The main goal of the present work was to develop a value-added product of biodegradable material for sustainable packaging. The use of agriculture waste-derived carboxymethyl cellulose (CMC) mainly is to reduce the cost involved in the development of the film, at present commercially available CMS is costly. The main focus of the research is to translate the agricultural waste-derived CMC to useful biodegradable polymer suitable for packaging material. During this process CMC was extracted from the agricultural waste mainly sugar cane bagasse and the blends were prepared using CMC (waste derived), gelatin, agar and varied concentrations of glycerol; 1.5% (sample A), 2% (sample B), and 2.5% (sample C) was added. Thus, the film derived from the sample C (gelatin + CMC + agar) with 2.0% glycerol as a plasticizer exhibited excellent properties than other samples A and B. The physiochemical properties of each developed biodegradable plastics (sample A, B, C) were characterized using Fourier Transform Infra-Red (FTIR) spectroscopy and Differential Scanning Calorimetry (DSC), Thermogravimetric analysis (TGA). The swelling test, solubility in different solvents, oil permeability coefficient, water permeability (WP), mechanical strength of the produced material was claimed to be a good material for packaging and meanwhile its biodegradability (soil burial method) indicated their environmental compatibility nature and commercial properties. The reflected work is a novel approach, and which is vital in the conversion of organic waste to value-added product development. There is also another way to utilize commercial CMC in preparation of polymeric blends for the packaging material, which can save considerable time involved in the recovery of CMC from sugarcane bagasse.
The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg-Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.
The use of solar energy is one of the most prominent strategies for addressing the present energy management challenges. Solar energy is used in numerous residential sectors through flat plate solar collectors. The thermal efficiency of flat plate solar collectors is improved when conventional heat transfer fluids are replaced with nanofluids because they offer superior thermo-physical properties to conventional heat transfer fluids. Concentrated chemicals are utilized in nanofluids' conventional synthesis techniques, which produce hazardous toxic bi-products. The present research investigates the effects of novel green covalently functionalized gallic acid-treated multiwall carbon nanotubes-water nanofluid on the performance of flat plate solar collectors. GAMWCNTs are highly stable in the base fluid, according to stability analysis techniques, including ultraviolet-visible spectroscopy and zeta potential. Experimental evaluation shows that the thermo-physical properties of nanofluid are better than those of base fluid deionized water. The energy, exergy and economic analysis are performed using 0.025%, 0.065% and 0.1% weight concentrations of GAMWCNT-water at varying mass flow rates 0.010, 0.0144, 0.0188 kg/s. The introduction of GAMWCNT nanofluid enhanced the thermal performance of flat plate solar collectors in terms of energy and exergy efficiency. There is an enhancement in efficiency with the rise in heat flux, mass flow rate and weight concentration, but a decline is seen as inlet temperature increases. As per experimental findings, the highest improvement in energy efficiency is 30.88% for a 0.1% weight concentration of GAMWCNT nanofluid at 0.0188 kg/s compared to the base fluid. The collector's exergy efficiency increases with the rise in weight concentration while it decreases with an increase in flow rate. The highest exergy efficiency is achieved at 0.1% GAMWCNT concentration and 0.010 kg/s mass flow rate. GAMWCNT nanofluids have higher values for friction factor compared to the base fluid. There is a small increment in relative pumping power with increasing weight concentration of nanofluid. Performance index values of more than 1 are achieved for all GAMWCNT concentrations. When the solar thermal collector is operated at 0.0188 kg/s and 0.1% weight concentration of GAMWCNT nanofluid, the highest size reduction, 27.59%, is achieved as compared to a flat plate solar collector with water as a heat transfer fluid.
