Methyl Ester Biofuel Research Articles

Numerical and experimental analyses for performance and emissions assessment of a four-stroke engine powered by oleic acid methyl ester biofuel made from waste frying oil

<p style="text-align:justify"><span style="font-size:11pt"><span style="line-height:150%"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif">Recently, several earlier works conducted research works dedicated to replacing fossil-based fuels with environmentally friendly ones. The goal of the research is to construct a multivariate linear regression model for the attributes of a four-cylinder diesel engine fueled by biodiesel. In this context, the goal of the research is to structure a multiple linear regression example for characterizing a four-cylinder engine fueled by biodiesel. In fact, experimental research was conducted on an agricultural tractor equipped with a 4-cylinder direct-injection diesel engine. The biodiesel is made from waste frying oil without blending referred to as the oleic acid methyl ester (OAME) biofuel. The biodiesel was extracted through a chemical transesterification reaction. The four-stroke engine is tested for both pure OAME biofuel and conventional diesel. One-dimensional numerical simulations were conducted. A strong correlation between numerical and experimental data was observed, validating the numerical model. Using the engine model, in-cylinder pressure, heat release rate, in-cylinder temperature, and emissions including Carbon monoxide (CO), nitrogen oxides (NO<sub>x</sub>), and Carbon dioxide (CO<sub>2</sub>) were recorded across a broad range of engine operating speeds. Additionally, brake-specific fuel consumption (BSFC) and brake power were numerically calculated and compared to experimental data. The results showed that usage of the OAME as a biofuel could be a promising solution that enables similar performances with lower CO emissions compared to petroleum diesel particularly at low engine speeds.</span></span></span></span></span></span></p>

Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models

Data-driven machine learning (ML) methods are extensively employed for modeling and simulation of highly complicated processes. ML techniques confirmed their great predictive capability compared to conventional techniques for modeling and management of non-linear relationships between input and output parameters. Biofuels as renewable sources of energy are a significant potential alternative to fossil fuels. Due to the non-linearity and complexity of biofuels production processes and increasing energy conversion, accurate and fast modeling tools are necessary for design and optimization of these processes. Hence, in this research, ML modeling techniques were developed for simulation of biofuel production from energy conversion of Papaya oil through transesterification process. In order to simulate and optimize the content Papaya oil methyl ester (POME) production, Gaussian Process Regression (GPR), Multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models, as well as adaptive boosting for amplification, were employed. The temperature of reaction, catalyst quantity, time of process, and methanol to oil molar ratio were considered as the inputs of models while the POME yield was the model output. The obtained results showed that the R2-score of 0.988, 0.993, and 0.994 were obtained for Boosted MLP, Boosted GPR, and Boosted KNN, respectively, which demonstrate the high predictive ability of these models. Also, the RMSE metric error rates of 9.8071, 4.8150, and 6.5180 corresponded to Boosted MLP, Boosted GPR, and Boosted KNN, respectively. We examined performance using another metric, MAE: 8.38008, 2.3184, and 5.21954 errors were observed for Boosted MLP, Boosted GPR, and Boosted KNN, respectively. The optimized POME production yield of 99.89% was observed at temperature of 62.5 °C, 6.47 min of reaction, catalyst quantity of 0.8125 wt% and methanol to oil molar ratio of 10.33. The obtained results of this study show that the ML techniques are highly recommended for prediction of biofuels production as cost and time saving methods.

Performance Evaluation of Compression Ignition Direct Injection Diesel Engine on Dual Fuel Mode with Mango Oil Methyl Ester Biofuel

In this study, Mango seed Oil Methyl Esters (MOME) that is produced by base catalyzed transesterification method has been chosen as a bio-fuel. Experiments were conducted with fuels of known cetane number at various loads on a single cylinder, constant speed, air-cooled, four stroke, and direct injection vertical diesel engine with rated power of 4.4 kW. All experiments were conducted at injection pressures 200 bar. For the same engine, the performance, combustion and emission analysis was carried out for various blends(20% and 100%) of MOME, diesel with grain alcohol(ethanol 0.1pre-mixed ratio) on dual fuel mode. When the engine was operated with 20% of MOME and 0.1 pre-mixed ratio, the brake thermal efficiency was found to be very much closer to the BTE obtained with neat diesel due to more calorific value and more heat release rate during combustion. It is also observed that at this blend, Hydrocarbons (HC), Carbon monoxide (CO) and smoke density emissions were found to be less than that of diesel whereas NOx emissions were slightly more than that of diesel.

Thermodynamic analysis of diesel engine with sunflower biofuel

Thermodynamic performance, which is based on the first and second thermodynamic laws, is a significant tool to assess energy systems. In this study, the authors conducted an energy and exergy analysis of a single-cylinder, four-stroke compression ignition engine operated with sunflower methyl ester biofuel at different engine loads (20%, 40%, 60%, 80%, 100%). The energy and exergy efficiencies were subsequently calculated along with the effective work, heat exergy losses and exergy destruction values at ten different engine speeds for five loads. Finally, optimum operating conditions were defined and discussed.

