Yazar "Josephin, J. S. Femilda" seçeneğine göre listele

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    A comparative study on bayes classifier for detecting photovoltaic module visual faults using deep learning features
    (Elsevier, 2024) Venkatesh, S. Naveen; Sugumaran, V.; Subramanian, Balaji; Josephin, J. S. Femilda; Varuvel, Edwin Geo
    Renewable energy is found to be an effective alternative in the field of power production owing to the recent energy crises. Among the available renewable energy sources, solar energy is considered the front runner due to its ability to deliver clean energy, free availability and reduced cost. Photovoltaic (PV) modules are placed over large geographical regions for efficient solar energy harvesting, making it difficult to carry out maintenance and restoration works. Thermal stresses inherited by photovoltaic modules (PVM) under varying environmental conditions can lead to failure of internal components. Such failures when left undetected impart a number of complications in the system that will lead to unsafe operation and seizure. To avoid the aforementioned uncertainties, frequent monitoring of PVM is found necessary. The fault identification in PVM using essential features taken from aerial images is presented in this study. The feature extraction procedure was carried out using convolutional neural networks (CNN), while the feature selection process was carried out by the J48 decision tree method. Six test conditions were considered such as delamination, glass breakage, discoloration, burn marks, snail trail, and good panel. Bayes Net (BN) and Naive Bayes (NB) classifiers were utilized as primary classifiers for all the test conditions. Results obtained from the classifiers were compared and the best classifier for fault detection in PVM is suggested.
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    Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in underwater sensor networks
    (Wiley, 2023) Kaliraj, S.; Hariharan, B.; Sivakumar, V; Josephin, J. S. Femilda; Siva, R.; Prakash, P. N. Senthil
    The issue of underwater sensor network (UWSN) localization has led to the aim of techniques presented in recent years. In this paper, we develop Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in UWSN. The projected method predicts that sink node plays a major function in managing the computational load contrasted with the remaining nodes in the network of underwater. This node localization is proceeding with frequency shifts of sound waves contrasted toward real, which are present once observer in addition source can be mobile as they do in a marine atmosphere. The proposed technique is utilized to compute the estimated position of an unknown sensor node; here Archimedes' optimization algorithm is utilized to reduce the error during localization of nodes in UWSNs. This proposed technique can be enhancing the accuracy of the localization of nodes in UWSNs. This proposed methodology can be implemented and evaluated with the help of performance metrics. To validate the proposed technique's efficiency, it is contrasted with conventional techniques like Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA).
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    Efficacy of machine learning algorithms in estimating emissions in a dual fuel compression ignition engine operating on hydrogen and diesel
    (Pergamon-Elsevier Science Ltd, 2023) Venkatesh, S. Naveen; Sugumaran, V; Thangavel, Venugopal; Balaji, P. Arun; Vijayaragavan, Mathanraj; Subramanian, Balaji; Josephin, J. S. Femilda
    Emission created by combustion of fossil fuels are a major concern of the world for the past few decades. The stringent emission norms have impacted the automobile manufacturers to work on exhaust emissions and its impact. This research focused on using machine learning regression models to evaluate the efficacy of experimental results for a dual fuel compression ignition (CI) engine operating on hydrogen and diesel. In the present study, engine emissions were estimated using 29 regression algorithms. A total of 5 input data namely, concentration of hydrogen, engine load, diesel intake, speed and equivalence ratio were considered in the study to estimate various emissions like oxides of nitrogen (NOx), carbon dioxide (CO2), hydrocarbon (HC) and smoke. Correlation coefficient, mean absolute error, root mean squared error, relative absolute error and root relative squared error were adopted as the performance metrics in the present study. Amongst the algorithms considered, pace regression, radial basis function regressor, multilayer perceptron regressor and alternating model tree produced the highest correlation coefficient of 0.9985, 0.8958, 0.9950 and 0.9256 in estimating the engine emissions like CO2, smoke, NOx and HC respectively. Additionally, an attempt was made to establish an individual algorithm that can estimate all the emissions was identified as multilayer perceptron regressor with correlation coefficient values of 0.9977 (CO2), 0.9950 (NOx), 0.8501(smoke) and 0.8731(HC) respectively. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Enhancing the performance of renewable biogas powered engine employing oxyhydrogen: Optimization with desirability and D-optimal design
    (Elsevier Sci Ltd, 2023) Sharma, Prabhakar; Balasubramanian, Dhinesh; Khai, Chu Thanh; Venugopal, Inbanaathan Papla; Alruqi, Mansoor; Josephin, J. S. Femilda; Sonthalia, Ankit
    The performance and exhaust characteristics of a dual-fuel compression ignition engine were explored, with biogas as the primary fuel, diesel as the pilot-injected fuel, and oxyhydrogen as the fortifying agent. The trials were carried out with the use of an RSM-based D-optimal design. ANOVA was used to create the relationship functions between input and output. Except for nitrogen oxide emissions, oxyhydrogen fortification increased biogas-diesel engine combustion and decreased carbon-based pollutants. For each result, RSM-ANOVA was utilized to generate mathematical formulations (models). The output of the models was predicted and compared to the observed findings. The prediction models showed robust prediction efficiency (R2 greater than 99.21%). The optimal engine operating parameters were discovered by desirability approach-based optimization to be 24 degrees crank angles before the top dead center, 10.88 kg engine loading, and 1.1 lpm oxyhydrogen flow rate. All outcomes were within 3.75% of the model's predicted output when the optimized parameters were tested experimentally. The current research has the potential to be widely used in compression ignition engine-based transportation systems.
