Cetane Number Of Biodiesel Research Articles

Alternative Methods for Biodiesel Cetane Number Valuation: A Technical Note.

Biodiesel is an environmentally beneficial and clean energy source that may replace fossil fuels, which are detrimental to the environment and cannot be replenished. Therefore, the physicochemical parameters of biodiesel must be determined in order to verify its quality. The cetane number is a crucial dimensionless fuel property that gauges the fuel ignition quality in power diesel engines. A higher cetane number results in a shorter ignition delay time, and vice versa. Biodiesel's cetane number may fluctuate due to a variety of fatty acid compositions, including variations in carbon chain length and the degree of unsaturation. The cetane number generally increases with increasing saturation and chain length, while it decreases as chain length is reduced and degrees of unsaturation and branching increase. This is the main reason for why alkanes possess a higher cetane number than alkenes and aromatics. The standard protocols for evaluating the cetane number of biodiesel are ASTM D613 and ISO 5165 test techniques using a monocylindrical cetane engine. However, adhering to these conventional procedures is quite challenging and time-consuming, and the cetane number test result may also be affected by the presence of certain gases and fumes. As a result, many researchers are bothered with cetane number valuation, and occasionally they skip it due to a lack of other options. Consequently, the aim of this paper is to present a set of more straightforward and relevant alternative techniques that can be applied to predict the cetane number of biodiesel when engine-based measurement is not practical. The three techniques with their designed pictographic outlooks conferred in this article include color indicator titration, aniline point, and fatty acid composition-based methods. The reported values of these procedures meet the minimum cut point of the biodiesel cetane number required by ASTM D6751 (≥47) and exhibit minimal variation from the typical standard methods. Nevertheless, the above-mentioned techniques are not applicable to other alternative biofuels except biodiesel products because they have a direct implication on the characteristics of the fatty acid profiles of different oil precursors, such as carbon chain length, degree of saturation or unsaturation, and aromaticity, which make up monoalkyl esters.

Prediction of cetane number of biodiesel from its fatty acid ester composition using Artificial Neural Networks

Models for estimation of the cetane number of biodiesel from their methyl ester composition using artificial neural networks were obtained in this work. An experimental data that covers 48 and 15 biodiesels in the modeling step and validation step respectively were taken. The selection of biodiesel samples took into account a wide range of ester compounds with different unsaturation characteristics and number of carbon atoms. A model to predict cetane number using artificial neural networks was obtained with better accuracy than 95 %. The best neural network for predict the cetane number was a backpropagationnetwork(11:4:1)using a Conjugate Gradient Descend algorithm for the second training step and showing 96.3 % of correlation for the validation data and a mean absolute error of 1.5.The proposed network is useful for prediction of the cetane number of biodiesel in a wide range of composition but keeping the percent of total unsaturations lower than 80 %. The use of the artificial neural networks in this case let to study and understands the effect of individual fatty acids in the cetane number. Therefore they can be used for improving some biofuel properties related to the combustion process and its efficiency.

Reducing NOx emission from palm biodiesel/diesel blends in CI engine: A comparative study of cetane improvement techniques through hydrogenation and fuel additives

In this study, the trans-esterification process prepared palm oil biodiesel and 20% biodiesel blended with diesel (PB20) by volume as test fuel. Unsaturated fatty acids are majorly influenced by biodiesel cetane number. This work examines two-fuel reformulation methods of fuel hydrogenation and addition of fuel additive di-tert-butyl peroxide (DTBP) to enhance the engine characteristics of a PB20 blend while maintaining emission control. A reactor (autoclave) was utilized for hydrogenating the palm biodiesel and examining the change in fuel composition using gas chromatography. DTBP is selected as the cetane enhancer and combined with PB20 at a 2000 ppm concentration. Tamson FBT equipment was utilized to evaluate the test fuel's filterability. The filterability, engine characteristics, and emissions were investigated for diesel (D0), PB20, HPB20, and PB20 + DTBP fuel blends in the compression ignition test engine. The FBT results reveal that PB20 and HPB20 have better filtration quality, which aligns with the standard requirements (ASTM D2068-14). An improved NOx (10.2%) and brake thermal efficiency (6.7%) are seen at engine full loads when using modified B20 fuels. The hydrocarbon, carbon monoxide, and smoke emissions decreased by 8.3%, 6%, and 10.14% compared to diesel fuel. Hence, partial hydrogenation has been a better approach to improving biodiesel trade-off features than DTBP addition.

