In this research, the SAFT-VR Morse has been extended to polar system by considering the dipolar interactions between non-spherical molecules. The second order derivative thermodynamic properties of refrigerants such as speed of sound, specific heat capacity and Joule-Thomson coefficient have been predicted and compared to PPC-SAFT EoS. The model parameters have been obtained using vapor pressure and saturated liquid density data of pure refrigerants. Using obtained model parameters the aforementioned properties have been predicted in the vapor, liquid, saturation and supercritical phases. The effect of polar contribution on model prediction performance has been studied. The results show that, the polar contribution can be neglected without losing model accuracy. The obtained average error of the polar PC-SAFT and polar SAFT-VR Morse is very close to their original versions. As well, the PPC-SAFT EoS gives accurate results compared to SAFT-VR Morse and its polar version, especially in the case of Joule-Thomson coefficient prediction.
To improve the turnover of thermodynamic cycles, combined cycles have gained a great deal of interest today. The primary objective of these systems is to maximize the utilization of wasted energy from power cycles to initiate cooling, heating, and desalination cycles. In the context of this project, the general cycle comprises a primary portion of power generation, the generation of freshwater, and cooling along with the essential heating of water. Additionally, compressed air energy storage was utilized to lower the expense of the complete cycle. Because of this, we should switch to using compressed air during the off-peak hours of the day and night when the power demand is at its highest. This article also includes a simulation of the gasification process, in which the higher temperature of the generated products is utilized to pre-heat the air. Considering each set of decision variables, the duration of each simulation ranges from 10 to 15 s. It is vital to utilize machine learning techniques to decrease the time needed for optimization to discover the ideal points. In conclusion, the genetic algorithm demonstrated that the exergy turnover and economic cost of the optimal point of the newly introduced cycle are equivalent to 36.21% and 6.56 $/h, respectively.
Background: Although radiotherapy is one of the main cancer treatment modalities, exposing healthy organs/tissues to ionizing radiation during treatment can lead to different adverse effects. In this regard, it has been shown that the use of radioprotective agents may alleviate the ionizing radiation-induced toxicities. Objective: The present study aims to review the radioprotective potentials of silymarin/silibinin in the prevention/reduction of ionizing radiation-induced adverse effects on healthy cells/tissues. Methods: Based on PRISMA guideline, a comprehensive and systematic search was performed for identifying relevant literature on the “potential protective role of silymarin/silibinin in the treatment of radiotherapy-induced toxicities” in the different electronic databases of Web of Science, PubMed, and Scopus up to April 2022. Four hundred and fifty-five articles were obtained and screened in accordance with the inclusion and exclusion criteria of the current study. Finally, 19 papers were included in this systematic review. Results: The findings revealed that the ionizing radiation-treated groups had reduced survival rate and body weight in comparison with the control groups. It was also found that radiation can induce mild to severe adverse effects on skin, digestive, hematologic, lymphatic, respiratory, reproductive, and urinary systems. Nevertheless, the administration of silymarin/silibinin could mitigate the ionizing radiation-induced adverse effects in most cases. This herbal agent exerts its radioprotective effects through anti-oxidant, anti-apoptosis, anti-inflammatory activities, and other mechanisms. Conclusion: The results of the current systematic review showed that co-treatment of silymarin/silibinin with radiotherapy alleviates the radiotherapy-induced adverse effects in healthy cells/tissues.
Though bioenergy still emits some emissions, they are a lot lower than fossil fuels. Besides, the increase in water and power consumption keeps pace with the earth's growing population. Therefore, many studies have been conducted on multi-purpose cycles. Utilizing the biomass gasification process to produce the fuel needed for a gas turbine is a novel technology. The additional heat from the outlet gases is used to produce higher power in the Rankin cycle and cooling in the double-effect absorption chiller. The net power produced in this cycle will be used to empower the desalination system using reverse osmosis (RO) to increase the inlet pressure of the salty water so that it passes the water treatment membranes. Since the outlet water pressure is high, a water turbine is used to generate electricity. The genetic algorithm, along with machine learning methods, is used to achieve the optimal performance conditions and reduce the calculational time; because the time and calculational costs for modeling every cycle are high, and the optimization process will be prolonged. The results revealed that the proposed system is capable of producing a power of nearly 400 kW, with an exergy efficiency of 41 % and CO2 emission rate of 0.59 ton/MWh. Besides, the desalination rate and cooling capacities are 1.7 kg/s and 310 kW, respectively.
