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Optimization of the factors affecting performance and emissions in a diesel engine using biodiesel and EGR with Taguchi method
Fuel 261 (2020) 116371
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Full Length Article
Optimization of the factors affecting performance and emissions in a diesel engine using biodiesel and EGR with Taguchi method
T
⁎
Vezir Ayhana, , Çiçek Çangalb, İdris Cesura, Aslan Çobana, Gökhen Ergena, Yusuf Çaya, Ahmet Kolipa, İbrahim Özserta a b
Sakarya University of Applied Science, Technology Faculty, Sakarya, Turkey Sakarya University of Applied Science, Graduate Education Institute, Sakarya, Turkey
A R T I C LE I N FO
A B S T R A C T
Keywords: Taguchi method Corn oil methyl ester EGR Performance Emissions
Biodiesel obtained from different vegetable or animal fats is a renewable fuel that can be used without any modification in diesel engines. And using biodiesel gives close performance data compared to diesel fuel. However, it increases the NOx (Nitrogen oxide) emissions, which is a major problem for diesel engines. Therefore, considering both the amount of biodiesel in blend fuel and NOx emissions reduction measures can be much more effective. If various factors are used together, it is necessary to determine the optimum conditions and utilization amounts considering performance and emissions. In this study, using different proportions of corn oil methyl ester (COME) blends (B0, B10, B20 and B50) and EGR (EGR0, EGR10, EGR15 and EGR20) on a direct injection (DI) diesel engine at variable loads (40%, 60%, 80% and 100%) and speed (1600 and 2400 rpm) conditions, the optimum factor levels in terms of performance and emission characteristics were determined by Taguchi method. As a result of the experimental design, L16 orthogonal array (OA) was found to be suitable and the studies were performed according to the combinations given in this array. The effect levels of factors were analyzed by ANOVA analysis. According to the results of the analysis, optimum levels of all factors were determined in terms of engine performance and emission characteristics.
1. Introduction
engine performance parameters [14,15], some of the other studies focused on the engine emission characteristics [16,17]. When the studies were examined, it was found that biodiesel gave positive results in terms of engine performance and emissions. Thus biodiesel can be considered an alternative and sustainable diesel engine fuel [18–20]. Noting, biodiesel was used in diesel fuel in most of the studies. Keskin and Ekşi [21] used biodiesel fuel produced from corn oil and determined that engine torque and effective power decreased and specific fuel consumption increased, CO and smoke emissions decreased and NOx emissions increased with biodiesel. Ayhan and Tunca [20] used different amounts of sunflower oil methyl ester blends in their experimental study using a DI diesel engine. They found the optimum biodiesel blend in terms of engine performance was B20 and reported increase in NOx emissions, despite the improvement in performance. Canakci [22] was investigated the effects of diesel and 20% soybean biodiesel blends on engine performance and emissions experimentally and determined increase in SFC about 2.9%, reduction in smoke, CO
Fossil fuels were one of the primary energy sources that meet the energy needs. However, due to industrialization and population growth, the available resources are reduced rapidly. The use of alternative fuels have become widespread in internal combustion engines due to the reduction of fuel reserves, updated legal regulations on emissions and energy policies. Biodiesel is one of the alternative fuels that can be used in diesel engines [1–5]. Also, vegetable oils are renewable and biodegradable, contain no toxic substances, and have low emission profiles other than NOx [6–10] and their use in diesel engines are spreading [11–13]. In some countries, sellingdiesel fuel with biodiesel addition at different ratios in petroleum stations has become a legal requirement. There are many studies on the testing of methyl/ethyl esters derived from vegetable and animal fats in diesel engines. While some researchers focused on the effects of vegetable oils and their esters on
Abbreviations: ANOVA, analysis of variance; B10, 90% diesel + 10% biodiesel; COME, corn oil methyl ester; DI, direct injection; EGR, exhaust gas recirculation; OA, orthogonal array; SFC, specific fuel consumption; bTDC, before top dead center; rpm, revolutions per minute; NOx, nitrogen oxide; NO, nitrogen monoxide; CO, Carbon monoxide; CO2, Carbon dioxide; SOI, start of injection; °CA, degrees crank angle ⁎ Corresponding author. E-mail address: [email protected] (V. Ayhan). https://doi.org/10.1016/j.fuel.2019.116371 Received 15 May 2019; Received in revised form 11 September 2019; Accepted 7 October 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.
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are performed on DI diesel engine operating under different loads at maximum torque and speeds at maximum effective power. The factor levels that make each parameter optimum have been determined by considering the Signal/Noise (S/N) ratios proposed by the Taguchi method. The effects of factors and levels were determined by ANOVA analysis. Approximately 70% reduction in time and cost of experimental studies was achieved.
and HC emissions while increase in NOx emissions around 11.2% for 20% blend. Özener et al. [23] was tested diesel and biodiesel fuel blends (B10, B20 and B50) in a DI diesel engine and reported that engine torque decreased, SFC increased, CO, HC emission decreased, but NOx emissions increased up to 17.5% with using biodiesel blends. Al-Dawody and Bhatti [24] were carried out engine tests on a single cylinder diesel engine, and used diesel and the blends of diesel-biodiesel as fuel. They found that soot emissions reduced, but SFC and NOx emissions increased for all biodiesel fuels compared to diesel fuel. Nagaraja et al. [25] investigated emission and performance of single cylinder diesel engine fueled with diesel-COME biodiesel blends and stated the results give clear information that COME has lower exhaust emissions and causes increase in performance without any modifications. As seen in the literature, using biodiesels in diesel engines increase NOx emissions. Thus, this drawback is required to be overcome. One of the best ways to achieve is using of exhaust gas recirculation (EGR) [26–28]. So that, the NOx emissions can be significantly reduced. However, when the EGR is used at high rates, it worsens engine performance and other emissions [29,29]. Haşimoğlu et al. [26] investigated experimentally the effect of using EGR on engine performance and exhaust emissions in diesel engines and observed a significant decrease in NOx emissions with EGR application. He et al. [30] conducted an experimental study by applying EGR at different rates on a single cylinder diesel engine and found better results for engine performance and emissions than the standard situation by using EGR up to a certain rate. Venu and et al. [31] investigated the effects of diesel-biodiesel blends, EGR and nanoparticle additives on performance and emissions in a four stroke, air cooled DI diesel engine. They determined that for PBN-EGR (30% palm biodiesel +70% diesel fuel +25 ppm TiO2 – EGR), there is only a marginal drop in cylinder pressure whereas the heat release is higher in comparison with biodiesel. SFC was found higher and NOx emissions were found lower for PBN-10EGR and PBN-20EGR than PBN full load conditions. Can et al. [32] tested in a four-stroke DI diesel engine fueled by diesel-soybean biodiesel blend, diesel and soybean by 20% volume, at different EGR rates (5, 10, 15%) and at 2200 rpm speed, and found, when used together with biodiesel blends and EGR the maximum heat release rate and maximum in-cylinder pressure and SFC increased, NOx and smoke emissions were improved simultaneously at the high engine load. In this study, unlike the studies given above, with quite fewer experimental study steps, the optimum levels of much more factors and levels were determined. The experimental design of the study was done by Taguchi method. This method allows the detection and optimization of factor levels that give optimum performance characteristics by testing the combinations of the orthogonal array instead of testing each level of the factors in the experiments separately and in combination [33]. Because the results can be obtained with high accuracy by a lesser number of experiments, it provides a significant reduction in time and costs in experimental studies and is used by researchers for different applications in many areas [34–36]. Nowadays, Taguchi method is used in studies related to internal combustion engines [37,38]. Ansari et al. [39] studied the changes in engine performance and emissions on a diesel engine using different biodiesel blends using the Taguchi method and optimized the factor levels to give the best engine performance and emission. Ayhan et al. [20] examined the effects of different fuel injection timing and steam injection rates on performance and emissions in a diesel engine and determined the factor and levels for the optimum engine performance and NOx emissions by Taguchi method. Wu and Wu [35] determined the effects of different ratios of biodiesel-diesel and H2 blend and different ratios of EGR on engine emissions and combustion performance in a single cylinder diesel engine and used the L9 orthogonal array. And time saving by 67% is obtained using the Taguchi experiment design method in experiments. In this study, were investigated the effects of different ratios of biodiesel blends and different EGR ratios on performance parameters and emission characteristics by using Taguchi method. Therefore, tests
2. Materials and methods 2.1. Production of COME COME obtained from corn oil by using transesterification method was used. In transesterification process Methyl alcohol and KOH catalyst was used. Premixed methyl alcohol and catalysts mixtures were poured on glass beaker. After Corn oil (CO) was heated, the prepared alcohol–catalyst mixture was added into CO. The mixture was stirred and then it was taken into the separator funnel and waited until ester–glycerin separation took place. Then, hot-sterile distilled water was added to COME. After this process, COME–water mixture was left to settle, then pure water and glycerin glimmers were removed. Finally, COME sample was dried by heating. Table 1 shows the properties of the fuels used in the study. 2.2. EGR application In the experiments, 10%, 15%, 20% EGR were applied into the engine. In each step EGR application, standard engine experiments were carried out and the amount of CO2 exhausted for each test step was measured. So, the EGR ratio, according to the measured values, is calculated. In order to determine the EGR ratio sent into the suction line, the equation given below (Eq. (1)) is used.