Effects of the Degree of Unsaturation of Fatty Acid Esters on Engine Performance and Emission Characteristics

Biodiesel is considered an environmentally friendly alternative to petro-derived diesel. The cetane number indicates the degree of difficulty in the compression-ignition of liquid fuel-powered engines. The allylic position equivalent (APE), which represents the unsaturated degree of fatty acid esters, was one of the key parameters for the cetane number of biodiesel. Due to the significant attributes of APE for biodiesel properties, the impact of APE on engine performance and emission characteristics was investigated in this study. The engine characteristics could be improved by adjusting the biodiesel fuel structure accordingly. A four-stroke and four-cylinder diesel engine accompanied by an engine dynamometer and a gas analyzer were used to derive the optimum blending ratio of the two biodiesels from soybean oil and waste cooking oil. Three fuel samples composed of various proportions of those two biodiesels and ultra-low sulfur diesel (ULSD) were prepared. The amounts of saturated fatty acids and mono-unsaturated fatty acids of the biodiesel made from waste cooking oil were significantly higher than those of the soybean-oil biodiesel by 9.92 wt. % and 28.54 wt. %, respectively. This caused a higher APE of the soybean-oil biodiesel than that of the biodiesel from waste cooking oil. The APE II biodiesel appeared to have the highest APE value (80.68) among those fuel samples. When the engine speed was increased to 1600 rpm, in comparison with the ULSD sample, the APE II biodiesel sample was observed to have lower CO and O2 emissions and engine thermal efficiency by 15.66%, 0.6%, and 9.3%, while having higher CO2 and NOx emissions, exhaust gas temperature, and brake-specific fuel consumption (BSFC) by 2.56%, 13.8%, 8.9 °C, and 16.67%, respectively. Hence, the engine performance and emission characteristics could be enhanced by adequately adjusting the degree of unsaturation of fatty acid esters represented by the APE of biodiesel.

Determination of Cetane Number from Fatty Acid Compositions and Structures of Biodiesel

Biodiesel, which possesses the dominant advantages of low emissions and environmental friendliness, is a competitive alternative fuel to petroleum-derived diesel. The cetane number, which indicates ignition delay characteristics, is considered the most significant fuel property of biodiesel. Determining the cetane number for biodiesel by general testing equipment is time-consuming and costly; hence, a simple and convenient predictive formula for the cetane number of biodiesel is a significant task to be carried out. A reliable and convenient predictive method for determining the cetane number is proposed in this study. The key parameters for the cetane number of biodiesel were first screened out. The analysis of multiple linear regressions using the available software SPSS for statistical analysis was carried out to obtain the regression coefficients of those key parameters and intercepts to establish the predictive model. Other available experimental data verified the validity of the proposed predictive equation. The determination coefficient of the formula reaches as high as 94.7%, and the standard error is 3.486. The key parameters, including the number of carbon atoms (NC), allylic position equivalent (APE), and double-bond equivalent (DBE), were more significant for influencing the cetane number of biodiesel. In addition, the increase of NC or the decrease of either APE or DBE results in the increase of the cetane number. Moreover, the present formula is found to obtain closer cetane numbers to those experimental data and features superior prediction capability.

Retraction Note to: Empirical Approach for Predicting the Cetane Number of Biodiesel

Retraction Note to: Empirical Approach for Predicting the Cetane Number of Biodiesel

Reliable connectionist tools to determine biodiesel cetane number based on fatty acids methyl esters content

The self-ignition ability of fuels is characterized using a property called cetane number (CN). The main objective of this research work is to develop accurate models using smart algorithms to predict the CNs associated with 149 samples of biofuels collected from the literature. In this study, we use two machine learning (ML) algorithms, namely least squares support vector machine (LSSVM) and extra trees (ET) and an experimental dataset containing a variety of fatty acids methyl esters (FAME). To assess the predictive performance of the developed models, various statistical parameters including average relative deviation in percentage (ARD%), average absolute relative deviation in percentage (AARD%), and coefficient of determination (R2) are calculated. The statistical and graphical analyses presented in this study reveal that the ET model outperforms the LSSVM model in predicting the CN associated with the biodiesel systems from the collected dataset so that the value of AARD% is less than 3% for the ET method. It is also found that Linoleic (18:02) is the most important factor affecting the CN value of the biodiesels. According to the sensitivity analysis, the CN value decreases upon an increase in the concentration of Palmitoleic (16:01), Oleic (18:01), Linoleic (18:02), and Linolenic (18:03); this is in agreement with the experimental studies. The smart modelling techniques used in this study can be utilized as alternative methods to direct measurements for determination of biodiesel CN.

