Biodiesel Blend Ratio Research Articles

Prediction of Performance and Emissions Diesel Engines Fueled-Biodiesel Using Artificial Neural Network (ANN) Resilient Backpropagation Algorithm (Rprop)

In order to increase energy security and improve environmental quality, the Indonesian Goverment set a target of 23% renewable energy mix in 2025, one of which is the Mandatory Bioediesel Program. A higher biodiesel blending ratio will affect the performance and emissions of diesel engines because biodiesel is chemically different from diesel oil. Research related to the prediction of diesel engine performance and emissions using Artificial Neural Network (ANN) has been conducted, but the author sees a research opportunity for the implementation of the ANN Resilient Backpropagation (Rprop) algorithm. The data used to create the ANN model prediction was secondary data from previous research. The model designed multi input and multi output (MIMO) with 4 input variables and 7 output variables. Model building done by varying the number of neurons and hidden layers. Model evaluation selected based on the largest coefficient of determination parameter R2 and the smallest RMSE or MAPE. The results showed that the ANN single layer 4-20-7 network architecture is the best model for predicting diesel engine performance and emissions with test data R2 , RMSE and MAPE of 0.962532, 6.699428 and 6.0% respectively, while for overall data testing has a performance of 0.982869, 3.908542 and 4.3%. The results also show that based on the ANN prediction results, the increasing biodiesel ratio can increase NOx emissions and decrease HC, CO and CO emissions2 . In terms of performance, the addition of biodiesel can increase BSFC and BP and decrease BTE. The results also show that the addition of ZnO concentration can reduce emissions while in terms of performance it will increase BTE and reduce BSFC and BP.

The Effects of Biodiesel on the Performance and Gas Emissions of Farm Tractors’ Engines: A Systematic Review, Meta-Analysis, and Meta-Regression

The need for the decarbonization of heavy-duty vehicles requires a deep understanding about the effects of biofuels, which represent a viable pathway to cut the emissions in the hard-to-abate sectors, like agricultural tractors. A novel meta-analysis approach can help to thoroughly investigate the effects of biodiesel blends on farm tractor engines in terms of performance and emissions. Studies were identified using the main keywords related to internal combustion engines in prominent scientific databases. Standardized mean differences were calculated for each study to evaluate engine performance and gas emissions. Mixed-effects regression models were developed to investigate performance and environmental pollution changes over different biodiesel blending ratios, biodiesel sources, and engine types. The analysis revealed significant effects of biodiesel blending ratio on decreasing torque [−13.0%, CI 95% (6.7%–19.3%); I2 = 97.67; p = 0.000; Q = 129.94], engine power [−15.0%, CI 95% (10.0–20.0%); I2 = 54.82; p = 0.000; Q = 101.81], CO2 emissions [−24.1%(15.0–32.0%); I2 = 0.198; p = 0.000; Q = 20.04], and CO emissions [−17.5%, CI 95% (16.0–18.0%); I2 = 98.62; p = 0.000; Q = 97.69], while increasing specific fuel consumption [+5.2%, CI 95% (1.0–9.0%); I2 = 95.94; p = 0.000; Q = 129.74] and NO emissions [+11.0%, CI 95% (6.0–15.0%); I2 = 98.51; p = 0.000; Q = 157.56]. The same analysis did not show any influence of the sources of biodiesel and the engine type. Finally, meta-regression found a significant positive association between increasing ratios of biodiesels and decreasing torque, engine power, CO and CO2 emissions, and increasing fuel consumption and NO emissions in terms of linear equations. Although through these equations it is not possible to individuate an optimal range of blending ratios able to lower the emissions and not affect the engine parameters, the range from 9.1% to 13.0% of biodiesel is a good tradeoff. Within it, the only decrease in engine performance is in charge of the power, however contained within 4%, while CO and CO2 emissions are reduced (respectively by 0.0%/−2.8% and −3.6%/−6.0%) without using any specific pollutant abatement systems.

