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Öğe A deep learning multi-feature based fusion model for predicting the state of health of lithium-ion batteries(Elsevier ltd, 2025) Sonthalia, Ankit; Bai, Femilda Josephin Joseph Shobana; Varuvel, Edwin Geo; Chinnathambi, Arunachalam; Subramanian, Thiyagarajan; Kiani, FarzadLithium-ion batteries have become the preferred energy storage method with applications ranging from consumer electronics to electric vehicles. Utilization of the battery will eventually lead to degradation and capacity fade. Accurately predicting the state of health (SOH) of the cells holds significant importance in terms of reliability and safety of the cell during its operation. The battery degradation mechanism is strongly non-linear and the physics-based model have their inherent disadvantages. The machine learning method has become popular for estimating SOH due to its superior non-linear mapping, adaptive, and self-learning capabilities, made possible by advances in deep learning technologies. In this study parallel hybrid neural network is formulated for predicting the state of health of lithium-ion cell. Firstly, the factors that have an effect on the cell state were analysed. These factors are cell voltage, charging & discharging time and incremental capacity curve. The features were then processed for use as input to the model. Spearman correlation coefficient analysis shows that all the factors had a positive correlation with SOH. While charging time has a negative correlation with the other features. Next the deep learning models namely convolution neural network (CNN), temporal convolution network (TCN), long-short-term memory (LSTM) and bi-directional LSTM were used to make fusion models. The number of layers in CNN and TCN were also varied. The hyperparameters used in the models were optimized using Bayesian optimization algorithm. The models were validated through comparative experiments on the University of Maryland battery degradation dataset. The prediction accuracy with CNN 3-layer LSTM was found to be the best for the training and the test dataset. The overall R2 value, root mean squared error (RMSE) and mean absolute percentage error (MAPE) with the model was found to be 0.999646, 0.003807 and 0.3, respectively. The impact of the features on the model was also analysed by removing one feature and retraining the model with the other features. The effect of discharging time and the peak of the discharge incremental capacity curve was maximum. The analysis also reveals that either charging voltage or discharging voltage can be used. Further, the proposed model was also compared with the other studies. The comparison shows that the R2, RMSE and MAPE values of the proposed model was better.Öğe AO-SVM: a machine learning model for predicting water quality in the cauvery river(IOP publishing, 2024) Vellingiri, J.; Kalaivanan, K.; Shanmugaiah, Kaliraj; Bai, Femilda Josephin Joseph ShobanaWater pollution is a significant cause of death globally, resulting in 1.8 million deaths annually due to waterborne diseases. Assessing water quality is a complex process that involves identifying contaminants in water sources and determining whether it is safe for human consumption. In this study, we utilized the Cauvery River dataset to develop a model for evaluating water quality. The aim of our research was to proficiently perform feature selection and classification tasks. We introduced a novel technique called the Aquila Optimization Support Vector Machine (AO-SVM), an advanced and effective machine learning system for predicting water quality. Here SVM is used for the classification, and the Aquila algorithm is used for optimizing SVM. The results show that the proposed method achieved a maximum accuracy rate of 96.3%, an execution time of 0.75 s, a precision of 93.9%, a recall rate of 95.1%, and an F1-Score value of 94.7%. The suggested AO-SVM model outperformed all other existing classification models regarding classification accuracy and other parameters.