One of the important properties of fuel related to compression ignition (CI) engine applications is the cetane number. The present work aims to develop a universal model to predict the cetane number of any candidate biodiesel fuel based on its fatty acid methyl ester composition using a multi-linear regression approach. The biodiesel composition effects on the cetane number are captured through two new parameters, viz., straight-chain saturated factor (SCSF) and modified degree of unsaturation (DUm), which can be estimated directly from the measured biodiesel composition data. The proposed composition-based approach for predicting the cetane number of biodiesel is not limited to a specific data set. The predictions from the proposed correlation are compared to the measured cetane number of nine different biodiesel fuels of varied compositions, having wide variations of the cetane number in the range of 49-62. The comparison is found to be quite satisfactory, with a regression coefficient of 0.95 and an average absolute deviation of 1.63%. Further, the predictions from the present model are found to be much better than the existing cetane number prediction models for biodiesel fuels. © 2016 American Chemical Society.

K. Anand

Department of Mechanical Engineering

Biodiesel

Composition effects

Esters

Forecasting

Fuels

AVERAGE ABSOLUTE DEVIATION

COMPOSITION DATA

Compression-ignition engines

DEGREE OF UNSATURATIONS

FATTY ACID METHYL ESTER

Multi-linear regression

prediction model

Regression coefficient

Fatty acids

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Energy and Fuels

The concern over extensive pollution, including anthropogenic carbon dioxide emission caused by the use of fossil fuels, results in the transition of the fuel mix of the world toward renewable energy sources. One of the most promising biofuels is biodiesel, which is renewable, nontoxic, biodegradable, safe to store, handle, and transport, and produces lower pollutant emissions (except oxides of nitrogen) compared to fossil diesel. However, one of the potential problems associated with biodiesel is the variability in its fatty acid methyl ester composition owing to larger variations in the feedstock used for its production. The biodiesel composition variations leads to variations in fuel properties, and thereby engine characteristics, demanding engine recalibration every time a new biodiesel fuel is introduced. In the present study, biodiesel-composition-based models are developed using artificial neural networks (ANN) to predict combustion, performance, and emission characteristics of a light duty naturally aspirated and a heavy duty turbocharged engine fuelled with different types of biodiesel. The models provide predictive functions for estimating the engine performance, combustion, and emission parameters across a range of biodiesel composition, thus reducing extensive engine experiments. The predictions from the developed ANN models compare well with measurements with a higher regression coefficient of above 0.9 and less than 10% absolute error. Further, attempts are made to combine the developed ANN models with a genetic algorithm to arrive at an optimal biodiesel composition which could result in better fuel economy and lower oxides of nitrogen emission. The obtained results show that the total saturated methyl ester falls in the range of 36-43% by weight and that the total unsaturated methyl ester falls in the range of 55-63% by weight for the optimum biodiesel composition. © 2018 American Chemical Society.

A Composition-Based Model to Predict and Optimize Biodiesel-Fuelled Engine Characteristics Using Artificial Neural Networks and Genetic Algorithms

