4.1 Performance Parameters
Fig. 2 shows the variation of brake thermal efficiency (BTE) for diesel, PO and PO with additives at different load conditions. Improved physical properties of PO improved the BTE to 31.8 % at full load condition in comparison to 30.2 % for diesel. PO has very low viscosity and improved volatility which improves the fuel preparation leading to more fuel prepared for the instantaneous combustion. It is observed that a marginal increase in BTE was observed with addition of DEE and DGE. Both, DEE and DGE has higher flame speed which aids in further improvement in combustion process. However, it is observed that the improvement is not significant with 10 % addition of DEE and DGE in comparison to 5 % addition by volume. Similarly, addition of MTBE also showed improvement in BTE for 5 % addition by volume. With addition of Bn5 and Bn10 with PO, BTE was increased to 33 % and 33.5 % respectively. As an aromatic alcohol, benzyl alcohol has an indirect bond between the OH and the aromatic ring. This phenomenon results in an escalation in the rates of hydrogen abstraction, isomerization, and free radical propagation. These unique features of benzyl alcohol aided in improved BTE and maximum improvement was observed in comparison to other additives.
Fig. 3 shows the variation of BSEC for diesel, PO and PO with additives at different load conditions. It is observed that BSEC reduces with load for all the test fuels. At maximum load, BSEC for diesel and PO is 11.9 MJ/kWh and 10.1 MJ/kWh respectively. Due to lower viscosity of pine oil, its atomization is improved thus causing proper mixing of fuel and air. Thereby improving the combustion quality and reducing the energy consumption. By blending diethyl ether, benzyl alcohol and MTBE the reduction in energy consumption was insignificant, whereas a significant decrease in energy consumption at all loads was observed by blending diglyme to pine oil. With 5% blending of diglyme in pine oil maximum reduction in energy consumption of 8.67% was observed at full load in comparison to neat pine oil operation. The probable decrease in energy consumption with diglyme is caused by earlier and higher heat release in the premixed phase as compared to pine oil, such that the peak pressure is close to top dead center and most of the heat is utilized in producing useful power.
Fig. 2 Variation in brake thermal efficiency with brake power
Fig. 3 Variation in brake specific energy consumption with brake power
The modulation in carbon monoxide emission with load for the test fuel samples is depicted in Fig. 4. It is seen that the engine operation with neat pine oil at full load resulted in a 12.5% decrease in CO emission as compared to diesel. The reduction in carbon monoxide (CO) emissions can be attributed to the oxygen-promoting properties of pine oil. Further, the combustion temperature with pine oil engine operation is higher than diesel due to higher heat released resulting in superior oxidation of the intermediate carbon monoxide. Moreover, addition of additives resulted in increase in CO emission except benzyl alcohol which resulted in lower CO emission, but the emission was still higher than neat pine oil engine operation. 10% blending of benzyl alcohol with pine oil resulted in lowest CO emission among the additives. At full load the emission with Bn10 was 14.29% higher than pine oil engine operation and similar to that of diesel. The overall increase in the CO emission with oxygenates is caused by the high latent heat of vaporization. Among the additives benzyl alcohol has relatively lower latent heat of vaporization and as the hydroxyl group is present outside the ring, the oxygen present in the alcohol can be easily utilized for the oxidation of carbon monoxide.
Fig. 5 depicts the variation in unburned hydrocarbon emission with load for diesel, pine oil and various additives at 5% and 10% blending in pine oil. The engine operation with pine oil resulted in lower unburned hydrocarbon emission than diesel. At full load the HC emission with diesel is 19ppm which reduced to 16ppm with pine oil. The decrease in the emission is caused by complete combustion of fuel as can be seen in the heat release curve (Fig. 9). Also, the amount of fuel injected per cycle is lower with pine oil (Fig. 3) hence less amount of hydrocarbon needs to burn for producing the same power. Therefore, there is less chance of fuel escaping combustion and leave the cylinder as unburned hydrocarbon. Further, the addition of additives resulted in higher HC emissions than pine oil. The HC emissions follow the same trend as that of CO emission i.e., the highest HC emission was observed with MTBE followed by diglyme and diethyl ether. Also, higher percentage of additive in the blend resulted in higher emission. However, the trend reversed with benzyl alcohol and lower emission is formed with 10% of benzyl alcohol in the blend which is the lowest emission among the additives. The increase in HC emission with the additives is similar to that of CO emissions i.e., higher latent heat of vaporization of oxygenates. The highest HC emission was observed with MTBE as the energy consumption of the engine with MTBE blends was highest.