EGR (%) =
[(CO2 )intake manifold − (CO2 )ambient ] × 100 [(CO2 )exhaust manifold ]
(1)
For the application of the EGR, the required connection pipes and the EGR cooler are installed to transfer the exhaust gases into the intake manifold. To adjust the EGR ratios, a multi-turn, high precision manual control valve is added to the system. 2.3. Experimental setup In the tests a single cylinder, four stroke DI diesel engine was used. The specifications of the test engine are given in Table 2. The engine, which is water-cooled, was allowed to come to the regime temperature before starting the experiments. For this reason, water and oil conditioning systems were connected to the test setup to keep water and oil temperature constant at 85 °C during the experiments. The engine was loaded with a 50 kW hydraulic dynamometer and the power was determined by 0.1 kg sensitivity S type load cell. All the temperatures were measured using NiCr-Ni type thermo-couples. Before, the experiments were performed to determine the optimum fuel injection timing and it was checked to be at 29°CA BTDC) (crank angle before top dead center). Bosch BAE 060 emission device was used for the measurement of NO, HC, CO, CO2, and Bosch BAE 070 analyzer was used for the measurements of smoke. Fuel consumption was measured Table 1 Properties of diesel and biodiesel fuel.
2
Property
Diesel
COME-Biodiesel
Density (15 °C) Cetane number Kinematic viscosity mm2/s, (40 °C) Flash point (°C) Lower heating value (kJ/kg)
0.82–0.86 49 2.5–3.5 > 55 42,640
0.87–0.88 > 55 4.3 > 100 39,576
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Table 2 Specification of the test engine.
Table 3 The errors in parameters and total uncertainties.
Engine Type
Super Star
Parameters
Systematic Errors, ±
Bore [mm] Stroke [mm] Cylinder number Stroke volume [l] Power −2400 rpm, [kW] Injection pressure, [bar] Injection advance, [°CA bTDC] Maximum speed, [rpm] Cooling type Injection type
108 100 1 0.92 12 225 29 2500 Water DI
Load, kg Speed, rpm Time, s Temperature, °C Fuel consumption, s NO, ppm HC, ppm O2 CO, % CO2, % Smoke, %
0.1 1.0 0.1 1.0 0.01 5% of measured value 5% of measured value Vol. %0.1 Vol. %0.03 Vol. %0.5 1% Total uncertainty, % 1.5 1.3 1.5
Specific fuel consumption, g/kWh Brake power, kW Effective efficiency, %
with a volumetric flow measurement system. Air consumption was measured with a surge tank and a mass flow measurement device. The experiments were carried out at 1600 and 2400 rpm engine speeds and at 40%, 60%, 80% and 100%, loads conditions. Engine tests were performed according to L16 orthogonal array determined by Taguchi method. A schematic illustration of the experimental setup is shown in Fig. 1. The measurements were repeated 5 times for each test point and the average has been considered. All the total uncertainties of performance characteristics are calculated as described above. The accuracies and total uncertainties of characteristics calculated with respect to measured values are shown in Table 3.
Table 4 Factors and their levels. Symbol
Factor
Level 1
Level 2
Level 3
Level 4
A B C D
Engine Load, % Blend,% EGR ratio, % Engine Speed, rpm
40% B0 EGR0 1600
60% B10 EGR10 2400
80% B20 EGR15 –
100% B50 EGR20 –
experiment plans in Taguchi experimental design, multiple factors and factor levels are performed with a small number of experiments. The orthogonal array is the mapping of levels of a factor to levels of other factors during the trial [40]. In this study, 3 factors by 4 levels, 1 factor by 2 levels were selected and input to Minitab software and according to these factor and level numbers, a suitable orthogonal array (OA) was created by the program. The OA expressed as L16 (4^3) (2^1) is given in Table 5. According to this table, 16 experiments were performed with 3 repetitions in random order. As a result of the data obtained from the engine tests, the engine performance parameters, Effective power,
2.4. Design of experiments with Taguchi method Experimental design was done with Taguchi method. The controllable factors and levels selected in the study are given in Table 4. For factors and their levels; diesel-corn oil biodiesel blends (4 levels; B0, B10, B20 and B50), EGR (4 levels; EGR0, EGR10, EGR15 and EGR20), engine loads (4 levels; 40%, 60%, 80% and 100%) and engine speeds (2 levels; 1600 rpm – test engine maximum torque and 2400 rpm – maximum effective power) were selected. The large number of experiments in full factorial experimental plans has negative consequences in terms of time and testing costs. Thanks to the orthogonal
Fig. 1. Test setup. 3
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Table 5 L16 Orthogonal array for experiments. Exp. No
⎡1 S/N = −10log ⎢ n ⎣
Factors
n
∑ i=1
1⎤ yi2 ⎥ ⎦
(2)
Lower is better; 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
A
B
C
D
1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 2 1 4 3 3 4 1 2 4 3 2 1
1 1 2 2 2 2 1 1 1 1 2 2 2 2 1 1
⎡1 S/N = −10log ⎢ n ⎣
n
∑ yi2 ⎤⎥ i=1
⎦
(3)
where, n , yi and i indicate the number of repetitions in a trial under the same conditions, the measured value, and the number of design parameters in the OA, respectively. S/N ratios were analyzed for each performance parameter and emission. According to the results, the optimum combinations were determined and Analysis of Variance (ANOVA) was performed to determine the percentage effect of the factors. The equations used in the ANOVA analysis are given in (4)–(8). N
T2 ⎡ ⎤ SS T = ⎢∑ (S/N)i2⎥ − N ⎣ i=1 ⎦
(4)
K
specific fuel consumption (SFC) and effective efficiency, were calculated for each test step. Emission characteristics, NO, HC, CO2, CO and smoke emissions, were measured with gas analyzers. All the parameters were found and the analyzes were made according to the average values of the 3 repetitions. The most important feature of Taguchi's experimental design method that distinguishes it from conventional design methods is the generic Signal-to-Noise (S/N) ratio, which determines the performance criterion [41]. There are several S/N ratios available depending on the type of characteristics including ‘Higher is Better’ (HB), ‘Lower is Better’ (LB), and ‘Nominal is the Best’ (NB). Accordingly, for the effective power, specific fuel consumption, effective efficiency, NO, HC, CO2, CO and smoke emissions appropriate S/N ratio calculation formulas were determined. Because of the high level of results to be obtained for effective power and effective efficiency, Taguchi's ‘Higher is Better’ formula was used. Similarly, for SFC and emission characteristics, ‘Lower is Better’ formula was used because the desired values were the lowest values. The formulas of S/N ratios below are given by Eqs. (2) and (3). Higher is better;
A A2 ⎤ T2 ⎡ SSA = ⎢∑ ⎜⎛ i ⎟⎞ ⎥ − n N ⎣ i = 1 ⎝ Ai ⎠ ⎦
(5)
vtotal = N − 1
(6)
Vfactor =
SSfactor ϑfactor
(7)
Ffactor =
Vfactor Verror
(8)
where, SS T is the sum of the squares with total variability, N is the total number of experiments, SSA is the sum of the squares due to the factor A, KA is the number of levels for factor A, Ai is the sum of the total level of the factor A and nAi is the sum of the factor A levels. T represents the sum of the total (S/N) ratio of the experiments and ϑ indicates the degrees of freedom. Vfactor indicates the variance of the factor. SSfactor is the sum of the squares of the factor and Ffactor is the factor’s F ratio. The levels of factors according to the Taguchi method are significant in the 90% to 99% confidence interval. In Taguchi methods, the levels of factors given in the ANOVA are meaningful according to 90% and 99% confidence intervals. Design of experiments is done considering these confidence limits.
