1. Introduction
In recent years, the interest in alternate fuels and renewable energy [
1] has increased because of an increase in energy demand [
2] and stringent environmental policies [
3], which are because of the depleting fossil fuels, and an increase in the price of fossil fuels for the internal combustion engine [
4]. A compression ignition engine is being used by the transportation sector and holds a major share [
5]. It was also observed that conventional compression ignition (CI) engines had higher NO
x and soot. To control the emissions, exhaust gas recirculation and an after-treatment method (diesel particulate filter, diesel oxidation catalyst, and selective catalytic reduction), or both, were incorporated together. Incorporating these systems in the engine increases the complexity and cost of the engine. Advance combustion concepts and specific fuels were the topics of interest for many researchers [
6]. Energy demand has been increasing [
7] and demand for decarbonization is important for the environment [
8]. Therefore, an urgent need for alternate fuels is required for the CI engine [
9], both liquid as well as gases [
10,
11]. In the case of IC engines, gaseous alternate fuel can be considered because of their high compression ratio [
12] and good mixing characteristics, which in turn would decrease the emission and increase brake thermal efficiency [
13].
For the CI engine, advanced combustion strategies such as:
Reactivity controlled compression ignition (RCCI)
Homogeneous charge compression ignition (HCCI)
Partially premixed combustion (PPC) were used
NO
x and soot emissions were reduced by partial premix combustion when applied to the IC engine. PPC helped in reducing heat transfer losses and shortened the combustion duration compared to conventional diesel engines. Other researchers mixed low-temperature combustion with alternate fuels (ethanol, butanol, natural gas, butanol, bio-methane, etc.) [
14,
15]. Bio-methane has been a popular alternate fuel in Poland and Italy. It could be developed from animal slurry, biodegradable waste, and from maize grown on agricultural land [
16,
17]. This combination showed improvement in NO
x, soot emissions and efficiency. Generally, in dual-fuel combustion, one of the fuels is of high reactivity and the other is of low reactivity [
18]. Biomethanol, as an alternative fuel in the transport and industrial sector, needs to be investigated [
19].
A potential renewable energy is biogas, which could be produced from organic material under natural degradation by micro-organisms without the use of oxygen. Organic substances are converted to biogas by anaerobic digestion, which is used as fuel for vehicles and to generate heat and electricity. Biogas mainly constitutes of methane and carbon dioxide [
20]. Biogas for industrial purposes is developed at (1) landfills, (2) agricultural organic waste digestion plants, (3) sewage treatment plants, and (4) sites with industrial processing units [
21].
Biogas is environment friendly [
22] and is available abundantly [
23,
24]. The use of biogas in the CI engine is difficult, as there is no spark plug for combustion and ignition. Biogas is also low in cetane number and has a high self-ignition temperature [
25]. CO
2 composition of biogas helps in combustion at low temperature, which reduces the chance of NO
x emission formation at elevated temperature during combustion in dual fuel mode [
26,
27]. BTE remains unchanged for intermittent loads, whereas, at reduced loads, it decreases and increases at maximum loads [
28,
29]. Thus, by using biogas as a dual fuel mode [
30], smoke and NOx emission were reduced and controlled [
31,
32].
Karthic et al. used an artificial neural network to foreshow the performance and emission of a diesel engine. The ANN model has input parameters such as the load on the engine, the injection pressure of fuel, fuel flow rate and injection timing of fuel; and the output parameters of the model were brake thermal efficiency and emission. It was evident that the experimental results and the ANN prediction were similar for the dual-fuel engine in terms of emission and performance [
33]. Gul et al. obtained the optimum combination of engine speed, operating load and fuel nature by Taguchi DOE in a diesel engine that was run by 100% waste cooking oil and B20 (i.e., 20% biodiesel and 80% diesel). Experimental results and ANN simulation computed the best combination by guaranteeing refinement of the output response factors, thus ratifying the Gray–Taguchi method in curtailing emissions and enhancing combustion and performance simultaneously [
34]. To conduct the experiment, Kumar used RSM based on box-Behnken experimental design. For the production of jatropha-algae oil, parameters such as the molar ratio, reaction time, catalyst concentration, and reaction temperature were optimized. The predicted results showed a correlation with the RSM outcomes [
35]. Samuel et al. modeled the production of coconut oil ethyl ester by RSM and ANN. It was observed that the predicted yield by ANN agreed with the output of the experiment [
36]. Calik et al. (2018) used corn, sunflower and canola biodiesel blends in a diesel engine, injected hydrogen through a manifold inlet and predicted the emission, noise and vibration level with the help of a support vector machine and artificial neural network. It was concluded that ANN predicted better results than SVM [
37]. Najafi et al. (2019) experimented on a CI engine, which was simultaneously run by pilot fuel (oxygenated additive) and main fuel (natural gas). Artificial Neural Network and genetic algorithm modeling were used to reduce emission by establishing the ratio of pilot fuel in respect to biodiesel, gaseous fuel, and additive [
38]. Javed et al. used hydrogen fuel with ZnO nano additives biodiesel in a diesel engine. An artificial neural network was utilized to forebode noise with different engine criteria. ANN was also used so that extensive experimentation could be avoided.
This paper examined the performance and emission features under the influence of diesel and biogas used together at varying engine loads at different gas flow rates. The prediction of performance and emission was carried out by an artificial neural network [
39]. Biogas was introduced into the combustion chamber through an inlet manifold. The comparative analysis of the prediction and the actual data are presented in this paper.