The Green Energy Effect on an HCCI Engine from Used Cooking Oil-based Biodiesel from Malaysia

Emissions from internal combustion engines (ICEs) significantly impact the environment, leading continents worldwide to work towards reducing them. The industry is increasingly leaning towards electric powertrains. However, power plants still utilize ICEs as generators, contributing to global pollution. Consequently, ICE emissions are garnering international attention. Alternatives like the Homogeneous Charge Compression Ignition (HCCI) engine and biodiesel fuels are being explored. HCCI engines have not been extensively tested with Used Cooking Oil (UCO) biodiesel. This study investigates the performance and emissions of HCCI engines using UCO-based biodiesel. This study tested an air-cooled, single-cylinder, 4-stroke diesel engine operating at 3600 rpm with a displacement of 0.219 liters. The HCCI mode was activated during preheating and run at 2700 rpm under varying biodiesel blend percentages and intake temperatures. In HCCI mode, brake-specific fuel consumption (BSFC) increased, peaking at a 90°C intake temperature. Diesel fuel in-cylinder pressure reached a maximum of 81 bars at 90°C, decreasing to 79 bars at 70°C. The HCCI mode resulted in lower NOx, CO, and UHC emissions. Higher biodiesel blend ratios further reduced CO emissions. Raising the intake air temperature to 90°C lowered NOx emissions by 96.66%, from 150 ppm to 5 ppm. Using green energy sources as fuel in HCCI engines significantly reduced emissions in this study, suggesting their potential as a future fuel for advanced engines.

Inhibition effect of biodiesel on the formation of oxidized insoluble matter in diesel: characteristics and mechanism

ABSTRACT To address the problem that oxidized insoluble matter in diesel is easily generated in the process of using, we proposed the idea of blending biodiesel into diesel to inhibit the formation of oxidized insoluble matter. We also studied the effect of the biodiesel blending ratio on the generation of oxidized insoluble matter and revealed the mechanism of biodiesel inhibiting the formation of diesel oxidized insoluble matter. According to the results, the color of the blended fuel became darker with the increase in oxidation time for B0–B100 blended fuel. The generation of oxidized insoluble matter during the oxidation of B0–B30 was obvious since the amount increased with the enhancement of biodiesel ratio. The formation phenomenon of oxidizing insoluble matter in diesel disappeared, however,when the addition ratio of biodiesel increased from 0%–30% to 40%–100%. Based on the results from Fourier transform infrared spectroscopy and gas chromatography-mass spectroscopy, the main component of oxidizing insoluble matter was tetralone. At low blending ratios, small molecule compounds such as alcohols, aldehydes, and ketones, were rapidly generated with the oxidation process of biodiesel, which maybe acted as precursors for formation of oxidation insoluble matter, thereby increasing the amount of oxidized insoluble matter. For high blending ratios, oxidized insoluble matter was gradually dissolved in biodiesel, because the polarity between biodiesel and oxidized insoluble matter was similar. The dielectric constant of biodiesel was 3.35, which indicated that biodiesel was a weak polar liquid. Meanwhile the oxidized insoluble matter was a polar substance, and its dielectric constant was 6.28–13.59. The results showed that the blending of biodiesel was expected to slow down the appearance of oxidized insoluble matter in diesel, thereby optimizing the performance of diesel and reducing the carbon emissions of diesel.

Model construction and multi-objective performance optimization of a biodiesel-diesel dual-fuel engine based on CNN-GRU