Fig. 2. S/N values of factor levels for effective power. 4
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Fig. 3. S/N values of factor levels for SFC.
Fig. 4. S/N values of factor levels for effective efficiency.
3. Results and discussion
biodiesel. In the literature, using biodiesel mixtures has been stated to cause effective power increases due to the improvement in the efficiency of combustion [13,20]. Biodiesel in the blend fuel is more than 20%, and since the lower heating value is lower than diesel fuel, less heat addition causes decreases in the torque and effective force. Fig. 3 shows the factors affecting engine specific fuel consumption by applying the Taguchi method. The optimum value of the SFC was obtained at 80% load, 1600 rpm, B10 biodiesel mixture and EGR0 ratio. The best combination is “A3-B3-C1-D1”. In general, in the engine characteristics curves the maximum torque has been obtained at medium engine speed for atmospheric compression ignition direct injection engines. In the test engine, the highest torque has been obtained at 1600 rpm. The torque decreases as the speed increases above this speed. This is due to a reduction in the amount of mixture entering the cylinder, causes the decrease in the volumetric efficiency and increased friction losses of the engine. The effective power depends on the torque as well as on the engine speed
3.1. Performance parameters The optimum factor levels obtained in terms of performance parameters as a result of the experiments carried out by using Taguchi method are given in the figures below. Fig. 2 shows the changes in factors and their levels affecting the engine effective power using Taguchi method. In the figure, the factor and the optimum factor levels were determined in terms of effective power, 100% at full load, 2400 rpm at engine load, B20 mixture and EGR0 (standard condition). The best combination of factors for effective power is “A4-B3-C1-D2”. As a result of experimental studies, it is seen that when different rates of biodiesel blends are used, the effective power increases compared to the standard engine data. It is determined that the increases up to B20, and so the best level of B20 fuel is related to the amount of oxygen contained in 5
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Table 6 The Analysis of variance (ANOVA) for performance parameters.
Effective Power
Effective Efficiency
SFC
Factors
Sum of Squares, SS
Degree of Freedom, ϑ
Squares Mean, V
Ffactor
A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error
133,269 1,325 0,541 13,582 149,029 0,313 7,5178 0,1172 3,8157 5,6690 19,6388 2,5191 7,5178 0,2160 3,8157 5,6690 19,7376 2,5191
3 3 3 1 15 5 3 3 3 1 15 5 3 3 3 1 15 5
44,423 0,442 0,180 13,582 9,935 0,063 2,505 0,039 1,271 5,668 1,308 0,503 2,5059 0,0720 1,2719 5,6689 1,315 0,503
709,462*** 7,053* 2,879 216,910***
4,97 0,08 2,52 11,25*
4,97 0,14 2,52 11,25*
*%95, **%99, ***%99.99 confidence.
Fig. 5. S/N values of factor levels for NO emission.
100% load conditions. When biodiesel blends were used, SFC was reduced when B10 and B20 were used, and while in B50, SFC slightly increased compared to standard engine data. It is seen that this change is caused by the factors affecting the effective power. Therefore, the effective efficiency has reached its maximum value of 80% load. Fig. 4 shows the changes in the factors and levels affecting the engine effective efficiency. The optimum value of the effective efficiency was obtained at 80% load, 1600 rpm, B20 biodiesel blend and EGR0 ratio. The best combination of factor levels is “A3-B3-C1-D1”. At full load condition of the engine load, the engine works with a richer mixture than at 80% load. As the effective power increased in the use of B20 blend, the efficiency increase was obtained due to the increase in power at the same fuel flow rate. Since EGR affects the performance of the engine negatively, it is seen that the efficiency is the maximum for EGR0. The maximum value of the effective efficiency is reached at 1600 rpm. The results of the ANOVA analysis of the data obtained from the experiments are given in Table 6. According to these results, it is determined that the factors are effective in the range of 95% to 99.99% for effective power and specific fuel consumption.
(number of cycles per unit time). As the speed increases, the effective power increases due to the increase in the number of cycles. SFC is obtained at a speed between maximum torque and maximum power conditions, close to the maximum torque condition. In this study, minimum SFC was obtained at 1600 rpm by considering the selected factors. The best values of effective power and specific fuel consumption are obtained in standard cases where EGR is not applied. When different EGR ratios were applied into the engine, the engine performance parameters deteriorated. Since, the oxygen content in the mixture decreased and the burning speed slowed down. Increasing EGR ratios, especially at high speeds, cause the mixture to become poor and the combustion to deteriorate. The effective power from diesel engines varies linearly with the amount of fuel delivered into the cylinder, up to a certain load value. The fuel flow rate and the effective power change linearly up to approximately 60% load, after which the slope of the curve changes. The increase and change in the slope of the curve indicate that the fuel conversion efficiency was deteriorated. As seen in Fig. 4, the minimum SFC was obtained under 80% load. After this load, because the entire fuel injected to the cylinder is not converted into effective power, SFC and the effective efficiency was deteriorated under 6
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Fig. 6. S/N values of factor levels for Smoke emission.
Fig. 7. S/N values of factor levels for HC emission.
the lowest NO was obtained with standard diesel fuel. EGR is the most efficient method for reducing NO emissions from diesel engines. With EGR, as the specific heat capacity of the in-cylinder gas mixture increases, the maximum temperature in the cylinder is reduced. Therefore, as the EGR ratio increases, NO emissions decrease. The minimum NO was obtained at the highest level of EGR in study. The second parameter effective in the formation of NO emissions is the amount of reacted oxygen and nitrogen. As the engine speed increases, less air is supplied to the cylinder, since the volumetric efficiency is reduced. In the study, the minimum NO was measured when the engine speed was high. Fig. 6 illustrates the changes in the factors and levels affecting the smoke emission. The optimum value of the smoke emission was obtained at 40% load value, 1600 rpm, B10 biodiesel blend and EGR0 ratio. The best combination is “A1-B2-C1-D1”. Fig. 7 shows the changes in the factors and levels affecting HC emission. The optimum value of HC emission was obtained at 40% load
Engine load and speed change were the most effective parameters in terms of effective power as expected, while other factors were significant. Biodiesel blends were found to be less effective in terms of efficient efficiency and SFC.
3.2. Emission characteristics Fig. 5 shows the factors and levels affecting NO emission. The optimum value of NO emission was obtained at 2400 rpm, B0 biodiesel mixture and EGR20 ratio at 40% load value. The best combination is “A1-B1-C4-D2”. The most important factor that accelerates the formation reactions of NO emission is the high temperatures formed in the cylinder. NO emissions increase as the engine load increases, causing increasing temperatures. Examining the Fig. 5, the minimum NO was obtained at the lowest engine load value. Using biodiesel blends increased NO emissions due to oxygen content of biodiesel. In the experiment results, 7
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Fig. 8. S/N values of factor levels for CO emission.
