An experimental investigation of performance and emission of thumba biodiesel using butanol as an additive in an IDI CI engine and analysis of results using multiobjective fuzzybased genetic algorithm
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Abstract
The present work studies the effect of butanol in thumba (Citrullus colocynthis) biodiesel in an IDI CI engine at varying percentages of 5 and 10% in 15 and 10% thumba biodiesel respectively with 80% diesel in each blend. Another blend was introduced with 80% diesel and 20% biodiesel without any additive. The experiment was conducted in a single cylinder fourstroke IDI CI engine at 1500 rpm varying from 25% to fullload (100%) conditions. The results showed diesel with less bio diesel and higher butanol in percentage gives good performance and emission compared to diesel at higher loads. Blend containing 10% bio diesel, 10% butanol, and 80% diesel (D80B10Bu10) showed higher cylinder pressure, heat release rate, BThE, and less NO_{x}. Biodiesels gave less UHC, CO emissions. In this work, multiobjective fuzzybased genetic algorithm was introduced for the best fit result. Four parameters were used for optimization (BSFC, BThE, CO, NO_{x}). The result from genetic algorithm was taken for validation and the optimized result was found adequate after validation.
Keywords
Biodiesel Butanol Performance Emission Fuzzy MPCINomenclature
 D100
Diesel 100%
 BL
Blend
 BL 1
D80B10Bu10—diesel 80% thumba biodiesel 10% butanol 10%
 BL 2
D80B15Bu5—diesel 80% thumba biodiesel 15% butanol 5%
 BL 3
D80B20—diesel 80% thumba biodiesel 20%
 Pc
probability of crossover
 Pm
mutation probability
Abbreviation
 IDI
indirect injection
 PD
pure diesel
 BSFC
brake specific fuel consumption
 BTHE
brake thermal efficiency
 UHC
unburned hydrocarbon
 CO
carbon monoxide
 NOx
oxides of nitrogen
 VO
vegetable oil
 MPCI
multiperformance characteristic index
 B
bad
 A
average
 Good
good
 EB
extremely bad
 VVB
very very bad
 VB
very bad
 NGNB
not good nor bad
 VG
very good
 VVG
very very good
 EG
extremely good
 GA
genetic algorithm
Introduction
Diesel fuels are commonly used fuels for power generation in various fields, automobiles transportation, etc. Diesel is being used in numerous fields from long time. These uses at a huge number are polluting the environment from the emissions of diesel fuels, also depleting the layer of this fuel sources day by day (Rinaldini et al. 2016). Therefore substitute of fuel for originating power is getting more and more interests from the last few years for reducing the fossil fuel usages as well as pollution occurring from the fossil fuels. In the last few years, as alternate for diesel, bio diesels gained much more attraction (Murugesan et al. 2009; Shahid and Jamal 2008; Ramadhas et al. 2004). Biodiesels are gaining immense response as an environmental friendly source of engine fuel and as an alternative to fossil fuels. Sometimes higher viscosity of biodiesel can cause lower atomization, inadequate combustion, and choking of the injector. So, before using biodiesel as an alternate of diesel, the various properties has to be tested and also has to be improved. Biodiesel can be mixed with diesel up to 20% which was denoted in many papers (Yilmaz and Morton 2011; Ramadhas et al. 2005). Thumba is used as alternative sources of fuel in recent times. E. Sivakumar, et al. worked on thumba biodiesel–diesel blends. Results showed lower brake thermal efficiency of diesel compared to thumba biodiesel–diesel blends (Sivakumar et al. 2015). Kumbhar et al. investigated on thumba biodiesel blends with diesel. Thumba B100 showed better emission performance of CO_{2}. NO_{x} was less for diesel. Thumba B20 showed better emission than other blends of thumba biodiesel (Kumbhar and Dange 2014). Nadir Yilmaz et al. have seen that biodiesel–butanol blends showed higher BSFC, CO emissions, and HC emissions while exhaust gas temperature and NO_{x} emissions was lower compared to biodiesel (Yilmaz et al. 2014). Rakopoulos et al. found that the smoke emissions, NO_{x}, CO emissions were reduced, total hydrocarbon increased with butanol/diesel fuel blends. With increasing percentage of butanol higher specific fuel consumption and increase of brake thermal efficiency was noted (Rakopoulos et al. 2010).
