A Composition Based Model to Predict and Optimize Biodiesel Fuelled

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Biofuels and Biomass

A Composition Based Model to Predict and Optimize Biodiesel Fuelled Engine Characteristics Using Artificial Neural Networks and Genetic Algorithms P. Rishikesh Menon, and Anand Krishnasamy Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b02846 • Publication Date (Web): 05 Oct 2018 Downloaded from http://pubs.acs.org on October 9, 2018

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A Composition Based Model to Predict and Optimize Biodiesel Fuelled Engine Characteristics Using Artificial Neural Networks and Genetic Algorithms P. Rishikesh Menon and K. Anand* Indian Institute of Technology Madras, Chennai 600036, India. *corresponding author email: [email protected]

Abstract The concern over extensive pollution including anthropogenic carbon dioxide emission caused by the use of fossil fuels results in transition of fuel mix of the world towards renewable energy sources. One of the most promising biofuels is biodiesel, which is renewable, non-toxic, bio-degradable, 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 re-calibration 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 reduce 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 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

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36% to 43% by weight and the total unsaturated methyl ester falls in the range of 55% to 63% by weight for the optimum biodiesel composition. Keywords: biodiesel, engine characteristics, artificial neural network, genetic algorithm, optimum composition

1. Introduction Although fossil fuels play a major role in meeting the energy demands of transport and power sectors, depleting oil reserves and harmful impacts of fossil fuels on the environment create a need to divert focus on developing alternative fuels. In the current scenario, it is projected that the global energy demand would increase by 50% during the period of 2005 to 2030, as predicted by International Energy Outlook, 2008. According to the United States Energy Information Administration (EIA), the average global energy consumption grows at the rate of 1.6% per annum, necessitating the search for alternative fuels which can either replace or supplement conventional fossil fuels [1].

Fig. 1: Transition in the fuel mix over the years [2] 2 ACS Paragon Plus Environment

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There is a gradual transition in the fuel mix favouring the renewables, as illustrated in Figure 1. The renewable energy is the fastest growing source of energy (7.1% per annum), with its share in primary energy increasing up to 10% by 2035 as compared to 3% in 2015 [2]. The energy consumption in India grows rapidly at the rate of 4.2% per annum, outweighing all the major economies in the world. The growth in the consumption of fossil fuels by India is the largest in the world and India overtakes China as the largest market for energy in terms of volume by 2030. Among the various biofuels, biodiesel is produced for commercial applications in approximately 60 countries around the world [3]. According to a report by the International Energy Agency, there has been a tenfold increase in the global production of biodiesel in the past decade [3]. Biodiesel is a renewable, non-toxic, bio-degradable fuel which is safe to store, handle and transport and produces lower pollutant emissions (except oxides of nitrogen) compared to fossil diesel [4]. Biodiesel is produced by the conversion of agricultural lipids in presence of a shorter chain alcohol and a catalyst into fatty acid alkyl esters. According to ASTM D 6751 standards, biodiesel is defined as “mono-alkyl esters of fatty acids derived from vegetable oil or animal fats” [4]. Various edible and non-edible oils including rice bran, coconut, Jatropha curcus, castor, cotton seed, Mahua, Karanja, etc. could be used for producing bio-diesel [4]. Biodiesel is a promising alternative fuel with almost similar properties as that of diesel which makes it suitable for use in compression ignition engine applications without any major modifications [5]. Unlike diesel, one of the potential problems associated with biodiesel is the variability in its fatty acid methyl ester composition owing to variations in the feedstocks used for its production [6]. This could result in variations in biodiesel fuel properties and thereby, engine characteristics demanding engine re-calibration every time a new biodiesel fuel is introduced.


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The present study intends to develop biodiesel composition based mathematical models using Artificial Neural Networks to predict engine characteristics. Further, attempts are made to arrive at an optimal biodiesel composition by utilizing a multi-objective Genetic Algorithm that could simultaneously improve fuel economy and reduce oxides of nitrogen emission with biodiesel. 2. Literature Review Some of the existing research works in the focus area of present study are presented and briefly discussed here. Filho et al [7] predicted viscosity, iodine value and Rancimat induction period of biodiesel using ANN. The input variables are the 13 most common fatty acid methyl esters (FAMEs) present in biodiesels. Around 98 biodiesel samples comprising of 13 FAMEs are employed to simulate the real biodiesel fuels. The optimization process with ANN is executed in three steps, viz. testing of algorithms for adjusting weights, testing of stopping conditions and testing of activation functions. Sharma et al [8] predicted the performance and emission parameters of a single cylinder diesel engine using ANN models that are trained with measurements done at varying fuel injection timings and loads using polanga biodiesel-diesel blends. For training the neural networks, Levenberg-Marquardt (LM) algorithm is employed. In the training stage, to improve the predictions, the number of neurons in the hidden layer is increased in incremental steps from 5 to 20. For this purpose, quasi-Newton back-propagation, LM learning algorithm and scaled conjugate gradient learning algorithm are used in the network structure. In the study conducted by Garg et al [9], the engine operating parameters in terms of speed, load, and injection pressure are used as inputs for ANN models. The algorithms used in this study include Gradient Descent and Levenberg-Marquardt with feed-forward network and radial-basis function network architecture. Three separate neural networks are developed and the performance of those networks is adjudged based on the mean square error. 4 ACS Paragon Plus Environment

