The limited resources of fossil fuels and environmental pollution have led researchers to use renewable green fuels such as biodiesel. The important costs in the biodiesel production process are heating and raw materials. To reduce these costs, in this work, by transesterification of waste cooking oil in two T-shaped and helical glass microreactors, which are located inside the boxes equipped with mirror plates, the chamber temperature reached 63 °C and led to 90 % energy savings. The operating parameters of retention time were the catalyst concentration and the oil to methanol volume ratio. XRF, XRD, FTIR, and SEM tests were used to determine the elemental and morphological characteristics of KOH and CaO catalysts. This transesterification via methanol using the Box-Behnken method was designed which capable of producing biodiesel with a purity of 99.97 %. Under optimal conditions of 300 sec for CaO, a catalyst dose of 10 %, and the oil to methanol volume ratio 2 the purity was 99.97 % and for KOH at 420 sec, a catalyst dose of 3 %, and the oil to methanol volume ratio 2 the purity was 98.99 %. The performance of the two microreactors was evaluated under optimal conditions; the performance of the helical type was better due to its geometric structure and greater T-shape mixing. The results show that the use of the glass microreactor assisted by solar heat can significantly reduce the operating cost by increasing the mixing, shortening the residence time, and maintaining the reaction temperature in the range of 50–63 °C, resulting in a 90 % reduction in energy consumption.
This paper presents a multi-objective control scheme on the interconnected wind farms which are formed by different wind turbines including doubly fed induction generators (DFIG) and squirrel cage induction generators (SCIG). The proposed control system aims to control voltage, frequency, and mechanical power in the wind farms under wind/load alterations, faults, and outages. The voltage stability and fault ride through capability are improved. The proposed model also deals with unbalanced loading and faulty conditions. All the aforementioned points are realized under both off-grid and grid-tied conditions. For voltage stability improvement, each wind farm is integrated with one static VAR compensator (SVC) and the grid-side is integrated with one static synchronous compensator (STATCOM). In the grid-tied, the resilience following events is improved by proper control of STATCOM. In the off-grid, the installed SVC at each site is responsible for increasing resilience. The proposed control systems are modeled and implemented on a typical test grid, and numerical simulations are carried out in MATLAB/SIMULINK software. It is demonstrated that the proposed multi-objective control scheme efficiently controls all individual wind farms, achieves a coordinated management between different wind farms, deals with stability/unbalanced operating condition/faults, increases resilience and improves fault ride through capability.
The novelty of this numerical study is integrating strategy of dimpling the tube and using turbulators in the center of a tube under constant heat flux to improve heat transfer rate. This research focused on evaluating the effect of the geometric parameters in a dimpled tube equipped with a conical turbulator based on the first and second laws of thermodynamics. In CFD modeling, the uniform inlet velocity at the inlet of the dimpled tube, non-slip condition in the walls, and atmospheric pressure at the outlet of the dimpled tube, a uniform heat flux on the tube wall and an inlet temperature of 300 K are selected as boundary conditions. The effect of pitch (S) and number of dimples (N), Reynolds number (Re = in the range of 5000 to 20,000) and heat flux (3000, 5000, 10,000 W m−2) on entropy generation, Nusselt number and the pressure drop in a dimpled tube equipped with the turbulator was investigated. The results showed that increase in S reduces the Nusselt number, pressure drop and friction factor, but thermal entropy and friction entropy decrease and so the total entropy decreases. As the number of dimples increases, the Nusselt number increases, which has similar effect on the pressure drop and friction factor. Increase in the number of dimples leads to increase in the frictional and the overall entropy, but decrease in the thermal entropy. The results showed that in low Reynolds number, the share of thermal entropy generation is much higher than the friction entropy generation and as the Reynolds number increases the share of friction entropy generation in the total entropy generation increases. The maximum Nusselt number and friction factor are related to Re = 20,000, S = 14 mm and N = 4. Based on the obtained data from modeling, heat flux has not significant influence on heat transfer and pressure drop. The maximum thermal entropy and total entropy are at Re = 5000, S = 14 mm and N = 2, while the highest friction entropy is related to Re = 5000, S = 14 mm, N = 4 and heat flux = 10,000 W m–2. Finally, the results of this research confirm that the dimples on the tube body and inserting the turbulator in the center of the dimpled tube with the efficient pitch and the number of dimples can be a logical technique to intensify the heat transfer process.