From the literature survey, it was found that thumba provides a wide scope as an alternate source of diesel and since not much work was done on the biodiesel and butanol as an additive together. Moreover, thumba tree is easily available in many parts of India. In India, thumba is available in the western part of Rajsthan and Gujrat. Thumba can be implemented as a biodiesel due to their similar diesel properties, small crop cycles, high percentage of oil recovery, and quality of oil. Cultivation of thumba biodiesel can also increase the economy of Rajasthan, Gujrat in the near future. Thumba seed oil is also available at a higher quantity in those reasons, and it is consumed in local soap industries. It is found that thumba plant cultivation is going on the wastelands of Rajasthan and Gujrat on 5.33 Mha so that thumba biodiesel can be used as a 5% of diesel substitute. Also, cultivation of thumba oil on 10.145 Mha wastelands in those areas can provide India 9.5% biodiesel substitutes in near future. These all informations about thumba bio diesel and the availability and future scopes of thumba bio diesel motivated the author to work on thumba bio diesel and the performance and emission was tested in the work at 3 ratios for the alternative source to see the usability of the blends and genetic algorithm based multi objective optimization was used for finding the best blend among all the blends used in the work.
In this work, to investigate the performance and emission, thumba biodiesel was used in proportion of 10%, 15%, and 20% with 80% diesel. Ten percent and 15% thumba biodiesel was mixed with 10 and 5% butanol respectively along with 80% diesel after transesterification process for the experimental work.
Multiregression analysis was used to generate the regression equation for the alteration. Through multiregression, each output variable which was generated in the form of the both input variables by MINITAB was applied for the best fit for GA.
In this work, the independent variables were blend, load and BSFC, BThE, CO, NO_{x} were the dependent variables. Regression analysis is applied when a continuous dependent variable is to be anticipated from various independent variables. Continuous or categorical or both independent variable can be applied in regression. Independent variable of two or more levels can also be utilized in regression analysis (Habib 2009).
Regression analysis illustrates the relation between the predictor and response variable through an equation.
The relation between dependent and independent variables can be modeled by a firstdegree polynomial as given below in Eq. 1, which describes the multiple regression process for a formulated function which has three independent factors and an n data set:
The fitness of the model was generated by Minitab, which also tests the fit by analyzing the residual plots and the result was illustrated (Zarepour et al. 2007). The p value checks the null hypothesis for individual terms and the coefficient has to be equivalent to zero. Also, lower p value (< 0.05) is very much significant which indicates the null hypothesis can be rejected. If the predictor has lower p value, then it is most likely going to be an essential development to the model, since the predictor value and the response variable are correlated.
Fuzzy was utilized for the transitional result which has to be defined from given constraints. Four input variables were taken for the work, i.e., BSFC, BThE, CO, and NO_{x}. Every variable had triangular membership function where bad (B), average (A), and good (G); these three level of sets were used respectively ranging from 0 to 1. MPCI was divided into nine levels namely extremely bad (EB), very very bad (VVB), very bad (VB), bad (B), NGNB (not good nor bad), good (G), very good (VG), very very good (VVG), and extremely good (EG). Triangular membership function was applied for the MPCI using input and output variables (Guven 2009). Eightyone rules were applied to express the relation of the input and output variables. In this work, a single objective was desired; hence, concept of the desirability function, fuzzy sets, and fuzzy logic was combined to transform multiple responses into a single MPCI. GA was used for the optimized result of the different dependent and independent parameters.
Materials and method
Biodiesel used, production, and properties
In the present study, thumba biodiesel, butanol, and diesel blends were tested in a single cylinder fourstroke IDI CI engine at varying loads. Thumba biodiesel was produced from transesterification process of thumba seed oil.