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Ramdhas et al [10] utilized neural network models for predicting the cetane number of biodiesels based on their fatty acid composition. Four different neural networks, viz. multilayer feed forward (MLFFN), radial base function (RBFN), generalised regression (GRNN) and recurrent network (RNN) are chosen for this work. The input layer of the neural network includes biodiesel composition and a back propagation algorithm is used to train the network. Valdes et al [11] presented ANN models to predict the density, dynamic viscosity, and cetane number of methyl esters and biodiesel. An experimental database is used for developing the models, where the input variables in the neural networks include temperature, number of carbon and hydrogen atoms, and methyl ester composition. The learning task is done through hyperbolic and linear functions, while the Levenberg–Marquardt algorithm is used for the optimization process. Javed et al [12] developed ANN models for predicting the performance and emission characteristics of a single cylinder diesel engine operated with Jatropha biodiesel blends and hydrogen in a dual fuel mode. Silitonga et al [13] developed an ANN model based on standard back-propagation algorithm using Jatropha biodiesel blend percent and engine speed as input variables for predicting the engine combustion, performance and emission characteristics. The available research works in the existing literature [7-13] concerning the utility of ANN models to predict biodiesel properties and engine characteristics could be listed under two categories as follows: one that predicts the engine characteristics based on the biodiesel blend percent and the other one that predicts the properties of biodiesels based on their composition. There have been no ventures so far to predict the engine characteristics based on biodiesel composition which is of utmost importance owing to a larger variations in biodiesel composition. The biodiesels derived from various feedstocks show significant variations in their fuel properties owing to variations in the fatty acid composition of the vegetable oil sources [14]. For edible vegetable oils, the following trend is generally observed


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with respect to their fatty acid profile: oleic acid (C18:1) > linoleic acid (C18:2) > palmitic acid (C16:0) > stearic acid (C18:0) [15]. It is to be mentioned that the diesel fuel properties also vary in different parts of the world owing to variations in their hydrocarbon class composition [16, 17, 18]. The fundamental fuel properties including cetane number varies from 40 to 65 for diesel in different parts of the world [17]. Hence, any relative variations in the engine characteristics between diesel and biodiesel are also dependent upon the diesel fuel composition and properties chosen for comparison. It is a laborious task, apart from higher cost, to determine the variations in engine characteristics due to variations in biodiesel composition based on physical experimentation tests. Moreover, the knowledge of the dependence of biodiesel composition on engine characteristics helps to arrive at an optimal biodiesel composition for better engine characteristics, which is more of a least explored area of research. Thus, developing suitable mathematical models to predict engine characteristics based on compositional variations of biodiesel would be an effective and economical solution for achieving optimal engine performance with biodiesel and forms the motivation for the present research work.

3. Materials and Methods 3.1. Artificial Neural Networks and Genetic Algorithms Artificial Neural Networks (ANNs) are a class of logic programming method that emulates human brain [8]. It is a huge parallel distributed processor comprising of a collection of simple processing units that stores knowledge through experience and makes it available for future use [19]. An ANN module consists of inputs, weightage functions, summation functions, activation functions and outputs. A neural cell receives inputs, combine them through a non-linear operation and output the result. ANN is a preferred tool when the number of independent variables are large and when it is difficult to establish the dependency


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of output on these variables. In most practical cases, the relationships are highly non-linear and the ability of ANN to understand them makes it a suitable tool for a wide range of applications [20]. ANN makes use of learning algorithms which are prescribed set of welldefined rules for providing solution to a learning problem with minimal error [19]. One of the most popular learning algorithms is a Back Propagation Learning Algorithm which involves backward propagation of error from the output layer to the hidden layer so as to estimate the updated weight for the units in a hidden layer [21]. Genetic Algorithms (GAs), first developed in 1975 [22] are a class of heuristic search algorithms that make use of evolutionary ideas to generate solutions for optimization problems by carrying out stochastic transformation inspired by natural evolution, such as inheritance, mutation, selection, and crossover [23]. The principle of “survival of the fittest” is accomplished by evaluating each candidate’s fitness through an appropriate objective function and a biased random selection procedure of individuals for “reproduction”, wherein higher rated candidates are more likely to be selected [24]. GAs are non-calculus based search algorithms whose advantage over other search techniques is that it uses probabilistic transition rules and not deterministic ones which help them to search more globally. Moreover, it is very robust and uses information on the objective function only and not on any of the auxiliary information including the derivatives. Thus, it is suitable to couple an ANN predictive function with GA optimization algorithm to search for optimal set of parameters. A multi-objective optimization refers to the process of optimizing two or more functions simultaneously. There is no unique solution to a multi objective optimization problem, but a set of mathematically acceptable solutions known as non-dominated or Pareto optimal solutions that could be arrived at. The Pareto front captures the trade-off between competing objectives and identifies solution which are non-dominated. It also includes members of