The development of algae is seen as a potential and ecologically sound approach to address the increasing demands in multiple sectors. However, successful implementation of processes is highly dependent on effective growing and harvesting methods. The present study provides a complete examination of contemporary techniques employed in the production and harvesting of algae, with a particular emphasis on their sustainability. The review begins by examining several culture strategies, encompassing open ponds, closed photobioreactors, and raceway ponds. The analysis of each method is conducted in a systematic manner, with a particular focus on highlighting their advantages, limitations, and potential for expansion. This approach ensures that the conversation is in line with the objectives of sustainability. Moreover, this study explores essential elements of algae harvesting, including the processes of cell separation, dewatering, and biomass extraction. Traditional methods such as centrifugation, filtration, and sedimentation are examined in conjunction with novel, environmentally concerned strategies including flocculation, electro-coagulation, and membrane filtration. It evaluates the impacts on the environment that are caused by the cultivation process, including the usage of water and land, the use of energy, the production of carbon dioxide, and the runoff of nutrients. Furthermore, this study presents a thorough examination of the current body of research pertaining to Life Cycle Analysis (LCA) studies, presenting a perspective that emphasizes sustainability in the context of algae harvesting systems. In conclusion, the analysis ends up with an examination ahead at potential areas for future study in the cultivation and harvesting of algae. This review is an essential guide for scientists, policymakers, and industry experts associated with the advancement and implementation of algae-based technologies.
Vertical axis wind turbines (VAWT) are a source of renewable energy and are used for both industrial and domestic purposes. The study of noise characteristics of a VAWT is an important performance parameter for the turbine. This study focuses on the development of a linear microphone array and measuring acoustic signals on a cambered five-bladed 45 W VAWT in an anechoic chamber at different tip speed ratios. The sound pressure level spectrum of VAWT shows that tonal noises such as blade passing frequencies dominate at lower frequencies whereas broadband noise corresponds to all audible ranges of frequencies. This study shows that the major portion of noise from the source is dominated by aerodynamic noises generated due to vortex generation and trailing edge serrations. The research also predicts that dynamic stall is evident in the lower Tip speed ratio (TSR) region making smaller TSR values unsuitable for a quiet VAWT. This paper compares the results of linear aeroacoustic array with a 128-MEMS acoustic camera with higher resolution. The study depicts a 3 dB margin between two systems at lower TSR values. The research approves the usage of the 8 mic linear array for small radius rotary machinery considering the results comparison with a NORSONIC camera and its resolution. These observations serve as a basis for noise reduction and blade optimization techniques.
The current study investigates the effect of multi stenosis on the hemodynamic parameters such as wall pressure, velocity and wall shear stress in the realistic left coronary artery. Patients CT scan image data of normal and diseased left coronary artery was chosen for the reconstruction of 3D coronary artery models. The diseased 3D model of left coronary artery shows a narrowing of more than 70% and 80% of area stenosis (AS) at the left main stem (LMS) and left circumflex (LCX) respectively. The results show that the decrease in pressure was found downstream to the stenosis as compared to the coronary artery without stenosis. The maximum pressure drop was noted across the 80% AS at the left circumflex branch. The recirculation zone was also observed immediate to the stenosis and highest wall shear stress was found across the 80% area stenosis. Our analysis provides an insight into the distribution of wall shear stress and pressure drop, thus improving our understanding on the hemodynamics in realistic coronary artery.
Sustainable materials, such as vegetable oils, have become an effective alternative for liquid dielectrics in power transformers. However, currently available vegetable oils for transformer application are extracted from edible products with a negative impact on food supply. So, it is proposed in this study to develop cottonseed oil (CSO) as an electrical insulating material and cooling medium in transformers. This development is performed in two stages. The first stage is to treat CSO with tertiary butylhydroquinone (TBHQ) antioxidants in order to enhance its oxidation stability. The second and most important stage is to use the promising graphene oxide (GO) nanosheets to enhance the dielectric and thermal properties of such oil through synthesizing GO-based CSO nanofluids. Sodium dodecyl sulfate (SDS) surfactant was used as surfactant for GO nanosheets. The nanofluid synthesis process followed the two-step method. Proper characterization of GO nanosheets and prepared nanofluids was performed using various techniques to validate the structure of GO nanosheets and their stability into the prepared nanofluids. The considered weight percentages of GO nanosheets into CSO are 0.01, 0.02, 0.03 and 0.05. Dielectric and thermal properties were comprehensively evaluated. Through these evaluations, the proper weight percentage of GO nanosheets was adopted and the corresponding physical mechanisms were discussed.
