ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS https://doi.org/10.1080/15567036.2020.1856237 Effect of Soybean biodiesel and Copper coated Zinc oxide Nanoparticles on Enhancement of Diesel Engine Characteristics Rakhamaji S. Gavhanea, Ajit M. Kateb, Abhay Pawarc, Manzoore Elahi M. Soudagar and H. Fayaz e d , a Department of Mechanical Engineering, Amrutvahini College of Engineering, Sangamner, Ahmednagar, India; Department of Mechanical Engineering, Zeal College of Engineering and Research, Pune, India; cDepartment of Mechanical Engineering, D Y Patil College of Engineering, Ambi Talegaon Tal Maval, Pune, India; dDepartment of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; eModeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam b ABSTRACT ARTICLE HISTORY In the present research, the influence of metallic copper-coated zinc oxide (Cu-ZnO) nanoparticles (NPs) and soybean biodiesel on the improvement in efficiency and emission characteristics of a VCR engine are examined. The soybean methyl ester (SBME) was produced utilizing the transesterification reaction. Several characterization experiments were performed to determine the shape, scale, and contents of the synthesized Cu-ZnO NPs. The Cu-ZnO NPs and SDBS surfactant were steadily distributed utilizing the ultrasonic vibration in SBME25-diesel at three stages (25, 50, and 75 ppm). The prepared physicochemical properties of fuels are comparable with ASTM requirements. In comparison to SBME25, nanofuel mixtures displayed better fuel properties. A compression ratio of 21.5 was used and a comparison was made with the SBME25. The SBME25Cu-ZnO50 combination and the CR 21.5 have illustrated an increase in overall engine characteristics. For the SBME25Cu-ZnO50 mixture, BTE and HRR raised by 16.1% and 19.2%, BSFC and ID dropped by 18.9% and 14.6%, and hydrocarbon, carbon monoxide, and smoke emissions lowered by 24.1%, 34.5%, and 16.8%. In all nanofuel blends, the oxide of nitrogen raised owing to a higher oxygen supply to the CC. Received 23 September 2020 Revised 4 November 2020 Accepted 15 November 2020 KEYWORDS Cu-ZnO nanoparticles; VCR engine; soybean biodiesel; ultrasonication; performance and emission characteristics Introduction The growth in population and increase in urbanization have led to an increase in the demand and rapid utilization of fossil fuels and the reserves are nearing its end (Soudagar et al. 2018). Also, many toxic exhaust pollutants are emitted from the transportation and industrial sector when fossil fuels are used (Delvi et al. 2019). For example, 2.9 kg of GHG emissions are emitted into the atmosphere when just 1 L of diesel fuel is consumed (Ağbulut and Sarıdemir 2019). The use of cleaner and renewable energy sources is expanding considering the present scenario. One of the most reliable and promising techniques is the incorporation of renewable energy sources (biofuels) into diesel (Mujtaba et al. 2020b, 2020c; Soudagar, Afzal et al. 2020, 2020a). Biofuels have better properties compared to diesel fuels such as sustainable, nontoxic, environmentally safe, environmentally benign, and sulfur-free, biodegradable (Harari et al. 2020; Marikatti et al. 2020). In terms of the reduction of toxic exhaust emissions, the results (methyl esters, fatty acids, alcohol, etc.) were favorable (Mujtaba et al. 2020a). Xiao et al. (Xiao et al. 2019) carried an investigation on 2-Methylfuran biodiesel on a four-cylinder CONTACT H. Fayaz fayaz@tdtu.edu.vn Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam. © 2020 Taylor & Francis Group, LLC 2 R. S. GAVHANE ET AL. engine. The load of the engine was varied from 0.13 to 1.13 MPa at 1800 rpm speed. Lower combustion duration and lengthier ignition delay at high and medium loads using the biodiesel were observed. HC and CO emission was lower while NOx emission increased. Rapid and irregular emissions of 1, 3-butadiene, benzene, and acetaldehyde were reduced with the growing biodiesel volume fraction at both high and medium loads. Additional engine loading after this caused less acetaldehyde and benzene emission, while 1, 