Fig. 4 Variation in carbon monoxide emission with brake power
Fig. 5 Variation in unburned hydrocarbon emission with brake power
The NO emission for the test fuels at all load conditions is shown in Fig. 6. It is seen that NO emission increases with increase in load for all the test fuels. The NO emission with neat pine oil engine operation was highest for all the loads and at full load it increased by 7.65% in comparison to diesel. The increase in emission is caused by higher heat release which leads to an increase in combustion temperature. In general, higher combustion temperature causes the nitrogen to split in to atomic nitrogen which has high affinity for the oxygen present in the air, resulting in increase in oxides of nitrogen emission. The nitrogen content inherent in the pine oil is an additional factor contributing to the rise in emissions. Addition of 5% and 10% oxygenates to the pine oil resulted in a slight decrease in NO emission at all loads. As the additives replaces pine oil, the nitrogen present in the charge is reduced as the percentage of nitrogen containing pine oil is reduced. Further, increase in additive percentage further reduces the nitrogen percentage, thereby reducing the emission. Among the additives the lowest NO emission of 1327ppm was observed with Bn10.
Fig. 6 Variation in NO emission with brake power
The variation in smoke opacity with engine load for diesel, pine oil, and the additives is shown in Fig. 7. At full load, the smoke opacity of the exhaust gas with pine oil operation is 57%, whereas with diesel the smoke opacity is 62%. The decrease in the opacity with pine oil is caused by its low viscosity which improves the mixing of fuel and air such that the thermal pyrolysis of fuel molecules in the absence of oxygen is reduced. Additionally, the reduction of particulate emission is accelerated by the high combustion temperature and the presence of molecular oxygen in pine oil.. The soot emission with all oxygenates was lower than pine oil. At full load, the smoke opacity with DEE5, DEE10, DGE5, DGE10, Bn5, Bn10, MTBE5 and MTBE 10 is 55%, 53%, 56%, 52%, 49%, 46%, 56% and 55%, respectively. An improvement in fuel combustion results in elevated combustion temperatures, which facilitates additional particulate emission reduction when oxygen is present, thereby contributing to the decrease in emissions.
Fig. 7 Variation in smoke opacity with brake power
Fig. 8 depicts the effect of the additives on the combined reduction of NO and smoke emission at full load. It is seen that the addition of oxygenates resulted in a decrease in NO and smoke emission as compared to neat pine oil engine operation. There was a slight decrease in both the emissions with DEE5, DGE5 and MTBE5 addition to pine oil. With 10% addition of MTBE to pine oil the NO emission was lowest, however, the smoke emission was similar to pine oil. Whereas 10% addition of benzyl alcohol resulted in lowest smoke emission and slightly higher NO emission than MTBE5. The combined decrease in NO and smoke emission with Bn10 can be attributed to better combustion quality of the fuel blend.
Fig. 8 NO-smoke trade-off at full load
The NO-smoke trade off shows that addition of 10% of benzyl alcohol to pine oil resulted in the combined reduction in NO and smoke emission. Also, the HC and CO emission with the blend was lowest among additives therefore, the combustion data of only benzyl alcohol was compared with neat diesel and pine oil engine operation. The variation in heat release at full load for the three fuel samples is shown in Fig. 9. It is seen that the heat release with pine oil initiated later than diesel on account of low cetane number of the oil. Therefore, when the combustion started large amount of the accumulated fuel was ready to burn that resulted in higher heat release in the initial phase of combustion. With 10% addition of benzyl alcohol in pine oil the highest heat release rate is observed. The improvement in heat release is caused by the increase in calorific value of the fuel sample that tends to increase the heat release rate. Also, a decrease in ignition delay is observed as compared to pine oil, due to improvement in cetane number of the test sample.
Fig. 9 Variation in heat release rate at full load
The in-cylinder pressure at full load with diesel, pine oil and 10% addition of benzyl alcohol to pine oil is shown in Fig. 10. The peak cylinder pressure with diesel is 84.68bar at 9°aTDC, with pine oil it is 89.19bar at 14°aTDC and with Bn10 it is 93.47bar at 10°aTDC. The rise in peak pressure associated with pine oil can be attributed to enhanced combustion quality resulting from the fuel's increased volatility. Further substitution of pine oil with benzyl alcohol resulted in increase in-cylinder pressure which is caused by higher heat release due to improvement in combustion. It is also seen that the peak pressure with Bn10 reached 4°CA earlier than pine oil due to improvement in cetane number that reduces the ignition delay.