In transesterification, in the presence of adequate catalyst, alcohol reacts with vegetable oil. Usually ethyl or methyl alcohol is used for the production of ethyl/methyl esters. In this work, methanol was used as alcohol and KOH as the catalyst. The molar ratio for alcohol to oil was taken as 6:1, and the catalyst was taken as 0.75% by the weight of oil for the process. The temperature during the reaction was kept between 55 and 60 °C, and the reaction time for the process was 90 min approximately. After the reaction, methyl ester and glycerin are formed which appears separately in two distinct layers of liquids. The vegetable oil reacts with alcohol which is mixed in the catalyst. After that, crude biodiesel and crude glycerin is formed from the liquid. The glycerin obtained from the vegetable oil is refined and used afterwards. The alcohol and crude biodiesel is then separated; the biodiesel is also refined, and the alcohol is used for further processes. Three molecules of monoglyceride and one molecule of glycerol is obtained from one molecule of vegetable oil/tri glyceride and three molecules of alcohol. The reaction is shown in Eq. 4 (Sayin 2010; Rakopoulos et al. 2011).
Equation 2 shows the transesterification of vegetable oils (Lee et al. 2010; Bari et al. 2002).
After the reaction is completed, biodiesel and glycerol are gravitationally or centrifugally separated.
In the present work, diesel and thumba biodiesel with butanol additive used for the experiment were D100, diesel 100%; blend 1, diesel 80% thumba biodiesel 10% butanol 10%; blend 2, diesel 80% thumba biodiesel 15% butanol5%; and blend 3, diesel 80% thumba biodiesel 20%.
Fuel properties
Fuel property  Diesel  Thumba Biodiesel  Butanol  Blend 1 (D80B10Bu10)  Blend 2 (D80B15Bu5)  Blend 3 (D80B20) 

Density kg/m^{3}  850  880  809  850.94  853.8  859.5 
Viscosity mm^{2}/s  2.7  5  2.29  2.599  2.836  3.07 
Calorific value MJ/kg  42  40  33.1  40.94  41.26  41.58 
Cetane number  47  53  25  45.4  46.1  48.2 
Oxygen content (wt%)  > 0.6  10.2  21.6  4.8  3.96  3.61 
Flash point, °C  57  91  35  58.2  61  63.8 
Engine used for the experiment and its specifications
The engine specification is given below
Make/model  Kirloskar Varsha 

Type  Horizontal4stroke/singleCylinder dieselCylinder 
Combustion type  Precombustion chamber type 
Cooling  Air 
Displacement (swept volume)  0.381 L 
Fuel  Diesel 
Speed  1500 rpm 
HP  4 HP 
Max. load  6 kg 
Starting  Crank 
Lubrication  Forced 
Error analysis
Accuracy measurements and uncertainty of computed results
Measurements  Accuracy 

NO_{x}  ± 5 ppm 
CO  ± 2 ppm 
UHC  ± 0.5 ppm 
Time  ± 0.5% 
Speed  ± 2 rpm 
Torque  ± 0.5 N m 
Calculated results  Uncertainty (%) 
Fuel volumetric rate  ± 1 
Power  ± 1 
Specific fuel consumption  ± 1.5 
Efficiency  ± 1.5 
Results and discussion
Analysis of combustion characteristics, performance, and emission: variation of average cylinder pressure with load
Variation of heat release rate with load
Variation of brake specific fuel consumption with load
Variation of brake thermal efficiency with load
At full load, BThE was slightly (1%) higher for D80B10Bu10 and for blend D80B15Bu5, BThE was comparable to pure diesel. This occurred due to the rapid combustion at premixed stage during ignition delay, oxygen enrichment, and enhancing energy release rate leading towards higher brake thermal efficiency. Another reason is that biodiesels are more oxygenated fuel than diesel. The oxygen content for butanol is higher than diesel and thumba biodiesel. The blend D80B10Bu10 showed the highest oxygen content in the mixture, also D80B15Bu5 have higher percentage of oxygen content as shown in Table 1. Therefore, more oxygen molecules take part in combustion which results better complete combustion than diesel resulting higher BThE at higher loads. But for D80B20 blend, the viscosity played the major role for lower BThE which was lower than diesel.
