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Öğe Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector(Edp Sciences S A, 2024) Cinarer, Goekalp; Yesilyurt, Murat Kadir; Agbulut, Uemit; Yilbasi, Zeki; Kilic, KazimThis study applies three different artificial intelligence algorithms (Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)) to estimate CO2 emissions in Turkiye's transportation sector. The input parameters considered are Energy consumption (ENERGY), Vehicle Kilometers (VK), POPulation (POP), Year (Y), and Gross Domestic Product Per Capita (GDP). Strong correlations are observed, with ENERGY having the highest correlation followed by VK, POP, Y, and GDP. Four scenarios are designed based on the correlation effect: scenario 1 (ENERGY/VK/POP/Y/GDP), scenario 2 (ENERGY/VK/POP/Y), scenario 3 (ENERGY/VK/POP), and scenario 4 (ENERGY/VK). Experiments compare their effects on CO2 emissions using statistical indicators (R-2, RMSE, MSE, and MAE). Across all scenarios and algorithms, R-2 values range from 0.8969 to 0.9886, and RMSE values range from 0.0333 to 0.1007. The XGBoost algorithm performs best in scenario 4. Artificial intelligence algorithms prove successful in estimating CO2 emissions. This study has significant implications for policymakers and stakeholders. It highlights the need to review energy investments in transportation and implement regulations, restrictions, legislation, and obligations to reduce emissions. Artificial intelligence algorithms offer the potential for developing effective strategies. Policymakers can use these insights to prioritize sustainable energy investments. In conclusion, this study provides insights into the relationship between input parameters and CO2 emissions in the transportation sector. It emphasizes the importance of proactive measures and policies to address the sector's environmental impact. It also contributes to the understanding of AI-assisted CO2 emissions forecasting in the transport sector, potentially informing future policy decisions aimed at emission reduction and sustainable transport development.Öğe Battery thermal management of a novel helical channeled cylindrical Li-ion battery with nanofluid and hybrid nanoparticle-enhanced phase change material(Pergamon-Elsevier Science Ltd, 2023) Jilte, Ravindra; Afzal, Asif; Agbulut, Uemit; Shaik, Saboor; Khan, Sher Afghan; Linul, Emanoil; Asif, MohammadElectric vehicles (EVs) have emerged as a viable alternative to Internal Combustion (IC) engine-powered vehicles, and efforts have been directed toward developing EVs that are more reliable and safer to operate. The safe working of EVs necessitates the use of an efficient battery cooling system. In this paper, cooling of cylindrical type Li-ion battery embedded with helical coolant channels is proposed. The effects of nanoparticles on removing heat from the battery cooling system have been investigated for four different nanoparticle concentrations: 0, 2, 5, and 10% of Al2O3 in the base fluid. Two cases of base fluids are considered: phase change material kept in a concentric container surrounding battery volume and coolant water circulated through liquid channels attached to the outer walls of the PCM (phase change material) cylindrical container. This study presented the three configurations (i) base case PCM-WLC: battery cooling system with a cylindrical enclosure filled with RT-42 phase change material. (ii) base case nePCM-WLC: battery cooling system filled with nano-enhanced phase change material. (iii) nePCM-LC: battery cooling system with helical liquid channels and filled with nanoenhanced PCM. The nanofluid was circulated through the liquid passages connected to the PCM container. Results showed using the helical channels, the nePCM-LC arrangement efficiently removes accumulated heat from the phase change material and provides better battery cooling than straight rectangular channel-based BTMS (battery thermal management system).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 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 Parametric optimization of an impingement jet solar air heater for active green heating in buildings using hybrid CRITIC-COPRAS approach(Elsevier France-Editions Scientifiques Medicales Elsevier, 2024) Kumar, Raj; Kumar, Sushil; Agbulut, Uemit; Guerel, Ali Etem; Alwetaishi, Mamdooh; Shaik, Saboor; Saleel, C. AhamedThis work aimed to optimize the parameters of discrete multi-arc shaped ribs (DMASRs) in a solar air heating system (SAHS) through multi-criteria decision-making techniques. In the experiment, the roughness parameters of DMASRs were varied to find the best parameter combination for optimal SAHS performance. The relative rib height (Hr /H) was varied from 0.025 to 0.047 , and the relative rib pitch (Pr /H) was varied from 0.58 to 3.1. The results obtained for the Nusselt number and friction factor, which determine the performance of the SAHS system, depend on the geometrical parameters of the roughness. The parameters of DMASRs did not show any discernible