Composition Of Biodiesel Research Articles

Evaluating the feasibility of machine learning algorithms for combustion regime classification in biodiesel-fueled homogeneous charge compression ignition engines

The present study explores the feasibility of machine-learning algorithms in classifying the combustion regimes in a homogenous charge compression ignition (HCCI) engine fueled with biodiesels from diverse sources. A distinctive feature of this research is the extensive database of HCCI combustion regimesgenerated for neat biodiesel fuels of different compositions. The engine experiments reveal significant variability in the load range of operation of HCCI engines across different biodiesel fuels. Strategies including compression ratio variation and charge dilution were employed to achieve higher engine loads, and the results confirm that the interplay of fuel reactivity and engine load, along with compression ratio and charge dilution, dictates the combustion stability. This complex phenomenon necessitates ascertaining the prevailing combustion type under a given engine load for biodiesel fuels derived from diverse sources. Thus, the present study aims to investigate the applicability of machine learning algorithms for developing models that classify combustion regimes in a neat biodiesel-fueled light-duty HCCI diesel engine. Models are developed to categorize biodiesel HCCI combustion into four groups: engine misfire, stable combustion without dilution, stable combustion with dilution, and knocking combustion. Biodiesel composition (13 different methyl esters), compression ratio and engine load are considered the input variables for the machine learning (ML) models. The results show that Naive Bayes and logistic regression models exhibit poor applicability among the investigated ML models due to a low F1 score (<0.8). The k-NN and SVM models demonstrate practical suitability with reasonably good F1 scores. However, the decision tree model outperforms all, precisely classifying 94 % of calibration and validation samples. It could precisely classify all the validation samples corresponding to misfire combustion, 21 of 22 samples to stable combustion, 4 of 6 samples to stable combustion with dilution, and 30 of 31 to knocking combustion. Thus, it emerges as the best approach to classify the combustion regimes of HCCI engines operated with neat biodiesel fuels.

Evaluating the Composition of Biodiesel Synthesized from Black Soldier Fly (Hermetia illucens) Larvae

Evaluating the Composition of Biodiesel Synthesized from Black Soldier Fly (Hermetia illucens) Larvae

Biodiesel production and selected fuel qualities from prospective non-edible oils: Hevea brasiliensis, Madhuca longifolia, Azadirachta indica, and Gossypium hirsutum

ABSTRACT Biodiesel predominantly originates from edible oil. Exploring non-edible oil resources is crucial to biodiesel manufacturing’s commercial viability and food security. This study examines four non-edible seed oils Hevea brasiliensis, Madhuca longifolia, Azadirachta indica, and Gossypium hirsutum as biofuel feedstocks. This study optimized reaction parameters to improve base-catalyzed transesterification, oil extraction and biodiesel synthesis. The produced esters were identified by nuclear magnetic resonance, Fourier transform infrared, and gas chromatography mass spectrometry. Elements composition was determined using inductively coupled plasma-optical emission spectrometry and elemental analyzer. The biofuel’s physicochemical properties met American Society for Testing and Materials and European standards. The maximum amount of crude oil was seen in Hevea brasiliensis using the soxhlet extraction process, ranging from 52-63%. This was followed by Madhuca longifolia 38-55%, Azadirachta indica 24-33%, and Gossypium hirsutum with 19-27%. The density of the plant sources exhibited a range of 0.88-0.90 g/cm3 @ 15 °C, while the kinematic viscosity showed a range of 3.2-4.1 mm2/s. The elemental composition of biodiesel, expressed as percentages of carbon, hydrogen, nitrogen, and oxygen, varied between the ranges of 70.52-76.30, 11.25-14.02, 1.18-2.75, and 9.11-13%, respectively. Since they may be grown in infertile and unproductive settings, these non-consumable seeds are cost-effective for renewable energy. They also help solve the energy situation by boosting biodiesel production..