3-butadiene discharges dropped initially and then increased. (Sekhar et al. 2018) prepared biodiesel from Pithecellobium dulce and Fourier transform infrared characterization was conducted to characterize the emission and gases. The authors used biodiesel (5–20%) for the analysis with pure diesel and observed that the CO emissions reduced by 19.64% and similar reductions in HC and NOx were reported. (Yadav et al. 2019) obtained biofuel from Yellow Oleander oil by the transesterification process. Different cavitation methods were used by him for producing the biofuel. The hydrodynamic cavitation technique provided a better yield than the other methods. The oleander oil obtained was blended with 20% to 100% in diesel which resulted in performance parameter improvement and emissions. (Raman et al. 2019) used rapeseed non-edible seed to produce biofuel using the transesterification process. At 200 bar pressure, the analysis was carried out and the BTE, BSFC, other emission characters were studied. From the analysis of results, it was noted that, in the diesel, the B25 blend without creating somewhat or any alteration can be used directly in the engine with decent BTE and reduced exhaust emissions. Maintaining better efficiency and emission characteristics and fuel properties lately, several researchers have based their attention on fuel formulation. Nano-particles as additives in biodiesel have been illustrated as the promising novel fuel additives to increase production while reducing exhaust emissions as far as possible among the latest fuel additives to biodiesel. (Soudagar et al. 2020b, 2020d) studied the effect of zinc oxide and aluminum oxide nanoparticles in mahua and honge biodiesel. The Authors observed considerable reductions in the emissions and enhancement in the performance and combustion characteristics of the CMFIS and CRDI diesel engine. Experiments were performed by (Fangsuwannarak and Triratanasirichai 2013) to incorporate TiO2 NPs in the CI engine with biodiesel. The addition of TiO2 increases the triggering of carbon combustion and thus facilitates overall combustion. Emissions such as CO, CO2, and NOx are considerably reduced due to full fuel combustion. (Jung, Kittelson, and Zachariah 2005) performed an analysis of the effect on diesel of a cerium oxide additive. Adding cerium to diesel would reduce the ID period and smoke emissions. With the addition of CeO2 NPs, the oxidation rate in diesel fuel was improved significantly, although the dosing rate remained relatively insensitive (Basha and Anand 2014) performed several studies with the use of aluminum and carbon nanotubes as biodiesel and diesel additives on direct injection diesel engines and found a substantial improvement in BTE and a decrease in toxic pollutants relative to neat biodiesel and diesel. This research aims to examine the influence of 3% Cu coated ZnO NPs and Soybean biodiesel (SBME25)-diesel fuel blend on a diesel engine at 21.5 compression ratio at different loads. In the present analysis, Tween 80 surfactant is used and mixed using the ultrasonication method utilizing different dosage levels of Cu coated zinc oxide NPs with the SBME25. The loads range from 20% to 100% and the influence on engine characteristics (performance, emission, and combustion) of the selected fuel mixtures were compared. The current research centered on the enhancement of SBME25 mixture efficiency by incorporating Cu-ZnO NPs. Materials and methods Synthesis of 3% Cu doped ZnO nanosheets The characteristic synthesis process of 3% Cu doped zinc oxide is shown in Figure 1. FESEM morphology of the synthesized Cu-ZnO NPs illustrated in Figure 2c implicates that the nanoparticles are spherical-rosette-shaped, the size of the NPs was in the range 40–50 nm (Naveed Ul Haq et al. 2017). Also, the XRD analysis is shown in Fig. (a) illustrates the corresponding peaks are ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 3 Figure 1. Synthesis of Cu-ZnO nanoparticles. relative to the ZnO NPs (Hasnidawani et al. 2016; Naveed Ul Haq et al. 2017). The UV-vis absorbance shows the synthesized