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population for which there are no solution better in criteria than the Pareto set member [25]. In practice, to select one best optimum or to get a set of values for decision making, clustering algorithms may be employed. One such available techniques is TOPSIS (Technique for Order of Preference by Similarity to an Ideal Solution) [26], which could be used for solving Multiple Criteria Decision Making (MCDM) problems with a finite number of solutions. 3.2. Neural Network Development ANN learns from its environment and thus, initially it must be trained with the measured data. All the neural networks employed in the present work are Multi-Layer Feed Forward Networks (MLFFN) utilizing Levenberg Marquardt (LM) training algorithm, which is a backpropagation training algorithm. The selection of MLFFN is based on its capacity to approximate virtually any linear or non-linear function to an acceptable level of accuracy, if sufficient hidden layer neurons are provided with sigmoidal transfer functions [27]. The training algorithm is chosen to be LM because it is one of the most widely used and well validated back propagation training algorithms that converges quickly and proved to be accurate enough in most cases [27]. The models are characterised by the choice of neural network architecture and the performance of the models are evaluated to decide their fitness for predicting engine parameters. The validation of the models are adjudged based on graphical comparison to examine the correctness of the predicted trends and also by using the standard statistical parameters including the regression coefficient to examine the magnitude of deviation. The selection of optimal number of hidden layer neurons in the ANN architecture falls in the domain of bias-variance dilemma. Increasing the number of hidden layer neurons compromises the generalization ability of the ANN model at the cost of minimizing the training data set error [25]. The number of hidden neurons determines the complexity of an ANN model. This is because the model may over-fit and predictions would


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become too biased. The fitting nature of neural networks is data specific because the way in which the output relates to the input varies and thus, each characteristic may show the best results for different neural network architectures. Thus, individual networks are built for each engine parameter so as to improve their accuracy and data fitting characteristics. The relationship between the output characteristics and the input variables may vary across the engine parameters under study and thus, each network may show best fitting nature for different set of network architectures. Thus, it is necessary to determine the best set of network architectures and activation functions for each engine parameter. The network architectures may be varied by varying the number of hidden neurons/hidden layers suitably. Increasing the number of neurons minimizes the prediction error but at the cost of poor generalization. Thus, the proposed approach starts with including minimum number of neurons in the network architecture and is gradually increased on a trial basis, until the prediction errors are within the permissible limits. The activation functions are chosen to be sigmoidal which are either tansig or logsig. The available measured data with seven different biodiesel types [28] and the data generated from the present work for the two biodiesel types, viz. groundnut and corn-rice bran in two different engine configurations, viz. light duty naturally aspirated and heavy duty turbocharged are made use of to develop Artificial Neural Network (ANN) models to correlate biodiesel composition and engine characteristics. Out of the nine biodiesels used in the light duty engine, the data from seven biodiesels are used to train the network and the two data sets are used for validating the networks. Thus, in total, around 35 data points are used to train the neural networks. In case of heavy duty engine, three biodiesels data are used to train the networks and one data is used for validation. Thus, around 15 data points are used for training the networks. The compositions of biodiesels used for training and validating the networks are provided in Table 1 and Table 2 respectively for the light and heavy duty 9 ACS Paragon Plus Environment

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engines. The training range of biodiesel composition which are given as inputs to the neural networks are provided in Table 3.

Table 1. Compositions of biodiesels tested in the light duty engine Biodiesel Type

Sunflower biodiesel Coconut Biodiesel Palm Biodiesel Soybean biodiesel (German) Palm biodiesel (German) Rapeseed biodiesel (German) Corn-Rice bran biodiesel Groundnut biodiesel Rice bran biodiesel

Composition of Methyl Esters (wt %) C14:0 C16:0 Training Data Set 0 5.13 19.44 8.53 0.64 37.60 0 7.90 0 39.20 0 2.80 0 15.50 Validation Data Set 0 8.24 0.085 17.91

C18:0

C18:1

C18:2

1.88 2.10 3.18 2.50 3.00 0 1.94

26.58 8.94 46.13 26.50 44.60 65.30 37.65

65.83 3.95 12.24 56.70 12.30 20.60 43.15

2.64 0.35

57.92 43.95

23.86 36.03

Table 2. Compositions of biodiesels tested in the heavy duty engine Biodiesel Type

