Double-pipe counter-flow heat exchangers are considered more suitable for heat recovery in the heat transfer industry. Numerous studies have been conducted to develop static tools for optimizing operating parameters of heat exchangers. Using this study, an improved heat exchanger system will be developed. This is frequently used to solve optimization problems and find optimal solutions. The Taguchi method determines the critical factor affecting a specific performance parameter of the heat exchanger by identifying the significant level of the factor affecting that parameter. Gray relational analysis was adopted to determine the gray relational grade to represent the multi-factor optimization model, and the heat exchanger gray relation coefficient target values that were predicted have been achieved using ANN with a back propagation model with the Levenberg−Marquardt drive algorithm. The genetic algorithm improved the accuracy of the gray relational grade by assigning gray relational co... [more]

The super-growth approach for carbon nanotubes synthesis is frequently used to boost the growth rate, catalyst lifespan, and height of vertically aligned carbon nanotubes. The elimination of amorphous carbon from catalyst particles, commonly made of iron, by injecting water vapor into a chemical vapor deposition process can enhance the purity, alignment, and height of carbon nanotubes and prevent the partial oxidation of the metallic catalyst. We present the development of a modified growth-optimized water-assisted super-growth vertically aligned carbon nanotube process by optimizing the catalyst layer structure and water vapor concentration for a carbon nanotube growth process for 4” diameter Si wafers. A significant finding is that under optimized water-assisted growth conditions over 4 mm, highly uniform tall, vertically aligned carbon nanotube structures can be grown with a minimum top crust layer of about ~5−10 μm thickness. This was achieved with a catalyst film comprising a >400... [more]

The development of an optimized air or O2-assisted multi-wall vertically aligned carbon nanotubes (VACNT) process that adjusts the vertical height profile of a standard H2O vapor-assisted VACNT process is reported. The effect of the air or O2 chemical vapor deposition (CVD) precursor flow rate, the catalytic Fe layer thickness, the process growth temperature, and the H2/C2H4 ratio on VACNT length was first investigated to find the optimum growth conditions. Spatial distribution height mapping of VACNT structures on six patterned 4′′ catalyst Si wafers prepared with a 70−90 min long O2-assisted growth step shows an average growth height of 1.8−2.2 mm, with a standard deviation of less than 10%. Characterization techniques included Raman spectroscopy, scanning electron microscopy (SEM), and spatial height mapping analysis for a range of Fluid channel Array Brick (FAB) components with a length of 30 mm, a width range of 2.5−15 mm, a fluid channel diameter range of d = 5−100 mm, and a flui... [more]

The yields of chemical reactions are highly dependent on the mixing pattern between reactants. Herein, we report the modification of a meso-micromixing interaction reaction model which is applied in batch reactors by leveraging the flow characteristics in the continuous reactors. Both experimental and model-predicted yields were compared using the classical Villermaux−Dushman method in a self-designed split and recombination reactor. This modified model significantly reduced the error in predicted product yields from approximately 15% to within 3%, compared to a model containing the micromixing term only. The effects of flow rates and reactor structure parameters on mixing performance were analyzed. We found that increasing flow rates and the degree of twist in the mixing element’s grooves, as well as decreasing the cross-sectional area of grooves, improved mixing performance. The optimization of reactor flow rates and structural parameters was achieved by combining Gaussian process re... [more]

Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems—graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and -container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling... [more]

Aiming at the common problems of the high working resistance, low soil disturbance, and high rates of missed extraction in the operation of carrot combine harvesters, a high-efficiency drag-reducing bionic soil-loosening shovel was designed in this study. The physical parameters of the soil and carrots were measured, and the bionic drag-reducing shovel was designed using the badger claw toe as a bionic prototype. The shovel wing structures were designed. Based on the EDEM discrete element simulation technology, a multi-element simulation model of the shovel−soil−carrot contact was established to determine the effects of the operating speed and sliding angle of the shovel handle on the resistance. The effects of the blade inclination angle and blade opening angle on the resistance, carrot extraction force, and soil disturbance rate were also studied. The results show that the resistance increases with an increase in operating speed. With a blade angle (α) and blade inclination angle (β)... [more]

Ensuring the accuracy of flow measurement is crucial to promoting high-quality cigarette production. In order to monitor the working status of flowmeters, this paper proposes an anomaly detection method based on the sliding-window recursive Lasso (Least absolute shrinkage and selection operator), which is able to track the changes in flowmeter operating conditions by self-adapting model parameters based on observed measurements. Due to the frequent mode switch and high sampling frequency of flow data, this paper introduces the sliding-window strategy to remove the effect of outdated data and accelerate the optimization. The tracking errors are used as a measure of anomaly and different thresholds are introduced based on the operating manual of cigarette production, which are used to distinguish between mode switch and flowmeter anomalies. The method’s effectiveness is verified by detecting flowmeter anomalies in a real cigarette production line. The mean absolute error (MAE) is 8.1479... [more]

The Maximum Power Point Tracking method is a mainstream method for improving the operational efficiency of photovoltaic power generation, but it is difficult to adapt to the rapidly changing environment and lacks good steady-state and dynamic performance. To achieve fast and accurate tracking of the Maximum Power Point Tracking, the optimization of the contraction expansion coefficient of the Quantum Particle Swarm Optimization algorithm is studied, and then the Levy flight strategy is introduced to optimize the algorithm’s global convergence ability, thereby constructing the Hybrid Quantum Particle Swarm Optimization algorithm. Finally, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm is obtained. The research results showed that the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm can always converge to the theoretical minimum value with a probability of more than 94% in the Rose... [more]

Deep learning can realize the approximation of complex functions by learning deep nonlinear network structures, characterizing the distributed representation of input data, and demonstrating the powerful ability to learn the essential features of data sets from a small number of sample sets. A long short-term memory network (LSTM) is a deep learning neural network often used in research, which can effectively extract the dependency relationship between time series data. The LSTM model has many problems such as excessive reliance on empirical settings for network parameters, as well as low model accuracy and weak generalization ability caused by human parameter settings. Optimizing LSTM through swarm intelligence algorithms (SIA-LSTM) can effectively solve these problems. Group behavior has complex behavioral patterns, which makes swarm intelligence algorithms exhibit strong information exchange capabilities. The particle swarm optimization algorithm (PSO) and cuckoo search (CS) algorit... [more]

For liquid hydrogen transportation, thermal insulation materials that are lightweight, compact and exhibit high-performance have been pursued for several decades, and variable density multi-layer insulation (VD-MLI) has been regarded as a promising choice. The thermal insulation performance of the insulation materials is important, but is not at the top of the list; many constraints, such as the space and weight of the insulation structures, are imposed on the design of a VD-MLI. Consequently, this makes the optimization of VD-MLIs more complicated. The present authors conducted a multi-objective optimization of a VD-MLI stacked with specific insulation units. The number of repetitions of the basic insulation unit was regarded as the dimensionless design parameter of the VD-MLI. Based on the experimentally validated layer-by-layer (LBL) model for MLI design, the multi-objective optimization of VD-MLI for liquid hydrogen storage was conducted by the combination of proper orthogonal deco... [more]

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]