Cu-ZnO illustrates the characteristic peak in between 300 and 350 nm shown in Figure 2b. The fundamental composition of 3% Cu-ZnO has been verified (Figure 2d) by the EDS. The atomic concentration of Cu-ZnO of 3% in the composition of Cu, Zn, and O. The absence of peaks in comparison to all other components of impurity reveals that the synthesized substance only consists of Cu, Zn, and O. The preparation of SBME25 is carried out using the transesterification reaction. The synthesis method used in preceding researches by Gavhane et al. 2020 and Hussain et al. 2020 is utilized in the present study. Figure 3 demonstrates the synthesis of SBME using the transesterification reaction. Synthesis and physicochemical properties of nanofuel blends In the present study, Polysorbate 80 or Tween 80 (2% vol.) known as a nonionic surfactant and emulsifier which is extremely soluble and stable was mixed with D73 and SBME25. The ultrasonication method was carried out using a bath and probe sonicator referring to the preceding literature by (Soudagar et al. 2019) and (Gavhane et al. 2020). The amalgamation is achieved using an ultrasound method, initially, the diesel mixture, Tween 80 and copper-zinc oxide (25, 50, and 75 ppm), and the magnetic stirrer is used to stir up biodiesel and heat these at 60°C, to remove any traces of water followed by sonification in a bath for 60 min of agitation. Figure 4 displays the illustration of the probe sonicator used in the current study. Also, a probe sonication was utilized for the blending, the ultrasound waves were distributed at 20 kHz for 3 s ON/OFF for 20 min, thereby allowing for the right mixture of the nanoparticles in test fuel. The physicochemical characteristics of the test fuel blends are observed in Table 1. 4 R. S. GAVHANE ET AL. Figure 2. Characterization: (a) X-ray diffraction, (b) UV-Vis, (c) FESEM at 50000× mag. level and (d) EDS analysis. The engine used in the present study is the Kirloskar 1-C, DI, VCR CI engine. In Apex innovation laboratory Sangli, India, all tests were performed. A 5-gas and smoke-meter analyzer were connected to the engine to measure the emissions. The CC in the current analysis is hemispherical and the compression ratio is 21.5. As a line connecting equipment and engine sensors (plane-flow, temperature, fuel flow, and load-measuring sensors), the DAQ and Labview software are used. Table 2 displays the VCR test engine specification. The same engine setup illustrated in Figure 5 used in the preceding literature (Gavhane et al. 2020) was utilized in the current study. Uncertainty analysis In an uncertainty analysis, errors of the engines are measured, and errors are predicted, while the data generated are believed to be accessible in optimal conditions and to be completely established and ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 5 Figure 3. Synthesis of SBME utilizing transesterification reaction (Hussain et al. 2020). Figure 4. VCR test engine setup (Gavhane et al. 2020). usable from both machinery and devices (Soudagar, Afzal et al. 2020a, Soudagar et al. 2020c). The uncertainty % of parameters measured in Table 3 is highlighted. A mathematical equation shown in Eq. 3 describes the uncertainties. 6 R. S. GAVHANE ET AL. Table 1. Physicochemical properties of fuel blends. Properties Density Cal. Val. Flash pt. Kin. Vis. Cet. No. Unit kg/m3 at 15°C kJ/kg °C cSt at 40°C - ASTM standards D4052 D5865 D93 D445 D613 Test limit ASTM D6751 860–900 Min. 35000 >130 1.9–6 Min. 40 D100 810 45000 55 2.12 51 A (Hussain et al. 2020) 845.66 41684 65.71 3.56 48.66 A1 838.2 44100 59.4 3.2 50.5 A2 839.6 44550 58.5 3.4 51.4 A3 841.5 44525 59 3.4 51.6 Where D100: Diesel; A: SBME25; A1: SBME25Cu-ZnO25; A2: SBME25Cu-ZnO50; A3: SBME25Cu-ZnO75. Table 2. Engine specifications (Gavhane et al. 2020). Parameters Fuel type Cylinder Rated Power Speed Cyl. dia. Load indicator Fuel tank EGR Piezo sensor T sensor Load sensor Rotameter Dynamometer Model Make End flanges both sides Air gap Torque Hot coil voltage Continuous current (amp) Cold resistance ohm Values Diesel, Biodiesel and Nanofuel Single, WC 