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High-temperature fuel cells (HT-FCs) indeed hold significant promise for enhancing energy systems' efficiency and environmental sustainability. Consequently, there is growing interest in exploring and developing innovative strategies to optimize the integration of heat recovery processes with HT-FC technology. The current research presents an innovative and environmentally friendly poly-generation unit that integrates advanced subprocesses to generate essential products. These final products include electricity, refrigeration, pure water, and hydrogen. This study signifies a significant step towards sustainable and efficient energy production while meeting diverse needs for different utilities. The design unit incorporates a novel modified dual ejector-based organic flash cycle, reverse osmosis water purification, and a water electrolyzer for hydrogen extraction, all integrated with a high-temperature solid oxide fuel cell. Energy, exergy, economic, and environmental (4E) analysis is conducted to thoroughly evaluate the proposed plan. Furthermore, a comprehensive parametric analysis and sensitivity study are performed to pinpoint the key design parameters of the poly-generation unit. To attain the optimal operational status of the poly-generation unit, a three-objective NSGA-II optimization technique is employed to fine-tune the system's performance within the exergy-cost-environmental framework. This approach aims to strike a balance between maximizing efficiency, minimizing costs, and reducing environmental impact for sustainable operations. In addition, a net present value analysis spanning a 20-year timeframe was conducted to assess the profitability of the devised unit. Based on the findings, it is evident that the sensitivity index of the fuel cell operating temperature carries substantial importance, registering a notable value of 0.60. Additionally, the optimization outcomes reveal the system's enhanced performance metrics: an exergetic efficiency of 41.11 %, a unit cost of 58.96 $/GJ, and a carbon dioxide emission reduction rate of 418 kg/MWh. Also, the unit's payback period is shortened from 11.33 years to 8.817 years. Moreover, it improved the unit's sustainability index and net present value, raising them from 1.544 to 4.98 M$ to 1.66 and 7.38 M$.

With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed in many industries have been improved. Particles and impurities of any size, shape and in any condition can be detected using advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impurities. The publications featured in this review were mainly retrieved from the Web of Science (WoS) database, covering the timeframe from 2000 to 2023. This paper also covers the review on the impurity detection and removal specifically in edible bird’s nest (EBN). The aim of this paper is to provide a valuable resource for the future development of AI applications in particle and impurity detection and removal technologies that have not been addressed in this study. Through the review and analysis of AI for particle and impurity detection and removal techniques in recent years, this paper includes the following parts: research trend in particle and impurity detection in general and AI methods in particle and impurity detection, applications of AI in particle and impurity detection in related industries including in EBN and AI applications in particle and impurity removal. This review study will offer advantages to researchers engaged in the field of AI with regards to the detection and removal of particles and impurities.

Owing to the marine environmental pollution affected by cruises, the use of techniques to mitigate carbon dioxide emission (CO2) is vital. Since a quarter of the fuel energy input to the engine is lost, waste management for marine diesel engines can provide some valuable outputs by which the need for other energy conversion-based methods disappears. Hence, the current work proposes a novel model of waste heat recovery for a 1 MW marine diesel engine in a low-temperature framework by which outstanding results are predictable. The defined auxiliary model consists of an absorption power cycle and an ejector refrigeration cycle to produce useful electricity and cooling for air conditioning. This model is designed for the first time and comprehensively analyzed and optimized to set the most suitable state of operation. The potential of the model is measured through the exergy, environmental, economic, and net present value standpoints. Moreover, an advanced evolutionary algorithm based on the non-dominated sorting genetic algorithm-II is applied to reach the optimum cost and exergetic performance. The optimum state showed an exergy efficiency of 35.19 % and products’ specific cost of 53.01 $/GJ. Moreover, the optimum payback period and CO2 emission reduction equal 6.79 years and 21.5 kg/MWh, respectively.

The wind energy sector has grown significantly over the past several decades as enabling technologies advance. One of the major reasons of such rapid growth is due to the renewability of this eco-friendly energy source that reduces carbon footprints worldwide. The overall costs of large scale wind energy conversion systems can be extremely high, which is deemed to be one of the main dilemmas stakeholders have to face with during the design phase. Generators for large scale wind turbines are generally heavy and huge in sizes, which translate into a hike in upfront and maintenance costs. Design optimizations are crucial in wind turbine generator. Multi-objective optimization is a general approach to the design of the generator because there are always tradeoffs in considerations. In many studies, optimizations are applied on mathematical models and the results are verified by means such as Finite Element Method (FEM). This review paper captures the fact that recent advancements in design optimization of Permanent Magnet Synchronous Generator (PMSG) for wind turbine systems are able to deliver tangible benefits to the renewable energy technology (RET) industry as a whole. Different design approaches, objectives applied in optimization, key variables, and optimization algorithms across different research are also presented in this paper.