To tackle the challenges of low efficiency and excessive NOx emissions in biodiesel-diesel dual-fuel engines, this paper presents an optimization method using the Pareto-multiple objective snake optimizer (Pareto-MOSO) to drive a convolutional neural network-gated recurrent unit (CNN-GRU). In the Pareto-MOSO, brake specific fuel consumption (BSFC), NOx, Soot, and CO are optimized by changing the engine control parameters, including injection timing (Tinject), engine speed (n), biodiesel blending ratio, torque (Ttorque), exhaust gas recirculation (EGR rate), intake pressure (Pin), and rail pressure (Prail). The experiment generates training data for CNN-GRU and verifies the accuracy of the Pareto-MOSO optimization results. The findings suggest that the optimized engine exhibits a balanced correlation between economy and emissions under propulsion characteristics. Furthermore, the NOx emissions are all in accordance with the IMO Tier III emission regulations. Under the rated condition, Scheme 2 demonstrates a significant reduction of 76.57 % in NOx emissions. However, this optimization has resulted in a 1.63 % increase in BSFC compared to its pre-optimized state. Therefore, the adoption of suitable control strategies proves advantageous in addressing the trade-off between economy and emissions of engines.

Optimisation of Exhaust Emissions, Vibration, and Noise of Unmodified Diesel Engine Fuelled with Canola Biodiesel-Diesel Blends with Natural Gas Addition by Using Response Surface Methodology

The paper presents methods to determine the optimum input parameters of CNG addition, biodiesel blend ratio, and engine speed to improve engine responses in terms of exhaust emissions, vibration, and noise of CNG-biodiesel-diesel fuelled engine. Box-Behnken based on response surface methodology was used to predict and optimise input parameters. Variance analysis was applied to determine the significant relationship between the input parameters and engine responses. At optimum input parameters (CNG addition=9.24 L/min, biodiesel blend ratio=40%, engine speed=1524.24 rpm), the optimum engine responses of NOx, CO, CO2, O2, engine vibration acceleration, and noise were 93.77 ppm, 438.05 ppm, 1.47%, 18.59%, 37.17 m/s2 and 91.34 dB[A], respectively. In terms of coefficient determination of R2, the values were 99.11%, 99.22%, 99.41%, 99.70%, 98.65%, and 98.60% respectively. The correlation between the optimised result and the engine test result showed an acceptable error limit for NOx, CO, CO2, O2, engine vibration acceleration, and noise as 4.2%, 3.8%, 4.9%, 0.25%, 4.12%, and 0.17%, respectively.

Effects of an Exhaust System Equipped with a Thermoelectric Generator on Combustion, Performance, Emissions, and Energy Recovery in a Diesel Engine Using Biodiesel

The predominance of petroleum-based fuels is lessened by the preference for biodiesel as an alternative. However, one of the adverse effects arising from the use of biodiesel is the formation of waste heat. The novel aspect of this study proposes a sustainable solution that will decelerate global warming by recovering waste heat through a new exhaust design equipped with thermoelectric generators. The study obtained test fuels by blending vegetable-derived biodiesel in five different volumetric ratios (0, 10%, 20%, 50%, and 100%). The experiments were carried out at three different constant engine speeds (1000, 1250, and 1500 RPM) and five different engine loads (25%, 50%, 75%, and 100%) on a single-cylinder diesel engine. At the end of the experiment, the combustion characteristics, engine performance, exhaust emissions, waste heat values, and electrical energy gained from the thermoelectric system of biodiesel blend fuels compared to diesel were evaluated. Specific fuel consumption, effective efficiency, exhaust gas temperatures, exhaust emissions, and electrical power generation with TEG in the diesel engine were evaluated, focusing on the different biodiesel blend ratios, engine load, and engine speeds.

Numerical simulation of the combustion chamber and performance analysis of microalgae-based biodiesel/diesel-powered CI engine: An axisymmetric flow approach