The penetration of renewable resources and electric vehicles has increased in recent years due to various benefits such as reducing environmental pollution. This paper optimizes the energy management of a microgrid integrated with battery charging and swapping stations in the presence of renewable resources and crypto-currency miners as an emerging critical load with high energy consumption. In such structures, the fluctuation of renewable energies decreases reliability and increases energy market trading. Hence, the conditional value-at-risk index is utilized to analyze the risk of uncertainties. Furthermore, the influence of responsive local loads and incoming vehicles into the stations is investigated using demand response programs. In order to implement the presented programming, a real distribution network in Kermanshah, Iran, is selected as the case study. The results show that the risk-averse strategy with α = 0.85 and β = 0.8, reduces the expected revenue by about 636.355 $ compared to the risk-neutral strategy. In addition, the swapping station can be used as a bulk storage, where it stores about 43.56 MWh in low-price times and injects about 20.04 MWh in high-price times to support the local system. The demand side management also increases the revenue even under the worst-case conditions by about 79.623 $.
Recently, the resilient operation of distribution networks has attracted attention owing to the considerable growth in natural disasters. After isolating the faulted or damaged areas, restoring the intact parts is challenging. This can be exacerbated by high penetration of renewable resources. During the restoration, the access to available resources is limited by time, production capacity, or stored energy. Therefore, a balance must be made while considering load criticality. Accordingly, a new distribution restoration process is proposed in this paper to overcome these challenges while considering switching sequence. To be accounted for real-world cases, a new binary-based segmentation model is developed for multiple-transformer bus loads. Besides, a novel bus load supply prioritization is proposed to overcome the drawbacks of previous methods which have used weighting factors in the objective function. Also, a new energy-based objective function is developed, taking the degree of energy available from distributed generation and storage resources into account. Besides benefiting from network reconfiguration and stationary energy storage systems, integration of modern storage-integrated soft open points is also modeled and considered. The model is validated through a case study, demonstrating its functionality to deal with real-world cases with load priority steps, multiple-transformer bus loads, and limited energy access.
In this paper, several types of microstrip couplers are investigated in terms of structure, performance, and design methods. These planar 4-ports passive devices transmit a signal through two different channels. Designers' competition has always been in miniaturizing and improving performance of couplers. Proposing a novel structure is an advantage of some previously reported couplers. A high-performance coupler should have high isolation and low losses at both channels. The common port return loss in the pass band should have a low value. Among the couplers, those with balanced amplitude and phase are more popular. The popular mathematical analysis methods are even/odd mode analysis, extracting the information from the ABCD matrix and analyzing the equivalent LC circuit of a simple resonator. According to the phase shift value, couplers are classified as 90º and correct multiples of 90º, where a microstrip 0º coupler can be used as a power divider. Some couplers have filtering and harmonic elimination features that are superior to other couplers. However, few designers paid attention to suppressing the harmonics. If the operating frequency is set in according to the type of application, the coupler becomes particularly valuable.
One of the most severe problems in power plants, petroleum and petrochemical industries is the accurate determination of phase fractions in two-phase flows. In this paper, we carried out experimental investigations to validate the simulations for water–air, two-phase flow in an annular pattern. To this end, we performed finite element simulations with COMSOL Multiphysics, conducted experimental investigations in concave electrode shape and, finally, compared both results. Our experimental set-up was constructed for water–air, two-phase flow in a vertical tube. Afterwards, the simulated models in the water–air condition were validated against the measurements. Our results show a relatively low relative error between the simulation and experiment indicating the validation of our simulations. Finally, we designed an Artificial Neural Network (ANN) model in order to predict the void fractions in any two-phase flow consisting of petroleum products as the liquid phase in pipelines. In this regard, we simulated a range of various liquid–gas, two-phase flows including crude oil, oil, diesel fuel, gasoline and water using the validated simulation. We developed our ANN model by a multi-layer perceptron (MLP) neural network in MATLAB 188.8.131.52 software. The input parameters of the MLP model were set to the capacitance of the sensor and the liquid phase material, whereas the output parameter was set to the void fraction. The void fraction was predicted with an error of less than 2% for different liquids via our proposed methodology. Using the presented novel metering system, the void fraction of any annular two-phase flow with different liquids can be precisely measured.