Recently, most service or product-oriented industries have been focusing on their activities to uphold the green and sustainable environment protocol owing to the increased environmental pollution. Concerning this issue, industries are now concentrating on developing recyclable or waste materials products. This research advocates developing and validating a banana fiber sandwich composite to promote the beneficial usage of bio-waste. The composite sandwich specimens were fabricated with resin-impregnated woven banana fiber mat as a skin, and the core was reinforced with three different weight percentages (5, 7.5 and 10%) of chopped banana fiber. The sandwich specimens were pressed into a three-point bending test to validate the structural integrity. The flexural characteristics like flexural strength and modulus were examined experimentally, whereas the key strength indices like flexural stiffness and core shear modulus were evaluated analytically. Post-fracture surfaces were studied through a scanning electron microscope to investigate the failure mechanism. The experimental and analytical results indicate that 10% banana fiber content in the sandwich core increases the flexural strength and flexural modulus to 225% and 147%, respectively, compared to the neat epoxy core. The numerical simulation was also performed through FEA to validate the experimental findings. The numerical results are in good concurrence with the experimental one.
A comprehensive understanding of physiochemical properties, thermal degradation behavior and chemical composition is significant for biomass residues before their thermochemical conversion for energy production. In this investigation, teff straw (TS), coffee husk (CH), corn cob (CC), and sweet sorghum stalk (SSS) residues were characterized to assess their potential applications as value-added bioenergy and chemical products. The thermal degradation behavior of CC, CH, TS and SSS samples is calculated using four different heating rates. The activation energy values ranged from 81.919 to 262.238 and 85.737-212.349 kJ mol-1 and were generated by the KAS and FWO models and aided in understanding the biomass conversion process into bio-products. The cellulose, hemicellulose, and lignin contents of CC, CH, TS, and SSS were found to be in the ranges of 31.56-41.15%, 23.9-32.02%, and 19.85-25.07%, respectively. The calorific values of the residues ranged from 17.3 to 19.7 MJ/kg, comparable to crude biomass. Scanning electron micrographs revealed agglomerated, irregular, and rough textures, with parallel lines providing nutrient and water transport pathways in all biomass samples. Energy Dispersive X-ray spectra and X-ray diffraction analysis indicated the presence of high carbonaceous material and crystalline nature. FTIR analysis identified prominent band peaks at specific wave numbers. Based on these findings, it can be concluded that these residues hold potential as energy sources for various applications, such as the textile, plastics, paints, automobile, and food additive industries.
A typical ferrite/martensitic heat-resistant steel (T91) is widely used in reheaters, superheaters and power stations. Cr3C2-NiCr-based composite coatings are known for wear-resistant coatings at elevated temperature applications. The current work compares the microstructural studies of 75 wt% Cr3C2- 25 wt% NiCr-based composite clads developed through laser and microwave energy on a T91 steel substrate. The developed clads of both processes were characterized through a field emission scanning electron microscope (FE-SEM) attached with energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD) and assessment of Vickers microhardness. The Cr3C2-NiCr based clads of both processes revealed better metallurgical bonding with the chosen substrate. The microstructure of the developed laser clad shows a distinctive dense solidified structure, with a rich Ni phase occupying interdendritic spaces. In the case of microwave clad, the hard chromium carbide particles consistently dispersed within the soft nickel matrix. EDS study evidenced that the cell boundaries are lined with chromium where Fe and Ni were found inside the cells. The X-ray phase analysis of both the processes evidenced the common presence of phases like chromium carbides (Cr7C3, Cr3C2, Cr23C6), Iron Nickel (FeNi3) and chromium-nickel (Cr3Ni2, CrNi), despite these phases iron carbides (Fe7C3) are observed in the developed microwave clads. The homogeneous distributions of such carbides in the developed clad structure of both processes indicated higher hardness. The typical microhardness of the laser-clad (1142 ± 65HV) was about 22% higher than the microwave clad (940 ± 42 HV). Using a ball-on-plate test, the study analyzed microwave and laser-clad samples' wear behavior. Laser-cladding samples showed superior wear resistance due to hard carbide elements. At the same time, microwave-clad samples experienced more surface damage and material loss due to micro-cutting, loosening, and fatigue-induced fracture.