Öğe Comparative analysis of regression models to predict the performance of the dual fuel engine operating on diesel and hydrogen gas(Elsevier ltd, 2025) S, Priya; Feenita, C.; Goel, Uday; T, Manoranjitham; Duraisamy, Boopathi; Subramanian, Balaji; Ganeshan, Kavitha; Bai, Femilda Josephin Joseph Shobana; Albeshr, Mohammed F.; Pugazhendhi, Arivalagan; Varuvel, Edwin GeoInternal combustion engines (ICEs) have long been essential in both the transportation and industrial sectors, providing primary power for vehicles, ships, and machines globally. Optimising the efficiency of ICEs is vital for decreasing their environmental impart, as increased fuel efficiency and lower emissions play a significant role in mitigating the effects of climate change as well as improving air quality. This study employed 15 regression algorithms and machine learning approaches to analyse and anticipate the performance parameters of ICEs that run on hydrogen-diesel in dual fuel mode. The input parameters include engine torque, speed, hydrogen flow rate, brake power and diesel energy share to hydrogen supply and the output parameters are brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and actual air intake. The model's performance is evaluated using five different performance metrics. Among the studied algorithms, the RANSAC Regressor demonstrated exceptional predictive capability, reaching an R-squared value of 0.999, a mean squared error (MSE) of 0.0064, a root mean square error (RMSE) of 0.08, and a mean absolute error (MAE) of 0.057. These outcomes show the algorithm's accuracy and precision in capturing the complicated data of engine system. The equivalency ratio, volumetric efficiency, brake thermal efficiency, brake specific fuel consumption, and actual air intake are among the critical performance outputs that are optimised by utilizing key input parameters like engine load, rotational speed, hydrogen flow rate, brake power, and the diesel fuel energy share. This study highlights the significant potential of machine learning in optimising ICE performance, offering a reliable alternative to traditional experimental analysis by reducing both risk and economic costs. The research findings also support the paradigm shift towards intelligent and sustainable energy systems by compellingly advocating for the inclusion of data-driven methodologies in contemporary engine design and operational methodsÖğe Comparison of machine learning models for lung cancer prediction using different feature selection methodologies(Elsevier, 2024) Bai, Femilda Josephin Joseph Shobana; Aruna, S; Ashok Kumar, Saranya; Maheswari, M; Katyal, Krish; Vipat, Dhaivat; Parasar, SanjeebanLung cancer is one of the most widespread diseases with significant fatality rates worldwide. Machine learning (ML) algorithms have recently demonstrated great promise for predicting lung cancer. The proposed research focuses on the extraction of helpful features from patient data that can enhance the precision and understandability of machine learning models. Correlation-based feature selection, recursive feature elimination (RFE), and tree-based feature selection are examined to see which is the most effective feature selection technique for predicting lung cancer. The ML models Naive Bayes (NB), K-nearest neighbor (KNN), and support vector machines (SVMs) were trained to produce predictions using the chosen features. Accuracy, precision, recall, and F1-score metrics were used to assess the model's performance. When the models were trained with and without feature selection, KNN and SVM displayed the highest prediction accuracy compared with NB. One of the feature selection techniques that has helped machine learning models to be trained with the most relevant attributes, increasing prediction accuracy, is tree-based feature selection. © 2024 Elsevier Inc. All rights reserved.Öğe Development of artificial neural network and response surface methodology model to optimize the engine parameters of rubber seed oil - Hydrogen on PCCI operation(Pergamon-Elsevier Science Ltd, 2023) Varuvel, Edwin Geo; Seetharaman, Sathyanarayanan; Bai, Femilda Josephin Joseph Shobana; Devarajan, Yuvarajan; Balasubramanian, DhineshIdentifying the suitable alternative fuel and optimum blend concentration for diesel engine combustion is critical as most biodiesel emits excess smoke and has a lower thermal efficiency due to its high viscosity and carbon residue. In the previous work, rubber seed oil was tested in a single-cylinder diesel engine, and its performance and emission results were compared with those of pure diesel, an RSO-diesel (70:30 by volume) blend, RSOmethyl ester, RSO-diethyl ether, RSO-ethanol, and RSO-hydrogen in a dual fuel operation. The testing was performed at a constant speed of 1500 rpm, with the engine loads varying at 25% step intervals. Results showed that smoke and nitrogen oxides were significantly reduced for RSO, and engine performance was enhanced when RSO was operated with hydrogen and diethyl