It has long been proven that geothermal energy may be used to generate electricity and heat sustainably. It emits less pollution, has a greater heat source temperature, and is compatible with a wide variety of energy systems. This research aims to use an ORC to utilize the excess energy of Kalina cycle systems (KCS) driven by a geothermal unit to generate clean, sustainable, and cost-effective low-temperature electricity. The most amazing feature of the Kalina cycle is that it gains more heat during heat addition in its evaporator owing to its significant thermo physical effects, as seen in Fig. 5. The system's extensive modeling is based on energy, exergy, and economic considerations. Additionally, optimization is carried out in order to get the lowest Levelized cost of power (product). The sensitivity analysis is used to determine the most effective parameters for system implementation. The results indicate that the unit cost of the product for the hybrid system is at its minimum amount of 0.04898. For the Kalina, the system is 0.5023, in which the effectiveness hybrid scheme will be 48.57%, and the effectiveness of the Kalina system will be 44.21%. The most exergy destruction occurs in the evaporator and then the Kalina cycle condenser because these components have the highest temperature difference. Finally, the AI-based genetic algorithm is implemented to find the best solution point in terms of LCOC using neural networks.
In the foreseeable future, heat exchangers will continue to play an important role in environmental management and have numerous applications, their geometry has been the subject of interest for many researchers. The main goal of this research is improving the heat exchangers' thermal performance and investigating the exergy using SIMPLE algorithm, k-w turbulent model and the Eulerian- Eulerian method for multiphase flow. Hence, the operation of Al2O3CuO-water hybrid nanofluid in a 3D shell-and-tube heat exchanger is simulated to enhance the contact surface of hot and cold fluid streams using computation fluid mechanics techniques. Reynolds number is 10,000, 15,000, 20,000, and 25,000 and volume fraction of nanoparticle is 2–6%. The innovations of this research are the use of the use of hybrid nanofluid and turbulator. The hybrid nanoparticles are employed to enhance the thermal conductivity and the turbulator is utilized to increase flow turbulence. The results demonstrated that the hybrid nanofluid, the turbulator, and high amount of Reynolds number can have a remarkable impact on increasing the thermal performance. The obtained results revealed that a 6% increment in hybrid nanoparticles volume fraction and an enhancement in Reynolds number from minimum to maximum lead to a 126% improvement in thermal performance in the presence of the turbulator. Lastly, environmental damage, energy consumption, and emissions are reduced by nanofluids.
Quality of joint play a vital role in the production of any industry. Nowadays, it is due to the very rigid and sharp economic market conditions in the manufacturing industry. The main objective of the industries is to reveal the production of better-quality products at a minimum cost and increase productivity. Tungsten Inert Gas (TIG) welding is the most vital and common operation for joining similar or dissimilar materials by heating, applying pressure, or using the filler material. TIG welding gives a better response for the company's main goal in terms of good quality and higher productivity with less time at a lower cost. In this research project, finding the impact of the parametric study on weld properties in TIG welding. The selected machining parameters are welding current, speed, and gas flow rate, which influence receptive responses such as unnotched tensile strength and notch tensile strength using an optimization viewpoint. Determination of the ideal processing parameters and their impact on better welding quality and higher productivity is investigated.