Liquid structures such as droplets and slugs exist inside gas channels of polymer electrolyte fuel cells in low-temperature applications. The efficiency of these electrochemical devices depends on the effective removal of the produced water. The gas channels’ specifications like section geometry, corner angles, and surface wettability properties substantially control the liquid removal process. Here, five channels with various section geometries are modeled and the liquid-slug discharge process is investigated using a transient volume of fluid method. The numerical model consists of a segment of the cathode-side gas channel with the working conditions of an operational fuel cell. The dynamic two-phase flow simulations show that channels with smaller width and height eventuate in proper flow distribution at the gas feed. A channel with the sectional dimensions of 0.5 mm × 0.5 mm results in. 35.18% faster GDL (Gas-Diffusion Layer) clearance, 29.32% faster liquid expulsion compared to other channels having 2–3 times higher dimensions. Therefore, this channel is recommended as the best design for improved fuel cell performance.

Various semi-O/O-shape dimple patterns were applied on the flat spiral pipes as a regional contorting policy. In this method only the critical regions that may cause significant effect on the improvement of the hydrothermal performance can be subjected to structural modification. The all-round geometrical modifications causes excessive pressure drop while this method can solve this problem to some extent. Archimedean spiral geometry of spiral pipe makes uneven hydraulic-thermal characteristics along the pipe, so regional geometrical modification allows to pinpoint the optimal model. Using semi-O or O-shape dimples or combination of both on the inner or outer (or on both wall) leads to obtain unique cases. Constant wall temperature of 360 K was applied for the outer wall of all cases to evaluate the strength of each case based on heat extraction potential. The effect of arrangement, position, and type of dimples was taken under consideration. To get elaborate comprehension about the frictional and thermal behavior of each design, several criteria including Nusselt number (Nu), friction factor (f), thermal performance factor (TPF), temperature efficiency (ɛ), and J-factor were employed. Moreover, the effect of flow inlet position on the said was explored. All investigations were done in the flow velocity range of 0.1–0.6 m/s. The findings showed that case “c” (spiral pipe with dimples on the inner wall) has the highest heat extraction potential and improves heat extraction by 27% compared to the smooth spiral pipe. Also, case “c” makes the lowest pressure drop. Hence, by owning said two positive features case “c” provided the highest TPF, ɛ, and J-factor. Heat transfer improves when flow enters from the innermost turn.

With the growing demand for geospatial data, challenging aerial images with high spatial, spectral, and temporal resolution achieve excellent development. Currently, deep Convolutional Neural Network (CNN) structures are applied widely for object detection. Nevertheless, existing deep CNN-based models consist of complex network structures and require immense amounts of graphics processing unit (GPU) computation power with high energy consumption. Thus, achieving efficient real-time object detection for limited memory and processing capacity embedded device is a major challenge. This paper proposes a feasible and lightweight object detection model based on deep CNN where a mobile inverted bottleneck module is adopted in the backbone structure. Moreover, an enhanced spatial pyramid pooling is adopted to increase the receptive field in the network by concatenating the multi-scale local region features. The experimental results demonstrated that the proposed model achieved higher average precision and required the smallest memory storage compared to previous works. Moreover, the proposed model offers the best trade-offs in terms of detection accuracy, model size, and detection time which has excellent potential to be deployed on limited capacity embedded device.

Thermal management of microelectronic circuits will be one of the most difficult challenges facing engineering processes in the near future. High operating temperatures in these devices can degrade the reliability of the components and reduce their life. Therefore, effective cooling technologies that can disperse the significant heat load from the surface of microelectronic equipment are required. An appropriate microchannel heat sink (MCHS) system with optimized geometry can be one of the reliable choices. In the current work, an artificial neural network (ANN) is exerted to optimize the geometry of a finned-MCHS. The distance of fins from the inlet in the second row (l), the distance of fins from the side walls in the first and third rows (t), and the angle of hexagons (θ) are the input parameters. According to the obtained results, the ANN model with a coefficient of determination of 0.999 performed well in predicting the Nusselt number (Nu) and pressure drop (ΔP). Among the investigated input parameters, the variations of the parameter of t affected the thermal and hydrodynamic properties of the device noticeably. Besides, the ANN model suggested that when the optimum values of input parameters (i.e., l = 7.636 mm, t = 4 mm, and θ = 140) are used for the hexagonal fins inside a microchannel, the maximum relative efficiency index of 1.491 can be acquired.