ether in dual fuel mode. In this study, the experimental results were employed to develop an artificial neural network and response surface methodology model. Brake thermal efficiency, rate of pressure rise, carbon monoxide, hydrocarbon, oxides of nitrogen, and smoke were predicted using response surface methodology and artificial neural network. Though artificial neural network produced the best R2 values (0.87264-0.99929), mean absolute percentage error was relatively lesser in response surface methodology. Thus, the authors conclude that response surface methodology is the best suitable artificial intelligence tool to optimize the engine for accomplishing desired responses.Öğe Early detection of cardiovascular disease: Data visualization, feature selection, and machine learning algorithms for predictive diagnosis(Elsevier, 2024) Bai, Femilda Josephin Joseph Shobana; Ashok Kumar, Saranya; Maheswari M.; Aruna S. b; Krishnan, Aditya; Majid, AmaanAccurate and early diagnosis of cardiovascular disease is a big concern to improve the patient's well-being. The proposed research is focused toward the prediction of cardiovascular disease using a diversified dataset, which includes the patient's health history and diagnostic test results. The study focuses mainly on data visualization, feature selection, and predictive modeling. To identify the distribution of the features and the relationship between the features, data visualization was performed by using various plots and graphs. The important features in the dataset that can be helpful for better prediction are selected using embedded-based feature selection approach. The prediction of disease utilized machine learning (ML) techniques, including logistic regression (LR), decision trees (DTs), support vector machines (SVMs), and k-nearest neighbors (KNNs). F-1 score, precision, recall, accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves are useful metrics for assessing how well machine learning models perform at predicting disease. These metrics provide insights into the advantages and drawbacks of the models, helping researchers to understand their effectiveness and suitability for specific tasks. 0.98, 0.82, 0.80, and 0.78 are the accuracies, and 0.99, 0.94, 0.89, and 0.83 are the area under the ROC curve (AUC) values of DT, KNN, SVM, and LR predictive models, respectively. The findings provide an insight to the healthcare professionals and researchers to understand the usefulness of predictive modeling for early predictions of cardiovascular disease. © 2024 Elsevier Inc. All rights reserved.Öğe Early prediction of the remaining useful life of lithium-ion cells using ensemble and non-ensemble algorithms(John wiley and sons inc, 2025) Bai, Femilda Josephin Joseph Shobana; Sonthalia, Ankit; Subramanian, Thiyagarajan; Aloui, Fethi; Bhatt, Dhowmya; Varuvel, Edwin GeoLithium-ion cells have become an important part of our daily lives. They are used to power mobile phones, laptops and more recently electric vehicles (both two- and four-wheelers). The chemical behavior of the cells is rather complex and non-linear. For reliable and sustainable use of the cells for practical applications, it is imperative to predict the precise pace at which their capacity will degrade. More importantly, the lifetime of the cells must be predicted at an early stage, which would accelerate development and design optimization of the cells. However, most of the existing methods cannot predict the lifetime at an early stage, since there is a weak correlation between the cell capacity and lifetime. In this study for accurate forecasting of the battery lifetime, the patterns of the parameters such as cell current, voltage, temperature, charging time, internal resistance, and capacity were examined during charging and discharging cycle of the cell. Twelve manually crafted features were prepared from these parameters. The dataset for the features was created using the raw data of the first 100 cycles of 124 cells. Six ensemble and non-ensemble machine learning algorithms, namely, multiple linear regression (MLR), decision tree, support vector machine (SVM), gradient boosting machine (GBM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), were trained with the features for predicting the life-cycle of the cells. The R2 and root mean squared error (RMSE) values of MLR, decision tree, SVM, GBM, LGBM, and XGBoost were found to be 0.72 and 201, 0.83 and 155, 0.85 and 146, 0.92 and 100, 0.9 and 112, and 0.94 and 95, respectively. The prediction accuracy of lithium-ion cell life-time was found to be the best with the XGBoost algorithm. This shows that only first 100 cycles are required foraccurately predicting the number of cycles the lithium-ion cell can work for. Lastly, the results of the study were compared with the available studies in the literature. Three studies were chosen, and the RMSE of the method proposed in this study was found to be higher than the three studies by 43, 17, and 20. Therefore, the proposed method is a suitable option for predicting the lifetime of lithium-ion cells during the early stages of its development.