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Analysis of bubble departure and lift-off boiling model using computational intelligence techniques and hybrid algorithms
(Elsevier France-Editions Scientifiques Medicales Elsevier, 2024) Quadros, Jaimon Dennis; Mogul, Yakub Iqbal; Agbulut, Umit; Gurel, Ali Etem; Khan, Sher Afghan; Akhtar, Mohammad Nishat; Jilte, R. D.
The bubble departure and lift-off boiling (BDL) model was studied using computational intelligence techniques and hybrid algorithms. Quite a few studies have predicted the relationship between wall heat fluxes and wall temperature in the form of flow boiling curves. The output wall temperature is a performance indicator that depends on many operating parameters. The current study, therefore, analyses the predictability of the wall temperature in terms of operating pressure, bulk flow velocity, and wall heat flux, based on the BDL model developed by Zenginer, which included two suppression factors namely, flow-induced and subcooling factors, respectively. The soft computing techniques used for prediction were - the artificial neural network (ANN), and the Fuzzy Mamdani model, and the hybrid algorithms were adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained particle swarm optimization (ANN-PSO). In addition, the ANN-PSO conducted a parametric analysis to evaluate the best model configuration by considering various factors. The comparison of all four techniques showed that the ANFIS model exhibited the prediction performance for wall temperature. Moreover, the results obtained from the ANFIS model have been compared with the different flow boiling curves from the literature and observed that the curve fitted well for higher bulk flow velocities with an MSE and R2 was found to be 0.85 % and 0.9933, respectively.
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.
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, Umit
Hydrogen 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.
Assessment of machine learning, time series, response surface methodology and empirical models in prediction of global solar radiation
(Elsevier Sci Ltd, 2020) Gurel, Ali Etem; Agbulut, Umit; Bicen, Yunus
Solar radiation (SR) knowledge plays a vital role in the design, modelling, and operation of solar energy conversion systems and future energy investment policies of the governments. However, these data are not measured for all regions due to the non-availability of SR measurement equipment at the weather stations. Therefore, SR has to be accurately predicted using various prediction models. In this research, four models from different classes are being used to predict monthly average daily global SR data. The models used in this study are based on a machine-learning algorithm (feed-forward neural network), empirical models (3 Angstrom-type models), time series (Holt-Winters), and mathematical model (RSM). As the prediction locations, four provinces (Ankara, Karaman, Kilis, and Sirnak) in Turkey are selected. The dataset including pressure, relative humidity, wind speed, ambient temperature, and sunshine duration is supplied from the Turkish State Meteorological Service and it covers the years 2008-2018. In the study, monthly average daily global SR data for the year 2018 is being predicted, and the performance success of the models is discussed in terms of the following benchmarks R-2, MBE, RMSE, MAPE, and t-stat. In the results, R-2 value for all models is varying between 0.952 and 0.993 and MAPE and RMSE value for all models is smaller than 10% and 2 MJ/m(2)-day, respectively. Evaluation in terms of t-stat value, no models exceed the t-critic limit. Considering all the models together, ANN has presented the best results with an average R-2, MBE, RMSE, MAPE, and t-stat of 0.9911, 0.1323 MJ/m(2)-day, 0.78 MJ/m(2)-day, 4.9263%, and 0.582, respectively. Then Holt-Winters, RSM, and empirical models closely followed it, respectively. (C) 2020 Elsevier Ltd. All rights reserved.
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. Ahamed
The 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.
