Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability.... [more]

Both the unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) are attracting growing attention as enabling technologies for future wireless networks. In particular, IRS-assisted UAV communication, which incorporates IRSs into UAV communications, is emerging to overcome the limitations and problems of UAV communications and improve the system performance. This article aims to provide a comprehensive survey on IRS-assisted UAV communications. We first present six representative scenarios that integrate IRSs and UAVs according to the installation point of IRSs and the role of UAVs. Then, we introduce and discuss the technical features of the state-of-the-art relevant works on IRS-assisted UAV communications systems from the perspective of the main performance criteria, i.e., spectral efficiency, energy efficiency, security, etc. We also introduce machine learning algorithms adopted in the previous works. Finally, we highlight technical issues and research challenges that... [more]

Heat transfer is one of the most fundamental engineering subjects and is found in every moment of life. Heat transfer problems, such as heating and cooling, where the transfer of heat between regions is calculated, are problems that can give exact solutions with parametric equations, many of which were obtained by solving differential equations in the past. Today, the fact that heat transfer problems have a more complex structure has led to the emergence of multivariate models, and problems that are very difficult to solve with differential equations have emerged. Optimization techniques, which are also the subject of computer science, are frequently used to solve complex problems. In this study, laminar thermal boundary layers in flow over a flat plate, a sub-problem of heat transfer, is solved with recent metaheuristic algorithms. Teaching learning-based optimization (TLBO), sine cosine optimization (SCO), gray wolf optimization (GWO), whale optimization (WO), salp swarm optimization... [more]

The excitation pulse current used to drive the railgun needs to present very a high magnitude (hundreds of kA) flat-top with very low ripple. At present, the main method to obtain this current is to increase the number of the capacitive pulsed power supply (PPS) modules. However, low utilization and massive volume of the railgun system would occur with this method, hampering the application of railgun. Therefore, the utilization optimization technology of PPS is researched in this paper. In order to obtain highly stable flat-top current, the control strategy of the capacitive PPS is designed, and a new charging voltage configuration is proposed, which significantly decreases the use of the capacitive modules. Besides, a miniaturization transformation scheme of capacitive PPS is proposed based on the control strategy. The result shows that the flat-top current ripple has the biggest influence on the PPS utilization, and the smaller the flat-top current ripple, the lower the utilization.... [more]

The proportional integral derivative (PID) controller is one of the most robust and simplest configuration controllers used for industrial applications. However, its performance purely depends on the tuning of its proportional (KP), integral (KI) and derivative (KD) gains. Therefore, a proper combination of these gains is primarily required to achieve an optimal performance of the PID controllers. The conventional methods of PID tuning such as Cohen-Coon (CC) and Ziegler−Nichols (ZN) generate unwanted overshoots and long-lasting oscillations in the system. Owing to the mentioned problems, this paper attempts to achieve an optimized combination of PID controller gains by exploiting the intelligence of the whale optimization algorithm (WOA) and one of its recently introduced modified versions called improved whale optimization algorithm (IWOA) in an automatic voltage regulator (AVR) system. The stability of the IWOA-AVR system was studied by assessing its root-locus, bode maps, and pole/... [more]

To address the severity of the wind and light abandonment problem and the economics of hydrogen energy production and operation, this paper explores the problem of multi-cycle resource allocation optimization of hydrogen storage systems for coal−wind−solar power generation. In view of the seriousness of the problem of abandoning wind and photovoltaic power and the economy of hydrogen production and operation, the node selection and scale setting issues for hydrogen production and storage, as well as decision-making problems such as the capacity of new transmission lines and new pipelines and route planning, are studied. This research takes the satisfaction of energy supply as the basic constraint and constructs a multi-cycle resource allocation optimization model for an integrated energy system, aiming to achieve the maximum benefit of the whole system. Using data from Inner Mongolia, where wind abandonment and power limitation are severe, and Beijing and Shanxi provinces, where hydrog... [more]

A remodeled sperm swarm optimization (RMSSO) algorithm for a home energy management (HEM) system is proposed, and its real-time efficacy was evaluated using a hardware experimental model. This home environment comprised sixteen residential loads, a smart meter and a Raspberry Pi controller to optimize the energy consumption cost (ECC) in response to the Indian day-ahead pricing (DAP) scheme. A wired/wireless communication network was considered to communicate with the smart meter and controller. To address this optimization problem, the sperm swarm optimization (SSO) algorithm’s constriction coefficient was remodeled to improve its global searching capability and proposed as RMSSO. For the first time, salp swarm optimization (SSA), SSO, and RMSSO algorithms were employed to schedule home appliances in the Indian scenario. To validate the proposed technique’s outcome, the results were compared to those of the conventional SSO and SSA algorithms. This problem was solved using the Python/... [more]