Application of Evolutionary Computation to the Optimization of Biodiesel Mixtures Using a Nature-Inspired Adaptive Genetic Algorithm

The present research work introduces a novel mixture optimization methodology for biodiesel fuels using an Evolutionary Computation method inspired by biological evolution. Specifically, the optimal biodiesel composition is deduced from the application of a nature-inspired adaptive genetic algorithm that first examines percentages of the ingredients in the optimal mixtures. The innovative approach’s effectiveness lies in problem simulation with improvements in the evaluation of the specific function and the way to define and tune the genetic algorithm. Environmental imperatives in the era of climate change currently impose the optimized production of alternative environmentally friendly biofuels to replace fossil fuels. Biodiesel in particular, appears to be more attractive in recent years, as it originates from renewable bio-derived resources. The main ingredients of the specific biofuel mixture investigated in this research are diesel and biodiesel (100% from bioresources). The assessment of the new biodiesel examined was performed using a fitness function that estimated both the density and cost of the fuel. Beyond the evaluation criterion of cost, density also influences the suitability of this biofuel for commercial use and market sale. The outcomes from the modeling process can be beneficial in saving cost and time for new biodiesel production by using this novel decision-making tool in comparison with randomized laboratory experimentations.

Chemical composition of biodiesel produced in situ with Salvinia molesta DS Mitchell (Salviniaceae) by ethylic and methylic route

Macrophytes are aquatic plants that can cause environmental and economic damage due to their rapid growth in eutrophicated environments; however, this characteristic makes these biomasses promising alternatives for biodiesel production. Thus, this study aims to characterize and evaluate the chemical composition of the biodiesel produced from the macrophyte Salvinia molesta DC Mitchell (Salviniaceae). The biodiesel production was carried out in situ through the ethyl and methyl process. Attenuated total reflectance-Fourier transform infrared spectroscopy (FTIR-ATR) and gas chromatography with a flame ionization detector (GC-FID) were used to characterize the product. A commercial sample was also analyzed for comparison purposes. The biofuel produced with ethanol and methanol showed characteristic peaks between 900 to 1300 cm-1 and 1750 to 1735 cm-1 in the FTIR-ATR. Both samples showed less unsaturation degree compared to the commercial sample, with 34.44% of monounsaturated compounds (MUFA) and 36.73% of polyunsaturated compounds (PUFA) for methylic biodiesel, 34.79% of MUFA and 36.89% of PUFA for ethylic biodiesel, and 55.34% of MUFA and 24.14% of PUFA for commercial biodiesel. Samples produced by both routes showed similar chemical composition, with higher contents of saturated compounds than the commercial sample. The average chain size and the number of double bonds are smaller for S. molesta samples, 17.38 and 1.15 for S. molesta biodiesels and 17.65 and 1.41 for commercial biodiesel, respectively. The chemical composition of S. molesta biodiesel demonstrates the potential to be an alternative to commercial biodiesel.

Investigations on the applicability of machine learning algorithms to optimize biodiesel composition for improved engine fuel properties

Selecting suitable biodiesel for the intended application is challenging due to the significant variations in the feedstock for producing biodiesel. The available models to predict biodiesel properties have limited applicability and reliability. The present work addresses these two challenges by developing reliable models based on machine learning algorithms for predicting engine fuel properties of biodiesel and optimizing biodiesel composition for better fuel properties. The models are developed using multilinear regression (MLR), artificial neural networks (ANN), support vector machine regression with grid search (SVMGS), Bayesian optimization (SVMBO) and grey-wolf optimization (SVMGWO) for hyperparameter tuning, Gaussian process regression (GPR), random forest (RF), and adaptive neuro-fuzzy inference system (ANFIS) algorithms. The models are trained to predict viscosity, cetane number, and calorific value from 13 methyl ester constituents of 70 biodiesels. SVMGS models predicted the viscosity, cetane number, and calorific value of 33 validation samples with a mean absolute percentage error of 1.54%, 1%, and 0.43%. Biodiesel composition was optimized to minimize viscosity and maximize cetane number and calorific value. The optimized composition exhibits 3.72 cSt viscosity, 57 cetane number, and 43 MJ/kg calorific value, which can be prepared by blending 68% ± 1% camelina and 32% ± 1% coconut oil. Applying machine learning algorithms to predict biodiesel properties yielded more accurate predictions than available models. It helped find the optimal composition for improved engine characteristics.