Öğe Eco-friendly perspective of hydrogen fuel addition to diesel engine: An inclusive review of low-temperature combustion concepts(Elsevier ltd, 2025) Nguyen, Van Nhanh; Ganesan, Nataraj; Ashok, Bragadeshwaran; Balasubramanian, Dhinesh; Anabayan, K.; Lawrence, Krupakaran Radhakrishnan; Tamilvanan, A.; Le, Duc Trong Nguyen; Truong, Thanh Hai; Tran, Viet Dung; Cao, Dao Nam; Bai, Femilda Josephin Joseph ShobanaHydrogen is a probable alternative fuel for both stationary and automotive engine applications due to its properties like high energy content and persistent availability. However, using hydrogen only as a fuel for engines was almost impossible; thus, hydrogen co-combusting with diesel and several biomass-based biofuels will be advisable. As viscosity plays a significant role in combustion, the application of biodiesel was classified as high viscous fuel and low viscous fuel for investigation with hydrogen in compression ignition engines. The present study aims to reconnoitre the prospects of using hydrogen-enriched diesel-biodiesel blends with advanced combustion technology. The present work also examines advanced combustion technologies, including reactivity-controlled compression ignition (RCCI), homogenous charge compression ignition (HCCI), and laser ignition technology. This review shed light on the properties of hydrogen-enriched biodiesel blends, engine operating parameters, and their impact on engine characteristics. This comprehensive review offered a distinct view to the academics for improving the performance, combustion, and emission characteristics of CI engines fuelled with hydrogen-enriched biodiesel-diesel. Further, the review progressed with the aforesaid operating conditions and advanced combustion technology.Öğe EnConv: enhanced CNN for leaf disease classification(Springer science and business media deutschland GmbH, 2025) Thanjaivadivel, M.; Gobinath, C.; Vellingiri, J.; Kaliraj, S.; Bai, Femilda Josephin Joseph ShobanaDetecting leaf diseases in plants is essential to maintain crop yield and market value. Machine learning has shown promise in detecting these diseases as it can group data into predetermined categories after examining it from various angles. However, machine learning models require a thorough knowledge of plant diseases, and processing time can be lengthy. This study proposes an enhanced convolutional neural network that utilizes depthwise separable convolution and inverted residual blocks to detect leaf diseases in plants. The model considers the morphological properties and characteristics of the plant leaves, including color, intensity, and size, to categorize the data. The proposed model outperforms traditional machine learning approaches and deep learning models, achieving an accuracy of 99.87% for 39 classes of different plants such as tomato, corn, apple, potato, and more. To further improve the model, global average pooling was used in place of the flatten layer. Overall, this study presents a promising approach to detect leaf diseases in plants using an enhanced convolutional neural network with depthwise separable convolution and inverted residual blocks. The results show the potential benefits of using this model in agriculture to improve the early detection of plant diseases and maintain crop yield and market value.