Fig. 9. S/N values of factor levels for CO2 emission.
effective in the exhaust emissions between 95% and 99.99% confidence. When Table 6 is examined, the effect levels and significance values of each factor according to ANOVA can be seen.
value, 1600 rpm, B50 biodiesel blend and EGR0 ratio. The best combination of factor level is “A1-B4-C1-D1”. In the Fig. 8, the factors and levels affecting CO emission was given and the optimum value of CO emission was obtained at 40% load value, 1600 rpm, B50 biodiesel blend and EGR0 ratio. The best combination of factor levels is “A1-B4-C1-D1”. With the application of EGR, smoke, CO and HC emissions increase. The reason for this is the lack of complete combustion and the decrease in the amount of oxygen in the cylinder and the decrease of the flame temperature due to the effect of EGR. By using biodiesel blends as fuel, CO and HC emissions decreased. This is considered to be reasoned by the improved combustion effect of biodiesel due to oxygen content. In the Fig. 9 the factors and levels affecting CO2 emissions are depicted. The optimum value of CO2 emission was obtained at 40% load value, 1600 rpm, B0 (diesel) fuel and EGR0 ratio. The best combination is “A1-B1-C1-D1”. The results of ANOVA analysis for emission characteristics are given in Table 7. According to these results, the factors are found to be
4. Conclusions In this study, the effect of different biodiesel blends and EGR ratios for a DI diesel engine running at different loads and speeds by using the Taguchi experiment design method and the changes in engine performance parameters and emission characteristics were investigated. Examining the obtained data and the results of the analysis, it has been determined that engine load, engine speed, different biodiesel-diesel mixtures and different EGR ratios are effective on engine performance parameters and emissions. In general, the results obtained are as follows; – The Taguchi experimental design method revealed that choosing the optimum factor levels are important in terms of the maximization of 8
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Table 7 The Analysis of Variance (ANOVA) for emissions.
NO
HC
CO2
CO
Smoke
Factors
Sum of Squares, SS
Degree of Freedom, ϑ
Squares Mean, V
Ffactor
A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error
4,859 4,907 121,974 43,011 181,136 6,384 153,48 28,73 21,18 97,65 320,96 19,91 82,214 8,295 23,829 6,838 128,358 7,181 952,97 3,27 163,67 67,14 1419,27 232,23 447,475 70,815 232,367 4,924 772,819 17,238
3 3 3 1 15 5 3 3 3 1 15 5 3 3 3 1 15 5 3 3 3 1 15 5 3 3 3 1 15 5
1,62 1,636 40,658 43,011 12,07573 1,277 51,162 9,576 7,061 97,648 21,39733 3,983 27,405 2,765 7,943 6,838 8,5572 1,436 317,655 1,09 54,556 67,142 94,618 46,445 149,158 23,605 77,456 4,924 51,52127 3,448
1,27 1,28 31,84** 33,69**
12,85** 2,4 1,77 24,52**
19,08** 1,93 5,53* 4,76
6,84* 0,02 1,17 1,45
43,26*** 6,85* 22,47** 1,43
*%95, **%99, ***%99.99 confidence.
– – – – – – –
effective power, effective efficiency and the minimization of SFC and exhaust emissions. The optimum results for effective power were obtained at full load condition, in B20 fuel, when EGR was not applied and at 2400 rpm. The optimum results for SFC and effective efficiency were obtained at 80% load, B10 fuel, EGR and 1600 rpm conditions. The best results in terms of NO emissions were determined at 40% partial load, B0-diesel fuel, 20% EGR ratio and 2400 rpm. The best results for smoke emissions were obtained at 40% partial load, B10 fuel, no EGR applied and at 1600 rpm. The optimum values of HC and CO emissions were determined at 40% partial load, B50 fuel, no EGR applied and 1600 rpm. The optimum values of CO2 emissions were determined at 40% partial load, diesel fuel, no EGR applied and 1600 rpm. In this study, experimental studies were done by using Taguchi statistical experimental design method, saving 70% in terms of time and cost. The experiments were carried out between 95% and 99.99% confidence interval. The method can demonstrate the interactions between performance parameters and factors that affect emission characteristics.
[6] Buyukkaya E. Effects of biodiesel on a DI diesel engine performance, emission and combustion characteristics. Fuel 2010;89:3099–105. [7] Nagaraja S, SooryaPrakash K, Sudhakaran R, SathishKumar M. Investigation on the emission quality, performance and combustion characteristics of the compression ignition engine fueled with environmental friendly corn oil methylester – diesel blends. Ecotoxicol Environ Safety 2016;134:455–61. [8] Selvan V Arul Mozhi, Anand RB, Udayakumar M. Combustion characteristics of diesohol using biodiesel as an additive in a direct injection ignition engine under various compression ratios. Energy Fuels 2009;23:5413–22. [9] Ozsezen AN, Canakci M. Determination of the performance and combustion characteristics of a diesel engine with canola and waste palm oil methyl esters. Energy Conserv Manage 2011;52:108–16. [10] Devan PK, Mahalakshmi NV. Performance emission and combustion characteristics of poon oil and its diesel blends in a DI diesel engine. Fuel 2009;88:861–7. [11] Hasimoglu C, Ciniviz M, Ozsert I, Içingur Y, Parlak A, Sahir Salman M. Performance characteristics of a low heat rejection diesel engine operating with biodiesel. Renewable Energy 2008;33:1709–15. [12] Karaosmanoğlu F, Kurt G, Özaktaş T. Long term CI engine test of sunflower oil. Renewable Energy 2000;19:219–21. [13] Parlak A, Karabaş H, Özsert İ, Ayhan V, Cesur İ. Application of Taguchi’s methods to investigate factors affecting emissions of a diesel engine running with tobacco oil seed methyl ester. Int J Vehicle Design 2012;59(2/3). [14] Nwafor OMI, Rice G. Performance of rape seed oil blends in a diesel engine. Appl Energy 1996;54:345–54. [15] Sapaun SM, Masjuki HH, Azlan A. The use of palm oil as diesel fuel substitute. J Power Energy – Part A 1996;210:47–53. [16] Murayama T, Oh Y-T, Miyamoto N, Chikahisa T, Takagi, N. Low carbon flower build up, low smoke and efficient diesel operation with vegetable oils by conversion to monoesters and blending with diesel oil or alcohols. SAE Paper 841161; 1984. [17] Ali Y, Hanna Milford A, Borg Joseph E. Optimization of diesel. Methyl tallowate and ethanol blend for reducing emissions from diesel engine. Bioresour Technol 1995;52:237–43. [18] Manigandan S, Gunasekar P, Devipriya J, Nithya S. Emission and injection characteristics of corn biodiesel blends in diesel engine. Fuel 2019;235:723–35. [19] Usta N, Can Ö, ve Özgtürk E. Comparison of biodiesel and ethanol as alternative diesel engine fuel, Pamukkale University Faculty of Engineering. J Eng Sci 2005(3). [20] Ayhan V, Tunca S. Experimental investigation on using emulsified fuels with different biofuel additives in a DI diesel engine for performance and emissions. Appl Therm Eng 2018;129:841–54. [21] Keskin A, Ekşi K. The effect of the use of corn oil biodiesel as fuel on diesel engine performance and emission in diesel engines. Celal Bayar Univ J Sci 2006;2(1):49–55. [22] Canakci M. Performance and emissions characteristics of biodiesel from soybean oil. P I Mech Eng D-J Aut 2005;219:915–22. [23] Özener O, Yüksek L, Ergenç AT, Özkan M. Effects of soybean biodiesel on a DI diesel
References [1] Abed KA, Gad MS, El Morsi AK, Sayed MM, Elyazeed SA. Effect of biodiesel fuels on diesel engine emissions. Egypt J Pet 2019. https://doi.org/10.1016/j.ejpe.2019.03. 001. [2] Hirkude JB, Padalkar AS. Performance optimization of CI engine fuelled with waste fried oil methyl ester-diesel blend using response surface methodology. Fuel 2014;119:266–73. [3] Raheman H, Ghadge SV. Performance of diesel engine with biodiesel at varying compression ratio and ignition timing. Fuel 2008;87:2659–66. [4] Lebedevas S, Vaicekauskas A, Lebedeva G, Makareviciene V, Janulis P. Change in operational characteristics of diesel engines running on RME biodiesel fuel. Energy Fuel 2007;21:3010–6. [5] Makarevičienė V, Lebedevas S, Rapalis P, Gumbyte M, Skorupskaite V, Žaglinskis J. Performance and emission characteristics of diesel fuel containing microalgae oil methyl esters. Fuel 2014;120:233–9.