trend. Hence, a multi-decision criteria approach that uses criteria importance through inter-criteria correlation (CRITIC) and complex proportional assessment (COPRAS) hybrid techniques was employed to determine the best parameter combination for optimal performance. The novel aspect of this study includes the use of a hybrid method (experimental and analytical) to optimize the performance of SAHS roughened with DMASRs hindrance promoters and predictions of outcomes using a hybrid CRITIC-COPRAS approach. The experimental and analytical examination through the use of the hybrid CRITIC-COPRAS approach is an essential component of this research that contributes to the optimization of the design parameters of such SAHS. The finding demonstrated that when Re = 19000, Pr/H = 1.7, and Hr/H = 0.047 were reached, the SAHS obtained an optimal thermohydraulic performance of 4.1.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 Synergistic effects of hybrid nanoparticles along with conventional fuel on engine performance, combustion, and environmental characteristics(Pergamon-Elsevier Science Ltd, 2024) Agbulut, Uemit; Saridemir, SuatIn this experimental work, two different nanoparticle types (Al2O3 and bN) and their binary hybrid forms (Al2O3bN) were mixed along with conventional diesel fuel (D) at 500 ppm by mass using the ultrasonication process. The tests were also carried out with completely diesel fuel (D), and reference data were collected. A singlecylinder CI engine was used for the tests at a fixed speed of 2400 rpm and variable engine loads of 3, 6, 9, and 12 Nm. In the results, BSFC value totally exhibited a decline of 8.20 % for Al2O3, 8.48 % for bN, and 9.72 % for Al2O3-bN test fuels, and BTE value totally raised by 4.21 % for Al2O3, 5.03 % for bN, and 6.64 % for Al2O3-bN test fuels as compared to the reference (D) fuel. Shortening of combustion duration, superior heat conduction capabilities, large surface/volume ratio, and improved engine performance triggered lower exhaust gas temperature (EGT) and lower NOx emissions for nanoparticle-added test fuels. NOx emission was reduced by 4.56 %, 24.57 %, and 25.85 % for Al2O3, bN, and Al2O3-bN test fuel, respectively. In addition, significant reductions in incomplete combustion pollutants such as CO and HC were also detected in the tailpipe. Numerically, CO emission was reduced by 18.75 %, 15.62 %, and 21.87 % for Al2O3, bN, and Al2O3-bN test fuel, respectively, and HC emission reduced by 4.41 %, 3.68 %, and 9.56 % for Al2O3, bN, and Al2O3-bN test fuel, respectively. In conclusion, considering all the results together, the use of nanoparticles with diesel fuel offers very promising outputs in terms of both energy efficiency and environmental aspects; however, it is possible to say that the hybrid nanoparticle usage has provided better combustion, performance, and emission results according to the mono nanoparticle usage.Öğe Technological solutions for boosting hydrogen role in decarbonization strategies and net-zero goals of world shipping: Challenges and perspectives(Pergamon-Elsevier Science Ltd, 2023) Hoang, Anh Tuan; Pandey, Ashok; De Oses, Francisco Javier Martinez; Chen, Wei-Hsin; Said, Zafar; Ng, Kim Hoong; Agbulut, UemitFacing the problems concerning greenhouse gas (GHG) emissions from international ocean shipping has meant that the latest regulations of the International Maritime Organization, issued on 1st January 2023, have come into force, with the aim of reducing GHG emissions from maritime activities. Hydrogen has been suggested as an alternative fuel to achieve decarbonization ambitions in the near future. Although hydrogen has been investigated and developed over the years, its application in ocean freight is still at an embryonic stage, with a very limited number of studies exploring its feasibility. Therefore, this work comprehensively reviewed the pertinent knowledge in the field, associated with the production, storage, and energy-derivation of hydrogen on ships and aims to identify the potential issues and provide possible solutions for fueling the shipping industry with hydrogen energy. It was found that the under-par development of hydrogen-based energy for the shipping industry can be explained by the following reasons: (i) the inability of space-limited ships to use the currently available hydrogen technologies; (ii) difficulties in hydrogen storage; (iii) under-developed infrastructure at hydrogen-bunkering ports; (iv) high retrofitting, maintenance, and operating costs; (v) incomplete guidelines, international rules and regulations for the implementation of hydrogen in fueling global shipping; and (vi) cheaper conventional fuels leading to the reluctancy of industry players to become involved in such a green transition. Finally, several suggestions relating to technological aspects and policy implications were given aiming at advocating the green transition of hydrogen-powered maritime industries for cleaner and more sustainable global trading.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.