Öğe Evaluating the Effectiveness of Boosting and Bagging Ensemble techniques in forecasting lithium-ion battery useful life(Wiley, 2025) Sonthalia, Ankit; Bai, Femilda Josephin Joseph Shobana; Aloui, Fethi; Varuvel, Edwin GeoIt is essential to forecast the exact rate at which the cell's capacity would decline for practical uses, to comprehend the intricate and non-linear behavior of the cell. Furthermore, the majority of studies provided subpar prediction criteria, making early cell lifetime prediction difficult. Applying reliable and accurate aging models to the dynamic on-road conditions presents additional challenges. In this work, the battery lifetime during its earliest phases of use was accurately predicted using machine learning models. After analyzing the patterns of the parameters, 12 hand-crafted features were selected and the raw data of the first 100 cycles of 126 cells was used for creating the dataset for the features. The dataset was then used to train five machine learning models namely random forest, gradient boosting machine (GBM), light gradient boosting machine (LGBM), extreme gradient boosting machine (XGBoost), and gradient boost with categorical features (CATBoost). The statistical analysis reveals that XGBoost algorithm present the best result with a R2 value of 0.95 and root-mean-square-error (RMSE) of 97 cycles. Lastly, in comparison to existing studies, the RMSE significantly reduced from a maximum of 138 to 97 cycles.Öğe Impact of hydrogen-assisted combustion in a toroidal re-entrant combustion chamber powered by rapeseed oil/waste cooking oil biodiesel(Elsevier ltd, 2025) Thiagarajan, S.; Seetharaman, Sathyanarayanan; Lokesh, R.; Prasanth, G.; Karthick, B.; Bai, Femilda Josephin Joseph Shobana; Ali Alharbi, Sulaiman; Pugazhendhi, Arivalagan; Varuvel, Edwin GeoThis study investigates the performance and emission characteristics of biodiesel blends of rapeseed oil and waste cooking oil in a toroidal re-entrant combustion chamber (TCC) compression ignition engine. Hydrogen was allowed into the engine in dual fuel mode to enhance the engine performance. The presence of oxygen in the biodiesel and hydrogen induction increased the peak pressure and heat release rate significantly for all the engine loads. At a peak load of 4.88 kW, the maximum brake thermal efficiency (BTE) of 31.77% was recorded for the D70R20W10 (diesel 70%, rapeseed oil 20%, waste cooking oil 10%) biodiesel blend. Furthermore, hydrogen induction enhanced the BTE by around 3%. The biodiesel blending substantially lowered the emissions of unburnt hydrocarbons, carbon monoxide, and smoke opacity. Additionally, hydrogen supplementation facilitated 5-10% carbon monoxide reduction over biodiesel blends by enabling more complete oxidation. However, higher temperatures generated due to complete combustion resulted in more NOx formation. Thus, the authors propose that biodiesel blends of rapeseed oil, waste cooking oil, and diesel with hydrogen induction improve engine performance and reduce regulated emissions.Öğe A Machine Learning Approach for Carbon di oxide and Other Emissions Characteristics Prediction in a Low Carbon Biofuel-Hydrogen Dual Fuel Engine(Elsevier Sci Ltd, 2023) Bai, Femilda Josephin Joseph ShobanaTo lower the carbon dioxide and other emissions from a single cylinder common rail direct injection (CRDI) engine, it is important to investigate the combinations of several methods. Lemon peel oil (LPO) and camphor oil (CMO), which are low carbon content biofuels, are the methods that are used and are induced by hydrogen in the intake manifold and zeolite-based after-treatment system. At full load, the injection of hydrogen decreased CO2 and smoke emissions by 39.7% and 49%, respectively. Even though the NO emission increases with hydrogen induction, it was decreased with zeolite after-treatment system. Predictions can be made using machine learning techniques, which will reduce the amount of time and money needed for engine trials. This work focuses on the prediction of engine emissions like CO2, Nitrogen Oxides (NO), Smoke, Brake Thermal Efficiency (BTE), Hydrocarbons (HC) using the ensemble learning techniques. The predictions are made using the ensemble learning methods like Extreme Gradient Boosting (XGBoost), Light Gradient Boosted Machine (LGBM), CatBoost, and Random Forest (RF). The CatBoost model has produced high accuracy predictions which was followed by XGBoost, RF and LightGBM models. The predicted and actual values are compared each other and the performance of the algorithms were analysed using the evaluation metrics like R-Square(R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).