Yazar "Sharma, Prabhakar" seçeneğine göre listele
Listeleniyor 1 - 10 / 10
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Application of modern approaches to the synthesis of biohydrogen from organic waste(Pergamon-Elsevier Science Ltd, 2023) Sharma, Prabhakar; Jain, Akshay; Bora, Bhaskor Jyoti; Balakrishnan, Deepanraj; Show, Pau Loke; Ramaraj, Rameshprabu; Agbulut, UmitHydrogen production with the use of biological processes and renewable feedstock may be considered an economical and sustainable alternative fuel. The high calorific value and zero emission in the production of biohydrogen make it the best possible source for energy security and environmental sustainability. Solar energy, microorganisms, and feedstock such as organic waste and lignocellulosic biomasses of different feedstock are the only requirements of biohydrogen production along with specific environmental conditions for the growth of microorganisms. Hydrogen is also named as 'fuel of the future'. This study presents different pathways of biohydrogen production. Because of breakthroughs in R & D, biohydrogen has been elevated to the status of a viable biofuel for the future. However, significant problems such as the cost of preprocessing, oxygen-hypersensitive enzymes, a lack of uniform light illumination for photobiological processes, and other expenses requiring intensification process limits are faced throughout the biohydrogen production process. Despite concerns regarding nanoparticle (NP) toxicity at higher concentrations, proper NP concentrations may improve hydrogen production dramatically by dissolving the substrates for bacterial hydrogen transformation. The data-driven Machine Learning (ML) model allows for quick response approximation for fermentative biohydrogen production while accounting for non-linear interactions between input variables. Scaling up biohydrogen production for future commercial-scale applications requires combining cost-benefit evaluations and life cycle effects with machine learning. & COPY; 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches(Taylor & Francis Inc, 2024) Nguyen, Van Giao; Sharma, Prabhakar; Agbulut, Uemit; Le, Huu Son; Cao, Dao Nam; Dzida, Marek; Osman, Sameh M.Examining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass sources. The study argues that white-box models, which are more open and comprehensible, are crucial for biochar prediction in light of the increasing suspicion of black-box models. Accurate forecasts are guaranteed by these explainable AI systems, which also give detailed explanations of the mechanisms generating the outcomes. For prediction models to gain confidence and for biochar production processes to enable informed decision-making, there must be an emphasis on interpretability and openness. The paper comprehensively synthesizes the most critical features of biochar prediction by a rigorous assessment of current literature and relies on the authors' own experience. Explainable machine learning techniques encourage ecologically responsible decision-making by improving forecast accuracy and transparency. Biochar is positioned as a crucial participant in solving global concerns connected to soil health and climate change, and this ultimately contributes to the wider aims of environmental sustainability and renewable energy consumption.Öğe Investigations on a novel fuel water hyacinth biodiesel and Hydrogen-Powered engine in Dual-Fuel Model: Optimization with I-optimal design and desirability(Elsevier Sci Ltd, 2023) Bora, Bhaskor Jyoti; Sharma, Prabhakar; Deepanraj, B.; Agbulut, UmitHydrogen is one of the most promising green fuels. The present study explores the potential of novel water hyacinth biodiesel as pilot fuel as well as investigates the influence of the injection pressure of pilot fuel on the performance of hydrogen running a dual-fuel diesel engine. For experimentation, a 4.8 kW research test engine was considered. Three fuel injection pressure (FIP) of the pilot fuel, namely 220 bar, 240 bar, and 260 bar were considered at a ratio of compression as 17.5 and standard injection timing of 23 degrees before Top Dead Centre (bTDC) for different loading conditions were considered. The peak brake thermal efficiency (BTE) under dual fuel mode (DFM) was observed as 