Combustion, performance and emission discussion of soapberry seed oil methyl ester blends and exhaust gas recirculation in common rail direct fuel injection system
(Pergamon-Elsevier Science Ltd, 2023) Sajjad, Mohammed Owais Ahmed; Sathish, T.; Saravanan, R.; Asif, Mohammad; Linul, Emanoil; Agbulut, Umit
Research on alternative fuel production and compression ignition (CI) engine improvement techniques is more attractive recently. Waste-to-wealth concept refers that the utilization of the waste of soapberry seed and exhaust gas to run the common rail direct fuel injection type diesel engine by optimizing their contribution through experimental analysis is the novelty of this investigation. The recirculation of exhaust gas plays important role in improving the combustion in the engine, reducing the emissions, and improving the engine performance. Soapberry seed oil methyl ester economizes diesel consumption by mixing it into diesel. Transesterification is the method used to turn soapberry seed oil into biodiesel. In the common rail direct injection (CRDI) engine, a 10%, 20%, or 30% blend of soapberry seed oil methyl ester (SSOB) with diesel is utilized as fuel. For each mix, 10%- 30% of the volume of SSOB is added to the rest of the diesel. Along with these fuel types, exhaust gas recirculation (EGR) is used from 10% to 30% to test and optimize the best combinations for CRDI engines. The experimental results show that pure diesel with EGR recorded high heat release rate (HRR) and brake thermal efficiency (BTE) at the highest load possible. The maximum BTE of the tested tracks is 26.83% because of better combustion, which was achieved with 10% EGR and a combination of 30% SSOB and 70% Diesel. The increase in EGR such as 30% in 30% of SSOB with 70% of Diesel blend produced reduced NOx emission (865 ppm), smoke opacity (13%), and hydrocarbon (HC) emissions (10 ppm) than diesel fuel because of the dilution and chemical effects during combustion. Accordingly, the present research reveals that a 30% of soapberry seed oil methyl ester blend with 30% EGR is recommended for engine usage with lesser emission with better combustion and performance characteristics.
A comprehensive study on the influences of different types of nano-sized particles usage in diesel-bioethanol blends on combustion, performance, and environmental aspects
(Pergamon-Elsevier Science Ltd, 2021) Agbulut, Umit; Polat, Fikret; Saridemir, Suat
This paper aims to discuss the influences of the doping of different types of nanoparticles into the bioethanol-diesel fuel blends on the combustion, performance, and emission aspects. In this viewpoint, the tests are performed at a constant engine speed of 2400 rpm under the varying engine loads from 3 to 12 Nm with the gaps of 3 Nm. Test engine is fuelled with conventional diesel fuel (DF), the binary form of 90% diesel fuel and %10 ethanol (DF90E10), and then separately 100 ppm aluminium oxide (Al2O3) nanoparticles (DF90E10 + A100), and 100 ppm titanium oxide (TiO2) nanoparticles (DF90E10 + T100) into DF90E10 test fuel. In the results, DF90E10 increases brake specific fuel consumption (BSFC) by 6.25% and drops the brake thermal efficiency (BTE) by 2.1% in comparison to those of conventional DF. However, it is noticed that nanoparticles-doped DF90E10 test fuels are being pulled back the worsened performance results thanks to their higher surface to volume ratio, higher cetane number, higher calorific value, superior thermal properties, catalyst role of the accelerating chemical reactions in combustion proces, and high energy density of nanoparticles. Accordingly, BSFC is dropped by 2.25% and 1.26% whilst BTE is enhanced by 3.48% and 2.94% for DF90E10 + A100 and DF90E10 + T100 test fuels, respectively as compared to those of DF. Thanks to the excess oxygen content of ethanol and oxygen-donating catalyst role of nanoparticles, carbon monoxide (CO) is reduced by 14.29%, 25%, and 21.43%, and hydrocarbon (HC) is reduced by 21.32%, 30.15%, and 26.47% for DF90E10, DF90E10 + A100, and DF90E10 + T100, respectively as compared to those of conventional DF. NOx emission increases by 3.6% for DF90E10, and then nitrogen oxides (NOx) are reduced by 3.02%, and 1.57% for DF90E10 + A100 and DF90E10 + T100 due to the higher thermal conductivity value of nanoparticles and improving engine performance characteristics. On the other hand, the highest in-cylinder pressure (CPmax) and heat release rate (HRRmax) values, and longer ignition delay are generally noticed for the diesel-ethanol binary blend due to the lower cetane number, lower energy density and higher viscosity. In conclusion, this paper is proving that the doping of nanoparticles into the biofuels is presenting very satisfying results in pulling back the worsened engine characteristics arising from using diesel-biofuel binary blends. (C) 2021 Elsevier Ltd. All rights reserved.