The Crucial Impact of Microbial Growth and Bioenergy Conversion on Treating Livestock Manure and Antibiotics Using Chlorella sorokiniana

The residual antibiotics in livestock excreta (LE) have been regarded as a potential threat to the ecosystem and human society. Some photoautotrophic microalgae, however, were found to metabolize them during active biomass photosynthesis. This study investigates how the strength of the antibiotics impacts the overall biodiesel yield and composition of the harvested microalgal biomass grown from LE. The microalgal growth results demonstrate that increasing the concentration of residual antibiotics suppresses the microalgal growth rate from 0.87 d−1 to 0.34 d−1. This 61% lower biomass production rate supports the proposition that the kinetic impact of antibiotics may slow lipid synthesis. Moreover, the analytical results of fatty acid methyl ester (FAME) demonstrate that amoxicillin substantially reduces the C16:0 content by over 96%. This study evidences that the functional group similarity of amoxicillin may competitively inhibit the esterification reaction by consuming methanol. This explanation further highlights that residual antibiotics interfere with microalgal lipid synthesis and its transesterification. Moreover, it was confirmed that the presence of residual antibiotics may not affect the major nutrient removal (total nitrogen: 74.5~78.0%, total phosphorus: 95.6~96.8%). This indicates that residual antibiotics inhibit the metabolism associated with carbon rather than those associated with nitrogen and phosphorus, which is connected to the decrease in the biodiesel yield. Overall, these results reveal that the frequent abuse of antibiotics in livestock may harm the eco-friendly conversion of waste-into-bioenergy strategy.

Production of genetically engineered designer biodiesel from yeast lipids

Biodiesels constitute a growing class of fuel in a world that is increasingly inclined towards more ecological and sustainable energy. Despite their many advantages, biodiesels have limited cold flow properties and larger NOX emissions. These limitations are mostly attributed to the chemical compositions of biodiesels which are dictated by the chemical compositions of their feedstock oils. Accordingly, this study presents a novel approach to produce Genetically Engineered Biodiesel (GEB) whose chemical composition can be controlled by the genetic manipulation of oleaginous yeast oils for the production of designer biodiesels with improved properties and performances. Using full-factorial central composite design, the best chemical composition of an optimal biodiesel was predicted. Then, simple and combined MFE1, PEX10 and POX2 mutants of the oleaginous yeast Yarrowia lipolytica were constructed. These mutants showed interesting lipid profiles where their biodiesels are predicted to have better cold flow properties. These mutants showed also higher lipid titers by 2–3 folds compared to the parent strain. This study provides an approach for tailor designing of biodiesel properties and performances via genetic engineering. Moreover, it provides solutions potentially enabling biodiesel to be used as a standalone fuel in cold climates without any mixing with petrodiesel.

Biodiesel yield optimization from ternary (animal fat-cotton seed and rice bran) oils using response surface methodology and grey wolf optimizer