A numerical analysis of a CdTe/Si dual-junction solar cell in terms of defect density introduced at various defect energy levels in the absorber layer is provided. The impact of defect concentration is analyzed against the thickness of the CdTe layer, and variation of the top and bottom cell bandgaps is studied. The results show that CdTe thin film with defects density between 1014 and 1015 cm-3 is acceptable for the top cell of the designed dual-junction solar cell. The variations of the defect concentrations against the thickness of the CdTe layer indicate that the open circuit voltage, short circuit current density, and efficiency (ƞ) are more affected by the defect density at higher CdTe thickness. In contrast, the Fill factor is mainly affected by the defect density, regardless of the thin film's thickness. An acceptable defect density of up to 1015 cm-3 at a CdTe thickness of 300 nm was obtained from this work. The bandgap variation shows optimal results for a CdTe with bandgaps ranging from 1.45 to 1.7 eV in tandem with a Si bandgap of about 1.1 eV. This study highlights the significance of tailoring defect density at different energy levels to realize viable CdTe/Si dual junction tandem solar cells. It also demonstrates how the impact of defect concentration changes with the thickness of the solar cell absorber layer.
The critical impact of sodium-doped molybdenum (MoNa) in shaping the MoSe2 interfacial layer, influencing the electrical properties of CIGSe/Mo heterostructures, and achieving optimal MoSe2 formation conditions, leading to improved hetero-contact quality. Notably, samples with a 600-nm-thick MoNa layer demonstrate the highest resistivity (73 μΩcm) and sheet resistance (0.45 Ω/square), highlighting the substantial impact of MoNa layer thickness on electrical conductivity. Controlled sodium diffusion through MoNa layers is essential for achieving desirable electrical characteristics, influencing Na diffusion rates, grain sizes, and overall morphology, as elucidated by EDX and FESEM analyses. Additionally, XRD results provide insights into the spontaneous peeling-off phenomenon, with the sample featuring a ~ 600-nm MoNa layer displaying the strongest diffraction peak and the largest crystal size, indicative of enhanced Mo to MoSe2 conversion facilitated by sodium presence. Raman spectra further confirm the presence of MoSe2, with its thickness correlating with MoNa layer thickness. The observed increase in resistance and decrease in conductivity with rising MoSe2 layer thickness underscore the critical importance of optimal MoSe2 formation for transitioning from Schottky to ohmic contact in CIGSe/Mo heterostructures. Ultimately, significant factors to the advancement of CIGSe thin-film solar cell production are discussed, providing nuanced insights into the interplay of MoNa and MoSe2, elucidating their collective impact on the electrical characteristics of CIGSe/Mo heterostructures.
Perovskite solar cells (PSCs) hold potential for low-cost, high-efficiency solar energy, but their sensitivity to moisture limits practical application. Current fabrication requires controlled environments, limiting mass production. Researchers aim to develop stable PSCs with longer lifetimes under ambient conditions. In this research work, we investigated the stability of perovskite films and solar cells fabricated and annealed in natural air using four different anti-solvents: toluene, ethyl acetate, diethyl ether, and chlorobenzene. Films (about 300 nm thick) were deposited via single-step spin-coating and subjected to ambient air-atmosphere for up to 30 days. We monitored changes in crystallinity, electrical properties, and optics over time. Results showed a gradual degradation in the films' crystallinity, morphology, and electro-optical properties. Notably, films made with ethyl acetate exhibited superior stability compared to other solvents. These findings contribute to advancing stable and high-performance PSCs manufactured under normal ambient conditions. In addition, we also discuss the possible machine learning (ML) approach to our future work direction to optimize the materials structures, and synthesis process parameters for future high-efficient perovskite solar cells fabrication.