Fig. 10 Variation in in-cylinder pressure at full load
4. Response Surface Methodology Analysis
The experiments carried out can be modelled and analyzed statistically and mathematically using response surface methodology (RSM) (Kumar and Dinesha. 2018) . In this method, critical factors are used to form a model for an output and the model values are also optimized to predict the output (Abdalla.et al. 2019). This technique is effective for a vast array of applications in a variety of industries. Eqs. 1 and 2 illustrate that the function utilized in this methodology to ascertain the correlation between the input and output may be linear or polynomial in nature (Singh et al. 2021).
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)
In this study, brake power, percentage of pine oil and additives were considered as input. The levels of input are shown in Table 3. Central composite design and quadratic models were used for optimizing and predicting the responses. The experimental design, run order, and data for forming the models for each of the output parameters are given in Table 4.
Analysis of variance (ANOVA) statistical technique was applied to understand the relationships between the independent and dependent variables. The experimental data of brake thermal efficiency, brake specific energy consumption, CO, HC, NO and smoke emission was analyzed using ANOVA. The significance of the model was given by F-test & P-test values which are tabulated in Tables 5 and 6. A model is said to be significant, if the p-values are under 0.05, indicating that the probability values are moving away from null hypothesis, else if the p-value is higher than 0.05 then the model is considered insignificant (Rajmohan and Palanikumar 2013). It is seen in Tables 5 and 6 that the model is significant for BTE, BSEC, CO and NO emission while it is not significant for HC and smoke emission. The correlation coefficient ‘R2’, adjusted correlation coefficient ‘Adj. R2’ and predicted correlation coefficient ‘Pred. R2’ were used to find the fit statistics of the model. These values were calculated using Eqs. 3, 4 and 5, respectively (Sathyanarayanan et al. 2023).
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)
The correlation coefficient ‘R2’ evaluates the correctness of the model and its sufficiency. High correlation coefficient shows that the model can accurately predict the output parameters. The R2 value of BTE, BSEC, CO, NO, HC and smoke was found to be 0.989, 0.9879, 0.9918, 0.9956, 0.5394 and 0.4829, respectively. It is seen that the accuracy of the model in predicting the unburned hydrocarbon and smoke emission is low. The model has a desirability of 0.583. The operating settings, related responses and evaluation of the model for determining the optimum responses is listed in Table 8. The optimum desirability ramp graph for additive ‘Bn10’ is shown in Fig. 11. Confirmation tests were then carried out to test the accuracy of the model. The error percentages between the experimental data and predicted data for all the additives is tabulated in Table 9. The table shows that the model can predict BTE, BSEC, NO and CO emission within 5 error percentage. However, the model was unable to accurately predict HC and smoke emission as the model failed to pass p-test which is seen in the analysis of variance (Table 6).
Table 3. Engine input level
Table 4. Experimental Design Data
Table 5. Engine performance ANOVA result
Table 6. Engine emissions ANOVA result
Table 7. Fit statistics
Table 8. Optimization setup and model evaluation
Table 9. Validation of predicted data and experimental data
Fig. 11 Optimum desirability ramp graph for Bn10 additive
4.1. Interaction effects
4.1.1. Interaction effects of thermal efficiency and specific energy consumption
Fig. 12 shows the interaction between pine oil percentage, additives, brake power and thermal efficiency. It is seen that overall, 10 percentage of any of the additives and 90 percentage of pine oil resulted in higher improvements in thermal efficiency at all the engine loads. While lowest thermal efficiency was observed when engine was operated with only pine oil. Addition of alcohol lead to increase in oxygen content of the charge which aided the combustion process, thus more useful work was produced. Similar trends were observed with brake specific energy consumption as shown in Fig. 13. The actual factors of brake thermal efficiency and brake specific energy consumption is given in Eqs. 8 and 9, respectively.