A detailed analysis of CPV/T solar air heater system with thermal energy storage: A novel winter season application
(Elsevier, 2021) Ceylan, Ilhan; Gurel, Ali Etem; Ergun, Alper; Ali, Ismail Hamad Guma; Agbulut, Umit; Yildiz, Gokhan
The interest in solar energy is increasing day by day because it is clean and limitless. Concentrated photovoltaic and thermal systems (CPV/T) are one of the systems that use in the winter and the summer, attract great attention among solar energy systems. The main purpose of this research is to discuss the capacity of a CPV/T to simultaneously convert solar energy into electrical energy and thermal energy, especially in winter seasons. While only thermal energy is obtained in many concentrated air collectors (CAC) used in the literature, in this study, energy is stored with the help of phase change material (PCM). Ethyl alcohol and water blend were utilized as a working fluid and paraffin wax was also utilized as a PCM. In this study, system performance was handled by applying energy, exergy and environmental economic analyzes. In the results, the average solar radiation was concentrated from 536 W/m(2) to 737 W/m(2). The average overall thermal efficiency and PV module efficiency of the CPV/T were calculated as 73% and 15%, respectively. In other words, the overall system efficiency of the CPV/T was obtained as 88%. The average exergy efficiency of the CPV/T was calculated as 10%. Concerning the environmental aspect, 1.11 kg of CO2 emission per hour into the atmosphere could be prevented by using such a system. In the conclusions, the present paper has reported that the integration of a PCM and air collector into a CPV/T system provided higher energy efficiency in the winter season.
Effects of high-dosage copper oxide nanoparticles addition in diesel fuel on engine characteristics
(Pergamon-Elsevier Science Ltd, 2021) Agbulut, Umit; Saridemir, Suat; Rajak, Upendra; Polat, Fikret; Afzal, Asif; Verma, Tikendra Nath
This paper examines the effect of adding high dosage of copper oxide (CuO) nanomaterials (<77 nm) directly to conventional diesel fuel. The performance of the fuel with CuO added is assessed using a single cylinder, naturally aspirated, direct injection, air-cooled diesel engine. Examined were the char-acteristics of combustion and emissions for blends of 1000 and 2000 ppm CuO nanoparticles. The CuO blends were tested in the speed range between 2000 and 3000 rpm at intervals of 250 rpm. The CuO nanoparticles have the potential to accelerate the process of combustion by supplying molecules of oxygen and acting as a catalyst. The CuO enhances the thermal conductivity of the test fuels and in-creases heat dissipation from the combustion chamber. Experimental results show exhaust gas tem-perature (EGT) is reduced as well as unburnt hydro-carbons (HC) and oxides of carbon and nitrogen (CO and NOx). For CuO additions of 1000 and 2000 ppm, CO emissions fell by 14.6% and 20.8%, HC emissions by 6.2% and 13.4%, and NOx emissions by 4%, and 4.7%. Both blends of CuO increased the heating value of the diesel fuel. Brake-specific fuel consumption (BSFC) dropped by 4.5% and 8% while brake thermal efficiency (BTE) increased by 5.5% and 14.6% for 1000-CuO and 2000-CuO, respectively. On the other hand, nanoparticles accelerated the chemical reactions and the ignition delay (ID) period was shortened by 3.03% and 5.45% for CuO additions of 1000, and 2000 ppm, respectively. It was also observed that CuO nanoparticles up to 2000 ppm can be suspended in diesel fuel without clogging the filter on the injection system. (c) 2021 Elsevier Ltd. All rights reserved.
Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms
(Elsevier Sci Ltd, 2021) Bakay, Melahat Sevgul; Agbulut, Umit
Today, the world's primary energy demand has been met by the burning of fossil-based fuels at a rate of 85%. This dominant use of fossil-based fuels has led to an accelerating increase in the release of greenhouse gases (GHG) all across the world. The largest share in total GHG emissions belongs to the electricity and heat production sector with a rate of 25%. With this viewpoint, this paper is aiming to forecast the GHG emissions (CO2, CH4, N2O, F-gases, and total GHG) using deep learning (DL), support vector machine (SVM), and artificial neural network (ANN) algorithms from the electricity production sector in Turkey. The dataset is supplied from the Turkish Statistical Institute and covers the years 1990-2018. In the study, the last four years (2015-2018) is being forecasted. To evaluate the performance success of the algorithms, five metrics (RMSE, MBE, rRMSE, R-2, and MAPE) are discussed in detail. In the results, this research is reporting that all algorithms used in the study are giving separately satisfying results for the forecasting of GHG emissions in Turkey. Based on the forecasting results, it is seen that the highest R-2 value for the emissions varies from 0.861 to 0.998 and all results are categorized as excellent in terms of rRMSE (all rRMSE values < 10%). Besides, MBE changes between -2.427 and 2.235, and all MAPE values are smaller than 1.2%. Total GHG emission is forecasted in DL algorithm with very satisfied R-2, RMSE, MBE, rRMSE, and MAPE of 0.998, 2.046, 0.419, 0.406%, and 0.021%, respectively. On the other hand, CO2 accounted for 69.05% of total GHG emissions of Turkey in 1990 but rising by 80.48% in the year 2018. In comparison with those of 1990, electricity production and total GHG emissions of Turkey in 2018 increased by 429.7% and 137.4%, respectively. Total GHG emission corresponding to electricity production is calculated to be 0.3813 Mt-total GHG/MWh in 1990 and 0.1709 Mt-total GHG/MWh in 2018. In conclusion, GHG emissions have recently increased at a high rate, but it is noticed that this increase is considerably higher as compared to the increase in energy production for Turkey. (C) 2020 Elsevier Ltd. All rights reserved.
Energy recovery from waste animal fats and detailed testing on combustion, performance, and emission analysis of IC engine fueled with their blends enriched with metal oxide nanoparticles
(Pergamon-Elsevier Science Ltd, 2023) Sathish, T.; Agbulut, Umit; Kumari, Vinod; Rathinasabapathi, G.; Karthikumar, K.; Jyothi, N. Rama; Kandavalli, Sumanth Ratna
The interest in researching alternative fuels is vital due to the alarming levels of environmental hazards of petroleum fuels. Since biodiesel is biodegradable, renewable, and has lower emissions, it has emerged as an appropriate choice for further investigation. The biodiesel and diesel blends with nanoparticle additives also contribute to improving efficiency and reducing the engine's exhaust emissions. Accordingly, this research analyzed the engine combustion, performance, and emission characteristics using Sheep fat (SF) biofuel blended with diesel fuel. The transesterification process produced the SF biodiesel, and the Zinc oxide (ZnO) nanoparticles were added as additives to improve the engine behaviors. The Sheep fat (SF) biofuel of 20 % volumetrically blended with conventional diesel fuel of 80 % (B20). Results of in-cylinder pressure, HRR, and BTE were recorded at the higher value using B20+ZnO 100 ppm and B20+ZnO 50 ppm blend fuel and BSFC was lowered compared with the nanoparticles-free fuel blends at higher load. Furthermore, the carbon monoxide (CO), Hydrocarbons (HC), and smoke levels have significantly decreased for both the B20+ZnO 100 and B20+ZnO 50 blends. For the B20+ZnO 100 fuel, these levels have decreased by roughly 28 %, 41 %, and 22 %, respectively, while for the B20+ZnO 50 blend, they have decreased by 24 %, 38 %, and 14 % at higher engine load. On the other hand, UHC, NOX, and smoke reduction percentage by using B20+ZnO 100 blend fuel is about 54.5, 52.4, and 45.4 % were reduced in comparison to these of diesel fuel. Compared with other test fuels, B20+ZnO 100 and B20+ZnO 50 blends significantly improve all engine characteristics.