The attributes of constant convergence speed, capacity for reconnoitering the search space and absence of technical drawbacks viz. trapped in local minima and unswerving solutions have led to wider adoption of Metaheuristic Algorithms (MAs) compared to conventional modelling tool. Grey Wolf Optimizer (GWO) surpassed others such as Whale Optimizer and Anti-lion Algorithms among the MAs. For the first time, GWO was utilized to build novel composite biodiesel from ternary oils (TSOs), specifically animal waste fat, cottonseed, and crude rice bran. As a result, in this study, (1) Response Surface Methodology (RSM) and GWO were used to model the novel composite biodiesel (NCB) from the CTOs, (2) the efficacy in terms of biodiesel yield (BY), and (3) the commercial viability of the produced NCB were investigated. The 94.2% of yield for NCB was obtained for RSM determined optimal conditions set at the methanol/CTOs molar ratio of 9, catalyst amount of 0.45 wt%, reaction temperature of 50 oC, time of 105 min. A quadratic model was developed to predict the biodiesel yield, with an R2 value of 0.995, indicating high accuracy. Grey wolf optimization technique optimized the biodiesel yield to 98.6%, corresponding to catalyst concentration, methanol to TSO molar ratio, reaction temperature, and time set at 0.53 wt%, 9.32, 60 °C, and 105 min. The predicted biodiesel yield was 99.18%, closely matching the experimental yield of 98.60%. 1 H NMR and FTIR provide insights into biodiesel's composition and chemical structure, ensuring its compliance with quality standards. The high-quality NCB obtained from the optimized condition ensures TSO can be employed for higher-scale production in industries and to run in diesel engines.

Production of Raphanus Sativus Biodiesel and Its Performance Assessment in a Thermal Barrier‐Coated Agriculture Sector Diesel Engine

Herein, in order to estimate the optimized process specifications, an empirical study is done for biodiesel production, performance of standard and coated engine, combustion, and discharge aspects of Raphanus sativus (radish) biodiesel. Optimization process parameters of biodiesel production are done using the response surface method. The importance of this study is that optimized biodiesel production is used to improve the biodiesel properties and fatty acid content to find suitable vegetable oil as well as a new coating material for sustainable development of the country in the field of agriculture and ecological conditions. The mechanism used in this research work is a coating for internal combustion engine components done with partially stabilized zirconia, aluminum–20% silicon carbide (Al–20%SiC), and titanium dioxide that acts as a ceramic composite settled above the coating that has a thickness of 450 μm by the technique called air plasma spray for test purpose. To increase the performance of an engine and reduce the emission particles like smoke density, carbon monoxide, and hydrocarbon except NO x , the partially stabilized zirconia, aluminum–20% silicon carbide (Al–20%SiC) coated engine is used under various compositions of radish biodiesel and clean diesel. To reduce NO x , the engine operation is carried out along with the TiO2 coated combustion chamber steam, and water injection technique is added to overcome the NO x formation. From this study, the resulting factor shows that adding 25% radish biodiesel (B25) and 75% clean diesel shows a reasonable reduction in emissions with comparatively better performance and combustion in the engine under consideration.

Impacts of antioxidants on stability of biodiesel derived from waste frying oil

AbstractMeasurement of the composition and properties of biodiesel derived from waste frying oil (B100 WFO) demonstrated the impacts of antioxidant (AO) concentration on oxidative stability. The B100 WFO was found to have similar physicochemical properties to B100 derived from waste cooking oil and catfish oil but it possessed better oxidation stability owing to AO added to the waste frying oil (WFO) and because of the lower content of polyunsaturated fatty acid methyl esters (FAMEs). A modified American Society for Testing and Materials D525 method was employed to study the impacts of oxidation on B100 WFO containing 0–0.24% v/v commercial BioExtend 30 AO and its fuel properties. BioExtend 30 protected B100 WFO from both degradation and polymerization reactions and a higher concentration of AO was found to prolong the shelf life of biodiesel linearly.

Large eddy simulation of spray and combustion characteristics of biodiesel and biodiesel/butanol blend fuels in internal combustion engines