The formation of adventitious root primordia (ARP) is an ecological precious developmental event in the plant. The ARP evolution is a crucial step for the plant to fulfill two important phases of the life cycle, viz., vegetative and reproduction. The identification of several candidate genes has been known to provoke the induction, initiation, and maintenance of ARP-associated signaling cascading network. The expression of various genes, including WOUND INDUCED DEDIFFERENTIATION (WIND) genes induction such as APETALA2/ETHYLENE RESPONSE FACTORs (AP2/ERFs), CLAVATA3/EMBRYO SURROUNDING REGION-RELATED, ABERRANT LATERAL ROOT FORMATION4 (ALF4), was observed to be enhanced during ARP formation. This chapter highlights the histological documentation of adventitious root development, the role of phytohormones in adventitious root (AR) development, the role of sugars in AR development, the role of signaling peptides in AR development, epigenetic control in AR development, and cell wall modification during AR emergence. Additionally, the role of phytohormones and their molecular network is also explained during ARP initiation, elongation, and maturation in different plant species. Furthermore, the genetic signals that play an important role in the crosstalk generate complex network that regulates the generation of ARP.
Metal-air batteries (MABs) and fuel cells (FCs) critically rely on electrocatalytic O2 activation, and O2 reduction reaction (ORR), with noble metal-free materials. However, the inception of their synergist reactivity is still unclear due to several electronic and structural limitations. Therefore, the correlation between their science and engineering and their experimental as well as theoretical activity descriptors can pave the way for the development of novel cheap, and efficient catalysts. Moreover, with this framework, several volcanic correlations were established, indicating that catalyst activity increases linearly with increasing binding energy of ORR intermediates up to a certain point, but after that, the activity decreases as binding energy increases. The motivation of this review is to highlight (i) recent designs and developments on non-noble-metal-containing electrocatalysts for ORR, (ii) correlations between science and engineering and existing activity descriptors to improve the electrocatalyst’s ORR performance, and (iii) prospects and challenges with non-noble-metal-based electrocatalysts. The “science and engineering” of the electrode materials discussed in this review will aid researchers in selecting and designing ORR electrocatalysts for energy conversion processes.
In this study, the numerical analysis of the radiant floor system was investigated for a building in the presence of PCM inside the external walls as well as the roof at a thickness of 2 cm. By injecting cold/warm fluid into the radiant tubes inside the roof, the cooling/heating requirements were met. Several PCMs with identical thermal properties (except melting point) were selected and based on numerical analysis, the energy utilization in the heating/cooling sections was evaluated by comparison with the simple building (without PCM). Four main variables were defined for the neural network, and energy consumption was trended for two climate zones, Shenyang (41.7922°N, 123.4328°E), and Zhengzhou (34.7578°N, 113.6486° E). For each region, the PCM with the best phase transition was selected and it was realized that for the first region, energy consumption was diminished by 12.6% and for the second region by 15.9%. According to the temperature conditions and radiation intensity in the environment, the ANN could forecast annual energy utilization with an error of less than 6%.
This study shows the effect of integrating eutectic Phase Change Material (PCM) in concrete on thermal, physical, and mechanical properties. Lauric acid and Myristic acid were used to prepare Eutectic PCM. Eutectic PCM was mixed in Zeolite to form Shape Stabilized Composite Phase Change Material (SSCPCM). SSCPCM-39% was found to be most stable and prevents the leakage of the eutectic PCM. SSCPCM-39% was chemical, physically and morphologically stable using Fourier Transform Infrared Spectroscopy (FTIR), X-rays Diffraction (XRD), and Scanning Electron Microscope (SEM). Additionally, SSCPCM-39% possess excellent thermal stability and thermal energy storage capacity which was investigated using Thermo Gravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). SSCPCM was then integrated in concrete in varying percentage to form Thermal Energy Storage Concrete slab namely TES-C0, TES-C1, TES-C2, TES-C3, and TES-C4. These slabs were then tested in real outdoor environment for thermal behaviour. TES-C4 shows best performance in regulating indoor temperature showing maximum reduction of 14.51% in peak temperature and maximum time delay of 71 min in comparison to TES-C0. Compressive strength of 42 kN, 39 kN, 33 kN, and 29 kN was shown by TES-C1, TES-C2, TES-C3, and TES-C4 respectively after 28 days of hydration process.
