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]

Nowadays, the world is encountering multiple challenges of energy security, economic recovery, and the effect of global warming. Investing in new fossil fuels only locks in uneconomic practices, sustains existing risks and increases the threats of climate change. In contrast, renewable energies, such as photovoltaic energy, constitute one of the most promising technologies in combating global increase in temperatures. Given its simplicity and low maintenance costs, photovoltaic energy is the most effective alternative to address the issues above. However, the standard test conditions (STCs) of PV modules are, in most cases, different from the real working conditions of a solar module. For instance, high levels of incident irradiation in an arid climate may cause the temperature of a module to rise by many degrees above the STC temperature of 25 °C, lowering the module’s performance. To effectively simulate and control PV systems for a given location, it has become paramount to develop... [more]

This paper presents a fully-decentralized peer-to-peer (P2P) electricity and gas market for retailers and prosumers with coupled energy units, considering the uncertainties of wholesale electricity market price and prosumers’ demand. The goal is to improve the overall economy of the proposed market while increasing its flexibility. In this market, the retailers are equipped with self-generation and energy storage units and can bilaterally negotiate for electricity and gas transactions with prosumers to maximize their profit. Furthermore, they can sell power to the upstream market in addition to prosumers. The prosumers have access to several retailers to supply their required electricity and gas and can freely provide their energy needs from every retailer, contributing to dynamicity in the proposed market. Given that they have an energy hub consisting of boiler units, combined heat and electricity (CHP) units, and electric pumps, they can switch their energy supply source from electri... [more]

The wide application of power electronic devices brings an increasing amount of undesired harmonic and interharmonic tones, and accurate harmonic phasor estimation under a complex signal input is an important task for smart grid applications. In this paper, an optimization of least-square dynamic harmonic phasor estimators, considering multi-interference and harmonic frequency variance, is proposed. A comprehensive error index (CEI) composed of the fundamental-leakage-led harmonic amplitude estimation error, harmonic mutual interference, out-of-band interference, and harmonic frequency deviation is employed. The largest CEI part of least-square algorithms using three different signal decomposition models is analyzed for the first time, and variables to reduce this error component are then introduced using singular value decomposition. With the CEI and defined variables, a minimum-error estimation of harmonic phasors under various interference and harmonic frequency change is discussed.... [more]

In the rural regions of southern Brazil, electricity is largely directed to irrigation activities on rice crops at restricted periods of the year. Typically, customers in these regions are called “irrigators”, and have some characteristics different from loads in urban centers, such as high demand levels and sharp load variations. These characteristics can result in problems of excessive loading on distribution grids at certain times of the day, generating concerns for the power utilities in relation to the security of the electrical system, energy supply to customers, and the integrity of electrical equipment. An alternative to avoid or mitigate these possible problems may be the application of a demand management model to irrigator customers. In this context, a load shifting strategy can be inserted to reduce demand in more critical periods and move it to intervals with lower load on the power grid. In this context, this article presents a demand-side management methodology in distri... [more]

Comprising a total of seven articles divided into five research articles, one review article, and one editorial article, this Special Issue is dedicated to new techniques for piezoelectric energy harvesting and its design, optimization, applications, and analysis [...]

Optimization studies are an important task in reservoir engineering practices such as production optimization and EOR (Enhanced Oil Recovery) assessments. However, they are extensive studies with many simulations that require huge computational effort and resources. In terms of EOR, CO2 injection is one of the most common methods employed due to a high recovery potential and environmental benefits. To assess the feasibility of CO2-EOR projects, a reservoir design study must be conducted before optimization is performed. Some studies have demonstrated the advantages of employing proxy models to perform this task in terms of saving huge amounts of computer memory space and time. In this study, proxy models were developed to solve a multi-objective optimization problem using NSGA-II (Non-dominated Sorting Genetic Algorithm II) in two selected reservoir models. The study was performed for a CO2-WAG (Water Alternating Gas) application, where gas and water injection rates and half-cycle leng... [more]