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[32] Can Ö, Öztürk E, Solmaz H, Aksoy F, Çinar C, Yücesu HS. Combined effects of soybean biodiesel fuel addition and EGR application on the combustion and exhaust emissions in a diesel engine. Appl Therm Eng 2016;95:115–24. [33] Sarpkaya Ç. Optimization of sizing process by gray relations analysis based on Taguchi method Ph.D. Thesis Cukurova University, Institute of Science and Technology; 2014. [34] Wu Z, Wu H, Hung CH. Applying Taguchi method to combustion characteristics and optimal factors determination in diesel/biodiesel engines with port-injecting LPG. Fuel 2014;117:8–14. [35] Wu H, Wu Z. Using Taguchi method on combustion performance of a diesel engine with diesel/biodiesel blend and port-inducting H2. Appl Energy 2013;104:362–70. [36] Lee DH, Park JS, Ryu MR, Park JH. Development of a highly efficient low-emission diesel engine-powered co-generation system and its optimization using Taguchi method. Appl Therm Eng 2013;50:491–5. [37] Balkı M, Sayın C, Sarıkaya M. Optimization of the operating parameters based on Taguchi method in an SI engine used pure gasoline, ethanol and methanol. Fuel 2016;180:630–7. [38] Ganapathy T, Murugesan K, Gakkhar RP. Performance optimization of jatropha biodiesel engine model using Taguchi approach. Appl Energy 2009;86:2476–86. [39] Ansari NA, Sharma A, Singh Y. Performance and emission analysis of a diesel engine implementing polanga biodiesel and optimization using Taguchi method. Process Saf Environ Prot 2018;120:146–54. [40] İzgiz S. Experiment design and Taguchi method-optimization of products and processes. İstanbul: Federal Mogul; 2001. [41] Şirvancı M. Experimental design for quality. 1st ed. Istanbul: Literatür Publications; 1997.
engine performance, emission and combustion characteristics. Fuel 2014;115:875–83. Al-Dawody MF, Bhatti SK. Optimization strategies to reduce the biodiesel NOx effect in diesel engine with experimental verification. Energy Convers Manag 2013;68:96–104. Nagarajan S, Soorya Prakash K, Sudhakaran R, Sathish Kumar M. Investigation on the emission quality, performance and combustion characteristics of the compression ignition engine fueled with environmental friendly corn oil methyl ester – diesel blends. Ecotoxicol Environ Saf 2016;134:455–61. Haşimoğlu C, İçingür Y, ve Öğüt H. Experimental analysis of the effect of exhaust gas recirculation (EGR) in diesel engines on engine performance and exhaust emissions, TÜBİTAK; 2002, 26, pp. 127–135. Tat ME. Investigation of oxides of nitrogen emissions from biodiesel-fueled engine Ph. D. Iowa State University; 2003. p. 41. Ayhan V, Özsert İ, Çay Y, Çoban A, ve Cesur İ. Determination of optimum steam injection rate and fuel injection timing in a DI diesel engine with the Taguchi method experimentaly. J New Results Eng Nat Sci 2018;8:128–36. Tok A. Investigation of the effect of exhaust gas recirculation (EGR) on engine performance Master Thesis Uludag: University, Institute of Science and Technology, Department of Mechanical Engineering; 2010. He T, Chen Z, Zhu L, Zhang Q. The influence of alcohol additives and EGR on the combustion and emission characteristics of diesel engine under high-load condition. Appl Therm Eng 2018;140:363–72. Venu H, Subramani L, Raju VD. Emission reduction in a DI diesel engine using exhaust gas recirculation (EGR) of palm biodiesel blended with TiO2 nano additives. Renewable Energy 2019;140:245–63.
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Fuel journal homepage: www.elsevier.com/locate/fuel
Full Length Article
Optimization of the factors affecting performance and emissions in a diesel engine using biodiesel and EGR with Taguchi method
T
⁎
Vezir Ayhana, , Çiçek Çangalb, İdris Cesura, Aslan Çobana, Gökhen Ergena, Yusuf Çaya, Ahmet Kolipa, İbrahim Özserta a b
Sakarya University of Applied Science, Technology Faculty, Sakarya, Turkey Sakarya University of Applied Science, Graduate Education Institute, Sakarya, Turkey
A R T I C LE I N FO
A B S T R A C T
Keywords: Taguchi method Corn oil methyl ester EGR Performance Emissions
Biodiesel obtained from different vegetable or animal fats is a renewable fuel that can be used without any modification in diesel engines. And using biodiesel gives close performance data compared to diesel fuel. However, it increases the NOx (Nitrogen oxide) emissions, which is a major problem for diesel engines. Therefore, considering both the amount of biodiesel in blend fuel and NOx emissions reduction measures can be much more effective. If various factors are used together, it is necessary to determine the optimum conditions and utilization amounts considering performance and emissions. In this study, using different proportions of corn oil methyl ester (COME) blends (B0, B10, B20 and B50) and EGR (EGR0, EGR10, EGR15 and EGR20) on a direct injection (DI) diesel engine at variable loads (40%, 60%, 80% and 100%) and speed (1600 and 2400 rpm) conditions, the optimum factor levels in terms of performance and emission characteristics were determined by Taguchi method. As a result of the experimental design, L16 orthogonal array (OA) was found to be suitable and the studies were performed according to the combinations given in this array. The effect levels of factors were analyzed by ANOVA analysis. According to the results of the analysis, optimum levels of all factors were determined in terms of engine performance and emission characteristics.
1. Introduction
engine performance parameters [14,15], some of the other studies focused on the engine emission characteristics [16,17]. When the studies were examined, it was found that biodiesel gave positive results in terms of engine performance and emissions. Thus biodiesel can be considered an alternative and sustainable diesel engine fuel [18–20]. Noting, biodiesel was used in diesel fuel in most of the studies. Keskin and Ekşi [21] used biodiesel fuel produced from corn oil and determined that engine torque and effective power decreased and specific fuel consumption increased, CO and smoke emissions decreased and NOx emissions increased with biodiesel. Ayhan and Tunca [20] used different amounts of sunflower oil methyl ester blends in their experimental study using a DI diesel engine. They found the optimum biodiesel blend in terms of engine performance was B20 and reported increase in NOx emissions, despite the improvement in performance. Canakci [22] was investigated the effects of diesel and 20% soybean biodiesel blends on engine performance and emissions experimentally and determined increase in SFC about 2.9%, reduction in smoke, CO
Fossil fuels were one of the primary energy sources that meet the energy needs. However, due to industrialization and population growth, the available resources are reduced rapidly. The use of alternative fuels have become widespread in internal combustion engines due to the reduction of fuel reserves, updated legal regulations on emissions and energy policies. Biodiesel is one of the alternative fuels that can be used in diesel engines [1–5]. Also, vegetable oils are renewable and biodegradable, contain no toxic substances, and have low emission profiles other than NOx [6–10] and their use in diesel engines are spreading [11–13]. In some countries, sellingdiesel fuel with biodiesel addition at different ratios in petroleum stations has become a legal requirement. There are many studies on the testing of methyl/ethyl esters derived from vegetable and animal fats in diesel engines. While some researchers focused on the effects of vegetable oils and their esters on
Abbreviations: ANOVA, analysis of variance; B10, 90% diesel + 10% biodiesel; COME, corn oil methyl ester; DI, direct injection; EGR, exhaust gas recirculation; OA, orthogonal array; SFC, specific fuel consumption; bTDC, before top dead center; rpm, revolutions per minute; NOx, nitrogen oxide; NO, nitrogen monoxide; CO, Carbon monoxide; CO2, Carbon dioxide; SOI, start of injection; °CA, degrees crank angle ⁎ Corresponding author. E-mail address: [email protected] (V. Ayhan). https://doi.org/10.1016/j.fuel.2019.116371 Received 15 May 2019; Received in revised form 11 September 2019; Accepted 7 October 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.