Sunflower biodiesel Coconut Biodiesel Palm Biodiesel

Rice bran biodiesel

Composition of Methyl Esters (wt %) C14:0 C16:0 Training Data Set 0 5.13 19.44 8.53 0.64 37.60 Validation Data Set 0.085 17.91

C18:0

C18:1

C18:2

1.88 2.10 3.18

26.58 8.94 46.13

65.83 3.95 12.24

0.35

43.95

36.03

Table 3. Training range of biodiesel composition given as input to Artificial Neural Networks FAME Weight percentage (%)

C14:0

C16:0

C18:0

C18:1

C18:2

0 to 19

3 to 39

0 to 3

9 to 65

4 to 65

The Artificial Neural Network models are built using the ANN toolbox available in MATLAB 2013a. The ANN models consider the mass fractions of five major Fatty Acid Methyl Ester (FAME) constituents of biodiesel, viz. Methyl Myristate (C14:0), Methyl 10 ACS Paragon Plus Environment

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Palmitate (C16:0), Methyl Stearate (C18:0), Methyl Oleate (C18:1) and Methyl Linoleate (C18:2) as their input and predict the engine combustion, performance and emission parameters. It has to be mentioned that the predictions correspond to various combinations of biodiesel composition within the training range. To validate the models, two different biodiesels are considered, whose composition fall within the training range. The biodiesels are produced from the respective raw vegetable oils using transesterification process and are tested in the light duty diesel engine. In the present study, the mass fractions of five major methyl esters which are present in considerable amount in all the biodiesel types under study are employed to develop the ANN models. This is done to ensure that ANN, which is predominantly an interpolating tool, creates models that are consistent within the training range. If the training dataset is skewed towards typical methyl esters which are not present in all the biodiesels, the model may not be consistent in interpolating within the training range. Thus the developed models could not cover the full range of possible FAMEs and is confined to the study of the variations of engine characteristics with variations in the composition of five major methyl esters only. For the same reason, the biodiesel compositions do not add up to 100 per cent for all the biodiesel types. This is one of the limitations of interpolation based ANN models. However, the present biodiesel composition based ANN models can very well capture the effects of biodiesel composition variations on the engine characteristics which is the primary objective of the present work. The models could be revamped by extending the input variables to include mass fractions of more FAMEs by incorporating more biodiesel engine characteristic datasets, which widens the future scope of the present work. 3.3. Biodiesel Production and Characterization Two biodiesel types, viz. groundnut and corn-rice bran are produced from their respective raw vegetable oils using transesterification process. Based on the measured data generated


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from these biodiesels in the light duty engine, corn-rice bran biodiesel data is included in the training set along with other available data sets [28], while the groundnut biodiesel data is used for validation along with the available rice bran biodiesel data [28]. Since the free fatty acid (FFA) content of groundnut and corn-rice bran oils are well within 3%, a single stage alkali-catalysed transesterification is utilized [29] for producing biodiesel. The procedure followed for producing biodiesel from the raw vegetable oils is shown in Figure 2.

Fig. 2: Flowchart showing biodiesel production process The groundnut biodiesel is produced from refined groundnut oil, while the corn-rice bran biodiesel is produced from corn-rice bran oil blend with 60% corn oil and 40% rice bran oil by weight. The molar ratio of methanol to oil used is 6:1 (300 mL per litre of oil) for both the oils. The amount of catalyst used for groundnut biodiesel is 5.2g per litre of oil (corresponding to FFA value of 1.692) and that for corn-rice bran biodiesel is 4g per litre of


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oil (corresponding to FFA value of 0.508). The total reaction time for the transesterification process is about 2 hours. The products of transesterification are allowed to settle down in a conical flask so that the heavier glycerine molecules separates out at the bottom which can then be drained. The biodiesel obtained as a top layer in the conical flask is subjected to repeated water washing to remove soap, unreacted methanol and residual catalyst. Finally, pure biodiesel is obtained after heating it above 373 K so as to remove any moisture or unreacted methanol present. The methyl ester composition of biodiesel are measured by using a Nucon gas chromatograph (GC) fitted with a flame ionization detector (FID). A gas chromatograph with an FID determines the composition of biodiesel by separating the constituent methyl esters. It has a column of capillary fibre through which the sample is circulated and the individual methyl esters are separated at different times according to the variations in their physical and chemical properties. The sample is prepared with 250 mg of biodiesel in 5 mL of hexane. A 1 µL of sample thus prepared is injected through the biodiesel port on GC using a syringe. The measured composition of biodiesels along with measurement uncertainty is provided in Table 4. Table 4. Measured composition of groundnut and corn-rice bran biodiesels (in % mass) Biodiesel Variety Groundnut Corn-Rice bran Measurement uncertainty (%)

C14:0 0 0 -

C16:0 8.24 15.50 0.54

C18:0 2.64 1.94 0.91

C18:1 57.92 37.65 0.69

C18:2 23.86 43.15 0.58

3.4 Engine Studies The biodiesels are tested at rated speed, varying load conditions in a light duty diesel engine, whose important specifications are given in Table 5 and the schematic of experimental test setup is provided in Figure 3.