Covid-19 has given one positive perspective to look at our planet earth in terms of reducing the air and noise pollution thus improving the environmental conditions globally. This positive outcome of pandemic has given the indication that the future of energy belong to green energy and one of the emerging source of green energy is Lithium-ion batteries (LIBs). LIBs are the backbone of the electric vehicles but there are some major issues faced by the them like poor thermal performance, thermal runaway, fire hazards and faster rate of discharge under low and high temperature environment,. Therefore to overcome these problems most of the researchers have come up with new methods of controlling and maintaining the overall thermal performance of the LIBs. The present review paper mainly is focused on optimization of thermal and structural design parameters of the LIBs under different BTMSs. The optimized BTMS generally demonstrated in this paper are maximum temperature of battery cell, battery pack or battery module, temperature uniformity, maximum or average temperature difference, inlet temperature of coolant, flow velocity, and pressure drop. Whereas the major structural design optimization parameters highlighted in this paper are type of flow channel, number of channels, length of channel, diameter of channel, cell to cell spacing, inlet and outlet plenum angle and arrangement of channels. These optimized parameters investigated under different BTMS heads such as air, PCM (phase change material), mini-channel, heat pipe, and water cooling are reported profoundly in this review article. The data are categorized and the results of the recent studies are summarized for each method. Critical review on use of various optimization algorithms (like ant colony, genetic, particle swarm, response surface, NSGA-II, etc.) for design parameter optimization are presented and categorized for different BTMS to boost their objectives. The single objective optimization techniques helps in obtaining the optimal value of important design parameters related to the thermal performance of battery cooling systems. Finally, multi-objective optimization technique is also discussed to get an idea of how to get the trade-off between the various conflicting parameters of interest such as energy, cost, pressure drop, size, arrangement, etc. which is related to minimization and thermal efficiency/performance of the battery system related to maximization. This review will be very helpful for researchers working with an objective of improving the thermal performance and life span of the LIBs.
This study investigated the engine performance and emission characteristics of biodiesel blends with combined Graphene oxide nanoplatelets (GNPs) and 10% v/v dimethyl carbonate (DMC) as fuel additives as well as analysed the tribological characteristics of those blends. 10% by volume DMC was mixed with 30% palm oil biodiesel blends with diesel. Three different concentrations (40, 80 and 120 ppm) of GNPs were added to these blends via the ultrasonication process to prepare the nanofuels. Sodium dodecyl sulphate (SDS) surfactant was added to improve the stability of these blends. GNPs were characterised using Scanning Electron Microscope (SEM) and Fourier Transform Infrared (FTIR), while the viscosity of nanofuels was investigated by rheometer. UV-spectrometry was used to determine the stability of these nanoplatelets. A ratio of 1:4 GNP: SDS was found to produce maximum stability in biodiesel. Performance and emissions characteristics of these nanofuels have been investigated in a four-stroke compression ignition engine. The maximum reduction in BSFC of 5.05% and the maximum BTE of 22.80% was for B30GNP40DMC10 compared to all other tested blends. A reduction in HC (25%) and CO (4.41%) were observed for B30DMC10, while a reduction in NOx of 3.65% was observed for B30GNP40DMC10. The diesel-biodiesel fuel blends with the addition of GNP exhibited a promising reduction in the average coefficient of friction 15.05%, 8.68% and 3.61% for 120, 80 and 40 ppm concentrations compared to B30. Thus, combined GNP and DMC showed excellent potential for utilisation in diesel engine operation.
This study deals with an experimental investigation to assess the characteristics of a modified common rail direct injection (CRDI) engine utilizing diesel, Mahua biodiesel, and their blends with synthesized zinc oxide (ZnO) nano additives. The physicochemical properties of diesel, diesel + 30 ppm ZnO nanoparticles (D10030), 20% Mahua biodiesel (MOME20), and Mahua biodiesel (20%) + 30 ppm ZnO nanoparticles (MOME2030) were measured in accordance to the American Society for Testing and Materials standards. The effects of modification of fuel injectors (FI) holes (7-hole FI) and toroidal reentrant combustion chamber (TRCC) piston bowl design on the performance of CRDI using different fuel blends were assessed. For injection timings (IT) and injection opening pressure (IOP) average increase in brake thermal efficiency for fuel blend D10030 and MOME2030 was 9.65% and 16.4%, and 8.83% and 5.06%, respectively. Also, for IT and IOP, the average reductions in brake specific fuel consumption, smoke, carbon monoxide, hydrocarbon and nitrogen oxide emissions for D10030 and MOME2030 were 10.9% and 7.7%, 18.2% and 8.6%, 12.6% and 11.5%, 8.74% and 13.1%, and 5.75% and 7.79%, respectively and 15.5% and 5.06%, 20.33% and 6.20%, 11.12% and 24.8%, 18.32% and 6.29%, and 1.79% and 6.89%, respectively for 7-hole fuel injector and TRCC. The cylinder pressure and heat release rate for D10030 and MOME2030 were enhanced by 6.8% and 17.1%, and 7.35% and 12.28%. The 7-hole fuel injector with the nano fuel blends at an injection timing and pressure of 10° btdc and 900 bar demonstrated the overall improvement of the engine characteristics due to the better air quality for fuel mixing. Similarly, the TRCC cylinder bowl geometry illustrated advanced ignition due to an improved swirl and turbulence. Also, the engine test results demonstrated that 30 ppm of ZnO nanoparticles in Mahua biodiesel (MOME2030) and diesel (D10030) with diethyl ether resulted overall enhancement of CRDI engine characteristics.