Inflammation has a crucial role in COVID-19 pathogenesis, and previous studies have proposed an important function of IL-6 during inflammation. On the other hand, IL-6 levels and inflammation might be associated with hypoxia-inducible factor-1α (HIF-1 α ). Thus, due to the possible role of HIF-1 α in inflammation and COVID-19 pathogenesis, we aimed to investigate the levels of HIF-1 α and its correlation with inflammatory parameters (IL-6 and CRP) and D-dimer. In this case-control study, 84 patients (54 patients hospitalized in the ICU and 30 individuals as outpatient subjects) were included as the case group, and 50 healthy subjects were included as the control group. The levels of D-dimer, CRP, IL-6 (interleukin 6) and HIF-1 α were assessed in all studied groups. The results of the present investigation showed that the levels of D-dimer, CRP and IL-6 were significantly increased in COVID-19 patients compared to healthy individuals. On the other hand, the level of HIF-1 α significantly decreased in COVID-19 patients. In addition, there was a significant correlation between IL-6 and CRP and D-dimer, while HIF-1 α was indirectly correlated with IL-6, CRP and D-dimer. Ultimately, our data indicated that the levels of CRP, D-dimer, IL-6 and HIF-1 α were significantly different between ICU patients and the outpatient group with healthy individuals. Based on the study results, inflammation plays a crucial role in COVID-19 pathogenesis, and low HIF-1 α is a consequence of inflammation due to COVID-19 infection and might have a protective role in COVID-19.
In this study, a milling tool with variable mass and stiffness is developed for chatter reduction. The proposed milling tool is hollow with a solid core threaded inside it. As the core is screwed in or out of the tool body, the equal mass and stiffness of the tool are changed. Therefore, the tool's frequency response function (FRF) is changed to affect the stability lobe diagram (SLD) position. Moving the SLDs can stabilize an unstable cutting process. With respect to the FRFs obtained from modal test, the idea is proven using an experimental and analytical approach. The optimum core position for every spindle speed is also presented. The developed tool stability is then investigated in a realistic cutting condition. The cutting process sound analysis and surface finish visual inspection results reveal the performance of the proposed system in chatter reduction of a slender milling tool.
This paper presents the design of a novel lowpass–bandpass diplexer with compact size and good performance using microstrip cells. For this purpose, two microstrip lowpass and bandpass filters are separately designed and mathematically analyzed. The proposed microstrip diplexer has a novel and simple structure. It occupies a compact area of 0.037 λ g ² (722 mm ² ), and its insertion loss and S 11 at both channels are low. The insertion loss at the lower and upper channels is only 0.047 and 0.16 dB, respectively. Meanwhile, both channels are flat with the maximum group delay of 1.68 ns which is the lowest compared to the previously reported diplexers mentioned in this paper. To design the proposed diplexer, first, a lowpass diplexer with good performance is designed which has low losses and a good figure of merit. It has a flat passband with a sharp roll-off. Then, to achieve the proposed diplexer, a bandpass resonator is added to the lowpass filter without any extra matching circuit, which saves the overall size. The proposed diplexer is designed, simulated, and fabricated, where the simulation and measurement results are close.
The present paper addresses the thermodynamic modeling and multi-objective optimization of a solar-based multi-generation system producing hot water, heating, cooling, hydrogen, and freshwater using a humidification and dehumidification (HDH) unit. Usually, in areas with high radiation intensity, the shortage of drinking water is severe; therefore, using multi-generation systems with solar energy as the prime mover can be a promising option in these areas. The main goals of the current work are multi-aspect assessment and optimization of a solar system to generate potable water and other valuable products. The proposed system is examined using thermodynamic modeling and environmental simulation from different aspects in the present study. The exergy destruction evaluation rate showed that the heliostat had the highest exergy destruction rate, gauged at 1867 kW. Also, in terms of exergy efficiency, the pump and heliostat units had the lowest exergy efficiency, with values of 52.09 % and 65.39 %. Parametric analysis was implemented to find the effect of changing different parameters on the yield of produced fresh water, exergy efficiency, exergy destruction rate, coefficient of performance (COP), sustainability index (SI), and produced hydrogen. Results showed that increasing the compressor pressure ratio from 2 to 6 elicits a reduction in freshwater flow rate and COP. Similarly, increasing the outlet pressure from 70 to 80 bar reduced exergy efficiency and freshwater production. Furthermore, owing to the different effects of the parameters on the studied system, multi-objective optimization was performed using the evolutionary genetic algorithm.