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Fig. 3: Schematic of the test engine setup

The test engine is connected to an eddy current dynamometer to set different load conditions. The dynamometer is initially calibrated using standard weights so as to maintain the applied torque within ±0.2 N-m accuracy. The air and fuel flow rates are measured by a turbine type flow meter and a mass balance stop-watch arrangement respectively. The coolant temperature, lube oil temperature and exhaust gas temperatures are measured by K-type thermocouples. The in-cylinder gas pressures are measured by a cylinder head mount Kistler 6055 water cooled piezoelectric sensor whose accuracy is ±0.5% over a temperature range of 200 50 ⁰C. The fuel line pressures are measured by a Kistler 4067 piezo-resistive type pressure sensor capable of measuring up to 2000 bar. The crank angle is measured by using a 60-2 trigger wheel with an inductive sensor. The firing TDC is set in the test engine by finding out the crank angle at which peak pressure is reached during motoring after accounting for the thermodynamic loss angle. A Kistler make Ki-Box combustion analyzer compatible with 60-2 wheel is used to set the firing TDC which has an in-built standard TDC 14 ACS Paragon Plus Environment

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settings. The combustion analyzer is used to convert and process the data from the pressure sensors and inductive sensors, which is capable of delivering the cylinder pressure and energy release rate trends with respect to crank angle in a live mode.

Table 5. Specifications of Test Engines Parameter

Application

Light duty engine Stationary constant speed 4-stroke engine Agricultural pump sets

Aspiration system

Naturally aspirated

No. of Cylinders Compression Ratio Rated Speed (rpm) Rated Power (kW) Rated Torque (N-m) Bore (mm) Stroke (mm) Displacement (cc) Cooling Fuel injection pressure (bar) Static Fuel injection timing (ᵒ CA bTDC) Number of injections Injector orifice (number x diameter)

1 17:1 1500 3.5 @ 1500 rpm 23.5 @ 1500 rpm 87.5 80 481 Natural water circulation 210 23 Single 3 x 0.600

Fuel delivery System

Jerk type in-line pump

Exhaust gas re-circulation

Nil

Intake Pressure and Temperature

Ambient conditions

Engine test speed (rev/min) Engine load (BMEP in bar)

1500 0 to 6.1

Engine Type

Heavy duty engine Automotive variable speed 4-stroke engine Diesel truck Turbocharged with intercooler 4 17.5:1 3200 70 @ 3200 rpm 285 @ 1400 rpm 100 105 3298 Forced water circulation 230 17 Single 5 x 0.209 Rotary distributor type pump Nil Boost pressure: 1.12 bar, Temperature: 30 deg. C 1400 0 to 8.7

The acquired data is then processed to a readable form using Kibox version 1.3 software and the required combustion trends are generated. The engine exhaust gas analyses is carried out by using a chemiluminescence analyzer to detect the concentration of oxides of nitrogen (accuracy: 0.2ppm). The moisture content in the exhaust gas from the engine is condensed and only dry exhaust gas is fed into the analyzer. The carbon monoxide concentration is measured based on non-dispersive infra-red principle by using an AVL di gas analyzer whose 15 ACS Paragon Plus Environment

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accuracy is ±3%.The exhaust smoke emission is measured by using a smoke meter (AVL Make: 415SE) which utilizes filter paper method to determine the soot concentration in the exhaust. The measurement accuracy of smoke meter is ±3% of the measured value. All the experiments are conducted under steady state conditions. A 100 cycle averaged cylinder pressure and fuel line pressure data are recorded and are processed further to deduce the combustion parameters. The fuel injection timings are deduced from the fuel line pressure histories by considering the crank angle at which the nozzle opening pressure of 210 bar is reached. The peak pressure is taken as the maximum pressure in the cylinder pressure histories. The brake specific fuel consumption is taken as the ratio of total fuel consumption to the brake power output of the engine. The ANN models are also trained and validated based on the measurements done in a heavy duty engine whose important specifications in comparison to the light duty engine are provided in Table 5. The heavy duty engine is an automotive variable speed four cylinder diesel engine The engine has a turbocharger with intercooler and employs a forced water circulation type cooling system. The bore and stroke of the engine are 100 mm and 105 mm respectively. The engine has a mechanical type fuel injection system with a rotary distributor type fuel pump and the injector nozzle opening pressure of 230 bar.