This paper provides insight into the optimal configuration scheme of the grid-connected inverters based on harmonic amplification suppression. The connection of the inverters changes the natural resonance frequencies of the grid. Hence, a reasonable configuration of grid-connected inverters can optimize the impedance distribution and shift the natural resonance frequencies to frequency bands farther away from the harmonic sources. We proposed a scheme of site selection and determination of the number of inverters to suppress harmonic amplification. The resonance frequencies and modal frequency sensitivities (MFSs) were obtained by the resonance modal analysis (RMA). Moreover, the concepts of security region and insecurity region of resonance frequency were illustrated. The grid-connected sites can be obtained by calculating the participation factors (PFs) of the resonance frequencies in the insecurity region. Furthermore, the optimal number was determined by building the Norton equival... [more]

Magnetic gears (MGs) have gained increasing attention due to their sound performance in high torque density and low friction loss. Aiming to maximize the torque density, topology design has been a popular issue in recent years. However, studies on the optimization comparisons of a general MG topology pattern are very limited. This paper proposes a Taguchi-method-based optimization method for a general MG topology pattern, which can cover most of the common types of radially magnetized concentric-surface-mounted MGs (RMCSM-MGs). The Taguchi method is introduced to evaluate the influence of each parameter in MGs. Moreover, the parameter value range is re-examined based on the sensitivity analysis results. The genetic algorithm (GA) method is adopted to optimize the topology pattern in the study.

Parametric design, with its unique scientific and logical nature, is gradually applied in the field of landscape design. Therefore, the GIS (geographic information systems) technology of parametric software was applied to the optimization of traditional landscape architecture, and its practical application quality was discussed. The actual analysis results showed that the evaluation result of parametric design had the highest score of 7.71 in behavioral perception. The overall score was 7.28, showing a high scientific nature. In the evaluation of landscape environmental benefits, after the optimization of landscape greening by parametric design, the air cleanliness and living comfort were generally improved, compared with those before optimization, and the highest values were 11.97 ± 6.01 and 5.86 ± 2.11 respectively. In the evaluation of the economic benefits of gardens, the economic income of gardens in the past 8 years generally increased, reaching the highest of 3.5795 billion yuan... [more]

Many multi-objective optimization problems in the real world have conflicting objectives, and these objectives change over time, known as dynamic multi-objective optimization problems (DMOPs). In recent years, transfer learning has attracted growing attention to solve DMOPs, since it is capable of leveraging historical information to guide the evolutionary search. However, there is still much room for improvement in the transfer effect and the computational efficiency. In this paper, we propose a cluster-based regression transfer learning-based dynamic multi-objective evolutionary algorithm named CRTL-DMOEA. It consists of two components, which are the cluster-based selection and cluster-based regression transfer. In particular, once a change occurs, we employ a cluster-based selection mechanism to partition the previous Pareto optimal solutions and find the clustering centroids, which are then fed into autoregression prediction model. Afterwards, to improve the prediction accuracy, we... [more]

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth−Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes’ residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed... [more]

Gas content is an important parameter for evaluating coalbed methane reservoirs, so it is an important prerequisite for coalbed methane resource evaluation and favorable area optimization to predict the gas content accurately. To improve the accuracy of CBM gas content prediction, the Bayesian hyper-parameter optimization method (BO) is introduced into the random forest algorithm (RF) and gradient boosting decision tree algorithm (GBDT) to establish CBM gas content prediction models using well-logging data in the Zhengzhuang block, south of Qinshui Basin, China. As a result, the GBDT model based on the BO method (BO-GBDT model) and the RF model based on the BO method (BO-RF model) were proposed. The results show that the mean-square-error (MSE) of the BO-RF model and the BO-GBDT model can be reduced by 8.83% and 37.94% on average less than that of the RF and GBDT modes, indicating that the accuracy of the models optimized by the BO method is improved. The prediction effect of the BO-GB... [more]

