFEC. In order to evaluate the effectiveness and performance of the proposed technique, the simulations are carried out with three different scales of wind farms, while five familiar Pareto-based meta-heuristic algorithms are introduced for per... [more]

A smart hybrid energy system (SHES) is presented using a combination of battery, PV systems, and gas/diesel engines. The economic/environmental dispatch optimization algorithm (EEDOA) is employed to minimize the total operating cost or total CO2 emission. In the face of the uncertainty of renewable power generation, the constraints for loss-of-load probability (LOLP) and the operating reserve for the rechargeable battery are taken into account for compensating the imbalance between load demand and power supplies. The grid-connected and islanded modes of SHES are demonstrated to address a low-carbon community. For forecasting load demand, PV power, and locational-based marginal pricing (LBMP), the proper forecast model, such as long short-term memory (LSTM) or extreme gradient boosting (XGBoost), is implemented to improve the EEDOA. A few comparisons show that (i) the grid-connected mode of SHES is superior to the islanded-connected mode of SHES due to lower total operating cost and les... [more]

The rapid growth of the global population and urbanization has led to environmental degradation, resulting in a worldwide energy crisis. In response, the quality of architecture has evolved to prioritize energy efficiency, impacting indoor human health in the process. Green buildings have emerged as a solution to this problem, aiming to improve indoor environmental quality (IEQ) and human well-being while minimizing negative environmental impacts. This comprehensive review focuses on the role of green buildings in enhancing indoor human health and energy efficiency. It examines the published research on the effects of green buildings on IEQ and occupant health, highlighting sustainable architectural practices that promote good health. The study concludes that green buildings provide healthier environments for their occupants by creating healthy indoor environments, and minimizing negative environmental impacts. The study also explores the link between sustainable architecture and healt... [more]

Partial shading (PS) is a prevalent phenomenon that often affects photovoltaic (PV) installations, leads to the appearance of numerous peaks in the power-voltage characteristics of PV cells, caused by the uneven distribution of solar irradiance on the PV module surface, known as global and local maximum power point (GMPP and LMPP). In this paper, a new technique for achieving GMPP based on the dandelion optimizer (DO) algorithm is proposed, inspired by the movement of dandelion seeds in the wind. The proposed technique aimed to enhance the efficiency of power generation in PV systems, particularly under PS conditions. However, the DO-based MPPT is compared with other advanced maximum power point tracker (MPPT) algorithms, such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), and Bat Algorithm (BA). Simulation results establish the superiority and effectiveness of the used MPPT in terms of tracking efficiency... [more]

The main goal of this research was to propose a new method of polarimetric SAR data decomposition that will extract additional polarimetric information from the Synthetic Aperture Radar (SAR) images compared to other existing decomposition methods. Most of the current decomposition methods are based on scattering, covariance or coherence matrices describing the radar wave-scattering phenomenon represented in a single pixel of an SAR image. A lot of different decomposition methods have been proposed up to now, but the problem is still open since it has no unique solution. In this research, a new polarimetric decomposition method is proposed that is based on polarimetric signature matrices. Such matrices may be used to reveal hidden information about the image target. Since polarimetric signatures (size 18 × 9) are much larger than scattering (size 2 × 2), covariance (size 3 × 3 or 4 × 4) or coherence (size 3 × 3 or 4 × 4) matrices, it was essential to use appropriate computational tools... [more]

Electrical discharge machining (EDM) has recently become very popular for processing titanium alloys, but surface quality is a major problem. During machining, a defect layer inevitably forms on the surface, which can have a negative impact on surface quality. One of the ways to reduce the defect layer is to add powder to the dielectric. However, it is not yet completely clear which powder and in what quantity it should be added to reduce the defect layer. In this sense, the present study aims to investigate the effects of machining parameters on the defect layer in powder-mixed electrical discharge machining of titanium alloys. The main goal is to achieve the minimum thickness of the defect layer by optimally adjusting the input parameters. Experimental studies were performed using the Taguchi orthogonal array L9, considering discharge current, pulse duration, duty cycle, and graphite powder concentration as input parameters. Based on the Taguchi and ANOVA analyses, the discharge curr... [more]

The optimization of screening parameters will directly improve the screening performance of vibration screens, which has been a concern of the industry. In this work, the discrete element model of wet sand and gravel particles is established, and the vibration screening process is simulated using the discrete element method (DEM). The screening efficiency and time are used as evaluation indices, and the screening parameters including amplitude, vibration frequency, vibration direction angle, screen surface inclination, the long and short half-axis ratio of the track, feeding rate, and screen surface length are investigated. The results of an orthogonal experiment and range analysis show that the amplitude, screen surface inclination, and vibration frequency are significant factors affecting screening performance. Then, the support vector regression optimized with the grey wolf optimizer (GWO-SVR) algorithm is used to model the screening data. The screening model with excellent learning... [more]

The failing tube-to-tubesheet joint is identified as a primary quality defect in the fabrication of a shell-and-tube heat exchanger. Operating in conditions of high pressure and temperature, a shell-and-tube heat exchanger may be susceptible to leakage around faulty joints. Owing to the ongoing low performance of the adjacent tube-to-tubesheet expansion, the heat exchanger eventually experiences malfunction. A quality improvement study on the assembly process is necessary in order to delve into the tight-fitting of the tube-to-tubesheet joint. We present a non-linear screening and optimization study of the tight-fitting process of P215NL (EN 10216-4) tube samples on P265GH (EN 10028-2) tubesheet specimens. A saturated fractional factorial scheme was implemented to screen and optimize the tube-to-tubesheet expanded-joint performance by examining the four controlling factors: (1) the clearance, (2) the number of grooves, (3) the groove depth, and (4) the tube wall thickness reduction. Th... [more]