Öğe Optimization of tree-based machine learning algorithms for improving the predictive accuracy of hepatitis C disease(Elsevier, 2024) Bai, Femilda Josephin Joseph Shobana; Jasmine, R. AnitaHepatitis C is a globally prevalent viral infection that has the potential to cause significant liver-related complications if not appropriately managed. The timely and precise identification of the medical condition is imperative for the efficient administration of patient care and therapy. One of the precise and potential diagnosis methods in the identification of hepatitis C is the utilization of machine learning (ML) algorithms. The present investigation focuses on the optimization of four ML algorithms which are tree-based algorithms, namely, random forest (RF), gradient boosting machines (GBMs), light gradient boosting machines (LGBMs), and extreme gradient boosting (XGBoost) with the aim of enhancing the predictive accuracy of hepatitis C disease. The investigation utilized a reliable dataset from the University of California, Irvine (UCI) Machine Learning Repository. The research methodology encompasses various stages, including data preprocessing, feature selection, hyperparameter tuning, and model evaluation. Optimization techniques, including the synthetic minority oversampling technique (SMOTE) for data balancing and grid search optimization for hyperparameter tuning, were utilized to improve the models’ performance. The optimized models were assessed through the utilization of stratified k-fold cross-validation and performance metrics, which comprise accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve. The findings of our study indicate that the optimized tree-based algorithms exhibit superior performance compared to their nonoptimized counterparts. Specifically, LGBM demonstrated the highest level of predictive accuracy at 98.91%, followed by XGBoost at 98.70%, GBM at 97.83%, and RF at 97.29%. The LGBM learning approach has the potential to be broadly applied and extended to diverse medical datasets and use cases, thus advancing ML in the healthcare domain. The study highlights the importance of optimizing tree-based algorithms to improve the accuracy of early prediction of the prevalence of hepatitis C disease and promote patient health. This underscores the capacity of ML to improve healthcare outcomes. © 2024 Elsevier Inc. All rights reserved.Öğe Prediction of software faults using machine learning algorithms and mitigating risks with feature selection(Elsevier, 2024) Bai, Femilda Josephin Joseph Shobana; Kaliraj S.; Ukrit, M. Ferni; Sivakumar V.Software fault prediction, a crucial component of software engineering, strives to detect probable flaws before they appear, thus enhancing the quality and reliability of software. Effective risk analysis is essential for reducing the risks and uncertainties that could arise during the development of software. The proposed work uses machine learning approaches to predict software faults and highlights the significance of risk analysis and feature selection. The accuracy of predictions can be increased by using feature selection approaches to help discover the features that strongly influence the prediction of software fault occurrence. The feature importance was identified by the algorithms using the decision trees (DT), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) techniques. The models also underwent comparison by removing the features to understand the importance of the features and their correlation. Finally, a comparison is done to recognize the best model for software fault prediction.Öğe Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: a combined application of artificial neural network and response surface methodology(Pergamon-elsevier science, 2024) Seetharaman, Sathyanarayanan; Suresh, S.; Shivaranjani, R. S.; Dhamodaran, Gopinath; Bai, Femilda Josephin Joseph Shobana; Alharbi, Sulaiman Ali; Pugazhendhi, Arivalagan; Varuvel, Edwin GeoThis research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R 2 between 0.99426 and 0.9998 was observed by ANN whereas R 2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN 's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.Öğe Production of liquid hydrocarbon fuels through catalytic cracking of high and low-density polyethylene medical wastes using fly ash as a catalyst(Elsevier, 2024) Premkumar, P.; Saravanan, C. G.; Nalluri, Premdasu; Seeman, M.; Vikneswaran, M.; Madheswaran, Dinesh Kumar; Bai, Femilda Josephin Joseph Shobana; Chinnathambi, Arunachalam; Pugazhendhi, Arivalagan; Varuvel, Edwin GeoThis study explores the potential of converting High-Density Polyethylene (HDPE) and Low-Density Polyethylene (LDPE) waste into liquid hydrocarbon fuels through catalytic degradation using fly ash. It achieves significant conversion rates, with HDPE reaching over 95% total conversion and a 66.4% oil yield at a catalyst-to-polymer ratio of 0.20, while LDPE shows a 100% conversion rate at ratios of 0.15 and 0.20. The process not only yields hydrocarbons with decreasing density and increasing calorific values, up to 55 MJ/kg for HDPE and 47 MJ/kg for LDPE at optimal conditions but also produces fractions with properties similar to diesel, notably in terms of density and viscosity. The flashpoint and fire point values further affirm these products' potential as viable fuel sources, aligning closely with diesel standards. 