This study presents a comprehensive analysis of the combustion chamber characteristics of a variable compression ratio compression ignition engine powered with microalgae biodiesel/diesel and their impact on engine performance and emissions. Both numerical and experimental methods have been employed to study the problem. A computational fluid dynamics method is used to study the axisymmetric flow in the cylindrical combustion chamber. Numerical simulations are obtained by solving time-independent steady, incompressible Navier-Stokes equation in a two-dimensional cylindrical coordinate system. Fluid flow characteristics, such as velocity vector field, streamlines, and pressure distribution are presented for different biodiesel blends. The velocity vector field analysis shows that biodiesel blends exhibit high velocities near the axisymmetric wall region. Swirl motion generated near the outer periphery of the combustion chamber influences the flow patterns. The experimental study involves the engine performance and its emissions. Due to the increase of biodiesel blend ratios in B20 (20% biodiesel and 80% diesel), B40 (40% biodiesel and 60% diesel), B50 (50% biodiesel and 50% diesel), and B100 (100% biodiesel), the brake thermal efficiency (BTE) decreases for a given brake power. B20, B100, and diesel exhibit 32.87%, 29.15%, and 34.45% BTE respectively. Higher brake-specific fuel consumption is obtained at no-load engine operations due to incomplete combustion of fuel. B20, B40, B50, and B100 show reductions of 16.04%, 20.85%, 25.66%, and 32.08% CO, respectively, as compared to diesel. HC emissions are very low for all biodiesel blends, while B20 exhibits a decrease of 7.35% HC compared to diesel. However, NOx emissions increase with the biodiesel blend ratio, with B100 exhibiting a 22.56% increase in NOx compared to diesel. Smoke emissions decrease with an increase in the biodiesel blend ratio, while B20 shows a 4.64% reduction. Results show that B20 offers a favorable balance between emissions reduction and engine performance. This study determines the biodiesel combustion characteristics and insights for the practical implementation of biodiesel blends in the transportation and energy sectors.

Study on the effect of dimethyl ether and diesel-castor biodiesel blends on emission and combustion characteristics

The biodiesel from Castor was, investigated with a water-cooled four-stroke diesel engine of model CT 110 with B0, B5, and B10 without and with dimethyl ether (DME) of 2 % and with a fixed load of (80 %) to study the combustion and emission. The biodiesel was made by alkaline transesterification with NaOH as a catalyst. Using established test protocols, the fuels' characteristics, including their viscosity, surface tension, heating value, flash point, and elemental makeup, were measured. Experimental research is used to determine how the pressure and heat release rate affect the performance characteristics of both reference fuel and the blends. The combustion studies are conducted with engine speeds of 1800, 2400, and 3000 rpm and emissions were analyzed with engine speeds starting from 1600 rpm to 3000 rpm with exhaust gas analyzer Gunt, CT159.02 digital analyzer. From the combustion analysis when the blend ratio increases the cylinder pressure (CP) and heat release rate (HRR) also increase due to oxygen molecules in the biodiesel. The addition of DME to biodiesel blends reduces carbon monoxide (CO) emissions relative to neat diesel and biodiesel blends. With the increase of diesel castor biodiesel blends, nitrogen oxide (NOX) emissions decreased as a result of the reduction in the HRR. The effect of castor diesel biodiesel blend on the NOX emissions shows, that when the blend ratio increased the NOX emissions also increased. When DME is added to a higher blend ratio of castor biodiesel, the engine is operated only at higher engine speed. Specifically, for B10 NOX emission was detected after engine speed of 2500 rpm. When the engine ran with DME for blends of castor biodiesel the engine was not operated at low engine speed. The increase of diesel castor biodiesel blends in the case of DME mixture unburned hydrocarbon (UHC) emissions increased. The finding of this research work was as the biodiesel blend increased the cylinder pressure and heat release rate also increased. So, using sustainable biodiesel-diesel blends made from castor oil with DME additive it is advisable to operate diesel engines for better emission regulation.