Biofuel is a crucial renewable and environmentally friendly energy source for addressing greenhouse gas emissions and other energy-related issues. Biodiesel and butanol, among alternative biofuels, possess complementary physical and chemical properties, offering multiple possibilities for their use in existing internal combustion engines. However, biodiesel’s distinctly different physical and combustion properties from conventional diesel fuels make its combustion process substantially different. The complex composition of biodiesel presents significant challenges in accurately simulating its spray combustion characteristics. This paper presents a systematic evaluation of six single-component surrogate fuel models and a five-component model for the prediction of biodiesel spray characteristics under various conditions using large-eddy simulation (LES). The results show that single-component surrogate fuel models can only predict the gaseous penetration of biodiesel but not the liquid-phase penetration. A five-component fatty acid methyl ester surrogate fuel model is proposed, demonstrating an accurate simulation of biodiesel spray evaporation characteristics under different conditions. Based on the five-component evaporation model, LES is utilized to examine three strategies of biodiesel/butanol-fueled internal combustion engines: direct injection of pure biodiesel in conventional diffusion-controlled combustion (CDC) engines, direct injection of biodiesel–butanol blend in CDC engines, and biodiesel/butanol reactivity-controlled compression ignition (RCCI) engines. The simulation results are validated against engine experiment results, showing that the five-component model can successfully predict spray and combustion characteristics in internal combustion engines. The RCCI concept can significantly reduce NOx emissions; however, CO and UHC emissions are higher than in the CDC engines due to incomplete combustion in the fuel-lean butanol/air mixture.

Impact of titanium dioxide (TiO2) nanoparticles addition in Eichhornia Crassipes biodiesel used to fuel compression ignition engine at variable injection pressure

Power production through combustion of fuels plays a vital role in the progress of a nation. In liquid fuels, biodiesel has emerged as a potential replacement of fossil fuel. In this regard, the improvement of fuel properties or adjustment of the operating parameters can improve the efficiency and lower the emission for a biodiesel run diesel engine. In this regard, the current study focuses on the use of novel nano blended biodiesel, prepared by blending titanium dioxide nano particles along Eichhornia Crassipes biodiesel. Three different biodiesel blends are prepared having composition of titanium dioxide by 50 ppm, 100 ppm and 150 ppm which is tested in a four stroke, single cylinder, naturally aspirated water cooled diesel engine of 3.5 kW rated power for different loading conditions for performance, emission, and combustion evaluation. Further, at the same engine conditions, the biodiesel blend having 150 ppm of titanium dioxide is tested at fuel injection pressure of 220 bar. The findings suggests that the brake thermal efficiency improves and emissions lowers with the addition of nano particles at high fuel injection pressure. The maximum improvement of brake thermal efficiency of 1.01% in comparison to diesel mode has been found for nano blended biodiesel composition of 150 ppm of titanium dioxide at fuel injection pressure of 220 bar under full loading condition. For the same fuel injection pressure of 220 bar and same nano blended biodiesel composition, the hydrocarbon and carbon monoxide emission were found to minimum at 60% load.

Can We Find an Optimal Fatty Acid Composition of Biodiesel in Order to Improve Oxidation Stability?

Air quality currently poses a major risk for human health. Currently, diesel is widely used as fuel and is a significant source of nitrogen oxides (NOx) and particulate matter (PM), both hazardous to human health. A good alternative for mineral diesel is biodiesel, not only for the improvement of hazardous components in the exhaust gases but also because it can be produced in view of a circular economy. Biodiesel consists of a mix of different fatty acid methyl esters, which can react with oxygen. As a consequence, the oxidation stability of biodiesel has to be studied, because the oxidation of biodiesel could affect the performance of the engine due to the wear of injectors and fuel pumps. The oxidation stability could also affect the quality of the exhaust gases due to increases in NOx and PM. The basic question we try to answer in this communication is: ‘Can we find an optimal fatty acid composition in order to have a maximal oxidation stability?’ In this article, we try to find the optimal fatty acid composition according to the five most common fatty acid methyl esters present in biodiesel in order to reach a maximal oxidation stability. The measurements and statistical analysis show, however, that there is no useful regression model because there are statistically significant two- and three-way interactions among the different fatty acids.