26.77%, 28.11%, and 27.21% for FIP of the pilot fuel of 220 bar, 240 bar, and 260 bar, respectively in comparison to 25.11% for biodiesel mode at 100% load. The maximum drop in carbon monoxide (CO) and hydrocarbon (HC) emissions was found to be 15.48%, and 35.7%, respectively for the FIP of the pilot fuel of 240 bar under DFM in comparison to biodiesel mode. The fall in Oxides of Nitrogen (NOX) emission under DFM was found to be 23.66% for the FIP of the pilot fuel of 220 bar under DFM compared to biodiesel mode. Based on the performance and emission analysis, the optimum FIP of the pilot fuel is found to be 240 bar. For the same FIP, the maximum liquid fuel replacement of 85% was obtained. The experimental study's data were evaluated using analysis of variance (ANOVA) to create models in the form of mathematical expressions for each outcome. The desirability approach was employed to optimize the operating settings for maximum performance while emitting the least amount of emission. According to the desirability-based optimization research, ideal operating conditions were 83.61% engine load and 242 bar FIP, resulting in engine performance of 26.5% of BTE, 80.47% of LFR, and 51.82 bar peak cylinder pressure. The emission levels were 191.19 ppm of NOX, 106.41 ppm of HC, and 130.95 ppm of CO at this setting. A model validation test found that the model-predicted values were within 6% of the observed values.Öğe Investigations on biomass gasification derived producer gas and algal biodiesel to power a dual-fuel engines: Application of neural networks optimized with Bayesian approach and K-cross fold(Pergamon-Elsevier Science Ltd, 2023) Alruqi, Mansoor; Sharma, Prabhakar; Agbulut, UemitThe adoption of sustainable energy sources is a crucial step towards achieving a low-carbon economy and mitigating the effects of climate change. One promising approach is the use of Producer Gas (PG) derived from solid biomass materials, which can be burned as fuel in internal combustion engines to generate power. Biomass gasification, the process of converting solid biomass into PG through thermochemical means, offers a sustainable alternative to traditional fossil fuels. However, using PG in dual-fuel engines poses a significant challenge due to its complex combustion characteristics. Fortunately, modern machine learning techniques offer a promising solution to this problem. In this study, we propose a Bayesian optimized neural network (BONN) to predict the performance and emissions of PG-algal biodiesel (ABD) -powered dual-fuel engines. The BONN is trained using experimental data obtained from a single-cylinder diesel engine retrofitted to run on PG as the primary fuel and algal biodiesel as the pilot fuel. The performance and emissions data are collected under various operating conditions, such as engine load, fuel injection pressure, biodiesel blending ratio, and injection timings. K-cross fold validation was used to reduce the chances of model overfitting while the Bayesian approach was used for hyperparameters finetuning. This strategy helped in the reduction of predicting errors and improved the accu-racy of the model. The coefficient of determination was in the range of 0.9421-0.9989 and mean squared errors were in the range of 0.0026-15.77. The mean absolute errors in model-predicted values were in the range of 0.0027-2.945. In all the cases of the prediction results during the model, the test improved upon the model training predictions, indicating a robust generalization and negated the chances of model overfitting. The results demonstrate that the BONN can accurately predict the performance and emissions of the engine, making it a valuable tool for engine optimization and control. This approach offers a promising pathway toward achieving net-zero targets and a sustainable future.Öğe Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy(Wiley, 2024) Nguyen, Van Giao; Sharma, Prabhakar; Agbulut, Uemit; Le, Huu Son; Truong, Thanh Hai; Dzida, Marek; Tran, Minh HoBiochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far-reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data-driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.