7. Results and Discussion
The model was trained with different algorithms and training functions, but the best training algorithm was Levenberge Marquardt and the training function was Tansig (Hyperbolic tangent sigmoid). The Logsig (logarithmic sigmoid) showed the best result with (R > 0.98) and (MSE < 0.001). The best model was trained by evaluating the mean square error and regression coefficient.
ANN predictions were used for the experimental values with regression coefficient and predictions. ANN predictions were matched with the actual data. Regression coefficient for emission for BSEC, BTE, NOx, CO, HC and smoke opacity were 0.99939, 0.99866, 0.99699, 0.99942, 0.99706, and 0.99865 respectively.
Figure 4 shows the accuracy of the training data, validation of the data, and test data. The closeness of data points with the Fit line shows that the accuracy of the predicted data and the regression coefficient (R = 0.99939) will be higher. The variation of BSEC at different engine loads is given in
Figure 5. From the study, it is clear that BSEC was the highest and as we increase the flow of biogas from 1 kg/h to 15 kg/h, the BSEC increases linearly. At 100% engine load, BSEC was the lowest. The predicted values of BSEC at 20% load 1 kg/h biogas was 38.51 MJ/kWh, whereas at 15 kg/h BSEC was 84.26 MJ/kWh. As the engine load was increased, the BSEC decreased. BSEC at 100% engine load at 1 kg/h was 18.27 MJ/kWh. By increasing the biogas to 15 kg/h, the BSEC increased to 26.00 MJ/kWh. It can be concluded that the increase of biogas flow rate resulted in an overall lower heating value. From the figure, the maximum value was obtained for 20% engine load at 15 kg/h biogas mass flow rate.
Figure 6 shows the regression coefficient (R = 0.99866) for BTE predictions for the accuracy of the training data, validation of the data, and test data. A variation of BTE with the variation of the mass flow rate of biogas at different engine loads is given in
Figure 7. From the figure, it is clear that BTE is lower at 20% engine load, whereas at higher engine load BTE also increases. At 20% engine load at 1 kg/h biogas mass flow rate, the value of BTE was 8.57% and on increasing the gas flow rate, the BTE reduced to 3.38%. The highest BTE was at 100% engine load at 1 kg/h biogas flow rate with 21.99% BTE. On increasing the flow rate, the BTE was reduced to 17.54% at 15 kg/h. It could be observed that upon increasing the engine load, BTE increased. It was because poor utilization of gaseous fuel mixture resulted in reduced BTE under dual fuel mode.
Figure 8 shows the regression coefficient (R = 0.99966) for NO
x prediction. This shows how closely the training data and test data match closely with each other. Variation of NO
x with the variation in the mass flow rate of biogas at different engine loads is given in
Figure 9. From the figure, it is clear that at a lower engine load, NO
x emission is higher. At 20% engine load and 1 kg/h biogas flow rate, the value of NO
x was 42.78 g/kWh and on increasing the gas flow rate, it reduced to 19.40 g/kWh. At 100% engine load and 1 kg/h biogas flow rate, NO
x was 14.40 g/kWh and on increasing the flow rate further, it reduces to 1.23 g/kWh. Increasing the engine load reduces the NO
x. It further reduces on increasing the biogas flow rate in the engine. It could be justified as the use of biogas diminishes the harmful emissions.
Figure 10 shows the regression coefficient of CO emission, i.e., R = 0.99942. CO emission increases with an increase in engine load and biogas flow rate. CO emission increases with a decrease in load and increase in biogas flow into the engine, as shown in
Figure 11. At 20% engine load and 1 kg/h biogas flow rate, CO emission was 0.312% Vol, increasing the biogas flow rate to 15 kg/h increases the CO to 0.367% Vol. On increasing the engine load at 1 kg/h biogas flow rate, CO emission were 0.119% Vol, 0.061% Vol, 0.090% Vol, and 0.170% Vol, respectively for 40%, 60%, 80%, and 100% engine load. Increasing the mass flow rate of biogas in the engine decreases the oxygen supply in the engine, which leads to higher CO emissions.
Figure 12 shows the regression coefficient (R = 0.99706) for HC prediction. Variation of HC with the variation of the mass flow rate of biogas at different engine loads is given in
Figure 13. From the figure, it is clear that HC emissions are lower at lower engine loads. At 20% engine load and 1 kg/h biogas flow rate, the value of HC was 1.18 g/kWh and on increasing the gas flow rate it increases to 2.59 g/kWh. At 100% engine load and 1 kg/h biogas flow rate, the HC was 0.28 g/kWh and on increasing the flow rate further, it increases to 0.47 g/kWh. Increasing the engine load reduces the HC. It increases by increasing the biogas flow rate in the engine. At 15 kg/h biogas flow rate, HC was 2.59, 1.66, 1.18, 1.08, and 0.47 g/kWh for 20%, 40%, 60%, 80%, and 100% engine load, respectively. An increase in HC with an increase in biogas flow rate could be justified by the lower flame velocity of biogas. The regression coefficient for smoke opacity was 0.099865, as shown in
Figure 14. Variation of smoke opacity with the variation of the mass flow rate of biogas at different engine loads is given in
Figure 15. From the figure, it is clear that smoke opacity increases the engine load. At 20% engine load and 1 kg/h biogas flow rate, smoke opacity was 15.75%. Increasing the biogas flow rate to 15 kg/h reduces the smoke opacity to 5.78%. At 100% engine load, 1 kg/h biogas flow rate smoke opacity was 44.93% and increasing the biogas flow reduced the smoke opacity to 24.33%. A decrease in smoke opacity with an increase in biogas is because of the absence of aromatic compounds in the biogas composition.