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Öğe Analysis of CRDI diesel engine characteristics operated on dual fuel mode fueled with biodiesel-hydrogen enriched producer gas under the single and multi-injection scheme(Pergamon-Elsevier Science Ltd, 2023) Lalsangi, Sadashiva; Yaliwal, V. S.; Banapurmath, N. R.; Soudagar, Manzoore Elahi M.; Agbulut, Umit; Kalam, M. A.The present work aims to investigate the consequences of pilot fuel (PF) multiple injections and hydrogen manifold injection (HMI) on the combustion and tailpipe gas characteristics of a common rail direct injection (CRDI) compression ignition (CI) engine operated on dual fuel (DF) mode. The CI engine can perform on a wide variety of fuels and under high pilot fuel (PF) pressure. Pilot fuel injection (PFI) is achieved at TDC, 5, 10, and 15oCA before the top dead center (bTDC), and divided injection consists of injecting fuel in three different magnitudes on a time basis and PF is injected into the engine cylinder at a pressure of 600 bar. In this work, the hydrogen flow rate (HFR) was fixed at 8 lpm constant and producer gas was inducted without any restriction. The investigational engine setup has the ability to deliver a PF and hydrogen (H2) precisely in all operating circumstances using a separate electronic control unit (ECU). Results showed that diesel-hydrogen enriched producer gas (HPG) operation at maximum operating conditions provided amplified thermal efficiency by 4.01% with reduced emissions, except NOx levels, compared to biodiesel-HPG operation. Further, DiSOME with the multi-injection strategy of 60 + 20+20 and 50 + 25+25, lowered thermal efficiency by 4.8% and 9.12%, respectively compared to identical fuel combinations under a single injection scheme. However, reductions in NOx levels, cylinder pressure, and HRR were observed with a multi-injection scheme. It is concluded that multi-injection results in lower BTE, changes carbon-based emissions marginally, and decreases cylinder pressure and heat release rate than the traditional fuel injection method. & COPY; 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Blends of scum oil methyl ester, alcohols, silver nanoparticles and the operating conditions affecting the diesel engine performance and emission: an optimization study using Dragon fly algorithm(Springer Heidelberg, 2021) Afzal, Asif; Agbulut, Umit; Soudagar, Manzoore Elahi M.; Razak, R. K. Abdul; Buradi, Abdulrajak; Saleel, C. AhamedThe effect of the addition of different proportions of silver (Ag) nanoparticles and alcohols in milk scum oil methyl ester on the performance of engine and emission are studied. B20 blend is added with 5% of ethanol, n-butanol, and iso-butanol as ternary additives for the experimental analysis from no load to full load. Furthermore, at a fixed load, operating conditions such as injection pressure (12 and 15 bar) and injection timing (23 degrees and 26 degrees) are varied without and with the addition of 0.8 vol% of Ag (silver) nanoparticles to the fuel blends. Also, the concentrations of Ag nanoparticles are increased from 0.2 to 1 vol% and comparisons are made with diesel and B60 blend. Mathematical models are developed for selected features of engine performance which fits with the experimental values for the purpose of optimization using the Dragon fly algorithm (DA) by considering these models as the objective functions. The concentration of nanoparticles lowers the BSFC significantly and helps in reducing the emission with an increased percentage. Using full biodiesel, 16.6% reduction in BTE was obtained, while use of alcohols prevented this reduction approximately by 5%. A highest of 4.6% improvement was obtained with the addition of Ag nanoparticles. 4.5% reduction in HC and 13% in NOx emission using nanoparticles are obtained. The DA algorithm provided the same optimized value at the end of 30 iterations in different cycles of execution. Nanoparticle addition and use of pressure in the range of 20 bar gives the lowest emission from the engine.Öğe A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning(Cell Press, 2024) Jathar, Laxmikant D.; Nikam, Keval; V. Awasarmol, Umesh; Gurav, Raviraj; Patil, Jitendra D.; Shahapurkar, Kiran; Soudagar, Manzoore Elahi M.Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era of remarkable research and innovation. This review article thoroughly examines the recent advancements in the field, focusing on the interplay between PV systems and water desalination within the framework of AI and ML applications, along with it analyses current research to identify significant patterns, obstacles, and prospects in this interdisciplinary field. Furthermore, review examines the incorporation of AI and ML methods in improving the performance of PV systems. This includes raising their efficiency, implementing predictive maintenance strategies, and enabling real-time monitoring. It also explores the transformative influence of intelligent algorithms on desalination techniques, specifically addressing concerns pertaining to energy usage, scalability, and environmental sustainability. This article provides a thorough analysis of the current literature, identifying areas where research is lacking and suggesting potential future avenues for investigation. These advancements have resulted in increased efficiency, decreased expenses, and improved sustainability of PV system. By utilizing artificial intelligence technologies, freshwater productivity can increase by 10 % and efficiency. This review offers significant and informative perspectives for researchers, engineers, and policymakers involved in renewable energy and water technology. It sheds light on the latest advancements in photovoltaic systems and desalination, which are facilitated by AI and ML. The review aims to guide towards a more sustainable and technologically advanced future.