Microscopic investigation of soot and ash particulate matter derived from biofuel and diesel: implications for the reactivity of soot

Investigation of soot and ash particulate matter deposited in diesel particulate filters (DPFs) operating with biofuel (B100) and diesel (pure diesel: B0 and diesel80/biofuel20 blend: B20) by means of optical microscopy, scanning electron microscopy, and high resolution transmission electron microscopy (HRTEM) reveals the following: the rapeseed methyl ester biofuel used for this study contributes to ash production, mainly of Ca–S– and P-bearing compounds ranging in size between 50 and 300 nm. Smaller ash particles are less common and build aggregates. Ash is deposited on the inlet DPF surface, the inlet channel walls, and in B100-DPF at the plugged ends of inlet channels. The presence of Fe–Cr–Ni fragments, down to tens of nanometers in size within the ash is attributed to engine wear. Pt particles (50–400 nm large) within the ash indicate that the diesel oxidation catalyst (DOC) upstream of the DPF shows aging effects. Radial cracks on the coating layer of the DOC confirm this assumption. The B100-DPF contains significantly less soot than B20 and B0. Based on the generally accepted view that soot reactivity correlates with the nanostructure of its primary particles, the length and curvature of graphene sheets from biofuel- and diesel-derived soot were measured and computed on the basis of HRTEM images. The results show that biofuel-derived soot can be more easily oxidized than diesel soot, not only during early formation but also during and after considerable particle growth. Differences in the graphene sheet separation distance, degree of crystalline order and size of primary soot particles between the two fuel types are in line with this inference.

Atomization and combustion of canola methyl ester biofuel spray

The spray atomization and combustion characteristics of canola methyl ester (CME) biofuel are compared to those of petroleum based No. 2 diesel fuel in this paper. The spray flame was contained in an optically accessible combustor which was operated at atmospheric pressure with a co-flow of heated air. Fuel was delivered through a swirl-type air-blast atomizer with an injector orifice diameter of 300 μm. A two-component phase Doppler particle analyzer was used to measure the spray droplet size, axial velocity, and radial velocity distributions. Radial and axial distributions of NO, CO, CO 2 and O 2 concentrations were also obtained. Axial and radial distributions of flame temperature were recorded with a Pt–Pt/13%Rh (type R) thermocouple. The volumetric flow rates of fuel, atomization air and co-flow air were kept constant for both fuels. The droplet Sauter mean diameter (SMD) at the nozzle exit for CME biofuel spray was smaller than that of the No. 2 diesel fuel spray, implying faster vaporization rates for the former. The flame temperature decreased more rapidly for the CME biofuel spray flame than for the No. 2 diesel fuel spray flame in both axial and radial directions. CME biofuel spray flames produced lower in-flame NO and CO peak concentrations than No. 2 diesel fuel spray flames.

Impacts of Biodiesel on Pollutant Emissions of a JP-8–Fueled Turbine Engine

The impacts of biodiesel on gaseous and particulate matter (PM) emissions of a JP-8–fueled T63 engine were investigated. Jet fuel was blended with the soybean oil-derived methyl ester biofuel at various concentrations and combusted in the turbine engine. The engine was operated at three power settings, namely ground idle, cruise, and takeoff power, to study the impact of the biodiesel at significantly different pressure and temperature conditions. Particulate emissions were characterized by measuring the particle number density (PND; particulate concentration), the particle size distribution, and the total particulate mass. PM samples were collected for off-line analysis to obtain information about the effect of the biodiesel on the polycyclic aromatic hydrocarbon (PAH) content. In addition, temperature-programmed oxidation was performed on the collected soot samples to obtain information about the carbonaceous content (elemental or organic). Major and minor gaseous emissions were quantified using a total hydrocarbon analyzer, an oxygen analyzer, and a Fourier Transform IR analyzer. Test results showed the potential of biodiesel to reduce soot emissions in the jet-fueled turbine engine without negatively impacting the engine performance. These reductions, however, were observed only at the higher power settings with relatively high concentrations of biodiesel. Specifically, reductions of ∼15% in the PND were observed at cruise and takeoff conditions with 20% biodiesel in the jet fuel. At the idle condition, slight increases in PND were observed; however, evidence shows this increase to be the result of condensed uncombusted biodiesel. Most of the gaseous emissions were unaffected under all of the conditions. The biodiesel was observed to have minimal effect on the formation of polycyclic aromatic hydrocarbons during this study. In addition to the combustion results, discussion of the physical and chemical characteristics of the blended fuels obtained using standard American Society for Testing and Materials (ASTM) fuel specifications methods are presented.