Energy recovery from waste plastic oils as an alternative fuel source and comparative assessment of engine characteristics at varying fuel injection timings
(Pergamon-Elsevier Science Ltd, 2023) Mohan, Revu Krishna; Sarojini, Jajimoggala; Agbulut, Umit; Rajak, Upendra; Verma, Tikendra Nath; Reddy, K. Thirupathi
The present study deals with the experimental results of diesel engine behaviors when using waste plastics oil blend (WPB) as a substitute at volumetrically 20%, 40%, and 100% to conventional diesel fuel. In order to conduct the engine testing, a direct-injection diesel engine with a typical 17.5 of CR and 210 bar was used (higher fuel injection pressure). In the current study, the load on the engine is altered from 0% to 100% at intervals of 25%, and the advanced fuel injection time (AFIT) is changed from 17.5 degrees to 25.0 degrees at intervals of 2.5 degrees. It is shown that the pursuit of optimal AFIT instants enhanced engine performance and permitted more advantageous energy conversions from waste plastics in comparison to those of conventional diesel. While it has been found that blending waste plastic oil with diesel at low concentrations improves engine combustion, performance, and production physiognomies, it has also been found that increasing the concentration of waste plastic oil in the test fuels has a negative impact on the engine characteristics. The WPB20D80 displays a maximum brake thermal efficiency of 26.1% at 22.5 AFITs and 1500 rpm, which is 2.6% higher than the WPB0D100. On the other hand, a reduction of 0.6% in brake-specific fuel consumption is noticed by WPB20D80. In contrast, diesel has a 2.6% higher NOx emission at 22.5 AFITs with 1500 rpm. WPB20D80 was found to have a reduction of 6.3% in smoke level when compared to diesel at 1500 rpm. In the conclusion, the present work shows that waste plastic oils at low concentrations can be used as a fuel substitute for diesel engines, and this case presents a promising solution to waste management.
ENERGY, EXERGY, AND ENVIRONMENTAL (3E) ASSESSMENTS OF VARIOUS REFRIGERANTS IN THE REFRIGERATION SYSTEMS WITH INTERNAL HEAT EXCHANGER
(Begell House Inc, 2020) Gurel, Ali Etem; Agbulut, Umit; Ergun, Alper; Yildiz, Gokhan
A comprehensive thermodynamic analysis of a refrigeration system with an internal heat exchanger was reported for four various refrigerants as an alternative to R134a. The preferred refrigerants in this paper have zero ozone-depleting potential and fairly low global warming potential value compared with reference R134a. These refrigerants are from both the HC group (R290 and R600a) and the HFO group (R1234yf and R1234ze(E)). Basically, the refrigeration system consists of a compressor, condenser, evaporator, expansion valve, and internal heat exchanger as well. Energy-exergy analyses and environmental impact assessments depending on the compressor energy consumptions are evaluated in the current study. The system performance was theoretically carried out at two different evaporation temperatures of 0 and -8 degrees C. Based on the obtained results from this study, the highest performance was achieved in R600a from HC group refrigerants and R1234ze(E) from HFO group refrigerants. As compared with R134a, in the COP value of R600a an increase of 3.2% at the evaporation temperature of 0 degrees C and 3.4% for the evaporation temperature of -8 degrees C was achieved. On the other hand, the COP value for R1234yf was decreased by 2% at the evaporation temperature of 0 degrees C and by 2.57% at the evaporation temperature of -8 degrees C. Considering the CO2 emissions, R600a was located at the first order in terms of the lowest CO2 emissions and R1234ze(E) follows R600a. In conclusion, R600a presented the highest performance compared with R134a in a refrigeration system with an internal heat exchanger.
Energy, exergy, economic and sustainability assessments of a compression ignition diesel engine fueled with tire pyrolytic oil - diesel blends
(Elsevier Sci Ltd, 2020) Karagoz, Mustafa; Uysal, Cuneyt; Agbulut, Umit; Saridemir, Suat
Every year, millions of tons of tire become unusable around the world and waste tire dumps threaten human health and the environment. Therefore, recycling of waste tires has attracted attention recently. In this study, energy, exergy, economic and sustainability analyses of a compression ignition diesel engine fueled with tire pyrolytic oil-diesel blends were performed and the results were compared with that of neat diesel. Tire pyrolytic oil was produced from waste tires with vacuum pyrolysis technique. Hydro-sulfuric acid treatment, vacuum distillation and oxidative desulfurization processes were applied to reduce emission values of tire pyrolytic oil. Tire pyrolytic oil was blended with neat diesel as 10 vol% (TPO10D90), 30 vol% (TPO30D70) and 50 vol% (TPO50D50). The test engine was single-cylinder, four-stroke, naturally aspirated, compression ignition diesel engine and the experiments were conducted for different test engine loads of 3 Nm, 6 Nm, 9 Nm and 12 Nm at constant crankshaft speed of 2000 rpm. The highest energy and exergy efficiencies were obtained for TPO10D90, while the lowest ones were obtained for neat diesel. At 12 Nm, the energy efficiency of test engine was obtained to be 26.89% for neat diesel and 28.15% for TPO10D90, while the exergy efficiency of test engine was found to be 25.19% for neat diesel and 26.36% for TPO10D90. The energy loss per capital investment cost was obtained to be 0.87 x 10(-4) kW/$ for TPO10D90 and 1.03 x 10(-4) kW/$ for neat diesel at 3Nm. At 12 Nm, the highest sustainability index was determined to be 1.358 for TPO10D90, while the lowest sustainability index was 1.337 for neat diesel. Results showed that TPO10D90 had better performance at each test engine load in terms of energy, exergy, economic and sustainability and the increase in tire pyrolytic oil content of blend made the results worse but better than neat diesel. As a conclusion, it can be said that tire pyrolytic oil production from waste tires is important fact from the viewpoint of both waste management and protection of fossil fuel resources depletion. (C) 2020 Elsevier Ltd. All rights reserved.