In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets from the Cleveland and IEEE Dataport. Optimizing model accuracy, GridsearchCV, and five-fold cross-validation are employed. In the Cleveland dataset, logistic regression surpassed others with 90.16% accuracy, while AdaBoost excelled in the IEEE Dataport dataset, achieving 90% accuracy. A soft voting ensemble classifier combining all six algorithms further enhanced accuracy, resulting in a 93.44% accuracy for the Cleveland dataset and 95% for the IEEE Dataport dataset. This surpassed the performance of the logistic regression and AdaBoost classifiers on both datasets. This study’s novelty lies in the use of GridSearchCV with five-fold cross-valida... [more]

This paper is concerned with the design and structural optimization of a training device for operators of a hydraulic rescue tool employed during traffic accidents, in conjunction with the improvement of the technical procedures used in such situations. Changes in the design process and subsequent production in the motor industry frequently result in an increased impact resistance of the used structural components. This applies, also, to extrication works and frequently used technical equipment. This paper presents its findings on the design process for the prototype of a training device designed for the extrication cutting drill with the assistance of a hydraulic rescue tool. The primary part of the research was dedicated to structural optimization; therefore, parameter dimensioning of the training device’s prototype was implemented. The device’s mechanical resistance, sturdiness, and stability during the implementation of hydraulic tools were also taken into account. A secondary part... [more]

A new Group Contribution Method based on elements and chemical bonds was proposed to predict the enthalpy of evaporation of organic compounds at their normal boiling points. A prediction model was built using 1266 experimental data points, and the accuracy of the model estimations was evaluated using 16 experimental data points. The new method has only 42 groups, a simple way of group splitting, and a wide range of predictions with an average relative error of 5.84%. Furthermore, the inclusion of silicon elements and their chemical bonds in the group library enables the effective prediction of silicon-containing compounds with an average relative error of 2.71%. By analyzing and comparing the other three commonly used methods, it can be concluded that the new method provides accurate and reliable estimation results and has a more comprehensive application range.

Due to financial limitations, power systems are being operated closer to their stability boundaries. Voltage stability analysis is crucial to preserve a power system’s equilibrium. However, this impacts a system’s dependability and security, and maintaining a power system’s voltage stability is a difficult challenge. Additionally, the inverters and converters in a high-voltage direct current (HVDC) system use a significant amount of reactive power, which exacerbates voltage instability. In this study, a new algorithm called Adaptive Neural Spider Monkey (ANSMA) was developed to improve the voltage stability security in an HVDC system. Additionally, the proposed ANSMA maintains voltage stability while scheduling the loads in the generator. Moreover, applying artificial-intelligence-related energy systems to these issues is considered an efficient solution. Fuzzy, neural, ANN, and other improvements in artificial intelligence approaches, along with power semiconductor devices, have signi... [more]

Sulfamethoxazole [SMX] and metronidazole [MNZ] are emergent pollutants commonly found in surface water and wastewater, which can cause public health and environmental issues even at trace levels. An efficient alternative for their removal is the application of adsorption technology. The present work evaluated single and binary adsorption processes using granular activated carbon (CAG F400) for SMX and MNZ in an aqueous solution. The binary adsorption process was studied using a Box−Behnken experimental design (RSD), and the results were statistically tested using an analysis of variance. Density functional theory (DFT) modeling was employed to characterize the interactions between the antibiotics and the CAG F400 surface. For the individual adsorption process, adsorption capacities (qe) of 1.61 mmol g−1 for SMX and 1.10 mmol g−1 for MNZ were obtained. The adsorption isotherm model that best fit experimental data was the Radke−Prausnitz isotherm model. The adsorption mechanism occurs th... [more]

Integrated solar combined cycle (ISCC) systems play a pivotal role in the utilization of non-fossil energy; however, the efficient application of solar energy has emerged as a primary issue in the study of ISCC systems. Therefore, it is extremely urgent to propose the best optimization scheme for ISCC under different operating conditions. In this paper, according to the idea of temperature matching and cascade utilization, the optimization of the ISCC system is carried out with the genetic algorithm for the whole working conditions, and the optimization schemes with the highest photoelectric efficiency and system efficiency under different working conditions are derived. In comparison with two optimization schemes with different objective functions, the conclusion can be drawn that: At 100% gas turbine load—30% DNI and 100% gas turbine load—100% DNI working conditions, respectively, the maximum system efficiency of 56.32% and the maximum solar photoelectric efficiency of 35.5% are atta... [more]

A number of canisters need to be lightweight designed to store the spherical fuel elements (SFE) used in high-temperature gas-cooled reactors (HTGR). The main challenge for engineering is pursuing high-accuracy and high-efficiency optimization simultaneously. Accordingly, a hybrid surrogate model-based multi-objective optimization method with the numerical method for the lightweight and safe design of the SFE canister is proposed. To be specific, the drop analysis model of the SFE canister is firstly established where the finite element method—discrete element method (FEM−DEM) coupled method is integrated to simulate the interaction force between the SFE and canister. Through simulation, the design variables, optimization objectives, and constraints are identified. Then the hybrid radial basis function—response surface method (RBF−RSM) surrogate method is carried out to approximate and simplify the accurate numerical model. A non-dominated sorting genetic algorithm (NSGA-II) is used fo... [more]