The influence of biodiesel with high saturated fatty acids on the performance of a CI engine fuelled by diesel and biodiesel blend fuels at low loads

Biodiesels are known as suitable alternatives for diesel fuel. In this research, WCO biodiesels with high saturated fatty acids have been used. To experimentally evaluate the engine performance, various volumetric blend ratios of biodiesel–diesel were employed. The tests were carried out under four different engine loads and a constant speed. According to the biodiesel cetane number the results clearly showed that the earlier heat release. In most cases, the use of WCO biodiesel with high saturated fatty acids improved the BSFC and BTE. The highest enhancements were observed at loads of 1.2 and 1.6 bars; so that at the load of 1.6 bars and for the B5, the BTE was enhanced by 2.54%. The maximum BTE of 27.5% was obtained with the B20 at the load of 2.2 bars. Overall, the results show that WCO with high saturated fatty acids can improve the combustion and operating conditions at low loads.

Reliable Connectionist Tools to Determine Biodiesel Cetane Number Based on Fatty Acids Methyl Esters Content

The self-ignition ability of fuels is characterized using a property called cetane number (CN). The main objective of this research work is to develop accurate models using smart algorithms to predict the CNs associated with 149 samples of biofuels collected from the literature. In this study, we use two machine learning (ML) algorithms, namely least squares support vector machine (LSSVM) and Extra Trees (ET) and an experimental dataset containing a variety of fatty acids methyl esters (FAME). To assess the predictive performance of the developed models, various statistical parameters including average relative deviation in percentage (%ARD), average absolute relative deviation in percentage (%AARD), and coefficient of determination (R2) are calculated. The statistical and graphical analyses presented in this study reveal that the ET model outperforms LSSVM model in predicting the CN associated with the biodiesel systems from the collected dataset so that the value of %AARD is less than 3% for the ET method. It is also found that Linoleic (18:02) is the most important factor affecting the CN magnitude of biodiesel. The CN value decreases upon an increase in concentration of Palmitoleic (16:01), Oleic (18:01), Linoleic (18:02), and Linolenic (18:03); this is in agreement with the experimental studies.

Assessing the correlation between fatty acid composition of biodiesel with the fuel property using artificial intelligence and optimization

AbstractThis work explores the underlying correlation between fatty acid composition and biodiesel fuel properties using artificial neural network (ANN) and establishes the complex non‐linear relationship between the fuel parameters and the significant fatty acids present in the composition of biodiesel. Six physico‐chemical properties of biodiesel, that is, specific gravity, oAPI, aniline point, cetane number, flash point, and calorific value have been assessed and a modeling framework has been developed. High value of R2 and low value of RMSE signify that ANN model captures the inherent relationship. Results demonstrate that with 1% change in arachidic acid, an increment of 21.74 MJ/Kg in heating value and a decrease of 7.17 in cetane number of biodiesel fuel take place, respectively. This suggests that the presence of a regulated amount of arachidic acid in biodiesel composition would assist in obtaining desired heat value and diesel index of biodiesel that would ultimately aid in improving fuel quality and hence, would help in obtaining higher grade biodiesel fuel. Furthermore, ANN‐based stochastic optimization techniques, genetic algorithm, and particle swarm optimization have been used to predict the maximum possible biodiesel blend that can be used in diesel engines retaining the fuel qualities.