Öğe Optimization of the pilot fuel injection and engine load for an algae biodiesel- hydrogen run dual fuel diesel engine using response surface methodology(Elsevier Sci Ltd, 2024) Mohite, Avadhoot; Bora, Bhaskor Jyoti; Agbulut, Uemit; Sharma, Prabhakar; Medhi, Bhaskar Jyoti; Barik, DebabrataThe main objective of the study is to enhance the performance and emissions of hydrogen and biodiesel dual-fuel engines by optimizing injection timing and engine load using response surface methodology. The pilot fuel considered for this study is Algae biodiesel. A mono-cylinder water-cooled diesel engine is tested for three different pilot fuel injection timings (23 degrees BTDC, 26 degrees BTDC, and 29 degrees BTDC) and five different engine loads (20%, 40%, 60%, 80%, and 100%). For a dual fuel operation, a maximum brake thermal efficiency of 28.21% and an 85% replacement of liquid charge was achieved at pilot fuel injection timing of 26 degrees BTDC and 100% load based on the experimental results. For the same setting of injection timing of 26 degrees BTDC, the emissions of CO and HC were significantly reduced by 12.12% and 36.13%, respectively, at the 80% load setting. While response surface optimum was found at 72.81% load and 25.73 degrees BTDC Injection timing. At this optimal operating parameter setting, a significant reduction of CO, HC, and NOx emissions by 20.98%, 29.15%, and 1.91%, respectively, was obtained while maintaining a comparable brake thermal efficiency of 25.06% and a replacement of liquid charge by 72.15%, respectively. Thus, a biodiesel-hydrogen dual-fuel diesel engine is one of the green solutions for power generation.Öğe Performance enhancement and emission control through adjustment of operating parameters of a biogas-biodiesel dual fuel diesel engine: An experimental and statistical study with biogas as a hydrogen carrier(Pergamon-Elsevier Science Ltd, 2024) Mohite, Avadhoot; Bora, Bhaskor Jyoti; Sharma, Prabhakar; Saridemir, Suat; Mallick, Debarshi; Sunil, S.; Agbulut, UmitThis study explores emissions regulation of a 3.5 kW single cylinder, direct injection, diesel engine fuelled with biogas and Mahua biodiesel. By varying the compression ratio from 17 to 18 with a step of 0.5, pilot fuel injection timing by 3 degrees BTDC from 23 degrees BTDC to 32 degrees BTDC, and engine load from 20% to 100% with a step of 20%, the optimal operating conditions are determined using response surface methodology. At the optimum engine operating parameter settings of a 17.73 compression ratio, 26.71 degrees BTDC pilot fuel injection timing, and 58.96% engine load, the optimum emissions are 42.89 ppm for NOx, 80.36 ppm for UHC, 4.23% Vol. for CO2, and 77.72 ppm for CO. Additionally, the study demonstrates a comparable brake thermal efficiency of 17.35% with 66.26% pilot fuel substitution, indicating biogas-biodiesel as a sustainable and renewable option for dual-fuel CI compressionignition engines.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Precise prognostics of biochar yield from various biomass sources by Bayesian approach with supervised machine learning and ensemble methods(Taylor & Francis Inc, 2024) Nguyen, Van Giao; Sharma, Prabhakar; Agbulut, Uemit; Le, Huu Son; Tran, Viet Dung; Cao, Dao NamBiomass pyrolysis is a sustainable process for generating biochar from agricultural waste, though it is generally energy-intensive and time-consuming. To address this issue, the researchers gathered data from published literature on various biomass types and employed ensemble methods (LSBoost) and supervised machine learning (Gaussian process regression) to construct predictive models. The results reveal that both models can predict well, with excellent correlations between expected and actual values. In comparison to the LSBoost model (0.9783 for training and 0.9879 for testing), the Gaussian process regression (GPR) model had higher R values for training (0.9883) and testing (0.9969). Likewise, the R2 values during training (0.9767) and testing (0.9938) were greater in the case of the GPR model than for the LSBoost model (0.9571 for training). Nash-Sutcliffe efficiency (NSE) revealed that both models captured the data precisely. However, the GPR model outperformed the LSBoost model in both during training as well as model test stages, providing higher (0.9766 for training and 0.9933 for testing) values. The GPR model outperforms the others due to superior correlation, improved variability capture, and lower errors. These findings offer useful insights for sustainable biomass utilization and provide valuable insights for optimizing pyrolysis operations.