3.5. Multi-Objective Optimization The engine brake thermal efficiency carries significance as it reflects efficient conversion of input fuel energy into brake work. However, minimization of brake specific fuel consumption is considered important from the commercial applications stand point since it is directly related with a better fuel economy. Nevertheless, these two parameters are inter related, a minimum brake specific fuel consumption also reflects a maximum brake thermal efficiency. Further, the major problems associated with biodiesel applications in diesel engines are


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higher brake specific fuel consumption and higher oxides of nitrogen emissions which could be reduced by optimizing their fatty acid methyl ester composition. Thus, it is intended to utilize them as the metrics to be minimized for biodiesel to achieve a comparable engine performance with that of diesel. A strategy to optimize biodiesel composition so as to simultaneously reduce BSFC and NOx emissions is investigated and it is found that a multiobjective optimization using Genetic Algorithm (GA) would be a suitable choice because of its robustness and better compatibility with Artificial Neural Networks [11]. The NOx and BSFC neural networks of the light duty engine are used as the fitness functions (objective functions) for GA, both of which are to be minimized. Initially, GA is run to obtain optimum set of biodiesel composition at individual loads. As the optimal biodiesel composition should be applicable across the entire load range, a TOPSIS algorithm [26] is used to determine a single optimal point from among the optimal points generated at individual loads in the Pareto front. A second stage multi-objective optimization is then done to minimize the penalties that arise when the new optimal composition is preferred over individual optima at all the load points. The penalty function is defined as the sum of absolute differences between the predicted engine parameter corresponding to the new optimum composition and the individual optimum at a certain load condition. The multi-objective optimization is executed using the Genetic Algorithm toolbox in MATLAB 2015a.

4. Results and Discussions The results pertaining to the development and validation of ANN models and arrival of optimal biodiesel composition using Genetic Algorithm are presented and discussed next. 4.1 ANN Models for Various Engine Parameters and their Validation As discussed earlier, it is necessary to determine the best set of network architectures and activation functions for each engine parameter. The activation functions are usually selected


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to be sigmoidal which are either tansig or logsig. Tansig functions are used commonly by default, but sometimes it is modified to log-sig to avoid negative values within the prediction range. The neural network architectures and the corresponding activation functions arrived at for the combustion, performance and emission parameters of the two engines are presented in Table 6. Table 6. Network architectures and activation functions for the two engine parameters Light duty engine Engine Parameters

BSFC BTE NOx Smoke Peak Pressure Ignition Delay

Network Architecture 6-3-1 6-3-6-1 6-6-1 6-6-1 6-6-1 6-6-1

Activation Functions

tansig-purelin tansig-tansig-purelin logsig-purelin logsig-purelin tansig-purelin tansig-purelin

Heavy duty engine Network Architecture 6-5-1 6-5-1 6-5-1 6-5-1 6-3-1 6-3-1

Activation Functions tansig -purelin tansig-purelin tansig-purelin logsig-purelin logsig-purelin logsig-purelin

To validate the neural networks developed for the light duty engine, the predicted engine parameters are compared with that of measured data obtained from groundnut and rice-bran biodiesels [28] whose composition are within the training range and are not used for training the networks. A total of 35 data points are used for training the networks and around 10 data points are used for the validation. A comparison of measured and predicted combustion, performance and emission trends of the light duty engine with ground nut biodiesel is provided in Figure 4. The measured and predicted engine characteristics of groundnut biodiesel is also compared with that of measured baseline diesel fuel data in Figure 4 to examine the variations in engine characteristics between diesel and biodiesel. Further, to examine the effects of biodiesel composition variations on the engine characteristics, a relative comparison of measured engine characteristics with diesel and nine different biodiesels (refer Table 1) at full load condition is shown in Figure 5. The discussions pertaining to the variations in engine characteristics between diesel and biodiesel shown in


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Figure 4 and also among the biodiesels shown in Figure 5 are presented in detail in ref. [28, 31, 32]. One of the major outcomes from those investigations [31, 32] include a strong correlation between biodiesel composition and engine characteristics. Within the scope of the present work, it is observed from Figures 4 and 5 that the variations in engine characteristics between diesel and biodiesel and also among the biodiesels owing to the variations in their compositions are significant. Thus, the present composition based approach to predict and optimize biodiesel fuelled engine characteristics is justified with the observed variations in engine characteristics with different biodiesel types.


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Fig. 4: Measured and predicted engine characteristics of the light duty engine in comparison to baseline diesel fuel

It is observed that the developed ANN models perform well in capturing the trends as well as the magnitude of the engine combustion, performance and emission parameters. Similar results are also obtained with rice-bran biodiesel which are not presented here for the sake of brevity. Since an optimum biodiesel composition for minimum BSFC and oxides of nitrogen emissions should be arrived at, it is important that the neural networks be stable within the training range. To ensure the stability, a computer code is written using MATLAB 2013a to simulate the neural networks with any random combinations of biodiesel composition (satisfying the constraints) and agreeable trends are observed. Further, regression plots drawn between the measured and predicted engine parameters agree well for all the predicted parameters with a higher regression coefficient of above 0.9.