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are performed on DI diesel engine operating under different loads at maximum torque and speeds at maximum effective power. The factor levels that make each parameter optimum have been determined by considering the Signal/Noise (S/N) ratios proposed by the Taguchi method. The effects of factors and levels were determined by ANOVA analysis. Approximately 70% reduction in time and cost of experimental studies was achieved.
and HC emissions while increase in NOx emissions around 11.2% for 20% blend. Özener et al. [23] was tested diesel and biodiesel fuel blends (B10, B20 and B50) in a DI diesel engine and reported that engine torque decreased, SFC increased, CO, HC emission decreased, but NOx emissions increased up to 17.5% with using biodiesel blends. Al-Dawody and Bhatti [24] were carried out engine tests on a single cylinder diesel engine, and used diesel and the blends of diesel-biodiesel as fuel. They found that soot emissions reduced, but SFC and NOx emissions increased for all biodiesel fuels compared to diesel fuel. Nagaraja et al. [25] investigated emission and performance of single cylinder diesel engine fueled with diesel-COME biodiesel blends and stated the results give clear information that COME has lower exhaust emissions and causes increase in performance without any modifications. As seen in the literature, using biodiesels in diesel engines increase NOx emissions. Thus, this drawback is required to be overcome. One of the best ways to achieve is using of exhaust gas recirculation (EGR) [26–28]. So that, the NOx emissions can be significantly reduced. However, when the EGR is used at high rates, it worsens engine performance and other emissions [29,29]. Haşimoğlu et al. [26] investigated experimentally the effect of using EGR on engine performance and exhaust emissions in diesel engines and observed a significant decrease in NOx emissions with EGR application. He et al. [30] conducted an experimental study by applying EGR at different rates on a single cylinder diesel engine and found better results for engine performance and emissions than the standard situation by using EGR up to a certain rate. Venu and et al. [31] investigated the effects of diesel-biodiesel blends, EGR and nanoparticle additives on performance and emissions in a four stroke, air cooled DI diesel engine. They determined that for PBN-EGR (30% palm biodiesel +70% diesel fuel +25 ppm TiO2 – EGR), there is only a marginal drop in cylinder pressure whereas the heat release is higher in comparison with biodiesel. SFC was found higher and NOx emissions were found lower for PBN-10EGR and PBN-20EGR than PBN full load conditions. Can et al. [32] tested in a four-stroke DI diesel engine fueled by diesel-soybean biodiesel blend, diesel and soybean by 20% volume, at different EGR rates (5, 10, 15%) and at 2200 rpm speed, and found, when used together with biodiesel blends and EGR the maximum heat release rate and maximum in-cylinder pressure and SFC increased, NOx and smoke emissions were improved simultaneously at the high engine load. In this study, unlike the studies given above, with quite fewer experimental study steps, the optimum levels of much more factors and levels were determined. The experimental design of the study was done by Taguchi method. This method allows the detection and optimization of factor levels that give optimum performance characteristics by testing the combinations of the orthogonal array instead of testing each level of the factors in the experiments separately and in combination [33]. Because the results can be obtained with high accuracy by a lesser number of experiments, it provides a significant reduction in time and costs in experimental studies and is used by researchers for different applications in many areas [34–36]. Nowadays, Taguchi method is used in studies related to internal combustion engines [37,38]. Ansari et al. [39] studied the changes in engine performance and emissions on a diesel engine using different biodiesel blends using the Taguchi method and optimized the factor levels to give the best engine performance and emission. Ayhan et al. [20] examined the effects of different fuel injection timing and steam injection rates on performance and emissions in a diesel engine and determined the factor and levels for the optimum engine performance and NOx emissions by Taguchi method. Wu and Wu [35] determined the effects of different ratios of biodiesel-diesel and H2 blend and different ratios of EGR on engine emissions and combustion performance in a single cylinder diesel engine and used the L9 orthogonal array. And time saving by 67% is obtained using the Taguchi experiment design method in experiments. In this study, were investigated the effects of different ratios of biodiesel blends and different EGR ratios on performance parameters and emission characteristics by using Taguchi method. Therefore, tests
2. Materials and methods 2.1. Production of COME COME obtained from corn oil by using transesterification method was used. In transesterification process Methyl alcohol and KOH catalyst was used. Premixed methyl alcohol and catalysts mixtures were poured on glass beaker. After Corn oil (CO) was heated, the prepared alcohol–catalyst mixture was added into CO. The mixture was stirred and then it was taken into the separator funnel and waited until ester–glycerin separation took place. Then, hot-sterile distilled water was added to COME. After this process, COME–water mixture was left to settle, then pure water and glycerin glimmers were removed. Finally, COME sample was dried by heating. Table 1 shows the properties of the fuels used in the study. 2.2. EGR application In the experiments, 10%, 15%, 20% EGR were applied into the engine. In each step EGR application, standard engine experiments were carried out and the amount of CO2 exhausted for each test step was measured. So, the EGR ratio, according to the measured values, is calculated. In order to determine the EGR ratio sent into the suction line, the equation given below (Eq. (1)) is used.
EGR (%) =
[(CO2 )intake manifold − (CO2 )ambient ] × 100 [(CO2 )exhaust manifold ]
(1)
For the application of the EGR, the required connection pipes and the EGR cooler are installed to transfer the exhaust gases into the intake manifold. To adjust the EGR ratios, a multi-turn, high precision manual control valve is added to the system. 2.3. Experimental setup In the tests a single cylinder, four stroke DI diesel engine was used. The specifications of the test engine are given in Table 2. The engine, which is water-cooled, was allowed to come to the regime temperature before starting the experiments. For this reason, water and oil conditioning systems were connected to the test setup to keep water and oil temperature constant at 85 °C during the experiments. The engine was loaded with a 50 kW hydraulic dynamometer and the power was determined by 0.1 kg sensitivity S type load cell. All the temperatures were measured using NiCr-Ni type thermo-couples. Before, the experiments were performed to determine the optimum fuel injection timing and it was checked to be at 29°CA BTDC) (crank angle before top dead center). Bosch BAE 060 emission device was used for the measurement of NO, HC, CO, CO2, and Bosch BAE 070 analyzer was used for the measurements of smoke. Fuel consumption was measured Table 1 Properties of diesel and biodiesel fuel.
2
Property
Diesel
COME-Biodiesel
Density (15 °C) Cetane number Kinematic viscosity mm2/s, (40 °C) Flash point (°C) Lower heating value (kJ/kg)
0.82–0.86 49 2.5–3.5 > 55 42,640
0.87–0.88 > 55 4.3 > 100 39,576
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Table 2 Specification of the test engine.
Table 3 The errors in parameters and total uncertainties.
Engine Type
Super Star
Parameters
Systematic Errors, ±
Bore [mm] Stroke [mm] Cylinder number Stroke volume [l] Power −2400 rpm, [kW] Injection pressure, [bar] Injection advance, [°CA bTDC] Maximum speed, [rpm] Cooling type Injection type
108 100 1 0.92 12 225 29 2500 Water DI
Load, kg Speed, rpm Time, s Temperature, °C Fuel consumption, s NO, ppm HC, ppm O2 CO, % CO2, % Smoke, %
0.1 1.0 0.1 1.0 0.01 5% of measured value 5% of measured value Vol. %0.1 Vol. %0.03 Vol. %0.5 1% Total uncertainty, % 1.5 1.3 1.5
Specific fuel consumption, g/kWh Brake power, kW Effective efficiency, %
with a volumetric flow measurement system. Air consumption was measured with a surge tank and a mass flow measurement device. The experiments were carried out at 1600 and 2400 rpm engine speeds and at 40%, 60%, 80% and 100%, loads conditions. Engine tests were performed according to L16 orthogonal array determined by Taguchi method. A schematic illustration of the experimental setup is shown in Fig. 1. The measurements were repeated 5 times for each test point and the average has been considered. All the total uncertainties of performance characteristics are calculated as described above. The accuracies and total uncertainties of characteristics calculated with respect to measured values are shown in Table 3.