This work investigates the performance enhancement of a solar air heater using inclined impinging jets on the heater surface. Two main sets of arrangements are considered for the jet nozzles including the parallel and crossing orientation of nozzles in consecutive rows. The RNG k − ɛ model is employed to simulate the turbulent flow in the channel. The influence of different geometrical and operational parameters such as jet diameter ratio (Dj/Dh), inclination angle (α,β), stream wise pitch ratio (L/Dh), span wise pitch ratio (S/Dh), Re number and velocity ratio (Vr) on the hydraulic and thermal behavior of jet solar air heater was studied. Results showed that the proposed jet solar air heater improved the Nu number and thermohydraulic performance parameter compared to the conventional unimproved solar air heater. For the studied range of parameters, up to 4.26 times higher Nu number was obtained using the jet impingement compared to the smooth duct solar air heater. Moreover, it was shown that the amount of velocity ratio (Vr) is a significant factor when comparing the parallel and crossing orientations. Additionally, results indicated that the increment of stream wise and span wise pitch led to the increment of the Nu number while having a minor effect on the friction factor. For example, the increment of L/dh from 0.3 to 0.6 leads to the reduction of f/fs of case 1 and case 4 by 6.5% and 1.09%, respectively Also, the increment of the jet diameter first raises the thermohydraulic performance parameter, and then with a further increment of diameter, the amount of thermohydraulic performance parameter decreases.

It is common to use micro-channel heat sinks (MCHSs) in equipment such as; ICs, transistors, LEDs, and high-power lasers, which generate heat due to the passage of electric current. This heat is often a menace to harm these devices and their internal parts. For this reason, heat rejection in the MCHSs is an endless challenge for researchers. Placing vortex generators (VGs) within the MCHS improves the cooling capacity but incurs a considerable pressure drop. Meanwhile, the shape, geometric dimensions, and arrangement of the VGs significantly affect this heat transfer. In the current study, the placement angle (θ), the longitudinal distance (dl), and the transverse distance (dt) of the VGs were chosen to be altered. The Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) were exerted to study their variation's effect on the Nusselt number (Nu) and pressure drop (ΔP) of an MCHS. The presented data illustrated that the results of the ANN model were closer to the data provided by the numerical simulation. With the coefficient of determination of 0.995 and 0.992 in forecasting the Nu and ΔP, the ANN exhibited better performance than the RSM model. Besides, the ANN model recommended that to acquire the highest relative efficiency index, the optimum values of placement angle, the longitudinal and transverse distances of the VGs should be 60, 0.151 mm, and 0.166 mm, respectively.

Bi-evaporator technology is a robust tool, operating in two levels of coolant production for normal cooling and freezing purposes. The current study is motivated to assess the feasibility of integrating a bi-evaporator technology and a single-flash geothermal power plant. The main novelty of this paper is to design a novel combined cooling and power (CCP) production cycle relying on an organic Rankin cycle and a modified bi-evaporator refrigeration cycle. In addition, the use of a zeotropic mixture (Pentane/Isobutane) fed to the CCP is another difference between the current research and the available literature review. In addition to the power and cooling, the entire system is boosted by the use of a multi-generation process, producing heating via a heating production unit and hydrogen (H2) via a low-temperature electrolyzer. The proposed system was analyzed from the energy, exergy, specific exergy costing, and net present value points of view. Therefore, a coherent single- and dual-sensitivity study is done, and the variability of the main performance metrics is viewed against the decision parameters. In addition, owing to the incompatible trend of the newly devised plant's chief performance metrics, an advanced triple-objective optimization through a NSGA-II method is regraded from the standpoints of thermodynamics and economics. The objective functions include the coefficient of performance, exergy efficiency, and sum unit cost of products, and the optimum solution reveals their values of 33.56%, 63.2%, and 6.0 $/GJ, respectively.