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Fig. 5: Comparison of measured engine characteristics of the light duty engine with diesel and biodiesels at full load condition


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To validate the neural networks developed for the heavy duty engine, the predicted engine parameters are compared with that of measured data obtained from rice-bran biodiesel [28]. It has to be mentioned that the heavy duty engine is a typical automotive truck diesel engine which runs mostly at low speed and high torque conditions. Thus, the chosen rated torque speed condition (refer Table 5) is a representative of intended application of the test engine. Further, it is opined in the existing literature [33] that the fuel-effects on engine combustion and emission formation could be better captured at the rated torque speed condition owing to a higher rate of fuel consumption. A total of 15 data points are used for training and around 5 data points are used for the validation. A comparison of measured and predicted combustion, performance and emission parameters at rated speed, varying load conditions of the heavy duty engine fuelled with rice-bran biodiesel is provided in Figure 6. A relative comparison of measured and predicted engine characteristics of rice-bran biodiesel with that of measured baseline diesel fuel is also shown in Figure 6. From these plots it is observed that the ANN models predict well the trends as well as the absolute values of all the combustion, performance and emission parameters of the heavy duty engine. The regression plots between the measured and predicted data for all the engine parameters show a very good agreement with a higher correlation coefficient of above 0.9. Further, the measured engine characteristics with diesel and four different biodiesels (refer Table 2) at full load conditions are shown in Figure 7. From Figures 6 and 7, it is observed that the variations in engine characterises between diesel and biodiesel and also among the biodiesels owing to variations in their composition are significant. Thus, both in the light duty and the heavy duty engine, the variations in engine characteristics with biodiesels composition variations are significant, necessitating a composition based approach to model biodiesel fuelled engine characteristics.


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Fig. 6: Measured and predicted engine characteristics of the heavy duty engine in comparison to baseline diesel fuel 23 ACS Paragon Plus Environment

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Fig. 7: Comparison of measured engine characteristics of the heavy duty engine with diesel and biodiesels at full load condition


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It is interesting to note that the ignition delay and smoke emissions trends with biodiesel and diesel are opposite in the light duty and heavy duty engines (refer Figure 4 and 6). The differences in the ignition delay between biodiesel and diesel are more pronounced and are higher in the light duty naturally aspirated engine as compared to the heavy duty turbocharged engine. This may be attributed to the unfavourable in-cylinder conditions at the time of fuel injection in the light duty engine resulting in more pronounced fuel effects. The physical delay effects are more significant in the light duty engine owing to unfavourable ignition conditions interms of lower temperature and lower oxygen fraction at the time of fuel injection. The distillation range is narrow and higher for biodiesel compared to diesel and thus, contributes to a longer physical delay under unfavourable vaporization conditions at the time of fuel injection. Furthermore, higher density, viscosity and surface tension of biodiesel would result in poor spray characteristics leading to a longer physical delay as compared to diesel [31]. The physical delay effects are not significant in the heavy duty turbocharged engine because of favourable ignition conditions in terms of higher temperatures and higher oxygen fraction at the time of fuel injection owing to turbo charging. Thus, the ignition delay is lower for biodiesel compared to diesel owing to its higher cetane number. It is observed that the trends of smoke variations with diesel and biodiesel are opposite in the light duty engine and the heavy duty engine. Biodiesel reduces smoke emissions compared to diesel in the heavy duty engine, while, it exhibits higher smoke emissions in the light duty engine. A shorter ignition delay in the turbocharged engine leads to a lower premixed phase and longer diffusion phase combustion wherein the fuel bound oxygen content of biodiesel is more effective to reduce smoke emissions compared to diesel. However, in the naturally aspirated engine, a longer ignition delay results in more dominant premixed phase and a shorter diffusion phase combustion. Thus, the positive effects of fuel bound oxygen in biodiesel is overshadowed by poor air-fuel mixing owing to its inferior spray and 25 ACS Paragon Plus Environment

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vaporization characteristics resulting in more fuel rich pockets and thereby, higher smoke emissions [31]. Further, the light duty engine is a small bore production diesel engine used for agricultural water pumping applications (refer Table 5). Since the engine is designed for applications with conventional diesel fuel, the injector design and operating parameters are optimized for diesel fuel. Thus, un-optimized injector design and operating variable with biodiesel in this small bore diesel engine could also be a possible cause for longer ignition delay and higher smoke emissions compared to diesel. It should be noted that the same base line diesel fuel is used in both the light duty and heavy duty engines. The observed differences in engine parameters between diesel and biodiesels are not due to variations in the baseline diesel fuel rather it is attributable to the differences in the engine type. The maximum absolute error in the predictions for the various engine parameters of the two engines is provided in Table 7. A higher prediction error for the smoke emissions may be due to a smaller magnitude of absolute smoke emission values. Table 7. Maximum absolute error in the predictions for the various engine parameters

Engine Parameter BSFC BTE NOx Emissions Smoke Emissions Peak Pressure Ignition Delay

Light duty engine

Heavy duty engine

Maximum Error (%)