Biodiesel is an eco-friendly, renewable biofuel derived primarily from vegetable oils and is a potential carbon neutral alternative to fossil diesel for compression ignition (CI) engine applications. The biodiesels produced from different feedstocks vary significantly in their fatty acid methyl ester composition and physico-chemical properties and thereby, engine performance and emissions. In the present work, experimental investigations are done with four candidate biodiesel fuels, viz. sunflower, rice-bran, palm and coconut in two different engine configurations, viz. light duty naturally aspirated (NA) and heavy duty turbocharged (TC) to establish the effects of biodiesel composition variations as well as engine type variations on the engine characteristics. To establish biodiesel composition effects on engine characteristics, two new parameters, viz. straight chain saturation factor (SCSF) and modified degree of unsaturation (DUm) are developed in this work, which can be estimated directly from the measured biodiesel composition. The obtained results show some contradicting trends with biodiesel compared to diesel in the two engines. The ignition delay is lower for biodiesel by 0.9 degree in the turbocharged engine, while, it is higher by 1 degree in the naturally aspirated engine. The dynamic start of injection timings are advanced for biodiesel in both the engines but the advance is relatively higher in the turbocharged engine. The heat release rates with biodiesel are premixed and diffusion phase dominant in the naturally aspirated and turbocharged engines respectively. The peak cylinder pressures and nitric oxide emissions are generally higher for biodiesel in both the engines. The smoke emissions with biodiesel are higher and lower respectively in the naturally aspirated and turbocharged engines. Further, investigations on biodiesel composition effects on the engine characteristics revealed a strong correlation between DUm, SCSF and engine parameters. An increase in DUm of biodiesel is found to deteriorate combustion quality, resulting in poor engine performance and higher emissions. However, biodiesel having higher SCSF exhibit better engine performance along with lower nitric oxide and smoke emissions. Thus, an estimate of DUm and SCSF of candidate biodiesel fuels provides a first approximation of the extent of variations in engine parameters compared to diesel and thereby, helps in making a careful choice of biodiesel feedstock for automotive engine applications. Further, based on the results obtained with four different biodiesels in two different engine configurations, the present study reveals that the contradicting engine trends with biodiesel compared to diesel are more influenced by engine type variations rather than by biodiesel composition variations. © 2017 Elsevier Ltd

Applied Energy

Comparison of biodiesel fuel behavior in a heavy duty turbocharged and a light duty naturally aspirated engine

Biodiesel is accepted worldwide as an alternative for diesel fuel in compression ignition engine applications as it is renewable, non-toxic, biodegradable, safe to store, handle and transport, locally available, has zero sulfur content and a higher cetane number. Upon these advantages, the poor storage stability of biodiesel owing to its unsaturated methyl ester constituents is a major concern. The fatty acid methyl ester composition of biodiesel fuels vary significantly according to the nature of vegetable oil feedstock used in their production process and are found to influence fuel stability upon long term storage. In the present work, experiments are done to investigate the effects of changes in biodiesel composition on its long term storage stability. For this study, two biodiesel fuels with significantly different compositions, viz. Coconut biodiesel having a higher proportion of saturated methyl esters (∼90%) and Karanja biodiesel with higher amounts of unsaturated methyl esters (∼70%) are chosen. The different blends of these two biodiesel fuels are prepared (referred herein as bio-mix) to alter their composition. The fuel blends are also made by mixing Karanja biodiesel with petro diesel. Six fuel samples with two neat biodiesel fuels and four blends are made by using this approach and are stored under four different storage conditions for a period of 10 months. The changes in compositions and the important properties of these fuel samples are measured at regular intervals to assess their rate of degradation. The obtained results show that the rate of degradation is higher for neat Karanja biodiesel as compared to all other fuel samples due to its higher unsaturated methyl ester contents. Further, it is observed that by blending Karanja and Coconut biodiesel, the rate of degradation is reduced on long term storage. Surprisingly, blending with diesel results in a higher rate of degradation as compared to bio-mix. Among the different storage conditions investigated, the direct exposure to air and sunlight results in a higher rate of biodiesel degradation. © 2016 Elsevier Ltd. All rights reserved.

Fuel

Effects of biodiesel composition on its long term storage stability

Petroleum and Gas Field Processing

Composition, Types of Crude Oil, and Oil Products

Journal of Sustainable Bioenergy Systems

A Comparative Study on the Engine Performance and Exhaust Emissions of Biodiesel from Various Vegetable Oils and Animal Fat

A comprehensive evaluation of the cetane numbers of fatty acid methyl esters

Computational Methods in Engineering

Numerical Differentiation

Predicting the Cetane Number of Biodiesel Fuels from Their Fatty Acid Methyl Ester Composition

Journal	Data powered by TypesetEnergy and Fuels
Publisher	Data powered by TypesetAmerican Chemical Society
ISSN	08870624
Open Access	No