An experimental study on optimum temperature of B20 POBD for higher efficiency and lower emissions of a DI diesel engine in lean and ultra‐lean combustion conditions

AbstractThe impact of inlet fuel temperature on the performance and emission characteristics of a 406 cc, single‐cylinder, air‐cooled, 4‐stroke, direct injection (DI) diesel engine was investigated to determine the suitable temperature of fuel for the best conditions of the engine in lean and ultra‐lean mixture combustion mode. B20 palm oil biodiesel‐diesel blend fuel (B20 POBD), as common renewable fuel and favorite biodiesel blend ratio used in the research, was provided and injected in the engine at varying temperatures in the range of 10–40°C and the engine parameters including the output power, specific fuel consumption (SFC), thermal efficiency, flue gas heat recovery efficiency and CO and NOx pollutant emissions were measured at different air–fuel ratios corresponded to lean and ultra‐lean mixtures. The results showed that there is an optimum temperature for fuel (about 20°C) in which maximum thermal efficiency and minimum SFC and CO emission are obtained. The minimum SFC and CO emission were 0.45 Kg/kWh and 610 ppm at the optimum temperature and output power of 1.5 kW, respectively. The effect of fuel temperature on SFC and CO increases as the output power decreases and for all fuel temperatures, the output power is inversely related to the air–fuel ratio. In addition, the elevated output power reduces the CO emission in all inlet fuel temperatures. Furthermore, an ultra‐lean mixture (very high A/F ratio) can dramatically decrease the thermal efficiency of diesel engines at all fuel temperatures, such that at the inlet fuel temperature of 17°C, increase in A/F ratio from 51 to 108 decreases the thermal efficiency from 16.8% to 0.12%. Finally, low engine output power leads to low levels of NOx emission, such that decrease in output power from 1.5 to 0.5 kW reduces the NOx emission from 522 to 204 ppm, respectively.

A field-study of the feasibility of the use of biodiesel in the marine industry

ABSTRACT The International Maritime Organization is making multiple efforts to reduce greenhouse gas and air pollutant emissions. However, it is difficult to achieve this because of the absence of technological developments. Consequently, there is an urgent need to develop alternative fuels. In this study, marine gas oil and biodiesel were blended at ratios of 0%, 5%, 10%, and 20% to assess their applicability to ships (Component analysis, metal corrosion, and storage stability) and potential for emission reduction (engine performance, environmental effects, and engine durability). The composition of the test fuels meet the ISO 8217:2017 standard. Metal corrosion was insignificant for carbon steel, iron, aluminum, and nickel, but not copper. Storage stability showed no sludge generation or fuel separation, although biodiesel experienced some discoloration owing to its high oxygen content. Engine performance showed no significant differences between marine gas oil and biodiesel. Emissions of NOx increased with higher biodiesel blending ratios, whereas SOx, CO, CO2, and Total hydro carbons decreased. Engine durability was good throughout the 160 hours sea trial. This study demonstrated that even at a 20% biodiesel blending ratio exceeding the 7% ratio suggested by ISO 8217:2017, can be safely used as a long-term marine fuel.

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

The 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 accuracy 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.

The combustion and emission improvements for diesel–biodiesel hybrid engines based on response surface methodology

With the rapid technological progress of society and increasingly stringent environmental regulations, further reduction of emissions has become an important issue for environmental protection. This study developed a response surface model with the biodiesel blending ratio (BBR), load, and exhaust gas recirculation (EGR) as independent variables and brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and NOx, and CO emission rates as dependent variables. Simulations were performed and calculated. The results of the response surface approach with the objectives of maximizing the BTE of the engine and minimizing BSFC, NOx emissions, and CO emissions show that when the BBR is 20%, the EGR rate is 15%, and the engine load is 74.52%, pollutant emissions are significantly reduced while the engine power’s performance is maximized.

Effect of graphene nanoparticles on the behavior of a CI engine fueled with Jatropha biodiesel

In this study, the behavior of a 4-stroke, single-cylinder, CI engine are investigated using a second-generation Jatropha biodiesel with 100 ppm of graphene nanoparticles added to it. The blended fuel is named according to the different blending ratios of biodiesel and nanoparticles: JB100 (pure Jatropha biodiesel), JB20 (20% Jatropha biodiesel + 80% diesel), JB100Gn100 (pure Jatropha biodiesel + 100 ppm graphene nanoparticles), and JB20Gn100 (20% Jatropha biodiesel + 80% diesel + 100 ppm graphene nanoparticles). The engine behavior for heat generated inside the cylinder, efficiency, fuel consumption, smoke opacity and PM emissions, were analyzed and compared. The results showed that with the inclusion of graphene nanoparticles increased the thermal efficiency by 0.57–1.54%, and reduced the fuel consumption by 0.36–1.33%, smoke opacity by 0.45–4.12%, and PM emissions by 1.68–9.62% from the engine. The investigation revealed that CI engines are more efficient with the inclusion of graphene nanoparticles.