Variation of air excess ratio with load
Variation of UHC with load
UHC was 2.09% lower for D80B10Bu10 and D80B20 at full load and for D80B15Bu5 the UHC was 1.39% lower compared to pure diesel.
Variation of CO with load
Variation of NO_{x} with load
At low load condition, NO_{x} was 17.8%lower for D80B10Bu10, 9% less for D80B15Bu5 and for D80B20 NO_{x} was 3.63% lower compared to pure diesel. At full load, D80B10Bu10 and D80B15Bu5 showed 10.28 and 9.14% less NO_{x} emission respectively compared to pure diesel. The higher cetane number significantly reduces NO_{x} formation which occurs due to the shorter ignition delay times resulting lower combustion temperature. In this experimental work, D80B20 showed higher NO_{x} than diesel; due to its lower cetane number, resulting higher cylinder temperature, it leads to higher NO_{x}. But for the butanol blends (D80B10Bu10 and D80B15Bu5), the higher latent heat of vaporization have superior effect over lower cetane number of the blends leading towards lower in cylinder temperature during combustion which reduced NO_{x}. Butanol addition to bio diesel blends reduces the emission of NO_{x} (Yilmaz et al. 2014; Sharon et al. 2013).
Multiobjective fuzzybased genetics algorithm optimization technique
Four output variables were used for this optimization such as BThE, BSFC, CO, NO_{x}. The data was used for generating the regression equations for each input independent variables in the form of output variables. Fuzzy was introduced for all the constraints to convert the variables in a single MPCI which had nine levels EB, VVB, VB, B, NGNB, G, VG, VVG, and EG. Every level was taken within the range of 0 to 1. The results from the regression equation and fuzzy set were taken for the genetic algorithm coding for the output converged graph. The result from GA was validated afterwards.
Checking the model accuracy and development of regression equations
Design expert was used for the best fit equations to correlate the performance with the parameters by using standard statistical techniques of regression analysis. Leastsquare regression technique was used for analyzing the data. Equations between the engine performance and emission parameters incorporated with the input factors (load and blend) were generated, which were of polynomial type. The response of the altered function in the form of square root equations were used to resolve a nonlinear relation between the input and output variables.
In regression results, each output variable is attached to its p value or significance level, which is a percentage and p value does not explain the relationship. p value describes the variables associated with the coefficient. If for a regression p value is 0.05, then the chance for the relationship developed randomly is 5% and the chance for the relationship to be real is 95%. If p value is less than 1, then it is considered as significant as well as accepted (Luis et al. 2005; ElTaweel 2009; Ramasamy et al. 2002). R^{2}, is the coefficient of determination, also known as a multiple determination coefficient. In multi regression model it interprets how near is the data to the regression line. For higher value of R^{2} signifies the model is better for fitness of data.
The equation which was generated to describe the relationship between the variables for the model was developed by using least square method in ANOVA and the accuracy was measured. The model is acceptable if the generated equations show p values less than 0.05; according to this method and p value, more than 0.1 indicates the equations generated cannot be taken for further procedure. In this work, the p values generated from the equations were lower than 0.05; hence, the models considered to be adequate. For these models, the calculated coefficients of determination (R^{2}), R^{2} adjusted, R^{2} predicted values were also above 95% which indicates that the regression model was highly significant (Luis et al. 2005; ElTaweel 2009). The results from the regression analysis through ANOVA software are given below.