Modeling of the optimal component composition of biodiesel fuel

The article presents the results of study to determine the component composition of rape-based biodiesel. Modeling of the optimal component composition taking into account low-temperature properties and cetane number was carried out. According to the studies, mathematical models of changes in the low-temperature properties of biodiesel fuel depending on the percentage of rapeseed oil were obtained, the optimal ratio of its components was determined and justified. The tests were conducted in 2012-2019. The studied temperature limits correspond to the extreme temperature values of the central and southern regions of the Russian Federation. The second important task was to determine the optimal ratio of diesel fuel and rapeseed oil while meeting the requirements of GOST R52368-05 for the cetane number of biodiesel fuel. Studies of the optimal percentage ratio of rapeseed oil and diesel fuel per cetane number showed that biodiesel, which contains rapeseed oil in a concentration of up to 30 %, meets the requirements of GOST R52368-05 (EN 590:2009). Certified equipment was used in the studies, and the methodology met the requirements of the state standard for their implementation. To solve the problem of determining the optimal ratio of biodiesel components, a methodology for assessing temperature and cetane number properties, as well as foreign scientific literature was analyzed in the field of research data. According to the results of the research, hypotheses were put forward that the lowtemperature properties and cetane number of biodiesel fuel change with an increase in the proportion of rapeseed oil in it, as well as the possibility of mathematical modeling of its optimal component composition corresponding to environmental temperature conditions during operation of the equipment. The reliability of the approximation of the obtained dependences was 0.83...0.91 when studying the low-temperature properties of biodiesel samples within the specified temperatures from -40 to 0 °C.

High Vacuum Fractional Distillation (HVFD) Approach for Quality and Performance Improvement of Azadirachta indica Biodiesel

Biodiesel offers an advantage only if it can be used as a direct replacement for ordinary diesel. There are many reasons to promote biodiesel. However, biodiesel cannot get wide acceptance until its drawbacks have been overcome including poor low temperature flow properties, variation in the quality of biodiesel produced from different feedstocks and fuel filter blocking. In the present study, a much cheaper and simpler method called high vacuum fractional distillation (HVFD) has been used as an alternative to produce high-quality refined biodiesel and to improve on the abovementioned drawbacks of biodiesel. The results of the present study showed that none of biodiesel sample produced from crude Azadirachta indica (neem) oil met standard biodiesel cetane number requirements. The high vacuum fractional distillation (HVFD) process improved the cetane number of produced biodiesels which ranged from 44–87.3. Similarly, biodiesel produced from fractionated Azadirachta indica oil has shown lower iodine values (91.2) and much better cloud (−2.6 °C) and pour point (−4.9 °C) than pure Azadirachta indica oil. In conclusion, the crude oil needs to be vacuum fractioned for superior biodiesel production for direct utilization in engine and consistent quality production.

Estimation of cetane numbers of biodiesel and diesel oils using regression and PSO-ANFIS models

One of the essential characteristics of diesel oils is their cetane number (CN) which indicates the ignition delay of the fuel in the process. In this study, using PSO-ANFIS technique, CNs of biodiesel and conventional diesel (called petrodiesel) oils were modeled and estimated based on the fatty acid methyl ester compositions of biodiesel oils and carbon type structure of the diesel fuels. For the first time, novel correlations were developed for the estimation of CNs of biodiesel and conventional diesel oils. For the models development, as the raw data, 232 biodiesel and 134 conventional diesel oils samples were derived from the literature. Based on the obtained results, the coefficient determinations (R2) of 0.91 and 0.93 and %AARD of 6.4 and 9.6 were obtained by PSO-ANFIS and MNR models for the estimation of biodiesel oil CN, respectively. The R2 of 0.98 and 0.96 and %AARD of 2 and 2.9 were resulted using PSO-ANFIS and MNR models for the prognostication of diesel oil CN, respectively. Based on the gained results, PSO-ANFIS and MNR algorithms are capable of estimating the biodiesel and diesel oils CNs. The novelty of this study is the development of novel correlations and smart models with high accuracies.