The suitable thermal management of electrical devices leads to their reliable operation and durability. The present study investigates the application of ultrasonic surface vibration in a pin–fin heatsink to improve hydrothermal performance. To this end, a totally 31 scenarios were examined by altering the location of the vibrating transducers at four lateral walls and the top plate of the heatsink. The numerical calculations were performed within Reynolds number (Re) ranges of 500–2000 and frequency magnitude (F) of 15–30 kHz. Our results showed that the heatsink with three transducers at the top plate and two lateral adjacent walls (near and the opposite side of the inlet), namely case#27, exhibits the highest heat transfer coefficient and performance evaluation criteria (PEC) of 1.38 over the base case without transducers (WOT) at Re = 2000 and F = 30 among the studied cases. In addition, case#27 has its highest heat transfer coefficient and pressure drop (ΔP) at Re = 2000, while its maximum PEC is obtained as 1.78 at Re = 1000 for F = 30 kHz over the base case without vibration. Moreover, the maximum heat transfer coefficient and the lowest ΔP for case#27 at Re = 2000 were obtained at = 25 kHz. In such a case, the PEC was obtained as 1.06 over the base case.
The accurate implementation of biological neural networks, which is one of the important areas of research in the field of neuromorphic, can be studied in the case of diseases, embedded systems, the study of the function of neurons in the nervous system, and so on. The pancreas is one of the main organs of human that performs important and vital functions in the body. One part of the pancreas is an endocrine gland and produces insulin, while another part is an exocrine gland that produces enzymes for digesting fats, proteins and carbohydrates. In this paper, an optimal digital hardware implementation for pancreatic $\beta$ -cells, which is the endocrine type, is presented. Since the equations of the original model include nonlinear functions, and the implementation of these functions results in greater use of hardware resources as well as deceleration, to achieve optimal implementation, we have approximated these nonlinear functions using the base-2 functions and LUT. The results of dynamic analysis and simulation show the accuracy of the proposed model compared to the original model. Analysis of the synthesis results of the proposed model on the Spartan-3 XC3S50 (5TQ144) reconfigurable board (FPGA) shows the superiority of the proposed model over the original model. These advantages include using fewer hardware resources, a performance almost twice as fast, and 19% less power consumption, than the original model.
This paper proposes an ultrahigh step-up DC-DC converter composed of two boosting stages, a coupled inductor and switched capacitors. Advance features of the converter consist of its high voltage gain, low voltage stress on its switches, continuity of its input current and existence of a common ground between the source and load. In addition, it needs an inductor and a coupled inductor with smaller sizes in comparison to the compared references which lead to its performance with higher efficiency. Among the compared converters, the proposed converter boosts voltage with higher voltage gain per different turns ratio of the coupled inductor. Analysis of the converter has been performed for its main switching states to validate its quality and quantity factors. A prototype is built in order to experiment its performance per different conditions and evaluate the analysis. The experiments are rated for input voltage, output voltage and output power equal to 25V, 400V and 150W respectively.
In nature, arsenic, a metalloid found in soil, is one of the most dangerous elements that can be combined with heavy metals. Industrial wastewater containing heavy metals is considered one of the most dangerous environmental pollutants, especially for microorganisms and human health. An overabundance of heavy metals primarily leads to disturbances in the fundamental reactions and synthesis of essential macromolecules in living organisms. Among these contaminants, the presence of arsenic in the aquatic environment has always been a global concern. As (V) and As (III) are the two most common oxidation states of inorganic arsenic ions. This research concentrates on the kinetics, isotherms, and thermodynamics of metal-organic frameworks (MOFs), which have been applied for arsenic ions uptake from aqueous solutions. This review provides an overview of the current capabilities and properties of MOFs used for arsenic removal, focusing on its kinetics and isotherms of adsorption, as well as its thermodynamic behavior in water and wastewater.
This study incorporates artificial neural network (ANN) modeling to design an intelligent road signboard that uses internet of things (IoT) technology to assign the speed limit on interurban highways. The appropriate speed limit must be determined by traffic police experts based on weather conditions and times of the day. Here, an intelligent IoT‐based signboard is proposed to announce speed limits on roadways considering some effective parameters, such as temperature, humidity, time, and light. The signboard receives environmental data through its sensors and uses artificial neural networks to compute the speed limit. A feed‐forward neural network (FFNN) is provided as the most reliable model. A hybrid training method based on gray wolf optimization and Bayesian regularization is also developed to enhance model performance. The proposed hybrid model converges with an error of less than 3.0% to expert opinion. The model equations are extracted for use in a microcontroller that calculates a safe speed limit based on weather conditions. Additionally, the underlying IoT technology has enabled the police station to remotely monitor and control the developed system. Experimental results demonstrate the reliability of the designed signboard. In all experimental cases, the computed speed limits were in coincidence with the expert's estimations.