Optimum heat transfer in modern micro-channel heat sinks (MCHSs) plays a considerable role in ameliorating the efficiency and power of these devices. The response surface methodology (RSM) is one of the recently developed techniques to study and optimize the thermal and hydraulic behaviors of the MCHSs. In the current investigation, a multi-nozzle MCHS with circular fins on both sidewalls of the micro-channels was elected to analyze. The RSM method predicted the Nusselt number (Nu) of the MCHS and pressure drop (ΔP) of the coolant (the responses of the model). The diameter, the longitudinal pitch, and the transverse pitch of the circular fins were considered as the independent variables. These variables were changed in the ranges of 0.02–0.06 mm (diameter), 0.1–0.4 mm (longitudinal pitch), and 0.1–0.2 mm (transverse pitch). The impact of changing the mentioned variables on Nu and ΔP of the coolant to achieve the higher cooling capacity was studied. At first, Nu and ΔP values were calculated by the numerical procedure and then predicted by the RSM. Comparing the values derived by the numerical and the RSM models, it was observed that the values predicted by the RSM were close to the ones calculated by the numerical simulation. The RSM model with the coefficient of determination of 97.51% and 98.74% for Nu and ΔP could predict these responses accurately.

Abstract The adoption of automated crop harvesting system based on machine vision may improve productivity and optimize the operational cost. The scope of this study is to obtain visual information at the plantation which is crucial in developing an intelligent automated crop harvesting system. This paper aims to develop an automatic detection system with high accuracy performance, low computational cost and lightweight model. Considering the advantages of YOLOv3 tiny, an optimized YOLOv3 tiny network namely YOLO‐P is proposed to detect and localize three objects at palm oil plantation which include fresh fruit bunch, grabber and palm tree under various environment conditions. The proposed YOLO‐P model incorporated lightweight backbone based on densely connected neural network, multi‐scale detection architecture and optimized anchor box size. The experimental results demonstrated that the proposed YOLO‐P model achieved good mean average precision and F1 score of 98.68% and 0.97 respectively. Besides, the proposed model performed faster training process and generated lightweight model of 76 MB. The proposed model was also tested to identify fresh fruit bunch of various maturities with accuracy of 98.91%. The comprehensive experimental results show that the proposed YOLO‐P model can effectively perform robust and accurate detection at the palm oil plantation.

Latent heat thermal energy storage (LHTES) systems are widely used due to their ability to store a lot of heat energy as latent, and few studies have directly or indirectly investigated on the heat exchange methods with LHTES systems. In the present study, the free convection of a mixture of water and Nano-encapsulated phase change material (NEPCM) with a porous medium was used to exchange heat energy between a cold-water stream and a hot tank filled phase change material (PCM). NEPCM are nanostructures consisted of a solid shell and a PCM core, and the addition of these nanostructures to water has remarkable effects on the heat transfer parameters. In this research, a number of cases were simulated using computational fluid dynamic (CFD) to observe the impacts of injecting NEPCM to water, the porosity of the porous media and the location of the phase change zone on the average heat flux of the PCM tank. The results showed that increment of porosity number from 0.85 to 0.95 decreased the average heat flux of the PCM tank by 13.8%. Moreover, adding 3% NEPCM to water enhanced the average heat flux of the PCM tank by 22.2% and 18.4% when porosity numbers were 0.85 and 0.95, respectively.

This study has designed and employed computational fluid dynamics to cool lithium-ion batteries (LIBs) to keep the temperature constant between 35and45°C. A duct was designed for installing three hot LIBs to be cooled by water and nano-encapsulated phase change material (NEPCM) flow. for this simulation, n-nonadecane with the fusion temperature of 30.44°C was used for NEPCM's core and also water entered the channel with temperature of 30°C. The impacts of the various surface temperature of LIB, Reynolds numbers, and the concentration of NEPCM were analyzed on flow pattern, dimensionless isotherms, melted NEPCM, and heat transfer rate of each LIB. Increasing Reynolds number from 70 to 100 raised the heat transfer rate of LIBs in order of 12.1–17.2%. Furthermore, increasing the volume fraction from 0 to 0.03 escalated the heat transfer rate by 8.2–13.6%.

Considering the thermal processes with the help of smart heat recovery, this study proposes a novel auxiliary trigeneration system for a ship based on the waste heat of its engine to produce electricity, cooling, and freshwater. The system consists of a regenerative organic Rankine cycle (RORC) with R600 working fluid, a lithium-bromide/water single-effect absorption chiller, and a humidification dehumidification (HDH) desalination unit. A multi-heat recovery technique is implemented in the design framework, having a well-organized waste-to-energy system. Technical 3E (energy, exergy, and exergoeconomic) analysis together with a multi-criteria optimization using a genetic algorithm is conducted. Furthermore, a parametric study is employed regarding the impact of changing design parameters, namely, pinch point temperature difference of the high recovery vapor generator (HRVG), turbine inlet pressure, and top temperature of the HDH on the thermodynamic and exergoeconomic criteria. The results indicated the high sensitivity of the outputs from varying the turbine inlet pressure. Besides, the optimum net output power, cooling, and generated freshwater are calculated to be 783.9 kW, 959.8 kW, and 98.1 m3/day, respectively. Also, the optimum energy and exergy efficiencies and total cost per unit exergy are computed to be 58.4%, 43.0%, and 0.1494 $/kWh, respectively.