Öğe Effect of injection timing and duration on the performance of diesel engine fueled with port injection of oxygenated fuels(Taylor & Francis Inc, 2023) Swamy, L. Ranganatha; Banapurmath, N. R.; Chandrashekar, T. K.; Soudagar, Manzoore Elahi M.; Gül, M.; Nik-Ghazali, Nik-Nazri; Mujtaba, M. A.An alarming adulteration of nature with automotive tail pipe emissions causing global warming demands suitable engine modification. Further use of alternative renewable fuels with fine-tuning the engine and required modifications could reduce the diesel engine emissions. Oxygenated fuels (such as methanol, ethanol and Butanol) act as stand-in fuel that could enrich the global energy requisites and affirmatively downsize the emissions by accelerating combustion efficiency. Under this circumstance, the experimental analysis was done on a single-cylinder four-stroke DI CI engine using conventional diesel as fuel by injection of ethanol, diethyl ether (DEE) and Butanol independently to into intake manifold at three injection timings viz., TDC, 5 degrees ATDC and 10 degrees ATDC respectively under the constant 27 degrees CA injection duration with the help of a distinct setup comprises of the electronic control unit (ECU) and injector system. The resolutions drawn from the recorded results at 80% load during experimentation summarized that DEE exhibited improved BTE of 2.5% and 1% and reduced smoke emissions of 12.54% and 10.6% compared to that obtained with ethanol and Butanol, respectively. On the other hand, DEE emitted excessive NOx and inferior HC, CO emissions compared to ethanol and Butanol apart from shortened ignition delay and combustion duration. The conclusions are drawn from the injection of oxygenates that apart from the perspective of performance, DEE injection at the 5 degrees ATDC was noticed to be optimal at 27 degrees CA injection duration (3 ms) too for emissions characteristics of a diesel engine.Öğe Forecasting of future greenhouse gas emission trajectory for India using energy and economic indexes with various metaheuristic algorithms(Elsevier Sci Ltd, 2022) Bakır, Hüseyin; Ağbulut, Ümit; Gürel, Ali Etem; Yıldız, Gökhan; Güvenç, Uğur; Soudagar, Manzoore Elahi M.; Hoang, Anh TuanThe accelerating increment of greenhouse gas (GHG) concentration in the atmosphere already reached an alarming level, and nowadays its adverse impacts on the living organisms, environment, and ecological balance of nature have been well-understood. India is one of the top countries that contribute the most to global GHG emissions. Therefore, it is of great significance to forecast the future GHG trends of the country in advance and accordingly take measures against the parameters that cause these emissions, considerably. In this direction, the present research has centered on forecasting the greenhouse gas trajectory of India with various metaheuristic algorithms. In this framework, marine predators algorithm (MPA), lightning search algorithm (LSA), equilibrium optimizer (EO), symbiotic organisms search (SOS), and backtracking search algorithm (BSA) are used for modeling the future GHG emission trajectory of India. Accordingly, the significant economic and energy indicators of India such as renewable energy generation, electricity generation from coal, electricity generation from gas, electricity generation from oil, gross domestic product, and population between 1990 and 2018 are collected to make a nexus with GHG emissions. As GHG emissions, CO2, CH4, F-gases, N2O, as well as total GHG emissions are separately forecasted by the year 2050. To make a better comparison, each GHG emission data in the last year five years is used for the testing phase of the algorithms, and then statistically discussed in terms of R2, MBE, rRMSE, and MAPE benchmarks. In the results, it is found that the R2 value changes between 0.8822 and 0.9923 for CO2, 0.2855-0.9945 for CH4, 0.9-0.9904 for F-gases, 0.4655-0.9964 for N2O, and 0.9016-0.9943 for total GHG emission, and the results in terms of rRMSE are very satisfying for all algorithms. In the study, it is forecasted that the two greenhouse gas emissions with the highest increase rate in 2050 will be between 2.5 and 2.87 times for CO2 emissions and between 2.8 and 3.5 times for F-gases, compared to today's data. According to the results of the present paper, the total GHG emission for India is forecasted to be 2.1-2.4 times higher in the year 2050 as compared to today. Given all forecasting results together, it is seen that the MPA algorithm generally gives the best results according to the statistical metric results, while the LSA algorithm generally gives the worst results. Consequently, the present paper strongly reports that the decision-makers and policy-makersÖğe Optimization of Thermal and Structural Design in Lithium-Ion Batteries to Obtain Energy Efficient Battery Thermal Management System (BTMS): A Critical Review(Springer, 2021) Fayaz, H.; Afzal, Asif; Samee, A. D. Mohammed; Soudagar, Manzoore Elahi M.; Akram, Naveed; Mujtaba, M. A.; Saleel, C. AhamedCovid-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.