1H NMR spectroscopy analysis reveals a composition rich in longchain alkanes and alkenes, indicating the efficient transformation of plastic waste into valuable energy resources. This research presents a promising avenue for recycling plastic waste into alternative fuels, highlighting a sustainable approach to waste management and energy recovery.Öğe Production of Raphanus Sativus Biodiesel and Its Performance Assessment in a Thermal Barrier-Coated Agriculture Sector Diesel Engine(Wiley-V C H Verlag Gmbh, 2023) Ravikumar, Venkatachalam; Senthilkumar, Duraisamy; Vellaiyan, Suresh; Saravanan, Chidambaram Ganapathy; Vikneswaran, Malaiperumal; Bai, Femilda Josephin Joseph Shobana; Varuvel, Edwin GeoHerein, in order to estimate the optimized process specifications, an empirical study is done for biodiesel production, performance of standard and coated engine, combustion, and discharge aspects of Raphanus sativus (radish) biodiesel. Optimization process parameters of biodiesel production are done using the response surface method. The importance of this study is that optimized biodiesel production is used to improve the biodiesel properties and fatty acid content to find suitable vegetable oil as well as a new coating material for sustainable development of the country in the field of agriculture and ecological conditions. The mechanism used in this research work is a coating for internal combustion engine components done with partially stabilized zirconia, aluminum-20% silicon carbide (Al-20%SiC), and titanium dioxide that acts as a ceramic composite settled above the coating that has a thickness of 450 mu m by the technique called air plasma spray for test purpose. To increase the performance of an engine and reduce the emission particles like smoke density, carbon monoxide, and hydrocarbon except NOx, the partially stabilized zirconia, aluminum-20% silicon carbide (Al-20%SiC) coated engine is used under various compositions of radish biodiesel and clean diesel. To reduce NOx, the engine operation is carried out along with the TiO2 coated combustion chamber steam, and water injection technique is added to overcome the NOx formation. From this study, the resulting factor shows that adding 25% radish biodiesel (B25) and 75% clean diesel shows a reasonable reduction in emissions with comparatively better performance and combustion in the engine under consideration.Öğe Response surface methodology optimization of characteristics of biodiesel powered diesel engine and its effective integration to autonomous microgrid(Elsevier, 2024) Kasimani, Ramesh; Sakthivel, R.; Balasubramanian, Dhinesh; Bai, Femilda Josephin Joseph Shobana; Chinnathambi, Arunachalam; Varuvel, Edwin GeoAn optimum diesel engine load condition with higher thermal efficiency and energy management has been the foremost challenge in the renewable energy research field. Modern trends involve nano additives for obtaining high performing biodiesel blends. Considering the above-mentioned factors, an experimental study along with statistical analysis has been performed with variation of compression ratio (16, 17.5 & 18), ZrO2 nano additive (50, 100 & 150 ppm) and load (50, 75 & 100 %). The optimization was accomplished by diminishing the BSFC, NOx, HC, CO, and increasing the BTE, CO2 using the approach of desirability. From the obtained results, the optimum operating conditions of the engine were found to be 75 % load, 18:1 compression ratio, and 150 ppm ZrO2 nano additive. The actual values at optimized conditions are obtained as BSFC (0.27 kg/kWh), BTE (33.37 %), CO (0.71 %), CO2 (8.69 %), NOx (1270 ppm), HC (62 %) and Smoke (25.20 %) with error less than 5 %. It has been observed that the BSFC of the proposed biodiesel-diesel blend is lower than that of pure diesel in all levels of power output. The biodiesel fuelled diesel engine is integrated as backup power in autonomous microgrid with main power as solar PV system operated at MPPT mode. A hybrid power system based on solar PV and biodiesel generator set up is the better alternative to emission-intensive fossil fuel and intermittent renewable.