Table 4 Factors and their levels. Symbol
Factor
Level 1
Level 2
Level 3
Level 4
A B C D
Engine Load, % Blend,% EGR ratio, % Engine Speed, rpm
40% B0 EGR0 1600
60% B10 EGR10 2400
80% B20 EGR15 –
100% B50 EGR20 –
experiment plans in Taguchi experimental design, multiple factors and factor levels are performed with a small number of experiments. The orthogonal array is the mapping of levels of a factor to levels of other factors during the trial [40]. In this study, 3 factors by 4 levels, 1 factor by 2 levels were selected and input to Minitab software and according to these factor and level numbers, a suitable orthogonal array (OA) was created by the program. The OA expressed as L16 (4^3) (2^1) is given in Table 5. According to this table, 16 experiments were performed with 3 repetitions in random order. As a result of the data obtained from the engine tests, the engine performance parameters, Effective power,
2.4. Design of experiments with Taguchi method Experimental design was done with Taguchi method. The controllable factors and levels selected in the study are given in Table 4. For factors and their levels; diesel-corn oil biodiesel blends (4 levels; B0, B10, B20 and B50), EGR (4 levels; EGR0, EGR10, EGR15 and EGR20), engine loads (4 levels; 40%, 60%, 80% and 100%) and engine speeds (2 levels; 1600 rpm – test engine maximum torque and 2400 rpm – maximum effective power) were selected. The large number of experiments in full factorial experimental plans has negative consequences in terms of time and testing costs. Thanks to the orthogonal
Fig. 1. Test setup. 3
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Table 5 L16 Orthogonal array for experiments. Exp. No
⎡1 S/N = −10log ⎢ n ⎣
Factors
n
∑ i=1
1⎤ yi2 ⎥ ⎦
(2)
Lower is better; 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
A
B
C
D
1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 2 1 4 3 3 4 1 2 4 3 2 1
1 1 2 2 2 2 1 1 1 1 2 2 2 2 1 1
⎡1 S/N = −10log ⎢ n ⎣
n
∑ yi2 ⎤⎥ i=1
⎦
(3)
where, n , yi and i indicate the number of repetitions in a trial under the same conditions, the measured value, and the number of design parameters in the OA, respectively. S/N ratios were analyzed for each performance parameter and emission. According to the results, the optimum combinations were determined and Analysis of Variance (ANOVA) was performed to determine the percentage effect of the factors. The equations used in the ANOVA analysis are given in (4)–(8). N
T2 ⎡ ⎤ SS T = ⎢∑ (S/N)i2⎥ − N ⎣ i=1 ⎦
(4)
K
specific fuel consumption (SFC) and effective efficiency, were calculated for each test step. Emission characteristics, NO, HC, CO2, CO and smoke emissions, were measured with gas analyzers. All the parameters were found and the analyzes were made according to the average values of the 3 repetitions. The most important feature of Taguchi's experimental design method that distinguishes it from conventional design methods is the generic Signal-to-Noise (S/N) ratio, which determines the performance criterion [41]. There are several S/N ratios available depending on the type of characteristics including ‘Higher is Better’ (HB), ‘Lower is Better’ (LB), and ‘Nominal is the Best’ (NB). Accordingly, for the effective power, specific fuel consumption, effective efficiency, NO, HC, CO2, CO and smoke emissions appropriate S/N ratio calculation formulas were determined. Because of the high level of results to be obtained for effective power and effective efficiency, Taguchi's ‘Higher is Better’ formula was used. Similarly, for SFC and emission characteristics, ‘Lower is Better’ formula was used because the desired values were the lowest values. The formulas of S/N ratios below are given by Eqs. (2) and (3). Higher is better;
A A2 ⎤ T2 ⎡ SSA = ⎢∑ ⎜⎛ i ⎟⎞ ⎥ − n N ⎣ i = 1 ⎝ Ai ⎠ ⎦
(5)
vtotal = N − 1
(6)
Vfactor =
SSfactor ϑfactor
(7)
Ffactor =
Vfactor Verror
(8)
where, SS T is the sum of the squares with total variability, N is the total number of experiments, SSA is the sum of the squares due to the factor A, KA is the number of levels for factor A, Ai is the sum of the total level of the factor A and nAi is the sum of the factor A levels. T represents the sum of the total (S/N) ratio of the experiments and ϑ indicates the degrees of freedom. Vfactor indicates the variance of the factor. SSfactor is the sum of the squares of the factor and Ffactor is the factor’s F ratio. The levels of factors according to the Taguchi method are significant in the 90% to 99% confidence interval. In Taguchi methods, the levels of factors given in the ANOVA are meaningful according to 90% and 99% confidence intervals. Design of experiments is done considering these confidence limits.
Fig. 2. S/N values of factor levels for effective power. 4
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Fig. 3. S/N values of factor levels for SFC.
Fig. 4. S/N values of factor levels for effective efficiency.
3. Results and discussion
biodiesel. In the literature, using biodiesel mixtures has been stated to cause effective power increases due to the improvement in the efficiency of combustion [13,20]. Biodiesel in the blend fuel is more than 20%, and since the lower heating value is lower than diesel fuel, less heat addition causes decreases in the torque and effective force. Fig. 3 shows the factors affecting engine specific fuel consumption by applying the Taguchi method. The optimum value of the SFC was obtained at 80% load, 1600 rpm, B10 biodiesel mixture and EGR0 ratio. The best combination is “A3-B3-C1-D1”. In general, in the engine characteristics curves the maximum torque has been obtained at medium engine speed for atmospheric compression ignition direct injection engines. In the test engine, the highest torque has been obtained at 1600 rpm. The torque decreases as the speed increases above this speed. This is due to a reduction in the amount of mixture entering the cylinder, causes the decrease in the volumetric efficiency and increased friction losses of the engine. The effective power depends on the torque as well as on the engine speed
3.1. Performance parameters The optimum factor levels obtained in terms of performance parameters as a result of the experiments carried out by using Taguchi method are given in the figures below. Fig. 2 shows the changes in factors and their levels affecting the engine effective power using Taguchi method. In the figure, the factor and the optimum factor levels were determined in terms of effective power, 100% at full load, 2400 rpm at engine load, B20 mixture and EGR0 (standard condition). The best combination of factors for effective power is “A4-B3-C1-D2”. As a result of experimental studies, it is seen that when different rates of biodiesel blends are used, the effective power increases compared to the standard engine data. It is determined that the increases up to B20, and so the best level of B20 fuel is related to the amount of oxygen contained in 5
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Table 6 The Analysis of variance (ANOVA) for performance parameters.
Effective Power
Effective Efficiency
SFC
Factors
Sum of Squares, SS
Degree of Freedom, ϑ
Squares Mean, V
Ffactor
A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error
133,269 1,325 0,541 13,582 149,029 0,313 7,5178 0,1172 3,8157 5,6690 19,6388 2,5191 7,5178 0,2160 3,8157 5,6690 19,7376 2,5191
3 3 3 1 15 5 3 3 3 1 15 5 3 3 3 1 15 5
44,423 0,442 0,180 13,582 9,935 0,063 2,505 0,039 1,271 5,668 1,308 0,503 2,5059 0,0720 1,2719 5,6689 1,315 0,503
709,462*** 7,053* 2,879 216,910***
4,97 0,08 2,52 11,25*
4,97 0,14 2,52 11,25*
*%95, **%99, ***%99.99 confidence.
Fig. 5. S/N values of factor levels for NO emission.