Calculated coefficient of determination (R^{2}), R^{2} (adjusted), R^{2} (predicted), and p value for BSFC, BThE, CO, NOx
Name of the process parameters  R ^{2}  R^{2} (adjusted)  R^{2} (predicted)  p value 

BSFC  99.82%  99.62%  99.09%  0.014 
BThE  99.79%  99.54%  98.94%  0.031 
CO  99.66%  99.27%  98.13%  0.002 
NO_{x}  97.60%  97.19%  97.06%  0.012 
Development of predictive model for fuzzy
Fuzzy if and then rules (fuzzy rating)
Sl. no  BSFC  BThE  CO  NO_{x}  MPCI 

1  Bad  Bad  Bad  Bad  EB 
2  Bad  Bad  Bad  Average  VVB 
3  Bad  Bad  Average  Bad  VVB 
4  Bad  Bad  Bad  Good  VB 
5  Bad  Bad  Good  Bad  VB 
6  Bad  Bad  Average  Average  VB 
7  Bad  Bad  Average  Good  B 
8  Bad  Bad  Good  Average  B 
9  Bad  Bad  Good  Good  NGNB 
10  Bad  Average  Bad  Bad  VVB 
11  Bad  Average  Average  Bad  VB 
12  Bad  Average  Bad  Average  VB 
13  Bad  Average  Average  Average  B 
14  Bad  Average  Good  Bad  B 
15  Bad  Average  Bad  Good  B 
16  Bad  Average  Good  Average  NGNB 
17  Bad  Average  Average  Good  NGNB 
18  Bad  Average  Good  Good  G 
19  Bad  Good  Bad  Bad  VB 
20  Bad  Good  Bad  Average  B 
21  Bad  Good  Average  Bad  B 
22  Bad  Good  Good  Bad  NGNB 
23  Bad  Good  Bad  Good  NGNB 
24  Bad  Good  Average  Average  NGNB 
25  Bad  Good  Average  Good  G 
26  Bad  Good  Good  Average  G 
27  Bad  Good  Good  Good  VG 
28  Average  Bad  Bad  Bad  VVB 
29  Average  Bad  Bad  Average  VB 
30  Average  Bad  Average  Bad  VB 
31  Average  Bad  Bad  Good  B 
32  Average  Bad  Good  Bad  B 
33  Average  Bad  Average  Average  B 
34  Average  Bad  Average  Good  NGNB 
35  Average  Bad  Good  Average  NGNB 
36  Average  Bad  Good  Good  G 
37  Average  Average  Bad  Bad  VB 
38  Average  Average  Bad  Average  B 
39  Average  Average  Average  Bad  B 
40  Average  Average  Bad  Good  NGNB 
41  Average  Average  Good  Bad  NGNB 
42  Average  Average  Average  Average  NGNB 
43  Average  Average  Average  Good  G 
44  Average  Average  Good  Average  G 
45  Average  Average  Good  Good  VG 
46  Average  Good  Bad  Bad  B 
47  Average  Good  Bad  Average  NGNB 
48  Average  Good  Average  Bad  NGNB 
49  Average  Good  Bad  Good  G 
50  Average  Good  Good  Bad  G 
51  Average  Good  Average  Average  G 
52  Average  Good  Average  Good  VG 
53  Average  Good  Good  Average  VG 
54  Average  Good  Good  Good  VVG 
55  Good  Bad  Bad  Bad  VB 
56  Good  Bad  Bad  Average  B 
57  Good  Bad  Average  Bad  B 
58  Good  Bad  Bad  Good  NGNB 
59  Good  Bad  Good  Bad  NGNB 
60  Good  Bad  Average  Average  NGNB 
61  Good  Bad  Average  Good  G 
62  Good  Bad  Good  Average  G 
63  Good  Bad  Good  Good  VG 
64  Good  Average  Bad  Bad  B 
65  Good  Average  Bad  Average  NGNB 
66  Good  Average  Average  Bad  NGNB 
67  Good  Average  Bad  Good  G 
68  Good  Average  Good  Bad  G 
69  Good  Average  Average  Average  G 
70  Good  Average  Average  Good  VG 
71  Good  Average  Good  Average  VG 
72  Good  Average  Good  Good  VVG 
73  Good  Good  Bad  Bad  NGNB 
74  Good  Good  Bad  Average  G 
75  Good  Good  Average  Bad  G 
76  Good  Good  Bad  Good  VG 
77  Good  Good  Good  Bad  VG 
78  Good  Good  Average  Average  VG 
79  Good  Good  Average  Good  VVG 
80  Good  Good  Good  Average  VVG 
81  Good  Good  Good  Good  EG 
Eightyone rules are plotted as shown in Table 5.