Food recommendation systems have been increasingly developed in online food services to make recommendations to users according to their previous diets. Although unhealthy diets may cause challenging diseases such as diabetes, cancer, and premature heart diseases, most of the developed food recommendation systems neglect considering health factors in their recommendation process. This emphasizes the importance of the reliability of the recommendation from the health content perspective. This paper proposes a new food recommendation system based on health-aware reliability measurement. In particular, we develop a time-aware community detection approach that groups users into disjoint sets and utilizes the identified communities as the nearest neighbors set in rating prediction. Then, a novel reliability measurement is introduced by considering both the health and accuracy criteria of predictions to evaluate the reliability of predicted ratings. Also, the unreliable predictions are recalculated by removing ineffective users from the nearest neighbors set. Finally, the recalculated predictions are utilized to generate a list of foods as recommendations. Different experiments on a crawled dataset demonstrate that the proposed method enhances the performance around 7.63%, 6.97%, 7.37%, 15.09%, and 16.17% based on precision, recall, F1, normalized discounted cumulative gain (NDCG), and health metrics, respectively, compared to the second-best model.
Low efficiency in freshwater production is one of the primary disadvantages of solar still desalination systems. Increasing the performance of solar still desalination systems has been one of the main goals of researchers in recent years. For solar still desalination systems, the double-glazing cooling technique combined with a thermoelectric module can increase the efficiency of these systems. Therefore, the main objective of this study is to investigate the impact of the double-glazing cooling technique integrated with a thermoelectric module on the performance of a solar still desalation system. The thermoelectric module generates cold air that is passed through the two glasses to provide cooling. The modified unit and a conventional one are experiemntally tested for three days under the same weather conditions in Kermanshah, Iran. A comparative analysis of results shows that cooling the double-glazed glass cover using cooled air passing through the two glasses continuously during all test hours results in a 30% increament in average freshwater production compared to the conventional system. Experimental results indicate that the solar still desalination performance can be enhanced further by increasing the linear air stream velocity. As the average air velocity increases from 1.5 to 2.5 m/s and 4 m/s, freshwater production increases by 14 % and 25 %, respectively. In addition, the results of the economic analysis represent that the cost of distilled water for the improved system is 0.1033 ($/L/ m2). Hence, cooling the double-glazed glass cover using cooled air is a cost-effective and viable method for enhancing the evaporation rate, condensation rate, and freshwater production.
This article investigates a geothermal energy-based cycle to produce power, cooling, and fresh water, using humidification-dehumidification technology. Energy, exergy, and exergo-economic analysis have been performed for a geothermal cycle and a proposed cycle that is an improvement of the basic cycle. Comparative analysis for the new cycle has been extracted and exergy-economic parameters have also been calculated. Moreover, by using artificial neural network and multi-objective optimization, optimal parameters of the system have been extracted. The primary novel part of this study is that different subsystems combined in a way generate different products and the optimum performance parameters are introduced based on the multi-objective optimization. The obtained results show that the highest exergy destruction is related to heat exchanger 1 (HX1) with a value of 670.5 kW. The proposed system can produce 1.104 kg/s fresh water and its net power production capacity is 2251 kW. Also, the exergy destruction in the proposed system is 964.4 kW higher than the basic cycle. Based on the multi-objective optimization, the optimal point is selected based on the ideal result of 32.35 % efficiency and 2322.32 kW exergy destruction, and the parameter unit cost of product is 8.81 $/kW.
Finding optical soliton solutions to nonlinear partial differential equations has become a popular topic in recent decades. The primary goal of this study is to identify a diverse collection of wave solutions to a generalized version of the nonlinear Schrödinger equation. We investigate two modifications to the generalized exponential rational function method to derive the expected results for this model. The first method is primarily based on using elementary functions such as exponential, trigonometric, and hyperbolic forms, which are commonly used to calculate the results. As for the second method, it is based on applying Jacobi elliptic functions to formulate solutions, whereas the underlying idea is the same as with the first method. As a means of enhancing the reader’s understanding of the results, we plot the graphical properties of our solutions. Based on this article’s results, it can be concluded that both techniques are easy to follow, and yet very efficient. These integration methods can determine different categories of solutions all in a unified framework. Therefore, it can be concluded from the manuscript that the approaches adopted in the manuscript may be regarded as efficient tools for determining wave solutions of a variety of partial differential equations. Due to the high computational complexity, the main requirement for applying our proposed methods is to employ an efficient computing software. Here, symbolic packages in Wolfram Mathematica have been used to validate the entire results of the paper.
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