Liquid–Liquid Equilibria of Glycerol + Alcohol + Safflower Biodiesel Systems: Measurement and Modeling

Liquid–liquid equilibrium (LLE) for the biodiesel + glycerol + alcohol system is essential for the design, operation, process optimization, and economic evaluation of a biodiesel production plant. In this work, methyl and ethyl safflower biodiesels were produced from safflower (Carthamus tinctorius L.) seed oil. New LLE experimental datasets were obtained for the glycerol (1) + methanol/ethanol (2) + methyl/ethyl safflower biodiesel (3) systems at 298.15 and 318.15 K under atmospheric pressure. Binodal curves, tie-lines, distribution coefficients, and selectivity were determined. Data reliability was confirmed using the Othmer–Tobias correlation. LLE data were correlated with the UNIQUAC model satisfactorily, with deviations between 0.70 and 2.79% for all studied systems. The consistency of the estimated binary interaction parameters of the UNIQUAC model was ascertained. Finally, through process simulations in Aspen Hysys software, it was verified that the UNIFAC LLE model, along with the UNIQUAC model, can also be used adequately to describe the liquid–liquid behavior of biodiesel separators, provided that the biodiesel composition is accurately known.

Effective utilization of biodiesel blends with nano additives on diesel engine towards eco-sustainability

The sustainable fuel known as biodiesel can be produced from either vegetable oils, animal fats, or even recycled cooking oils. It has the potential to cut greenhouse gas emissions and lessen our dependence on oil when compared to traditional diesel derived from petroleum. Burning fossil fuels releases greenhouse gases, which are a key contributor to climate change as well as other pollution. In India’s agriculture industry, making biodiesel from plant sources, especially oils that can’t be eaten, has gained a lot of popularity in recent years. In addition, there are not enough of these biodiesels on the market to match the surging demand for fossil fuel. A new biodiesel composition that can be used in diesel engines is made from non-edible oils. The development of a new combination of biodiesel is crucial in order to keep up with the surging demand for fossil fuels. The purpose of this study is to create a novel biodiesel blend of 50 % cottonseed biodiesel and 50 % rapeseed biodiesel and diesel with cerium oxide. The results of CR20N50 show a 15–18 % reduction in fuel consumption, a 12–22 % reduction in carbon monoxide emissions, and a 13–20 % reduction in hydrocarbon emissions compared with base diesel.

Performance of diesel engine with biodiesel fuel from cooking oil

Biodiesel from used cooking oil is a very potential material to replace diesel fuel. Besides being cheap, it can also reduce waste. The purpose of the study was to determine the performance of the Nissan SD22 diesel engine on the use of biodiesel as fuel and the optimum composition of biodiesel and diesel mixtures. The results of the diesel engine performance show that a high rotational speed of 2800 rpm could produce a torque of 112 N.m at B10, while the results for the power parameter at a rotational speed of 2750 rpm produced a large power of 33.46 KW for B10. As for fuel consumption for a rotation speed of 2750 rpm, B10 fuel provided 0.293 kg/(kW.hour) of specific fuel while Pertamina Dex requires 0.310 kg/(kW.hour) of specific fuel, meaning that the use of B10 saved 5.48% more than B10. As for fuel consumption for a rotation speed of 2750 rpm, B10 fuel provides 0.293 kg/kW.hour of specific fuel while Pertamina Dex requires 0.310 kg/kW.hour of specific fuel, meaning that the use of B10 saves 5.48% more than B10. Biodiesel B10 is able to provide good performance for use as a diesel engine fuel. The resulting gas emissions are smaller than diesel emissions. This proves that biodiesel is an environmentally friendly fuel.