BTE = 28.77+3.98*A-1.81*B+1.58*C[1]+0.4408*C[2]+0.1613*C[3]+1.14*C[4]+0.1508*C[5]-0.8473* C[6]-1.12*C[7]+0.0064*AB-0.0374*AC[1]-0.0254*AC[2]-0.213*AC[3]+0.1425*AC[4]+0.1435* AC[5] +0.061*AC[6]-0.1581*AC[7]-0.9575*A2 (8)
BSEC=10.7-1.6*A-0.1251*B+0.6961*C[1]-0.5895*C[2]+0.2125*C[3]+0.2823*C[4]+0.122 * C[5]-0.7146*C[6]-0.0053*C[7]-0.0649*AB-0.0217*AC[1]+0.1005*AC[2]+0.0766*AC[3]-0.0438*AC[4] -0.0842*AC[5]+0.0356*AC[6]+0.0462*AC[7]+0.608*A2 (9)
4.1.2. Interaction effects of carbon monoxide emission
The interactions between alcohol-based additives, pine oil and brake power on carbon monoxide emission is depicted in Fig. 14. Overall, the emissions were lower with additives as compared to pine oil due to improvement in combustion ability caused by enrichment of oxygen and wide flammability. Eq. 10 shows the actual factor of carbon monoxide emission.
CO=0.0966-0.0352*A+0.0413*B-0.09*C[1]-0.0088*C[2]-0.0162*C[3]-0.0312*C[4]+0.037*C[5]+ 0.0475 *C[6]+0.04*C[7]+0.0082*AB-0.018*AC[1]-0.0007*AC[2]-0.0052*AC[3]-0.0082*AC[4] +0.0037*AC[5]+0.0075*AC[6]+0.009*AC[7]-0.0006*A2 (10)
Fig. 12 Interaction effect of pine oil, additives and brake power on thermal efficiency
Fig. 13 Interaction effect of pine oil, additives and brake power on energy consumption
4.1.3. Interaction effects of unburned hydrocarbon emission
Fig. 15 shows the effect of brake power, pine oil percentage and additive percentage on unburned hydrocarbon emission. The emissions were reduced with additives due to improvement in combustion caused by higher flame speed of alcohols. The actual factors for unburned hydrocarbon emission are shown in Eq. 11.
HC=15.2-1.3*A-1.08*B+2.17*C[1]-0.67*C[2]+1.58*C[3]-2.17*C[4] +1.08*C[5]-0.25*C[6]+0.5*C[7] +1.55*AB-3.7*AC[1]+0.7*AC[2]+2.95*AC[3]-0.2*AC[4] -1.85*AC[5]-0.15*AC[6]+1.2*AC[7]-0.0625*A2 (11)
4.1.4. Interaction effects of oxides of nitrogen emission
The interaction effect of additive and pine oil percentage and brake power on oxides of nitrogen is shown in Fig. 16. The emissions were found to be lower with addition of alcohols as they have a cooling effect due to high latent heat of vaporization resulting in lowering of combustion temperature, thereby reducing the NO emission. The actual factor of the emission is mentioned in Eq. 12.
NO=1231.99+297*A+93.42*B-66.08*C[1]+3.33*C[2]-18.42*C[3]-51.92*C[4] -15.17*C[5]+65.25* C[6]+42.5*C[7]-3.9*AB+6.15*AC[1]+2.85*AC[2]+1.2*AC[3]+17.7*AC[4]-0.15*AC[5] -7.35*AC[6] +7.5*AC[7]-112.63*A2 (12)
Fig. 14 Interaction effect of pine oil, additives and brake power on emission of carbon monoxide
Fig. 15 Interaction effect of pine oil, additives and brake power on emission of unburned hydrocarbons
4.1.5. Interaction effects of particulate emission
The interaction between brake power & pine oil concentration for all the additives and smoke emission is depicted in Fig. 17. The emission with the additives was higher than neat pine oil at low load condition while the trend reverses at high load condition. The actual factor of the emission is mentioned in Eq. 13.
Smoke=33.17-0.39*A+1.82*B-9.13*C[1]-4.19*C[2]+7.93*C[3]-16.57*C[4] +6.76*C[5]+5.2* C[6] + 14.5*C[7]+15.51*AB-33.72*AC[1]-2.69*AC[2]+11.04*AC[3]-4.86*AC[4]-9.86*AC[5] +8.28*AC[6] +19.2*AC[7]-1.54*A2 (13)
Fig. 16 Interaction effect of pine oil, additives and brake power on emission of oxides of nitrogen
Fig. 17 Interaction effect of pine oil, additives and brake power on emission of particulates