The present work aims to investigate the theoretical model of a flat-plate solar collector considering nanofluids as heat transport medium. For this purpose, solar irradiance decomposition and transposition models have been implemented. Also, the different models for determining the thermophysical properties of nanofluids were implemented and compared with experimental data on these properties. The theoretical model was implemented in Matlab software and validated by comparison with experimental data from a flat-plate solar collector with MgO water. The results show that the maximum relative error was 5.36%, the minimum relative error was 0.20%, and the mean relative error was 2.02% when the model was validated with experimental data for MgO-water nanofluid with volume concentrations between 0 and 1.5%. Therefore, the theoretical model was successfully extended to simulate flat-plate solar collectors with MgO-water nanofluid. A parametric study showed that a nanofluid with a volume concentration of 0.75% MgO exhibited a higher relative increase in thermal efficiency compared to pure water. Moreover, the theoretical model was applied to a case study by simulating the annual performance of the collector in Porto Alegre, Brazil, when it was operated with MgO water. This showed satisfactory effects and great potential for application.
This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.
In this work, the predictive PC-SAFT EoS has been developed to predict the speed of sound, the isochoric and isobaric heat capacity of ionic liquids (ILs) over a wide range of pressures and temperatures. In this new methodology, the segment number and diameter of ILs have been estimated using the surface area and molecular volume of the UNIFAC model. The segment energy and association energy parameters have been obtained using an inverse relationship between dispersion/association energy and segment diameter. The results show that the predictive PC-SAFT model can predict the specific isochoric and isobaric heat capacity of ILs up to high pressure, accurately. In the case of the speed of sound, the model performance is acceptable by considering using no adjustable model parameters. To check the model capability, the new model results have been compared to the electrolyte PC-SAFT EoS.
The long-term viability of energy resources as a main input is essential to achieve long-term economic growth of a country and the energy efficiency significantly reduces energy consumption and greenhouse gas emissions, supporting environmental sustainability. As a result, a number of governments, led by those in the developed world, are making an effort to enact laws governing energy efficiency. This study suggests cutting-edge methods for forecasting greenhouse gas emissions and reducing energy demand from renewable sources based on a sustainable environment. Utilizing the statistical regression neural network (SRNN), greenhouse gas emissions have been predicted, and the deep neural network's (DNN) energy efficiency has increased. The SRNN_DNN intensity method out predicts evaluated MLR (multiple linear regression) and second- and third-order non-linear MPR (multiple polynomial regression) techniques according to MAPE (mean absolute percentage error) results. Furthermore, presented methods are considered suitable for computing GHG emissions due to the high accuracy of the SRNN DNN model. The anticipated greenhouse gas emissions related to energy were remarkably similar to the actual emissions of EU (European Union) nations.
In this article, natural alumina/water nanofluid (NF) convection in an isosceles equilateral rhombus-shaped enclosure was simulated using the Simplex algorithm and the control volume method. The enclosure under study had two insulation walls, i.e., a cold wall and a warm wall. Two blades were installed on the warm wall with a temperature equal to that of the warm wall. There was also a fin in the center of the enclosure with a temperature equal to that of the warm wall. The enclosure was horizontally under a magnetic field at Hartmann number (Ha) of 20. The average Nusselt number (Nu), entropy production, Bejan number (Be), and flow and temperature contours were studied while altering the length and thickness of the blades from 0.1 to 0.8 and 0.05 to 0.15, respectively, and the aspect ratio (AR) of the fin from 0.1 to 0.4. The obtained results were then optimized to catch the best results. The two-phase method was used to simulate nanofluid flow. By altering the width and length of the blades and the fin AR, the average Nu varies from 6.52 to 8.31. According to the results, within the range of the above variables, Nu, entropy production, and Be varied from 5.62 to 8.31, 7.55 to 12.36, and 0.48 to 0.6, respectively.