Maximum Error (%)

9.1 8.8 9.6 56.1 2.7 6.9

10.8 12.3 6.9 53.9 10.4 3.0

4.2. Multi Objective Optimization A multi-objective optimization using Genetic Algorithms is utilized to determine the optimum biodiesel composition for better fuel economy and lower oxides of nitrogen 26 ACS Paragon Plus Environment

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emissions. The two fitness functions for multi-objective optimization are chosen to be the neural networks of NOx emission and BSFC for the light duty engine. Initially, individual optima are obtained at varying load points viz. 20%, 40%, 60%, 80% and 100%. The total saturated and unsaturated methyl ester constituents of biodiesel corresponding to the individual optima at various load conditions are provided in Table 8. Table 8. Optimal biodiesel composition at individual loads Load (%)

20 40 60 80 100

Optimal Biodiesel Composition (% mass) Total Saturation Total Unsaturation Grand Total 40.72 58.8 99.52 39.53 59.77 99.3 42.71 56.43 99.14 43.51 56.17 99.68 36.71 62.55 99.26

To arrive at an optimal biodiesel composition suitable for all the loads, a second-stage multiobjective optimization is done to minimize the NOx and BSFC penalties. A penalty function is defined as the sum of absolute differences between the predicted values of BSFC or NOx with any arbitrary biodiesel composition and their predicted optimal values at individual loads based on first stage optimization. By multi-objectively minimizing the penalty functions for BSFC and NOx, an optimum biodiesel composition that is suitable across all the load conditions is arrived at. The Pareto front plot for this second stage optimization for five different iterations is depicted in Figure 8.


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Fig. 8: Pareto plot for multi-objective optimization of BSFC and NOx penalties Based on the results obtained in second stage optimization, five prominent points are chosen by using TOPSIS method with 50-50 weightage function given to both BSFC and NOx. The final optimal biodiesel composition to reduce BSFC and NOx simultaneously is provided in Table 9. Table 9. Optimal biodiesel composition to reduce BSFC and NOx simultaneously

C14:0 5 to 12

Optimal Biodiesel Composition (% mass) C16:0 C18:0 C18:1 C18:2 25 to 33 0-3 16.5 to 26 31 to 40 Total Saturation = 36% to 43% Total Unsaturation= 55% to 63%

It is observed that the total saturated and unsaturated methyl ester content falls in the range of 36-43 and 55-63 percentage by weight respectively for the optimum biodiesel composition. The biodiesels having higher proportion of palmitic (C16:0) and linoleic (C18:2) methyl esters have the potential to simultaneously reduce BSFC and NOx emissions.

5. Conclusions The objective of the present study is to develop mathematical models to correlate engine parameters with biodiesel composition and suggest an optimum biodiesel composition for 28 ACS Paragon Plus Environment

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better fuel economy and lower oxides of nitrogen emission. The biodiesel composition based ANN models are developed to predict combustion, performance and emission parameters of a light duty naturally aspirated engine and a heavy duty turbo charged diesel engine operated with various biodiesels. The developed models perform well in predicting the combustion, performance and emission parameters of both the engines with a higher correlation coefficient of above 0.9 and less than 10% absolute error. The validation of ANN models are done based on measured data generated with two biodiesels, which are produced by transesterification process under optimal conditions. The composition of those biodiesels are not included in the training data set. This gives further credibility to the developed models. Moreover, the stability of the developed models to predict engine parameters with various combinations of biodiesel composition within the training range as inputs is tested and observed to be satisfactory. Further, by combining ANN with genetic algorithm, optimal biodiesel composition is arrived at so as to simultaneously reduce brake specific fuel consumption and oxides of nitrogen emission. Using multi-objective optimization with genetic algorithms, individual optima are obtained at each load points. But, since a common optimum suitable for all the load conditions should be the target, the penalties corresponding to the common optimum at individual loads corresponding to both BSFC and NOx emission are minimized. Thus, a common optimum biodiesel composition across the load conditions is arrived at. From this study, it is observed that the biodiesels having higher proportions of palmitic (C16:0) and linoleic (C18:2) methyl esters have the potential to simultaneously reduce BSFC and NOx emission. The total saturated and unsaturated methyl ester content falls in the range of 36 to 43 and 55 to 63 percentage by weight respectively for the optimum biodiesel composition. As such, palm biodiesel would be a suitable candidate that matches well with the ideal biodiesel composition suggested in the present work, within the limitations of the present model and


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also engine type variations. However, a detailed life cycle analysis needs to be done to recommend the credibility of palm biodiesel for automotive engine applications.

Acknowledgements The authors wish to acknowledge the Department of Science and Technology (DST), Government of India and the Indo-German Centre for Sustainability (IGCS) for providing necessary funding (MEE/13-14/313/IITM/PRAM) to carry out the present research work.

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A Composition Based Model to Predict and Optimize Biodiesel Fuelled

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