3.5 KW 1500 rpm 87.5 mm Digital, Range 0–50 Kg, Supply 230 V AC Volume 15 L with glass fuel metering column Water cooled, Stainless Steel, Range 0–15% Range 5000 PSI, with low noise cable RTD, PT100 and TC, Type K Load cell, type strain gauge, range 0–50 Kg Engine cooling 40–400 LPH; Cal. 25–250 LPH AG10 Saj test plant rig Cardon shaft model 1260 type 0.77 mm 11.5 Nm 60 V 5 9.8 ffiffiffi vffi"ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi# u n � �2 X 1 @y Uy u U xi ¼t y @xi y i¼1 (1) In a variable ‘y’ is a factor that depends on the ‘xi’ and ‘Uy’ variables indicating ‘y’ parameter for an uncertainty. Analyses and readings are reported at different engine loads and uncertainty is confirmed by multiple trials. The % uncertainty in the research parameters is calculated as follows: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Overall uncertainty ¼ � BTE2 þ BSFC2 þ HC2 þ CO2 þ NOx2 þ Smoke2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi � ð0:8Þ2 þ ð0:7Þ2 þ ð0:6Þ2 þ ð0:7Þ2 þ ð0:8Þ2 þ ð0:5Þ2 ¼ �1:69 Results and discussion RSM based on Box–Behnken design Design expert 10.0 version was used to design the experiments for the optimization of diesel engine emission and performance characteristics. Three input factors (X1: compression ratio, X2: nanoparticle ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 7 Figure 5. (a) Schematic diagram of ultrasound-assisted probe sonicator (Hussain et al. 2020). Table 3. Uncertainty % of measured factors. Measurement CO HC NOx Smoke BTE BSFC Range 0–12% vol 0–15000 ppm 0–3000 ppm 0–99.9 - Accuracy ±0.03% ±10 ppm ±50 ppm ±1% - Uncertainty 0.7 0.6 0.8 0.5 0.8 0.7 concentration, and X3: engine load) were taken for current optimization. A quadratic polynomial Eq. 2 was used to obtain the optimum values for response factors referring to (Ferreira et al. 2007). Tables 4 and 5 Y ¼ C0 þ k X i¼1 Ci Xi þ k X i¼1 Cii Xi2 þ k k X X : Cij Xij j¼iþ1 (2) i¼1 ‘Y’ predicted the ultimate value response from the above equation. ‘Xi’ is the i/p independent factor, ‘Co’ and ‘Ci’ are the intercept and regression of the coefficient of the first-order RSM model. ‘Cii’ is the quadratic regression component of the ‘ith’ factor model. Cij’ is the coefficient of regression between the i/p factor of ‘ith’ and ‘jth’ and the number of i/p factor represented by ‘k.’ Second-degree equations related to optimizing output factors (Smoke, HC, and CO) are presented in Table 6. Optimum predicted values can be obtained from the above equations to measure the percentage of error. The percentage error can be calculated by using the following Eq. 3: 8 R. S. GAVHANE ET AL. Table 4. Experimental design. Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Compression ratio X1 19 19 21.5 16.5 19 21.5 19 16.5 21.5 16.5 21.5 19 19 19 19 19 16.5 NP concentration X2 (ppm) 50 75 25 75 50 50 50 50 75 25 50 50 25 25 50 75 50 Engine load X3 (%) 50 100 50 50 50 0 50 0 50 50 100 50 0 100 50 0 100 Smoke (%) 38.971 52.49 42.74 43.147 38.971 12.85 38.971 15.88 40.12 46.789 48 38.971 19.21 55.741 38.971 16.67 51.55 HC (g/kWh) 0.14 0.1914 0.16 0.341 0.14 0.135 0.14 0.334 0.145 0.354 0.158 0.14 0.156 0.21 0.14 0.143 0.357 CO (g/kWh) 0.0838 0.295 0.125 0.225 0.0838 0.05 0.0838 0.18 0.09 0.304 0.23 0.0838 0.16 0.35 0.0838 0.0638 0.381 Table 5. Independent input process variables. Input process parameters Units Coded factors Compression ratio (CR) Nanoparticle concentration (NC) Engine load ppm % X1 X2 X3 Coded process variables levels −1 Level Center +1 Level 25 50 75 16.5 19 21.5 0 50 100 Table 6. Second-degree equations for emissions. Smoke +56.74137 −1.65676 −0.78569 HC +6.19269 * Compression ratio −0.58894 * NP Concentration −3.21800E003 +0.66624 * Engine Load −1.95000E004 +4.08800E- * Compression ratio * NP −8.00000E003 Concentartion 006 −1.04000E- * Compression ratio * +1.57163E003 Engine Load 019 −1.42200E- * NP Concentration * Engine −1.12000E004 Load 006 +0.021620 * Compression ratio2 +0.014472 +6.54860E- * NP Concentartion2 003 −2.81445E- * Engine Load2 003 * Compression ratio * NP Concentration −7.61500E004 * Compression ratio * NP +1.76000EConcentartion 004 * Compression ratio * −4.20000EEngine Load 005 * NP Concentration * Engine +8.24000ELoad 006 * Compression ratio2 +7.62000E003 +3.12800E- * NP Concentartion2 +8.73200E005 005 +6.22000E- * Engine Load2 +3.15300E006 005 Percentage of error ¼ * Engine Load CO +3.81092 −0.32601 −0.013814 * Compression ratio * NP Concentration * Engine Load * Compression ratio * NP Concentartion * Compression ratio * Engine Load * NP Concentration * Engine Load * Compression ratio2 * NP Concentartion2 * Engine Load2 Experimentalresult Optimizedresult � 100 Experimentalresult (3) Figure 6a-c shows the contour plots of variation of smoke emissions with the input parameters. It is observed that smoke is strongly dependent on load. Smoke increases as the load increases. The blend type and injection pressure have less variation effect on smoke. Figure 6d-f shows the contour plots of ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 9 Figure 6. RSM contour plots. variation of HC with the input parameters. It is observed that HC is strongly dependent on injection pressure. HC decreases as the injection increases. The load has less variation effect on HC. The interaction effect of load and injection pressure has increased the HC. Figure 6g-i shows that the CO increases as the load increases. The interaction effect of load and blend type has increased the CO. The interaction effect of blend type and injection pressure has less variation effect on CO. Emission characteristics Carbon monoxide The variations of CO emissions vs. load for biodiesel, diesel, and nano fuel blends are shown in Figure 7. CO emissions from exhaust gases signify the lost chemical energy that was not used in the CC. The dearth of oxygen molecules in the combustion chamber results in higher emissions of CO due to incomplete fuel combustion. The addition of metal-oxide-based nanoparticles behaves as an oxidation catalyst which results in higher carbon combustion activation and therefore encourages complete fuel combustion (Heidari-Maleni et al. 2020; Hosseinzadeh-Bandbafha et al. 2020). The carbon monoxide emissions for the SBME25-(Cu-ZnO25, Cu-ZnO50 and Cu-ZnO75) are 20%, 34.5% and 27.4%, respectively, compared to SBME25. 10 R. S. GAVHANE ET AL. Figure 7. Variation of CO emissions and load. Smoke The inclusion of Cu doped ZnO NPs-biodiesel lowers the smoke density compared to B25 fuel blend at all loading conditions illustrated in Figure 8. Oxygen present in nanoparticles causes smoke density to decrease. The decrease in smoke density was also observed with rising Cu-ZnO NPs concentration. The dosage level of 50 ppm of Cu-ZnO indicates a maximum smoke density reduction, 16.8% compared to SBME25, while for 25 and 75 ppm the smoke reduced by 7.5% and 14.2%. The SBME25 smoke density decreased by 8–20%, in particular with a maximum load close to the fuel pattern when Cu-ZnO nanoparticles are added. This is attributed to improvement in the combustion phenomenon which allows for absolute combustion of H-C molecules (Heidari-Maleni et al. 2020). Hydrocarbon Biodiesel indicated the highest hydrocarbon emissions owing to high viscosity, thus resulting in improper fuel spray characteristics which results in partial combustion of fuel blends (El-Seesy et al. 2020; Venu et al. 2020). The variation of HC emissions is shown in Figure 9. The test fuel blends at CR 21.5 showed lower HC emissions that are primarily influenced by the A: F ratio. Also, the addition of 2% Tween 80 surfactant lowers viscosity of test fuel blends and aids in converting from rich to lean mixture. The HC emissions for SBME25Cu-ZnO25 decreases by 16.85%, 24.1%, and 21.1%, respectively, compared to SBME25. Nitrogen oxide The incorporation of Cu-ZnO NPs boosts combustion and thus increases the combustion rate by providing optimum energy during combustion. With an increase in engine load, NOx emissions raised as illustrated in Figure 10. Emissions of high NOx are increased owing to the increase in in- ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS Figure 8. Variation of smoke emissions and load. Figure 