Effects of Blended Biodiesel Coupled with Miller Cycle on Combustion and Emissions of Marine Diesel Engines

In order to study the effect of blended biodiesel coupled Miller cycle technology on the combustion and emission performance of diesel engine, the 4190 diesel engine was simulated by AVL-FIRE software. The Miller degree and different blending ratios of biodiesel are simulated and analyzed. The results show that: under the same Miller degree, with the increase of the blending ratio, the explosion pressure decreases significantly, the cylinder temperature decreases, the CO emission decreases significantly, and the NO emission increases. Soot emissions have dropped significantly. Under the same blending ratio, with the increase of Miller degree, the explosion pressure increases, the cylinder temperature decreases gradually but not obviously, the CO emission decreases, the NO emission increases first and then decreases, and the Soot emission decreases. Therefore, selecting the appropriate blending ratio and Miller degree can better reduce NO emissions, and at the same time have a better inhibitory effect on the generation of Soot and CO, and the power performance of the diesel engine is slightly improved. The results of this study provide a solution for the practical application of biodiesel.

Effect of biodiesel blend ratio derived from rubber seed oil on the fuel consumption when using in the diesel engine without structural modification by simulation and experiment

In this study, The model Mazda WL diesel engine,4-cylinder, 4 straight-line cylinder with biodiesel blends ratio of 0%, 15%, 20%, 25%, 30% derived from rubber seed oil and DO has been simulated on ANSYS FLUENT software to analyze fuel consumption characteristics. Experimental engine has been using fuel with diesel (DO) and biodiesel blends (B15, B20, B25, B30) derived from rubber seed oil for a Mazda WL diesel engine installed on Mazda 2500 and Ford Ranger cars. The simulation results show that the average increase in fuel consumption rates for all modes for B15, B20, B25 and B30 compared to DO fuel are 0.71%, 1.69%, 2.77% and 3,83% respectively. Experimental results show that the average increase in fuel consumption rates for all modes for B15, B20, B25 and B30 compared to DO fuel are 0.58%, 1.39%, 2.19% and 3,19% respectively.

Effects of palm oil biodiesel addition on exhaust emissions and particle physicochemical characteristics of a common-rail diesel engine

In this study, the effects of varying diesel/biodiesel blending ratios on exhaust emissions and particle physicochemical characteristics were conducted on a four-cylinder high-pressure common-rail diesel engine. The experiment involved the application of reference diesel (B0) and several diesel/biodiesel blends (B20, B50, B70 and B100) as the alternative fuels for diesel engine. The results indicated that the addition of biodiesel resulted in a significant reduction of HC, CO and soot emissions except for NOx, the generation of soot precursors such as polycyclic aromatic hydrocarbons (PAHs), ethylene and acetylene were inhibited, and the unregulated emissions of formaldehyde and acetaldehyde increased slightly, while they still were <45 ppm. At rated load, the soot and PAHs emissions of B70 fuel decreased by 28% and 24%, respectively. With the increase of biodiesel blending ratio, the molar and mass ratios of O/C, aliphatic hydrocarbon functional groups (CH) and oxygen-containing functional groups (-OH and -C=O), as well as the degree of disorder (sp3/sp2) on soot surface increased. Furthermore, the initial oxidation temperature and burnout temperature of B20, B50 and B70 soot samples were lower than those of B0 soot samples, and their apparent activation energy decreased by 4.67%, 9.76% and 17.09%, respectively.