The aim of optimization methods is to identify the best results in the search area. In this research, we focused on a mixture of the interior point method, opposite gradient method, and mean-variance mapping optimization, named IPOG-MVMO, where the solutions can be obtained from the gradient field of the cost function on the constraint manifold. The process was divided into three main phases. In the first phase, the interior point method was applied for local searching. Secondly, the opposite gradient method was used to generate a population of candidate solutions. The last phase involved updating the population according to the mean and variance of the solutions. In the experiments on real parameter optimization problems, three types of functions, which were unimodal, multimodal, and continuous composition functions, were considered and used to compare our proposed method with other meta-heuristics techniques. The results showed that our proposed algorithms outperformed other algorith... [more]

The extraction of xylanase was performed using an aqueous two-phase system (ATPS) based on polyethylene glycol (PEG1540) and various salts. Preliminary studies in a batch extractor showed that the highest extraction efficiency, E = 79.63 ± 5.21%, and purification factor, PF = 1.26 ± 0.25, were obtained with sodium citrate dihydrate-H2O-PEG1540-based ATPS for an extraction time of 10 min. The process was optimized using the experimental Box-Behnken design at three levels with three factors: extraction time (t), xylanase concentration (γ), and mass fraction of PEG in the ATPS (wPEG). Under optimal process conditions (γ = 0.3 mg/mL, wPEG = 0.21 w/w, and t = 15 min), E = 99.13 ± 1.20% and PF = 6.49 ± 0.05 were achieved. In order to intensify the process, the extraction was performed continuously in microextractors at optimal process conditions. The influence of residence time, different feeding strategies, and channel diameter on extraction efficiency and purification factor was further ex... [more]

HYW (Hong Yi Wu line) heavy oil emulsion in Xinjiang Oilfield (Karamay, China) is a kind of heavy oil with high viscosity and high emulsification. Its viscosity reaches 120,000 mPa·s at 40 °C. The emulsion has no demulsification. Even if the demulsification temperature reaches 90 degrees, the concentration of demulsifier reaches 260 mg/L. In this paper, a new process of thermochemical demulsification of heavy oil after blending is studied. First, SE low-viscosity oil with viscosity of 640 mPa·s and water cut of 90% was selected as blended oil. Study the viscosity of SE line and HYW line at different temperatures after fully blended. The results show that the heavy oil blended model conforms to Bingham model. When the temperature is 40 °C and the content of SE line is 30%, the viscosity is less than 10,000 mPa·s. With the increase of temperature, the viscosity continues to decline. When the temperature exceeds 80 °C, the viscosity is less than 1000 mPa·s. The final design SE line conten... [more]

This research aims to develop ensemble machine-learning methods for forecasting the ultimate tensile strength (UTS) of friction stir welding (FSW). The substance utilized in the experiment was a mixture of aluminum alloys AA5083 and AA5061. An ensemble machine learning model was created to predict the UTS of the friction stir-welded seam, utilizing 11 FSW parameters as input factors and the UTS as a response variable. The proposed approach used the Gaussian process regression (GPR) and the support vector machine (SVM) model of machine learning to build the ensemble machine learning model. In addition, an efficient technique using a differential evolution algorithm to optimize the weight for the decision fusion was incorporated into the proposed model. The effectiveness of the model was evaluated using three datasets. The first and second datasets were divided into two groups, with 80% for the training dataset and 20% for the testing dataset, while the third dataset comprised the test d... [more]

Hydroponic production raises economic and environmental issues related to the treatment, recovery or disposal of hydroponic wastewater, which can be rich in eutrophication-related nutrients, nitrogen (N) and phosphorus (P). Little focus has been put on the influence of the growth conditions on the N and P content in hydroponic wastewater, which is of uttermost importance when it is intended to reuse the wastewater for irrigation or other purposes with reduced impact on the environment. This study aimed to optimize an indoor non-recirculating deep-water culture (DWC) hydroponic system for lettuce (Lactuca sativa L. var. crispa) production, in terms of daily light integral (DLI) and volume of nutrient solution (NS) per plant, to maximize both the biomass production and the N and P removal, allowing for the wastewater to meet the criteria established for reusing in irrigation and minimizing the eutrophication impacts. A small-scale DWC hydroponic system with a fluorescent light fixture wa... [more]

The rising amount of carbon fiber reinforced polymer (CFRP) composite waste requires new processes for reintroducing waste into the production cycle. In the present research, the objective is the design and study of a reuse process for carbon fibers and CFRP by mechanical recycling consisting of length and width reduction, obtaining rods and reintegrating them as fillers into a polymeric matrix. Preliminary studies are carried out with continuous and discontinuous unidirectional fibers of various lengths. The processing conditions are then optimized, including the length of the reinforcement, the need for a plasma surface treatment and/or for resin post-curing. The resin is thermally characterized by differential scanning calorimetry (DSC), while the composites are mechanically characterized by tensile strength tests, completed by a factorial design. In addition, the composites tested are observed by scanning electron microscopy (SEM) to study the fracture mechanics. Optimal processing... [more]