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    Evaluation of wheat germ oil biofuel in diesel engine with hydrogen, bioethanol dual fuel and fuel ionization strategies
    (Pergamon-Elsevier Science Ltd, 2024) Nibin, Mohammed; Varuvel, Edwin Geo; Josephin, J. S. Femilda; Vikneswaran, M.
    The work has been done with the objective of overcoming the sustainability and environmental degradation pertaining to the increasing consumption of diesel fuel. This can be done by replacing it with vegetable oils because they are renewable and eco-friendly. In this regard, initially, this study investigated the performance of a single-cylinder diesel engine fuelled by neat wheat germ oil (WTGO). The results proved that the performance of WTGO was way inferior to that of sole diesel due to its very high viscosity nature. The BTE given by WTGO at full load was 2.1% lesser than diesel. Apart from NOx and CO2, other emissions like CO, smoke, and HC were higher for WTGO in comparison to diesel. To improve the performance and emission of WTGO, various fuel modification methods were employed with it, and the results of those methods were compared with neat diesel and WTGO. The methods adopted in this study are: i) Trans-esterified WTGO (biodiesel), ii) Fuel ionization using a magnetic field (Permanent and electrical type), iii) Dual fuel mode: WTGO operated in combination with ethanol and hydrogen. Among these, dual fuel operation of WTGO and hydrogen resulted in maximum brake thermal efficiency, followed by dual fuel operation with ethanol (30% energy share), fuel ionization (both types), and WTGO biodiesel. The WTGO, with a 15% hydrogen energy share, showed the highest BTE of 29.8%, which was higher than neat diesel (28.7%) and WTGO (26.6%). The same method reduced the HC and CO emissions by 39.3% and 40.5%, respectively, when compared to neat WTGO. All methods decreased the smoke emission, and the lowest was recorded by WTGO biodiesel, which was lesser by 21.7% and 6.1% compared to WTGO and diesel, respectively. The peak heat release rate and pressure were higher for all fuel modifications as compared to neat WTGO, but only WTGO and 15% hydrogen energy share of dual fuel operation exhibited higher peak values than diesel. The neat WTGO experienced the most delayed start of combustion, and it was improved with the implication of the above methods. The operation of WTGO in dual fuel mode resulted in the least delay for the start of combustion but was not equivalent to neat diesel. Finally, it is recommended that using hydrogen in dual fuel mode is the best way to achieve maximum performance with WTGO as a fuel for diesel engines without any major modifications to the engine.
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    Exhaust emission control of SI engines using ZSM-5 zeolite supported bimetals as a catalyst synthesized from coal fly ash
    (Elsevier Sci Ltd, 2023) Rajakrishnamoorthy, P.; Saravanan, C. G.; Natarajan, Ramesh; Karthikeyan, D.; Sasikala, J.; Josephin, J. S. Femilda; Vikneswaran, M.
    This paper synthesizes ZSM-5 zeolite from coal fly ash and uses it as a catalyst for reduction of NOx emissions in gasoline powered engine. It suggests a mono and bimetallic doped zeolite coated in honeycomb structure cordierite monolith for effectively reducing the NOx emissions. The synthesized ZSM-5 zeolite was subjected to SEM, XRF and XRD analysis and compared with commercial ZSM-5 zeolite. The experimental study of measuring emissions using AVL DI-gas analyzer on a Tata nano twin-cylinder spark ignition engine clearly indicated that inhouse made bimetallic of Ce.Cu-ZSM5 and Ce.Fe-ZSM5 were able to reduce the NOx by 69 % and 75 % at 16 kW. The NOx reductions were much better than those of the commercial catalytic converters.
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    Experimental investigations on in-cylinder flame and emission characteristics of butanol-gasoline blends in SI engine using combustion endoscopic system
    (Elsevier, 2024) Kumaravel, S.; Saravanan, C. G.; Raman, Vallinayagam; Vikneswaran, M.; Sasikala, J.; Josephin, J. S. Femilda; Alharbi, Sulaiman Ali
    The objective of this study is to characterize the in-cylinder flames of butanol-gasoline blends in a spark ignition (SI) engine. The experiments were performed using butanol-gasoline blends prepared in the ratio of 10:90, 20:80, and 30:70 by volume. The in-cylinder combustion was visualized and captured using a combustion endoscopic system. From the captured combustion images, spatial flame distribution was evaluated for butanol-gasoline fuel blends. Furthermore, combustion, emission, and performance characteristics were investigated in a SI engine for the same blends. The engine test results were rationalized from the flame characterization results of butanol-gasoline combustion to improve the fundamental understanding. The experimental outcome is that the flame spread region (%) was found to be higher for butanol blends when compared to sole gasoline fuel. The addition of butanol to gasoline increased the flame speed and consequently increased the combustion burn rate, as well as the pressure and heat release rate within the cylinder. The brake thermal efficiency of the engine increased with increasing butanol concentration in the blend. In addition, the butanol-gasoline blends showed decreased CO and HC emissions when compared to gasoline but reportedly increased NO emission for butanol-blended gasoline blend fuels. Overall, this study concludes that butanol has the potential to be used as a supplement to gasoline due to improved flame and engine characteristics and can be used in the conventional gasoline engine without any major engine modification.