Feature selection, which picks the optimal subset of characteristics related to the target data by deleting unnecessary data, is one of the most important aspects of the machine learning area. A major part of big data preprocessing is feature selection (reduction). There are 2ⁿ alternative feature subsets for every n features, making it difficult to choose the best set of features from a dataset using typical feature selection techniques. Consequently, the present study proposes and suggests a unique feature selection method based on the Eagle Strategy(ESO) Optimization, Gravitational Search Optimization (GSO) algorithm, and their hybrid algorithm. We chose this infection as our subject of investigation since the number of women with breast cancer is increasing rapidly on a global scale. After lung cancer, which affects more women than any other kind of cancer, breast cancer is the second leading cause of cancer mortality. The goal of this study is to categorize breast cancer into two groups using the benchmark feature set (Wisconsin Diagnostic Breast Cancer (WDBC)) and to choose the fewest features (feature selection) to achieve maximum accuracy. This work also provides a hybrid technique for finding important features that combines two algorithms, ESO and the GSO algorithm, while reducing insignificant characteristics (features) and complexity. Soft computing technologies and machine learning algorithms provide a framework for prognostic research by classifying data instances as relevant or irrelevant depending on cancer severity. Thus, this work presented a new approach for classifying breast cancer tumors. In this research, we coupled soft computing methodologies—our implemented algorithms are applied for the first time to this problem—with artificial intelligence-based machine learning strategies to create a prediction model. The efficacy of our suggested technique was evaluated using WDBC breast cancer data sets, and the findings show that our proposed hybrid algorithm performs very well in breast cancer classification. We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9705, specificity up to 1.000, precision up to 1.000, F1-score up to 0.9696, and an AUC up to 0.9980 (close to maximum, i.e., 1.0000). Our study's goal is to incorporate our findings into a valid clinical prediction system, allowing visual science specialists to make more accurate and effective judgments in the future. Furthermore, our suggested technology might be used to detect a wide range of diseases.
In this work, the two-phase mixture model is applied to solve the velocity field, the pressure field and obtain the entropy generation rates in a dual-height plate-fin heatsink. The governing equations were solved using the second-order upwind discretization scheme as well as SIMPLE scheme used in coupling the pressure and velocity fields. The Re, nanoparticle concentration (ψ), secondary fin height (hsf), and fin spacing (dsf) were considered within the ranges of 500-2000, 0%-1%, 5 mm-17.5 mm, and 0.25 mm-0.5 mm, respectively. The results demonstrated that S˙th almost decrease by 30% as hsf increases from 5 mm to 17.5 mm. The increase in hsf escalates S˙fr by 308% and 238% at Re numbers of 500 and 2000, respectively. In addition, the escalation of Re from 500 to 2000 increases S˙fr by 2125% and 1748% for hsf 5 mm and 17.5 mm, respectively. The fin spacing analysis revealed that the lowest S˙th and highest S˙fr belong to dsf=0.5 mm. The lower the dsf the higher the backward flow and mixing flow at the heatsink outlet, thereby delay in flow passage and intensification of S˙th and S˙fr.
The significance of optimal fuel consumption is crucial due to the lack of energy resources and the need to preserve the environment. Also, it is unavoidable and toxic for nanoparticles to be released into the environment, as a result of their widespread use. In this study, performance evaluation criteria (PEC), and entropy production of a U-shaped tube heat exchanger with internal fins are numerically examined in the presence of hybrid nanofluid flow. The hybrid nanofluid is water/TiO2-SWCNT. The volume fraction of nanoparticles (φ) changes from 0 to 4% and the considered Reynolds numbers (Res) are 10000, 12500, 15000, 17500, and 20000. The height of needle fins is 1 and 1.25 mm. As Re and fin size are enhanced, the average Nusselt number (Nu) is intensified. For the fin size of 1.25 cm, the maximum enhancement in the PEC is 8.45% when φ is enhanced from 0 to 4% and Re=10000. For the fin size of 1cm, the maximum enhancement in thermal entropy production is equal to 81.5% as the Re is enhanced from 10000 to 20000 and φ=0%. Thermo- hydraulic analysis recommends using a U-shaped heat exchanger with a fin size of 1.25 cm. The use of nanofluid decreased energy consumption, emissions, and damage to the environment.
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