100% load conditions. When biodiesel blends were used, SFC was reduced when B10 and B20 were used, and while in B50, SFC slightly increased compared to standard engine data. It is seen that this change is caused by the factors affecting the effective power. Therefore, the effective efficiency has reached its maximum value of 80% load. Fig. 4 shows the changes in the factors and levels affecting the engine effective efficiency. The optimum value of the effective efficiency was obtained at 80% load, 1600 rpm, B20 biodiesel blend and EGR0 ratio. The best combination of factor levels is “A3-B3-C1-D1”. At full load condition of the engine load, the engine works with a richer mixture than at 80% load. As the effective power increased in the use of B20 blend, the efficiency increase was obtained due to the increase in power at the same fuel flow rate. Since EGR affects the performance of the engine negatively, it is seen that the efficiency is the maximum for EGR0. The maximum value of the effective efficiency is reached at 1600 rpm. The results of the ANOVA analysis of the data obtained from the experiments are given in Table 6. According to these results, it is determined that the factors are effective in the range of 95% to 99.99% for effective power and specific fuel consumption.
(number of cycles per unit time). As the speed increases, the effective power increases due to the increase in the number of cycles. SFC is obtained at a speed between maximum torque and maximum power conditions, close to the maximum torque condition. In this study, minimum SFC was obtained at 1600 rpm by considering the selected factors. The best values of effective power and specific fuel consumption are obtained in standard cases where EGR is not applied. When different EGR ratios were applied into the engine, the engine performance parameters deteriorated. Since, the oxygen content in the mixture decreased and the burning speed slowed down. Increasing EGR ratios, especially at high speeds, cause the mixture to become poor and the combustion to deteriorate. The effective power from diesel engines varies linearly with the amount of fuel delivered into the cylinder, up to a certain load value. The fuel flow rate and the effective power change linearly up to approximately 60% load, after which the slope of the curve changes. The increase and change in the slope of the curve indicate that the fuel conversion efficiency was deteriorated. As seen in Fig. 4, the minimum SFC was obtained under 80% load. After this load, because the entire fuel injected to the cylinder is not converted into effective power, SFC and the effective efficiency was deteriorated under 6
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Fig. 6. S/N values of factor levels for Smoke emission.
Fig. 7. S/N values of factor levels for HC emission.
the lowest NO was obtained with standard diesel fuel. EGR is the most efficient method for reducing NO emissions from diesel engines. With EGR, as the specific heat capacity of the in-cylinder gas mixture increases, the maximum temperature in the cylinder is reduced. Therefore, as the EGR ratio increases, NO emissions decrease. The minimum NO was obtained at the highest level of EGR in study. The second parameter effective in the formation of NO emissions is the amount of reacted oxygen and nitrogen. As the engine speed increases, less air is supplied to the cylinder, since the volumetric efficiency is reduced. In the study, the minimum NO was measured when the engine speed was high. Fig. 6 illustrates the changes in the factors and levels affecting the smoke emission. The optimum value of the smoke emission was obtained at 40% load value, 1600 rpm, B10 biodiesel blend and EGR0 ratio. The best combination is “A1-B2-C1-D1”. Fig. 7 shows the changes in the factors and levels affecting HC emission. The optimum value of HC emission was obtained at 40% load
Engine load and speed change were the most effective parameters in terms of effective power as expected, while other factors were significant. Biodiesel blends were found to be less effective in terms of efficient efficiency and SFC.
3.2. Emission characteristics Fig. 5 shows the factors and levels affecting NO emission. The optimum value of NO emission was obtained at 2400 rpm, B0 biodiesel mixture and EGR20 ratio at 40% load value. The best combination is “A1-B1-C4-D2”. The most important factor that accelerates the formation reactions of NO emission is the high temperatures formed in the cylinder. NO emissions increase as the engine load increases, causing increasing temperatures. Examining the Fig. 5, the minimum NO was obtained at the lowest engine load value. Using biodiesel blends increased NO emissions due to oxygen content of biodiesel. In the experiment results, 7
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Fig. 8. S/N values of factor levels for CO emission.
Fig. 9. S/N values of factor levels for CO2 emission.
effective in the exhaust emissions between 95% and 99.99% confidence. When Table 6 is examined, the effect levels and significance values of each factor according to ANOVA can be seen.
value, 1600 rpm, B50 biodiesel blend and EGR0 ratio. The best combination of factor level is “A1-B4-C1-D1”. In the Fig. 8, the factors and levels affecting CO emission was given and the optimum value of CO emission was obtained at 40% load value, 1600 rpm, B50 biodiesel blend and EGR0 ratio. The best combination of factor levels is “A1-B4-C1-D1”. With the application of EGR, smoke, CO and HC emissions increase. The reason for this is the lack of complete combustion and the decrease in the amount of oxygen in the cylinder and the decrease of the flame temperature due to the effect of EGR. By using biodiesel blends as fuel, CO and HC emissions decreased. This is considered to be reasoned by the improved combustion effect of biodiesel due to oxygen content. In the Fig. 9 the factors and levels affecting CO2 emissions are depicted. The optimum value of CO2 emission was obtained at 40% load value, 1600 rpm, B0 (diesel) fuel and EGR0 ratio. The best combination is “A1-B1-C1-D1”. The results of ANOVA analysis for emission characteristics are given in Table 7. According to these results, the factors are found to be
4. Conclusions In this study, the effect of different biodiesel blends and EGR ratios for a DI diesel engine running at different loads and speeds by using the Taguchi experiment design method and the changes in engine performance parameters and emission characteristics were investigated. Examining the obtained data and the results of the analysis, it has been determined that engine load, engine speed, different biodiesel-diesel mixtures and different EGR ratios are effective on engine performance parameters and emissions. In general, the results obtained are as follows; – The Taguchi experimental design method revealed that choosing the optimum factor levels are important in terms of the maximization of 8
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Table 7 The Analysis of Variance (ANOVA) for emissions.
NO
HC
CO2
CO
Smoke
Factors
Sum of Squares, SS
Degree of Freedom, ϑ
Squares Mean, V
Ffactor
A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error A – Load, % B – Biodiesel blends C – EGR rate, % D – Speed, rpm Total Error
4,859 4,907 121,974 43,011 181,136 6,384 153,48 28,73 21,18 97,65 320,96 19,91 82,214 8,295 23,829 6,838 128,358 7,181 952,97 3,27 163,67 67,14 1419,27 232,23 447,475 70,815 232,367 4,924 772,819 17,238
3 3 3 1 15 5 3 3 3 1 15 5 3 3 3 1 15 5 3 3 3 1 15 5 3 3 3 1 15 5
1,62 1,636 40,658 43,011 12,07573 1,277 51,162 9,576 7,061 97,648 21,39733 3,983 27,405 2,765 7,943 6,838 8,5572 1,436 317,655 1,09 54,556 67,142 94,618 46,445 149,158 23,605 77,456 4,924 51,52127 3,448
1,27 1,28 31,84** 33,69**
12,85** 2,4 1,77 24,52**
19,08** 1,93 5,53* 4,76
6,84* 0,02 1,17 1,45
43,26*** 6,85* 22,47** 1,43
*%95, **%99, ***%99.99 confidence.
– – – – – – –
effective power, effective efficiency and the minimization of SFC and exhaust emissions. The optimum results for effective power were obtained at full load condition, in B20 fuel, when EGR was not applied and at 2400 rpm. The optimum results for SFC and effective efficiency were obtained at 80% load, B10 fuel, EGR and 1600 rpm conditions. The best results in terms of NO emissions were determined at 40% partial load, B0-diesel fuel, 20% EGR ratio and 2400 rpm. The best results for smoke emissions were obtained at 40% partial load, B10 fuel, no EGR applied and at 1600 rpm. The optimum values of HC and CO emissions were determined at 40% partial load, B50 fuel, no EGR applied and 1600 rpm. The optimum values of CO2 emissions were determined at 40% partial load, diesel fuel, no EGR applied and 1600 rpm. In this study, experimental studies were done by using Taguchi statistical experimental design method, saving 70% in terms of time and cost. The experiments were carried out between 95% and 99.99% confidence interval. The method can demonstrate the interactions between performance parameters and factors that affect emission characteristics.
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