The effect of crump rubber on the dry sliding wear behavior of epoxy composites is investigated in the present study. Wear tests are carried out for three levels of crump rubber (10, 20, and 30 vol.%), normal applied load (30, 40, and 50 N), and sliding distance (1, 3, and 5 km). The wear behavior of crump rubber-epoxy composites is investigated against EN31 steel discs. The hybrid mathematical approach of Taguchi-coupled Grey Relational Analysis (GRA)-Principal Component Analysis (PCA) is used to examine the influence of crump rubber on the tribological response of composites. Mathematical and experimental results reveal that increasing crump rubber content reduces the wear rate of composites. Composites also show a significant decrease in specific wear values at higher applied loads. Furthermore, the coefficient of friction also shows a decreasing trend with an increase in crump rubber content, indicating the effectiveness of reinforcing crump rubber in a widely used epoxy matrix. Analysis of Variance (ANOVA) results also reveal that the crump rubber content in the composite is a significant parameter to influence the wear characteristic. The post-test temperature of discs increases with an increase in the applied load, while decreasing with an increase in filler loading. Worn surfaces are analyzed using scanning electron microscopy to understand structure-property correlations. Finally, existing studies available in the literature are compared with the wear data of the present study in the form of a property map.
Recently, solar photovoltaic (PV) technology has shown tremendous growth among all renewable energy sectors. The attractiveness of a PV system depends deeply of the module and it is primarily determined by its performance. The quantity of electricity and power generated by a PV cell is contingent upon a number of parameters that can be intrinsic to the PV system itself, external or environmental. Thus, to improve the PV panel performance and lifetime, it is crucial to recognize the main parameters that directly influence the module during its operational lifetime. Among these parameters there are numerous factors that positively impact a PV system including the temperature of the solar panel, humidity, wind speed, amount of light, altitude and barometric pressure. On the other hand, the module can be exposed to simultaneous environmental stresses such as dust accumulation, shading and pollution factors. All these factors can gradually decrease the performance of the PV panel. This review not only provides the factors impacting PV panel's performance but also discusses the degradation and failure parameters that can usually affect the PV technology. The major points include: 1) Total quantity of energy extracted from a photovoltaic module is impacted on a daily, quarterly, seasonal, and yearly scale by the amount of dust formed on the surface of the module. 2) Climatic conditions as high temperatures and relative humidity affect the operation of solar cells by more than 70% and lead to a considerable decrease in solar cells efficiency. 3) The PV module current can be affected by soft shading while the voltage does not vary. In the case of hard shadowing, the performance of the photovoltaic module is determined by whether some or all of the cells of the module are shaded. 4) Compared to more traditional forms of energy production, PV systems offer a significant number of advantages to the environment. Nevertheless, these systems can procure greenhouse gas emissions, especially during the production stages. In conclusion, this study underlines the importance of considering multiple parameters while evaluating the performance of photovoltaic modules. Environmental factors can have a major impact on the performance of a PV system. It is critical to consider these factors, as well as intrinsic and other intermediate factors, to optimize the performance of solar energy systems. In addition, continuous monitoring and maintenance of PV systems is essential to ensure maximum efficiency and performance.
It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.