Öğe Waste bull bone based reusable and biodegradable heterogeneous catalyst for alternate fuel production from WCO, and investigation of its usability as fuel substitute(Elsevier Sci Ltd, 2024) Saravanan, R.; Sathish, T.; Agbulut, Umit; Sathyamurthy, Ravishankar; Sharma, Prabhakar; Linul, Emanoil; Asif, MohammadFast-growing fuel demand by an increase in diesel vehicles and diesel engine applications for various sectors motivates researchers to develop alternate fuels. Though many approaches have been proposed, this investigation is unique by producing alternate fuels from the waste cooking oil (WCO) using a biodegradable, reusable, easy-to-handle, eco-friendly, and heterogeneous catalyst developed to form the waste bull bone and characterized for alternate fuel production from WCO. The zero-waste approach, eco-friendly fuel blends, and low-cost production factors were considered. The preprocessing of WCO was carried out by the bubble washes method, followed by transesterification processing for producing biofuel. The fuel blends were tested with different ratios like 20% to 80% with diesel and short out B20 grade. Further, the blends were prepared with Diethyl ether (DEE) and Ethanol. Total eight fuels (Diesel, B20, B20 + 5 wt% DEE, B20 + 10 wt% DEE, B20 + 5 wt% Ethanol, B20 + 10 wt% Ethanol, B20 + 5 wt% Methanol, and B20 + 10 wt% Methanol) were tested including pure diesel from No load to full load engine condition at two different compression ratios (15:1 & 18:1). The results reveal that B20 + 5 wt% Methanol at 15:1 compression ratio outperformed in terms of brake power of 2.64 kW, indicated power of 6.35 kW, brake thermal efficiency of 33.21%, Indicated thermal efficiency of 67.18%, mechanical efficiency of 59.14%, low brake specific fuel consumption of 0.27 kg/kWh at full load. In conclusion, the heterogeneous catalyst obtained from the waste bull bone can be used in biodiesel production, which ensures the efficient usability of the waste bull bone in the fuel-processing sector.Öğe Waste to fuel: Synergetic effect of hybrid nanoparticle usage for the improvement of CI engine characteristics fuelled with waste fish oils(Pergamon-Elsevier Science Ltd, 2023) Sathish, T.; Agbulut, Uemit; George, Santhi M.; Ramesh, K.; Saravanan, R.; Roberts, Kenneth L.; Sharma, PrabhakarReducing waste products into energy sources is valuable and essential for the waste-management policies of governments. Globally fish waste and their by-products have been widely dumped in dustbins. The utilization of such wastes for producing high-grade fuel for diesel engines is discussed in this research. This investigation extracted fish oil from fish wastes and produced biodiesel through transesterification method. Then TiO2 and CeO2 nanoparticles were added in mono and hybrid forms to biodiesel of fish oil blends to improve the poor fish biodiesel properties. The prepared test fuels were characterized and compared with conventional diesel fuel. All these fuels were tested in a single-cylinder, water-cooled diesel engine at varying engine loads from 0 to 100% with intervals of 25%. The engine response is handled in terms of the engine performance, combustion, and emission characteristics. The results revealed at full load that the TiO2 mono nano fuels outperformed CeO2 Nano fuel, FWOBD and diesel fuels, but hybrid nano fuel finally outperformed all other fuels considered in this investigation. The hybrid nano-fuel recorded 17% higher brake thermal efficiency, 7.5% higher peak pressure, 36.6% heat release rate, 16% lesser NOx emission, 15% lesser HC emission, and 5% lesser carbon monoxide emission. The metallic nanoparticles were employed as suitable catalysts for combustion at CI engines, improved engine performance, and reduced emissions significantly. In conclusion, it was well-noticed that the addition of hybrid nanoparticles into WFO biodiesel blends has more significant contribution to enhancing engine performance, combustion, and emission behaviors in comparison with the addition of mono-nanoparticle usage.