Exergetic and exergoeconomic analyses of a CI engine fueled with diesel-biodiesel blends containing various metal-oxide nanoparticles
(Pergamon-Elsevier Science Ltd, 2021) Karagoz, Mustafa; Uysal, Cuneyt; Agbulut, Umit; Saridemir, Suat
Comprehensive exergetic and exergoeconomic analyses of a single-cylinder, four-stroke, naturally aspirated compression ignition (CI) diesel engine were conducted in the present paper. Exergy-based sustainability indicators were also determined in the study. The test engine was fueled with diesel fuel (D100), %90 diesel+10% waste cooking oil methyl ester blend (D90B10), D90B10 with Al2O3 nanoparticle of 100 ppm (D90B10Al(2)O(3)), D90B10 with TiO2 nanoparticle of 100 ppm (D90B10TiO(2)), and D90B10 with SiO2 nanoparticle of 100 ppm (D90B10SiO(2)) nanofuels, separately. The tests were performed at a constant engine speed of 2000 rpm and at varying engine loads from 2.5 to 10 Nm with an increment of 2.5 Nm. As a result, the exergy efficiencies of the test engine for D90B10 and D90B10Al(2)O(3) were determined to be 25.57% and 28.12%, respectively. The lowest cost flow rate of crankshaft work was found to be 0.4247 US$/h at 2.5 Nm, 0.5154 US$/h at 5 Nm for D90B10Al(2)O(3), and 0.6029 US$/h at 7.5 Nm, 0.7253 US$/h at 10 Nm for D90B10SiO(2). At 10 Nm, the highest and lowest sustainability index values were determined to be 1.391 for D90B10Al(2)O(3) and 1.344 for D90B10, respectively. From the perspective of exergy and sustainability, D90B10Al(2)O(3) had the best results. Besides, from the perspective of exergoeconomics, D90B10Al(2)O(3) had the best results at lower engine loads. As a conclusion, it can be said that nanofuels showed better performances compared to neat diesel fuel and diesel-biodiesel blend in the terms of in terms of exergy, exergoeconomics, and sustainability analyzes. Considering all analyses together, it is noticed that Al2O3-doped nanofuel is the best test fuel for this study, and then it is followed by SiO2 and TiO2-doped nanofuels, respectively. (C) 2020 Elsevier Ltd. All rights reserved.