In the present study, the natural convection flow of water with Nano-encapsulated phase change material (NPCM) was simulated inside an insulated chamber, which a pair of pipes were considered as a heater and cooler sources with the boundary condition of uniform temperature. The NPCM's core was made of n-nonadecane with melting temperature of 30.44°C. This core has ability to change the liquid-solid phase to transfer heat between the heater and the cooler sources. Current simulation was steady state and was solved by SIMPLE algorithm based on FVM to investigate the effects of Rayleigh number, volume fraction and location of phase change zone on the convective heat transfer coefficient. Observations showed that, phase change of NPCM occurs at low Rayleigh numbers but had no effect on the convective heat transfer coefficient, but it was directly related to the thermal conductivity of mixture. Moreover, adding volume fraction of NPCM 0.02 into water increased the convective heat transfer coefficient by 10.43%, 19.1% and 18.3% compared to pure water for Rayleigh numbers 102,104,and106, respectively.

UAVs have been contributing substantially to multi-disciplinary research and around 70% of the articles have been published in just about the last five years, with an exponential increase. Primarily, while exploring the literature from the scientific databases for various aspects within the autonomous UAV path planning, such as type and configuration of UAVs, the complexity of their environments or workspaces, choices of path generating algorithms, nature of solutions and efficacy of the generated paths, necessitates an increased number of search keywords as a prerequisite. However, the addition of more and more keywords might as well curtail some conducive and worthwhile search results in the same pursuit. This article presents a Systematic Literature Review (SLR) for 20 useful parameters, organized into six distinct categories that researchers and industry practitioners usually consider. In this work, Web of Science (WOS) was selected to search the primary studies based on three keywords: “Autonomous” + “Path Planning” + “UAV” and following the exclusion and inclusion criteria defined within the SLR methodology, 90 primary studies were considered. Through literature synthesis, a unique perspective to see through the literature is established in terms of characteristic research sectors for UAVs. Moreover, open research challenges from recent studies and state-of-the-art contributions to address them were highlighted. It was also discovered that the autonomy of UAVs and the extent of their mission complexities go hand-in-hand, and the benchmark to define a fully autonomous UAV is an arbitral goal yet to be achieved. To further this quest, the study cites two key models to measure a drone’s autonomy and offers a novel complexity matrix to measure the extent of a drone’s autonomy. Additionally, since preliminary-level researchers often look for technical means to assess their ideas, the technologies used in academic research are also tabulated with references.

In the current research, the natural convection of water-Al2O3 nanofluid has been simulated inside a cold semi-annulus chamber with a hot block at the its bottom. Also, an oblique magnetic field (MHD) has been implemented to improve and control the flow. The unstructured triangular mesh and FVM with SIMPLE algorithm have been employed for discretization and coupling of pressure and velocity equations. In this simulation, the parameters of Hartmann, Rayleigh numbers and volume fraction of nanoparticles have been investigated on temperature contours, flow pattern, and hot wall Nusselt number. The observations show that, increasing the volume fraction increases the Nusselt number at low Rayleigh numbers. Moreover, the Nusselt number is inversely related to the Hartmann number for high Rayleigh numbers.

The free convection of Cu-water nanofluid is simulated and investigated inside a square heat exchanger chamber in the presence of MHD magnetic field. The Buongiorno model with the effects of Brownian and thermophoresis motion is considered to nanoparticles distribution inside the chamber. The geometry consists of a square chamber with two cylinders on the right and left sides as heater and cooler in order to create the buoyancy force, respectively. These cylinders represent hot and cold pipes, and the walls of the chamber are heat and mass insulation. the FVM with SIMPLE algorithm are used for velocity and pressure coupling. In current two-phase simulation, the effects of Rayleigh number, Hartmann number, inclination angle of chamber and volume fraction on streamline contours, isothermal lines, Lorentz force lines, nanoparticle distribution and Nusselt number are investigated. By modeling the motion of nanoparticles and evaluating it, a nanoparticle transport zone was observed. The diffusion effects of thermophoresis were significant in this zone. The nanoparticles were thrown from the hot cylinder to the cold cylinder. The application of a magnetic field enlarged the nanoparticle transport zone. However, increasing the Rayleigh number and decreasing the inclination angle of the enclosure caused the nanoparticles to disperse evenly.