Optimizing cetane improver concentration in biodiesel-diesel blend via grey wolf optimizer algorithm

Biodiesel and their blends with diesel have long been used as alternative fuels in diesel engines. In particular, B20 is recommended in most studies since it reduces the exhaust emissions and provides satisfactory engine torque close to diesel fuel. On the other hand, low cetane number of biodiesel leads to higher oxides of nitrogene (NOx) emission when compared to those of the diesel fuel. Formation of NOx emissions has a strong correlation with cetane number of fuels, which increases with the reduction of the cetane number. The current paper focuses on finding the optimum 2-ethylhexyl nitrate (EHN) (cetane improver) concentration and the engine speed for 20 vol% canola oil methyl ester and 80 vol% diesel fuel blend (B20). For that reason, experimental design is created and the experiments have been done on TDI diesel engine at full load and different engine speed conditions to be able to model the problem as an optimization problem by means of the regression modelling. Accordingly, the developed model in consequence of the test results of the engine is optimized via the grey wolf optimizer (GWO) algorithm taking into account of engine performance and emission parameters viz. brake torque, brake power, brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), NOx, and carbon dioxide (CO2), to identify the rate of concentration of EHN in B20 and engine speed. Finally, confirmation tests were employed to compare the output values of the concentration that were identified through the GWO algorithm, and further statistical analyses indicate the consistency between the real experimental results and the results obtained through the GWO algorithm. The optimum EHN concentration and engine speed was determined as 743 mg/L and 3221 rpm respectively. Test results of engine performance indicated that brake power and BSFC of optimum blend at 3221 rpm decreased while brake torque and BTE increased in comparison with those of B20 without EHN. CO2 and NOx exhaust emissions decreased as 11.19% and 4.63% respectively.

Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models

One of the major properties of biodiesel fuels is cetane number (CN) which expresses the ignition characteristics and quality of motor power. The main idea of this work was proposing an accurate model for estimation of the cetane number of biodiesel in terms of fatty acid methyl esters composition. In doing so, least-square support vector machine (LSSVM) approach was coupled with Genetic algorithm (GA), particle swarm optimization (PSO) and hybrid of GA and PSO (HGAPSO) algorithms and a total number of 232 samples of fuels were extracted from literature. The coefficient of determination (R2), mean relative errors (MREs), mean squared errors (MSEs) and standard deviations (STD) were calculated for evaluation of the models. The R2 values in the testing phase for LSSVM-GA, LSSVM-PSO, and LSSVM-HGAPSO were estimated by 0.965, 0.966 and 0.978 respectively. The statistical and graphical analyses showed that the LSSVM algorithm coupled with GA, PSO or HGAPSO algorithms can be used as an accurate model for estimation of the cetane number of the biodiesel fuels.

Effects of Fuel Injection Pressure on Combustion and Emission Characteristics under Low Speed Conditions in a Diesel Engine Fueled with Palm Oil Biodiesel

In this study, the effect of injection pressure on combustion and emission characteristics was evaluated on a common rail direct injection diesel engine fueled with palm oil biodiesel. Recently, many studies have been conducted to utilize biodiesel produced from various sources to prevent environmental pollution and the depletion of petroleum resources. The oxygen content and high cetane number of biodiesel can reduce the production of exhaust pollutants by improving the combustion, but its high viscosity deteriorates the atomization of the injected fuel. Particularly at low engine speed conditions like idle, poor atomization and low airflow in the cylinder deteriorates the combustion efficiency. Increasing the fuel injection pressure is one of the effective methods to improve the atomization of biodiesel without mechanical modification of the current diesel engine. In this study, combustion characteristics and emission levels of pollutants were measured by varying the fuel injection pressure applying palm oil biodiesel. As a result, it was confirmed that increasing the injection pressure to apply palm oil biodiesel at low engine speed can reduce ignition delay and improve combustion efficiency so that nitrogen oxides (NOx) is increased but soot formation is reduced. Carbon monoxide (CO) and hydrocarbon (HC) are slightly reduced but these are increased again when using 100% palm oil biodiesel. The increased NOx due to increased injection pressure can be reduced by applying exhaust gas recirculation (EGR).