For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms—the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale Optimization, and the Particle Swarm Optimization—to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyper... [more]

The optimal power flow (OPF) is an important tool for the secure and economic operation of the power system. It attracts many researchers to pay close attention. Many algorithms are used to solve the OPF problem. The decomposition-based multi-objective algorithm (MOEA/D) is one of them. However, the effectiveness of the algorithm decreases as the size of the power system increases. Therefore, an improved MOEA/D (IMOEA/D) is proposed in this paper to solve the OPF problem. The main goal of IMOEA/D is to speed up the convergence of the algorithm and increase species diversity. To achieve this goal, three improvement strategies are introduced. Firstly, the competition strategy between the barnacle optimization algorithm and differential evolution algorithm is adopted to overcome the reduced species diversity. Secondly, an adaptive mutation strategy is employed to enhance species diversity at the latter stage of iteration. Finally, the selective candidate with similarity selection is used... [more]

Among the pillars of decarbonization of the global energy system, energy efficiency plays a key role in reducing energy consumption across end-use (industry, transport and buildings) sectors. In industrial processes, energy efficiency can be improved by exploiting heat recovery via heat exchange between process streams. This paper develops a stage-wise superstructure-based mathematical programming model for the optimization of heat exchanger networks. The model incorporates rigorous formulation to handle process streams with phase change (condensation or evaporation), and is applied to a case study of an ethylene glycol production plant in Taiwan for minimizing utility consumption. The results show a compromise between steam savings and process feasibility, as well as how the model is modified to reflect practical considerations. In the preliminary analysis, with a substantial potential steam saving of 15,476 kW (28%), the solution involves forbidden matches that pose a hazard to the p... [more]

To improve the hydraulic performance of a new type of dishwasher pump and solve the multi-parameter optimization problem, a genetic algorithm was introduced to optimize the special design of the twin-volute structure. Six curvature radii of the twin-volute structure were defined as the optimization parameters, and 100 groups of design samples were generated based on the Latin hypercube sampling (LHS) method. The pump head and the efficiency were taken as the optimization objectives, i.e., to improve the efficiency as much as possible while ensuring that the head would not be lower than 2 m. The important parameters were identified via sensitivity analysis, and the optimization problem was solved in detail by using the multi-objective genetic algorithm (MOGA). The results showed that the external profile of the first to the fourth section of the twin-volute structure had the most significant effect on the pump head and efficiency. The response surface method (RSM) was used to select the... [more]

Electric vehicles (EVs) will be dominating the modes of transport in the future. Current limitations discouraging the use of EVs are mainly due to the characteristics of the EV battery and lack of easy access to charging stations. Charging schedules of EVs are usually uncoordinated, whereas coordinated charging offers several advantages, including grid stability. For a solar photovoltaic (PV)-based charging station (CS), optimal utilization of solar power results in an increased revenue and efficient utilization of related equipment. The solar PV and the arrival of EVs for charging are both highly stochastic. This work considers the solar PV forecast and the probability of EV arrival to optimize the operation of an off-grid, solar PV-based commercial CS with a battery energy storage system (BESS) to realize maximum profit. BESS supports the sale of power when the solar PV generation is low and subsequently captures energy from the solar PV when the generation is high. Due to contrastin... [more]

The helium-cooled pebble bed (HCPB) breeding blanket (BB) is one of the two driver-blanket candidates for the European DEMO fusion reactor. Recent design activities were focused, among other objectives, on the achievement of an efficient shielding system to adequately protect the vacuum vessel (VV) and toroidal field coils (TFCs). Several shielding options have been studied in terms of architecture (e.g., in-BB shield and ex-BB shield) and materials (e.g., B4C, WC, WB, YHx, and ZrHx). In this study, the B4C material was selected as the most attractive option considering not only shielding performance but also availability, industrialization, experience, and cost factors. Subsequently, we performed a parametric study by implementing different thicknesses of a B4C external shield and reporting information of its effect on shielding performance, structural behavior, swelling and tritium breeding. Furthermore, a detailed structure for the VV was developed considering an internal layered co... [more]