Optimization of performance and emission characteristics of CI engine fueled with waste safflower oil biodiesel and its blends

Biodiesel production represents a great opportunity to reduce marine and land pollution while incorporating circular economy principles into modern society. The objective of this research is to use the response surface methodology to evaluate the best engine performance and emission characteristics of safflower-based biodiesel and diesel mix dual-fuel Compression ignition engines. The optimization aims to increase brake power, and brake thermal efficiency, and reduce brake-specific fuel consumption and exhaust emissions. The independent input variables were biodiesel blending ratio, compression ratio, and engine load. The novelty of this study is to optimize the operating parameters of a dual-fuel compression ignition engine fueled with safflower biodiesel and diesel blend fraction. A total of 48 experimental trials were conducted. The oil output from safflower seed in the lab and the biodiesel yield obtained were 26% and 91%, respectively. RSM results predict the optimized magnitude of the biodiesel blending ratio, CR, and EL are B16.66, 16.68, and 12 kg, respectively. The optimal values for dependent parameters, that is, BP, BTE, BSFC, CO, UHC, CO2, and NO were 3.27 kW, 19.60%, 3.30 kg/kWh, 0.0006 (% vol.), 0.8 (ppm), 0.78 (% vol.), and 140.87 ppm, respectively. The cumulative composite desirability was found to be 0.5877.

Effects of waste-cooking-oil biodiesel blends on diesel vehicle emissions and their reducing characteristics with exhaust after-treatment system

The waste-cooking-oil biodiesel is proven to be a clean alternative fuel, its effect on the vehicle emissions at different driving conditions needs further quantitative evaluation, especially with the exhaust after-treatment. The objective of this study is to quantify the effect of waste-cooking-oil biodiesel on the gaseous and particulate emissions from an urban bus at different driving conditions, and with a combination of diesel oxidation catalyst (DOC) and a catalyzed diesel particulate filter (CDPF) after-treatment. Results showed that the emissions depended on the driving conditions, vehicle acceleration and high speed increased the emissions. The biodiesel blends produced a reduction in the carbon monoxide (CO), total hydrocarbon (THC), particle number (PN) and particle mass (PM) emissions, but an increase in the carbon dioxide (CO2) and nitrogen oxide (NOx) emission, and this changing trend increased with not only the biodiesel blending ratio but also the vehicle speed and acceleration. The particle size distribution remained bimodal logarithm distribution after using the biodiesel, and more accumulation mode particles reduced compared with nucleation mode ones, increasing the proportion of nucleation mode particles. The after-treatment system composed of DOC and CDPF could reduce 58.0% of the CO and 53.1% of the THC when using B10 (10% biodiesel and 90% diesel by volume), and the reduction effect was enhanced with the increase of the biodiesel blending ratio, also with the increase of speed and acceleration. The after-treatment system reduced more than 99% of the PN and 94% of the PM, achieving excellent reduction effects, independent of fuel type and driving conditions.

Optimization of exhaust emissions, vibration, and noise of a hydrogen enriched fuelled diesel engine

In this study, the effects of various input parameters are examined on exhaust emissions, vibration, and noise of an unmodified diesel engine. The primary aim of this study is to optimize the vibration, noise, and exhaust emissions of the engine to get optimal configuration parameters. Experiments were carried out on a four-stroke, four-cylinder, diesel engine fuelled with diesel-biodiesel-hydrogen blends. To minimize the number of experiments Box-Behnken design (BBD) has been adopted. Optimum desirability is found as 0.862 with hydrogen addition of 4.63 L/min, fuel blend of 26.8% and 1500 rpm engine speed for the diesel engine. When the diesel engine is operated at 1500 rpm engine speed and fuelled with 4.63 L/min hydrogen addition and 26.8% biodiesel blend ratio; the optimum responses of CO, CO2, NOx, vibration, and noise are established as 214 ppm, 1.35%, 90.4 ppm, 38.6 m/s2, and 91.3 dB[A], respectively. The predicted values were confirmed experimentally and the errors in predicted values are found in a limit range.