Combustion and Emission Reduction Characteristics of GTL-Biodiesel Fuel in a Single-Cylinder Diesel Engine

The purpose of this paper is to investigate the effects of using gas to liquid (GTL)-biodiesel blends as an alternative fuel on the physical properties as well as the combustion and emission reduction characteristics in a diesel engine. In order to assess the influence of the GTL-biodiesel blending ratio, the biodiesel is blended with GTL fuel, which is a test fuel with various blending ratios. The effects of GTL-biodiesel blends on the fuel properties, heat release, and emission characteristics were studied at various fuel injection timing and blending ratios. The test fuels investigated here were GTL, biodiesel, and biodiesel blended GTL fuels. The biodiesel blending ratio was changed from 0%, 20% and 40% by a volume fraction. The GTL-biodiesel fuel properties such as the fuel density, viscosity, lower heating value, and cetane number were analyzed in order to compare the effects of different mixing ratios of the biodiesel fuel. Based on the experimental results, certain meaningful results were derived. The increasing rate of the density and kinematic viscosity of the GTL-biodiesel blended fuels at various temperature conditions was increased with the increase in the biodiesel volumetric fraction. The rate of density changes between biodiesel-GTL and GTL are 2.768% to 10.982%. The combustion pressure of the GTL fuel showed a higher pressure than the biodiesel blended GTL fuels. The biodiesel-GTL fuel resulted in reduced NOx and soot emissions compared to those of the unblended GTL fuel. Based on the experimental results, the ignition delay of the GTL-biodiesel blends increased with the increase of the biodiesel blending ratio because of the low cetane number of biodiesel compared to GTL. As the injection timing is advanced, the NOx emissions were significantly increased, while the effect of the injection timing on the soot emission was small compared to the NOx emissions. In the cases of the HC and CO emissions, the GTL-biodiesel blended fuels resulted in similar low emission trends and, in particular, the HC emissions showed a slight increase at the range of advanced injection timings.

A Comparative Assessment of Biodiesel Cetane Number Predictive Correlations Based on Fatty Acid Composition

Sixteen biodiesel cetane number (CN) predictive models developed since the early 1980s have been gathered and compared in order to assess their predictive capability, strengths and shortcomings. All are based on the fatty acid (FA) composition and/or the various metrics derived directly from it, namely, the degree of unsaturation, molecular weight, number of double bonds and chain length. The models were evaluated against a broad set of experimental data from the literature comprising 50 series of measured CNs and FA compositions. It was found that models based purely on compositional structure manifest the best predictive capability in the form of coefficient of determination R2. On the other hand, more complex models incorporating the effects of molecular weight, degree of unsaturation and chain length, although reliable in their predictions, exhibit lower accuracy. Average and maximum errors from each model’s predictions were also computed and assessed.

Cetane number prediction of waste cooking oil-derived biodiesel prior to transesterification reaction using near infrared spectroscopy

Fifty waste cooking oils (WCOs) were transesterified with methanol (1:8 WCO:methanol molar ratio) at 60 °C for 60 min using NaOH as catalyst (1% wt.). Fatty acid methyl ester (FAME) composition of the resulting biodiesels was analysed by gas chromatography, and near infrared (NIR) spectra of these biodiesels and those of the starting WCOs were acquired. Biodiesel cetane number was then calculated from both FAME composition and from biodiesel NIR spectra, this last technique using the former one as reference data. Because of transesterification does not modify fatty acid distribution of the starting WCO, and the similarity between biodiesel and WCO NIR spectra, biodiesel cetane number was successfully predicted from WCO NIR spectra, achieving RPD (ratio of performance to deviation) of 3.83. Therefore, biodiesel cetane number (and, as consequence, any other biodiesel property related to FAME composition) can be predicted by NIR spectroscopy before performing the transesterification reaction, which allows beforehand selecting the most suitable substrates for biodiesel production.

Eco-Friendly Enzymatic Production of 2,5-Bis(hydroxymethyl)furan Fatty Acid Diesters, Potential Biodiesel Additives

Due to the increasing need for greener and sustainable alternatives to fossil-based industrial chemicals and products, development of efficient synthesis of biomass-based platform chemicals is an attractive goal. 5-Hydroxymethylfurfural (HMF) derived 2,5-bis(hydroxymethyl)furan (BHMF) is one of the most targeted green chemicals, since it is a valuable building block for production of various useful compounds. Herein we described the efficient enzyme catalyzed double esterification of BHMF with saturated long-chain fatty acids using biomass-derived 2-methyl-tetrahydrofuran (2-MTHF) as solvent, providing an efficient synthetic route with >99% overall conversion (BHMF mono- and diesters) and between 89 and 99% conversion into the targeted BHMF diesters. The optimized large-scale procedure yielded easily isolable and chemically pure products useful to improve the cetane number of biodiesel.