Investments in the development of the district heating system require a thorough analysis of the technical, economic, and legal aspects. Regarding the technical and economic context, a mathematical model of the district heating system has been proposed. It optimizes both the technical and economic aspects of the function and development of a district heating system. To deal with non-linearities, the developed linear programming model is divided into three phases: flow, thermal, and pressure. Therein, non-linear dependencies are calculated between the linear optimization phases. This paper presents the main assumptions and equations that were used to calculate the parameters of the heating system, along with the optimal level of heat production, the flow rate of the heating medium in the heat nodes and edges of the network graph, the heat, power, and temperature losses at each edge, and the purchase costs of heat and its transmission. The operation of the model was tested on a real-worl... [more]

To address the problems of “difficult to consume” renewable energy and the randomness of power output, we propose the CHP unit joint-operation model with power to gas (P2G) and carbon capture system (CCS) technologies and analyze the operation cost, carbon emission, and “electric-heat coupling” characteristics of this model. A dispatch optimization model is constructed based on the information-gap decision theory under the strategy to further consider the interval uncertainty of renewable energy unit output and load forecast. The optimized-dispatching model effectively solves the fate of renewable unit output and electric-thermal load and provides dispatching strategies for decision-makers to balance risk and capital management.

The integration of distributed generation (DG) into the power grid has increased in recent years due to its techno-economic benefits for utilities and consumers. However, due to the fact that distribution systems were not originally designed to accommodate such DG units, many challenges are being faced by utilities to seamlessly integrate them into their systems. One of the critical challenges is their effect on protection system settings and coordination. The DG units will affect the pickup current settings of the protection relays, coordination between the primary and secondary relays, and even the direction of the fault current. Failing to consider DG’s effect on the protection system may lead to serious equipment damage or system failure, causing huge financial setbacks for utilities. To that end, this work proposes a new dynamic approach to optimally allocate different types of DG units over the planning horizon. The objective is to minimize the overall costs of the system while t... [more]

This study focuses on the determination of optimum layout configurations for a linear array of identical mutually interacting truncated cylinders. Optimum configurations correspond to those that maximize either the total heave exciting force acting on all cylinders of the array or the heave exciting force applied on pairs of cylinders within the array. For achieving this goal, we developed and applied an efficient optimization numerical process (ONP), where a robust hydrodynamic numerical model, capable of solving the diffraction problem of the examined multi-body arrangement in the frequency domain, was appropriately coupled with a genetic algorithm solver in an integrated computational environment. Initially, the efficiency of the ONP is demonstrated by comparing results with those of other investigations that resulted from the deployment of classical optimization methods. Then, ONP is applied for a linear array of nine cylinders for determining the optimum layout configurations unde... [more]

This study discusses a numerical study that was developed to optimize the ventilation system in a power substation prior to its installation. We established a multiobjective particle swarm optimizer to identify the best approach for simultaneously improving, first, the ventilation performance considering the most appropriate inlet size and outlet openings and second, the reduction of the synthetic noise of the ventilation and power consumption from the exhaust fan equipment and its operation. The study used building information modeling to construct indoor and outdoor models of the substation building and verified the overall performance using ANSYS FLUENT 18.0 software to simulate the air velocity and air temperature distribution within the building. Results show that the exhaust fan of the B1F cable finishing room and the 23 kV gas insulated switchgear (GIS) room optimize the reduction of horsepower by approximately 1 Hp and 0.5 Hp. The combined noise is reduced by 4 dBA and 2 dBA; t... [more]

This paper investigates the influence of a stochastic variation of both energy and economic parameters in an optimization loop applied to a refurbished social housing building. Usually, energy and economic optimization procedures rely on the results of an underlying numerical deterministic model which influences both energy gains and economic figures. However, an analyst must always face the random variation of input and parameter data. The unknown data can represent poor initial information or data that can change in a long time; this is the case of fuel cost and economic indexes in particular. This paper deals with both problems for building refurbishment optimization, the former related to the initial state of a building, and the latter to the energy cost variability. Reliability analysis considers a stochastic variation of parameters looking for solutions that incorporate a risk level; in this case, it deals with optimization objectives related to different impacts on economic, env... [more]