Exergetic and exergoeconomic assessments of a diesel engine operating on dual-fuel mode with biogas and diesel fuel containing boron nitride nanoparticles
(Springer, 2024) Uysal, Cuneyt; Agbulut, Umit; Topal, Halil Ibrahim; Karagoz, Mustafa; Polat, Fikret; Saridemir, Suat
This study investigates the exergetic and exergoeconomic analyses of a diesel engine operated on dual-fuel mode with fuelled both diesel fuel-boron nitride nanofuel and biogas purchased commercially. The experiments were performed for diesel fuel, diesel + 100 ppm boron nitride nanoparticle, diesel + 100 ppm boron nitride nanoparticle + 0.5 L min-1 biogas, diesel + 100 ppm boron nitride nanoparticle + 1.0 L min-1 biogas and diesel + 100 ppm boron nitride nanoparticle + 2.0 L min-1 biogas at various engine loads (2.5 Nm, 5.0 Nm, 7.5 Nm, and 10.0 Nm) and fixed crankshaft speed of 1500 rpm. The obtained experimental data were used to realize exergetic and exergoeconomic analyses. Among the fuels considered in this study, diesel + 100 ppm boron nitride nanoparticle nanofuel had the best exergetic and exergoeconomic results. As a result, at engine load of 10 Nm, the exergy efficiency of test engine and specific exergy cost of crankshaft work were obtained to be 29.12% and 124.86 US$ GJ-1 for diesel + 100 ppm boron nitride nanoparticle nanofuel, respectively. These values were 27.35% and 125.19 US$ GJ-1 for diesel fuel, 25.50% and 141.92 US$ GJ-1 for diesel + 100 ppm boron nitride nanoparticle + 0.5 L min-1 biogas, 23.10% and 156.33 US$ GJ-1 for diesel + 100 ppm boron nitride nanoparticle + 1.0 L min-1 biogas, and 21.09% and 171.92 US$ GJ-1 for diesel + 100 ppm boron nitride nanoparticle + 2.0 L min-1 biogas, respectively. It is clear that biogas addition to combustion made worse the exergetic and exergoeconomic performances of test engine. As a conclusion, it can be said that diesel + 100 ppm boron nitride nanoparticle nanofuel can be used as alternative fuel to D100 in terms of exergy and exergoeconomics.
Experimental analysis of CPV/T solar dryer with nano-enhanced PCM and prediction of drying parameters using ANN and SVM algorithms
(Pergamon-Elsevier Science Ltd, 2021) Karaagac, Mehmet Onur; Ergun, Alper; Agbulut, Umit; Gurel, Ali Etem; Ceylan, Ilhan
In this paper, a concentrated photovoltaic-thermal solar dryer (CPV/TSD) using nano-enhanced PCM (Al2O3Paraffin wax) is experimentally studied. A comprehensive thermodynamic analysis of the system according to the first and second laws is discussed. Besides, the drying parameters (moisture content and moisture ratio) are predicted using the two machine learning algorithms (ANN and SVM) and compared the prediction success with four evaluation metrics (R2, rRMSE, MBE, and rMAE). The overall thermal energy efficiency and exergy efficiency of the CPV/TSD system are found to be 20% and 8%, respectively. Although solar radiation to the environment has decreased a lot, it has been found that the thermal energy transferred to the nano-enhanced PCM prevents the decrease in greenhouse temperature for the first 100 min. In the system, mushrooms are dried from the initial moisture content of 17.45 g water/g dry matter to the final moisture content of 0.0515 g water/g dry matter. Then the drying rate value for CPV/TSD system is calculated to be 0.436 g matter/g dry matter.min. On the other hand, even if both ANN and SVM algorithms have exhibited very satisfying results, ANN is coming to the fore in the prediction of the drying parameters considering all evaluation metrics together.
An experimental assessment of combustion and performance characteristics of a spark ignition engine fueled with co-fermentation biogas and gasoline dual fuel
(Sage Publications Ltd, 2021) Agbulut, Umit; Aydin, Mustafa; Karagoz, Mustafa; Deniz, Emrah; Ciftci, Burak
Natural gas, biogas and alcohols are alternative fuels for spark ignition engines which can be used for reducing exhaust emissions and improving performance metrics. At the first stage of the study, a pilot scale biogas system was built, and biogas was produced from a mixture of manure and water called slurry, consisting of 40% cattle manure, 35% water, 17% whey and 8% poultry manure by co-fermentation method. Scrubbing and desulfurization were applied to remove the harmful gasses (CO2, H2S) from the produced biogas in two stages. In the end of the purification process, biogas with a CH4 content of 51%, 57% and 87% was produced. In the second stage, these biogas fuels were used in an SI engine, and their impacts on performance and combustion characteristics were investigated experimentally. A 4-cylinder, 4-stroke, water cooled SI engine with an 11:1 compression ratio was used in the experiments. Tests were conducted at various loads and constant speed. Results showed that daily amount of mean biogas production has reached 1.6 m(3)/day and biogas methane content has reached 72%. In engine tests, as the methane ratio in biogas increases, cylinder pressure and exhaust temperature values increase and brake specific fuel consumption decreases.
Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles-diesel blends using different machine learning algorithms
(Pergamon-Elsevier Science Ltd, 2021) Agbulut, Umit; Gurel, Ali Etem; Sandemir, Suat
Deep learning (DL), Artificial Neural Network (ANN), Kernel Nearest Neighbor (k-NN), and Support Vector Machine (SVM) have been applied to numerous fields owing to their high-accuracy and ability to analyze the non-linear problems. In this study, these machine learning algorithms (MLAs) are used to predict emission and performance characteristics of a CI engine fuelled with various metal-oxide based nano particles (Al2O3, CuO, and TiO2) at a mass fractions of 200 ppm. Assessed parameters in the study are carbon dioxide (CO), nitrogen oxide (NOx), exhaust gas temperature (EGT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE). To evaluate the success of algorithms, four metrics (R-2, RMSE, rRMSE, and MBE) are discussed in detail. Tests performed at varying engine speeds from 1500 rpm to 3400 rpm with the intervals of 100 rpm. The addition of nanoparticles simultaneously reduced CO and NOx emissions because they ensured more complete combustion thanks to their inherent oxygen, the higher surface to volume ratio, superior thermal conductivities and their catalytic activity role. Further, the nano-sized particles ensured an accelerated heat transfer from the combustion chamber. In comparison with that of neat diesel fuel, the reduction in NOx is found to be 3.28, 7.53, and 10.05%, and the reduction in CO is found to be 8.3, 11.6, and 15.5% for TiO2, Al2O3, and CuO test fuels, respectively. Moreover, the presence of nanoparticles in test fuels has improved engine performance. As compared with those of neat diesel fuel, the doping of nanoparticles drops the BSFC value by 5.54, 7.89, and 9.96% for TiO2, CuO, and Al2O3, respectively, and enhanced BTE value to be 6.15, 8.87, and 11.23% for TiO2, CuO, and Al2O3, respectively. On the other hand, it can be said that all algorithms presented very satisfying results in the prediction of CI engine responses. All R-2 has changed between 0.901 and 0.994, and DL has given the highest R-2 value for each engine response. In terms of rRMSE, all results (except for one result in k-NN) are categorized as excellent according to the classification in the literature. Considering all metrics together, DL is giving the best results in the prediction of engine responses for the dataset used in this paper. Then it is closely followed by ANN, SVM, and k-NN algorithms, respectively. In conclusion, this paper is proving that the nanoparticle addition for ICEs is significantly dropping the exhaust pollutants, and improving the engine performance, and further the results can be successfully predicted with the machine learning algorithms. (c) 2020 Elsevier Ltd. All rights reserved.
Experimental investigation of fusel oil (isoamyl alcohol) and diesel blends in a CI engine
(Elsevier Sci Ltd, 2020) Agbulut, Umit; Saridemir, Suat; Karagoz, Mustafa
The present paper details an experimental investigation of the combustion behaviours, exhaust emission and performance characteristics of a single-cylinder diesel engine fueled with fusel oil-diesel blends of volumetrically 10%, 15% and 20% into neat diesel fuel (F0) separately. Under steady-state conditions, the tests were performed at constant engine speed (2000 rpm), and four different engine loads (2.5, 5, 7.5 and 10 Nm). The results showed that CO and NOx emissions significantly reduced down to 52% and 20%, respectively with an increasing percentage of the fusel oil in the fusel oil-diesel blends. However, HC gradually increased up to 40% with the addition of fusel oil. With respect to the performance of the engine, the lowest BSFC and the highest BTE were seen in F0 test fuel owing to the higher heating value of F0. On the other hand, duration in ignition delay (ID) of fusel oil-diesel blends was longer than that of F0 due to the lower cetane number of the fusel oil. The maximum in-cylinder pressure (CPmax) and maximum heat release rates (HRRmax) of fusel oil containing fuels is higher in comparison with diesel fuel owing to the longer ID and oxygen atoms of excessive fusel oil. The combustion characteristics of fusel oil-diesel blends closely followed those of neat diesel fuel.

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