9. Variation of HC emissions and load. 11 12 R. S. GAVHANE ET AL. Figure 10. Variation of NOx emissions and load. cylinder temperature induced by complete fuel combustion. SBME25 and ternary blend molecules improved and generated the high combustion temperature due to a rise in O2. The pressure and temperature of the in-cylinder which contributes to increased NOx emissions. The NOx emissions for 25 ppm of Cu-ZnO in SBME25 increased by 7.5%, while for the 50 and 75 ppm of Cu-ZnO NPs increased the NOx emissions by 9.5% and 10.2% compared to SBME25. While the nitrogen oxide for the SBME25 test fuel was comparable to the D100. Combustion characteristics Heat release rate. The variations of HRR vs. load % for fuel blends are illustrated in Figure 11. The addition of Cu-ZnO NPs enhances the combustion process and the maximum amount of energy is produced during the combustion thus increasing the HRR. The trend is due to an elevated Cv of nano fuel blends compared to SBME25. The heat release rate for the SBME25-(Cu-ZnO percentage of 25 ppm, 50 ppm, and 75 ppm) nano fuel blends increased the HRR of 8.2%, 19.2%, and 14.85%. The improvement in the HRR is also owing to high momentum liquid droplets and elevated injection velocity and compression ratio (Basha and Anand 2013; Prabu 2018). Ignition delay. The variations of ID period vs. load % for fuel blends are illustrated in Figure 12. At lower load conditions, the ID is higher and progressively increases at higher loads due to improved compression and a sufficient A: F combination (Shaafi and Velraj 2015). At maximum load, the fuel ID values reduce by 6.5%, 14.6%, and 10.2% for SBME25 (25, 50, and 75 ppm of Cu-ZnO NPs), the reduction compared with the fuel combination for SBME25. The ID period depends on the release of energy for the duration of the pre-ignition phase (Basha and Anand 2013; Hussain et al. 2020). ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS Figure 11. Variation of HRR and crank angle. Figure 12. Variation of ID and load. 13 14 R. S. GAVHANE ET AL. Performance characteristics Brake thermal efficiency. The variations of BTE vs. load % for fuel blends are illustrated in Figure 13. The BTE increases with enhancement in the loading conditions. The presence of Cu-ZnO nanoparticles in the fuel blends resulted in an increase in the BTE due to enhanced HRR, also higher Cv, comparative lower density, and viscosity of fuel blends resulted in higher BTE. The BTE for fuel blend SBME25CuZnO50 was comparable to diesel fuel and increases by 16.1% compared to SBME25. Also, the addition of 25 ppm of Cu doped ZnO nanoparticles and 75 ppm in the SBME25 test fuel increased the BTE by 9.6% and 11.5% compared to SBME25. The NPs acts as a catalyst and oxygen buffer to enhance the micro-explosion phenomenon which increases the BTE (Khan et al. 2020; Mujtaba et al. 2020a, 2020b, 2020c). Brake specific fuel consumption The variations of BSFC vs. load % for fuel blends are illustrated in Figure 14. BSFC reduced with an increase in the loads. The BSFC is explained for maximum load, CR of 21.5 was considered in the current investigation. The inclusion of nanoparticles in the fuel blends resulted in lowered BSFC due to enhanced combustion phenomenon (Dhahad and Chaichan 2020; El-Seesy et al. 2017). For the SBME25Cu-ZnO50 fuel blend, the BSFC was lowest amongst all the fuel blends and comparable to diesel fuel. The NPs acts as a catalyst to enhance the micro-explosion phenomenon in the base fuel blends. The addition of 25 mg/ L of Cu doped zinc oxide nanoparticles in the B25 fuel lowered the SFC by 6.5% compared to SBME25; similarly, 50 ppm and 75 ppm Cu-ZnO in SBME25-diesel fuel reduced SFC by 18.9% and 14.8%. Figure 13. Variation of BTE and load. ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 15 Figure 14. Variation of BSFC emissions and load. Conclusions The current study deals with the inclusion of Cu coated ZnO NPs in the soybean (B25)-Diesel fuel. The engine was operated at a constant speed and injection timing of 1500 rpm 23°BTDC while the loads were varied, a constant compression ratio of 21.5 was selected based on the RSM results. Three fuel blends were used by varying the nanoparticle dosage. Based on the results obtained, the conclusion is drawn: (1) The ultrasonication process viz. bath and probe sonication assisted in the steady blending of the Cu-ZnO NPs in the SBME25 fuel blend. The physicochemical properties of SBME25 fuel improved with the addition of Cu-ZnO NPs. Furthermore, owing to a 2% addition of Tween 80 surfactant in SBME25 lowered the viscosity and density of the test fuel. (2) The effective lab synthesis of the Cu coated ZnO NPs enabled the use of the nanoparticles as a fuel additive in biodiesel, which significantly reduced the purchasing cost. The characterization tests carried out ensured the successful synthesis of Cu-ZnO NPs. (3) The nanofuel blend, SBME25Ce-ZnO50 reduced the carbon monoxide, HC, and smoke opacity emissions by 34.5%, 24.1%, and 16.8% in contract to B25 fuel owing to enhancement in fuel properties and combustion rate. (4) Higher compression ratio of 21.5 increased overall diesel engine characteristics compared with a compression ratio of 18.5. RSM was illustrated to be a powerful optimization tool for the diesel engine fueled with SBME25 and Cu-ZnO nanoparticles. Significant outcomes on the competence and emission attributes have been analyzed. A second-degree model was successfully established to narrate the linkages among input parameters, the blend type, and injection 16 R. S. GAVHANE ET AL. pressure. These optimized conditions were validated by experimenting and nearly 95% similar results were observed. (5) The performance characteristics viz. BTE was enhanced by 16.1% while the BSFC reduced by 18.9%, while the combustion characteristics viz. HRR increased by 19.2% and ID reduced by 14.6%. The outcomes validate that the copper-zinc oxide NPs in soybean B25 fuel at CR 21.5 enhance the engine characteristics of the tested VCR engine contrasted to the soybean B25 fuel. The fuel blend with 50 ppm of Cu-ZnO functioned as the best fuel blend. There is further scope in the study of engine wear on the CC, piston and piston rings, cylinder linings, fuel injectors, and exhaust pipe are necessary to confirm the reliability of NPs in VCR engine operation. Also, there is further scope on the impact of NPs additives on human health and environmental pollution. Use of bio-based nano-additives in further studies to improve the combustion characteristics of diesel engine. Nomenclature NPs VCR CI nm g/kWh CC ATDC FFA ASTM ID CO2 NOX BTE IP °CA D100 Nanoparticles Variable compression ratio Compression ignition Nanometer Grams per kilowatt hour Combustion chamber After top dead center Free fatty acid American Society for Testing and Materials Injection delay Carbon dioxide Oxides of nitrogen Brake thermal efficiency Injection pressure Crank angle (degrees) 100% diesel SBME25 25% Soybean methyl ester blended with diesel SBME25CuZnO25 SBME25 and 50 ppm Cu-ZnO NPs SBME25CuZnO75 SBME25CuZnO50 ZnO Cu Tween80 IC ppm BTDC CR PP HC CO PM BSFC HRR IT Zinc oxide Copper Polysorbate 80 Internal combustion Parts per million Before top dead center Compression ratio Peak pressure Hydrocarbon Carbon monoxide Particulate matter Brake specific fuel consumption Heat release rate Injection timing SBME Soybean methyl ester (Soybean biodiesel) SBME25 and 25 ppm Cu-ZnO NPs SBME25 and 75 ppm Cu-ZnO NPs Author contributions RSG, MEMS: Writing Original Draft, Methodology, Validation of Results. MEMS: Formal analysis and Data curation. FH, AMK, AP, MEMS: Review, and Editing. AMK, AP: Supervision. Conflict of interest The authors declare no conflict of interest. Notes on contributors Rakhamaji S.Gavhanecompleted the B.E. (Mechanical) in 2005 from Savitribai Phule Pune University, Maharashtra, India. M.E. (Heat Power) in 2010 from Shivaji University, Maharashtra, India. He is now pursuing PhD degree in effect of Nano-additives on soybean biodiesel from Savitribai Phule Pune University, Maharashtra, India. Recently he is working as Assistant Professor in Amrutvahini college of Engineering, Sangamner, Maharashtra, India. He is life time member of Indian Society of Technical Education(ISTE). He has received a grant of 07.00 lakhs for variable compression ratio engine, under MODROBS in 2006. His research interest includes Biodiesel, Nano-Additives, Heat transfer and I.C. Engines. ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 17 Ajit M. Katecompleted B.E. (Mechanical) in 1994 fromDr. B.A.M.U, Aurangabad, Maharashtra, India. M.E. (Heat Power) in 2004 & Ph.D. in Mechanical Engineering in 2012from Savitribai Phule Pune University, Maharashtra, India. Recently he is working as a Principal in Zeal College of Engineering & Research, Pune, Maharashtra, India. He is also working as BOS member of Mechanical Engineering in Savitribai Phule Pune University, Maharashtra, India. He is having 25 years of teaching experience in reputed institutes. He is working as a consultant for design & manufacturing of heat pipe to various industries. He is recognised PhD guide in in Savitribai Phule Pune University, Maharashtra, India. Abhay Pawarcompleted B.E. (Mechanical) in 1997 from Dr. B.A.M.U. University, Aurangabad Maharashtra, India. M.E. (Heat Power) in 2004 & Ph.D. in Mechanical Engineering in 2011from Savitribai Phule Pune University, Maharashtra, India. Recently he is working as a Principal in DYPCOE, Ambi, Pune, Maharashtra, India. He is Member of BOS Mechanical and Automobile Engineering appointed from 2016 in Savitribai Phule Pune University, Maharashtra, India. He is awarded with national level ‘SHIKSHAK RATNA PURASKAR’ by Mahatma Phule Shikshak Parishad, Maharashtra, India. He is Recognized Ph.D. guide of SPPU, Department of Technology and VTU, Karnataka. Total Research Scholars pursuing are 07 (01 Research Scholar completed Ph.D. from VTU). Received research fund (Rs. 20 Lacs) from Nostrum Technologies Pvt Ltd USA for Ph. D. research work at Vishwakarma Institute of Technology, Pune. Manzoore Elahi M. Soudagarreceived the bachelor’s (B. Tech) and master’s degree (M. Tech) in Mechanical Engineering from Visvesvaraya Technological University (VTU), Karnataka, India. He has several years of teaching experience in numerous Engineering Universities. He is currently pursuing his Ph.D. in Non-Conventional Energy Sources (Renewable Energy) from Department of Mechanical Engineering, University of Malaya, Kuala Lumpur, Malaysia. His research interests include biodiesel production technologies, diesel engine characteristics, finite element analysis, and solar energy. He has four years of extensive research experience, he has published total of 62 Articles till Dec'2020, 40 published in Web of Science, Clarivate Analytics (ISI/SCI/SCIE); 3 in Scopus indexed Journals; 18 Web of Science Conference Proceedings and one book chapter. He is reviewer of Elsevier, BV; Springer-Nature; Wiley; Taylor and Francis. Fayaz Hussainreceived the M.Sc. degree (2014) and the Ph.D. degree (2019) both supported by the scholarship achieved in Renewable Energy from the University of Malaya, Malaysia. He worked as research assistant and tutor from 2010 to 2019 in University of Malaya and currently working as Lecturer and Researcher in Ton Duc Thang University. He also haveindustrial experience of maintaining equipment in Oil and Gas Development Company, which provides better understanding of engineering systems research. He has worked onrenewable energy related projects mainly on solar energy and biofuels. He has published numerous papers in ISI/SCI high impact factor journals, book chapters and internationalconferences. His main research interests include energy, solar assisted refrigeration, energy storage in phase change materials, biofuels, renewable energy and other related energy projects. ORCID Manzoore Elahi M. Soudagar http://orcid.org/0000-0002-0935-2040 H. Fayaz http://orcid.org/0000-0001-6169-7911 References Ağbulut, Ü., and S. Sarıdemir. 2019. A general view to converting fossil fuels to cleaner energy source by adding nanoparticles. International Journal of Ambient Energy 1–6. doi:10.1080/01430750.2018.1563818. Basha, J. S., and R. Anand. 2013. 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