Fuzzybased multiobjective genetics algorithm for the formulation of optimal process parameters
For the formulation of the multiobjective function and for the convergence of the solution grey relation was implemented. In the present work, the objective for three parameters (CO, NO_{x}, BSFC) were to minimize and for one parameter the objective was to maximize (BThE), which was obtained from the grey relation and grey relation grade was also generated from the equation. The equations for the each parameter are given below:
Compared with the singleobjective optimization, multiobjective optimization includes the synchronized numerous variables for optimization, which are frequently between two different states. With a specific range of the design constraints, multiobjective optimization implements an optimal but not the only solutions which meet all the boundaries.
In this work, the objective was to maximize BThE and to minimize the BSFC, CO, and NO_{x}; hence, the maximization of MPCI was attained by minimizing the fitness function F(y), i.e., F(y) = 1/ (1 + f(y)) so that the lower value can correlate to the higher value of the objective function. GA was used for generating the code also for the fitness function.
There are many factors that affect the fitness function of the GA. Like population model, size, crossover operator, and mutation operator. In GA, the accuracy of the model is depended on size of the population, crossover operator, and mutation operator. The larger size of the population and the crossover rate enables the broad area of explore of the solution and reduces the chances of selection of the poor solution.
The initial population is replenished with completely random solutions. Each individuals of the initial population represents the various process parameters. Parent selection is done mostly by fitness proportionate selection and every individual has chance to become a parent. In this process mating, propagating of their features to the next generation, each individual fitter gets higher chances. Hence, selection strategy is applied so that better individuals can be appointed over time to the more fit individuals in the population. Roulette wheel method was applied in this study for selection. The advantage of this method is that the probability for selecting each individual depends directly on its fitness and the chance of the better chromosomes for selection increases also probabilistically adds more instances of the strings to the mating pool. The individuals are stored in a mating pool, also the algorithm until it generates new population for the next generation.
Biological crossover and reproduction is analogous to the crossover operator. In this process, one or more offspring is produced using the genetic material of one or two parents. Higher probability of crossover (Pc) is generally used in GA (Roy et al. 2013).
A new solution was formed from the remaining solutions by crossover operator, which was available in the mating pool. Exchange of gene information between the solutions and the operator takes place in the mating pool. Any of two strings solution is chosen from the mating pool by the most popular crossover. Meanwhile, in some part of the string, exchange takes place. All the strings are not chosen for crossover. Randomly, crossover probability is used to choose the strings. Determination of the solution for crossover is obtained by the introduction of crossover probability.
In this work, the crossover probability was 0.8 which implies that the 80% of the chromosomes were taken for crossover, and the remaining were left for the next generation.
Diversity is maintained in the population, by introducing mutation into the solution strings of the population pool. The optimal solution was selected by mutation and crossover which was used for finding the optimal solution. Mutation probability (Pm), mutation operator varies from 0 to 1.Thevalue of mutation probability is generally kept low for steady convergence.
The results of the experimental and predicted data from developed model with optimal parameter
Parameters  BSFC  BThE  CO  NO_{x} 

Results obtained from GA  0.184  29.92  0.042  126.56 
Experimental result  0.192  29.5  0.046  120 
From Table 6, it can be seen that after validation, the results from GA and the experimental results can be related to each other which reflects the accuracy and significance of the work.
Conclusion

At higher load, among the blends D80B10Bu10 showed better cylinder pressure, heat release rate as well as BSFC, BThE.

Air excess ratio was higher for the bio diesel blends. Almost all the blends showed better emission results compared to diesel. BThE was higher for D80B10Bu10 at full load. CO, UHC was also reduced for the blends at higher loads. NO_{x} was higher for D80B20 at full load.

Multiobjective fuzzybased GA which was implemented from the input variables which showed reliable values of regression. A total of nine sets of membership function was introduced for fuzzy ranking of MPCI which showed 81 fuzzy rules. Gray relation was implemented for the conversion of multiobjective function to a single objective for the process parameters. The GA result was significant for the experimental and predicted data which converged after 89th iteration and resulted blend 1 as the optimal blend at 5.1 kg load, and the results from GA were found to be significant to the experimental data under the process parameters.
Notes
Acknowledgments
The authors would like to acknowledge the National Institute of Technology Agartala, India for the technical support for the work, also to the technical staffs who were associated to the work.
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