Assessment of oxidative stability of biodiesel and biodiesel blends

ABSTRACT This research investigates the oxidative stability of biodiesel blends produced from macauba and soybean after being stored for 45 and 90 days. The stability was evaluated through specific mass, kinematic viscosity, and water content measurements. The results showed a significant increase in water content and acidity index values during storage, particularly in the first 45 days. However, at time zero, the acidity index of all samples was below the established limit, but after 90 days of storage, only the BM100 sample remained below the limit. The fatty acid composition of macauba oil was determined by gas chromatography, and it presented 65.01% saturated fatty acids and 34.99% unsaturated fatty acids, with a higher predominance of maluric fatty acid at 36.37%. The physical-chemical characteristics of macauba oil, including acidity index, viscosity, and oxidation stability, were also determined. The acidity index of macauba oil was found to be 1.00 mg KOH/g, which is at the maximum limit for alkaline transesterification for biodiesel synthesis. The low viscosity of macauba oil (31.92 mm2/s) is justified by its composition rich in c12 uric acid, while the fatty acid composition may be responsible for the high oxidative stability. Spectroscopy in the infrared region was used to determine the chemical composition of soybean biodiesel, macauba biodiesel, and their blends.

Performance enhancement and emissions reduction in a diesel engine using oleander and croton biodiesel doped with graphene nanoparticles

Biodiesel is considered a suitable substitute for petroleum diesel because it is renewable, environment-friendly, and has a low carbon footprint. However, its high density, high viscosity and low heating value prevents it from replacing petroleum diesel completely. This study investigates the performance and emission characteristics of a compression ignition engine operating on oleander and croton biodiesel doped with graphene nanoparticles. Five fuel samples are used, including diesel (D100), diesel - 80% blended with oleander and croton biodiesel - 20% (OCB20) and OCB20 dosed with graphene nanoparticles at mass fractions of 50 ppm (mg/L), 75 ppm (mg/L) and 100 ppm (mg/L), respectively. The chemical composition of biodiesel and graphene nanoparticles is analyzed using Fourier Transform Infrared (FTIR) spectroscopy while the morphology of the nanoparticles is analyzed using Scanning Electron Microscope (SEM). Engine tests reveal a significant improvement in brake thermal efficiency, especially at 75 ppm concentration which is 2.76% and 18.93% higher than diesel and OCB20, respectively, and a reduction in brake specific fuel consumption by 2.44% and 16.67% compared to diesel and OCB20, respectively. Carbon monoxide (CO) and unburnt hydrocarbon emissions (UHC) decreases for the 50 ppm sample, recording 8.58% and 21.65% reduction in CO and 52.2% and 50% in UHC compared to the diesel and OCB20, respectively. However, Oxides of Nitrogen (NOx) emissions increase. The results indicate that graphene nanoparticle-enhanced biodiesel can adequately substitute petroleum diesel, albeit with NOx reduction techniques.

Support vector regression approach to optimize the biodiesel composition for improved engine performance and lower exhaust emissions

The practical applicability of biodiesel is limited due to poor fuel economy and higher emissions of oxides of nitrogen. Moreover, engine characteristics vary significantly with biodiesel type, highlighting the necessity to determine the most suitable biodiesel for improved fuel economy and emissions. Choosing the most suitable biodiesel is challenging as the relationship between biodiesel composition and engine characteristics is complex. Thus, it is intended to determine the most suitable biodiesel by developing composition-based models that predict engine characteristics and use them for optimization. This study explores the applicability of support vector machine regression to correlate biodiesel composition with engine characteristics. Two additional biodiesels not used for calibration validate the developed models with an acceptable mean absolute percentage error of less than 7%. Further, the models are fed to the genetic algorithm to determine the optimal composition for lower brake specific fuel consumption (BSFC) and emissions of unburnt hydrocarbon (HC), carbon monoxide (CO), and oxides of nitrogen (NOx). The optimized biodiesel has 8% shorter chain esters and 92% longer chain esters. Sunflower biodiesel exhibits 23% higher BSFC than diesel, whereas for optimized biodiesel, it is 4% higher. The proposed biodiesel results in 42% and 2% less HC and CO emissions than diesel and are reasonably lower than other biodiesels. The optimized biodiesel results in 42% higher NOx emissions, while sunflower biodiesel exhibits 83% higher NOx emissions than diesel. Overall, the optimized biodiesel resulted in a minimum penalty in fuel economy and NOx emissions compared to other commercially available biodiesels.