Waste cooking oil characterization
Due to several chemical events such as hydrolysis and material transfer between oil and food occur during the frying process, the chemical and physical features of the oil are altered. The physic-chemical variables of the waste cooking oil sample collected are listed in Table 3.
Characterization of the catalyst
Scanning electron microscope. Fig.1a shows SEM pictures of the catalyst. The particles have irregular forms with voids, as shown in Fig.1a, as a result of the activating ingredient evaporating (NaOH). Fig.1b shows the spongy form of the catalyst. Fig.1b also shows the existence of rod-like particles and has a smoother surface than fig.1a, which contains a few cavities and hair line breaks. This could be due to the activation process, which may have filled in the holes and created a smooth surface. Because triglyceride molecules react with methanol molecules absorbed on these spongy surfaces, the spongy structure of the catalyst will boost biodiesel generation.
Fourier transform infra-red analysis. The FTIR spectra of the catalyst was carried out in the range from 1000 – 3500 cm-1 to study the catalytic transesterification effect. The FTIR spectra is shown in Fig 2. The changes in the functional groups provide the indication of the modifications that occurred during the impregnation process. Based on the area of each peak, major absorption peaks were observed at 3893.8, 3652.8, 1114.5, 998.9, 9096.1 ,779.0 and 685.8 cm-1. These major absorption bands and their corresponding functional groups are listed in Table 2.
Response surface method statistical analysis
Table 4 shows the results of the CCD experimental design for transesterification of waste cooking oil (WCO) to FAME using RSM. Tocreate an acceptable and usable regression model, the actual yield was analyzed. A suitable model was chosen from among mean, linear, quadratic, cubic, quartic, and other options. The software created a cubic regression model and used it to forecast ideal parameters for the transesterification of WCO to biodiesel. Equation (3) shows the best fit model for FAME yield.
The trial/experimental changes are: catalyst concentration, methanol to oil molar ratio, reaction temperature, reaction time, and agitation speed, and A, B, C, D, and E show the estimates of the trial/experimental changes: catalyst concentration, methanol to oil molar ratio, reaction temperature, reaction time, and agitation speed. The positive sign in front of the words indicates that the component has a synergistic effect in increasing FAME yield, whereas the negative sign indicates that the factor has an antagonistic effect29,30. The positive coefficients in the model regression (equation 3) showed a linear rise in FAME yield. The quadractic word, on the other hand, had detrimental consequences on the FAME yield.
As indicated in Table 4, the greatest FAME yield of 87.70% was obtained with catalyst weight of 4 wt.%, reaction time = 4hr, reaction temperature = 60 °C, methanol to oil molar ratio = 9:1, and agitation speed of 350 rpm, as predicted by the RSM regression model. The ANOVA test, R2, and Radj2 were used to assess the statistical significance of the model equation. ANOVA was used to test the regression model's accuracy and efficiency in predicting the response, and the results are displayed in Table 5. The model's F-value and p-value were calculated to be 125.02 and 0.0001, respectively, showing that it was statistically significant at the 95 percent confidence interval (p 0.05). A, C, E, AB, AC, AD, AE, BD, CD, DE, A2, B2, D2, and E2 are statistically significant model terms in that scenario, whereas B, D, BC, CE, and C2 are not. P-values less than 0.01 are regarded statistically significant at the 99% confidence level, indicating that the model is statistically significant (p-value 0.05). The lack of fit F-value of 2.69 and p-value of 0.1489 (p-value > 0.05 is not significant) indicated that the lack of fit was not significant in comparison to the pure error and that the model was successfully fitted to experimental data. A lack of fit F-value "this large may occur owing to noise" has a 99.59 percent chance of occurring. Significant lack of fit indicates that there may be contributions in the regressor response relationship that the model does not account for. Insignificant lack of fit is most needed because significant lack of fit indicates that there may be contributions in the regressor response relationship that the model does not account for. The model's regression coefficient R2 is 0.9956, suggesting that the experimental variables analyzed account for 99.56% of the overall variation in biodiesel yield, indicating that the model is well-fit. High values of projected R2 (0.9221) and adjusted coefficient of determination (R2Adj:0.9887), a standard deviation of 1.84, and a low coefficient of variation (C.V:2.70 %) are indicators of fitted model precision and a sign of strong accuracy and dependability as shown in table.6.
Physical property of biodiesel produced
94.10% biodiesel (FAME) was obtained under ideal conditions. Based on ASTM standards, the biodiesel generated in this situation was further examined to establish its viscosity, density, pour point, and cloud point. The physical properties of the biodiesel produced are shown in Table 7. Despite the fact that the percent biodiesel (FAME) yield attained in this study was less than 96.50%, the product's viscosity, density, acid value, pour point, and flash point all met ASTM D6751 and EN 14214 standards.
Effects of process parameter on biodiesel yield.
Effect of methanol/oil molar ratio on biodiesel yield. Fig. 3a shows the experimental results, which show that the molar ratio of methanol to oil has a substantial impact on biodiesel yield. With the molar ratio, the biodiesel yield was enhanced. At a 9:1 molar ratio of methanol to oil, a 76.4% yield was reported. The surplus methanol is required since it can speed up the methanolysis process. The high concentration of methanol increased the production of methoxyl species on the catalyst surface, causing an equilibrium shift to the forward direction and consequently an increase in biodiesel conversion rate31. Increasing the methanol to oil molar ratio after the optimal 9:1 methanol to oil molar ratio would diminish the biodiesel yield. This is due to the presence of too much methanol above the optimal concentration, which stymies the reaction. The glycerol produced as a by-product of the reaction would primarily dissolve in the excess methanol and therefore block the reaction of methanol to reactants and catalyst, interfering with glycerine separation, lowering conversion by shifting the equilibrium in the other way.
Effect of catalyst concentration on biodiesel yield. The concentration of the catalyst is critical for improving the yield of the transesterification reaction. Fig.3b shows that as the catalyst concentration is increased from 1% w/w to 5% w/w, the biodiesel yield increases, but the yield decreases marginally as the catalyst concentration is increased further. With a biodiesel production of 86.0%, the best catalyst concentration was determined to be 4% w/w clay catalyst. Due to soap generation in the presence of a large amount of catalyst, the biodiesel yield has been marginally reduced. Furthermore, the presence of too much catalyst raises the viscosity of the reactants, decreasing the biodiesel production. The basic sites generated on the surface of the catalyst, as well as the soluble substance leached away from the catalyst, catalyze the transesterification32.
Effect of temperature on biodiesel yield. Fig.3c shows the biodiesel output from waste cooking oil transesterification at reaction temperatures ranging from 45 to 75°C. The biodiesel yield rises with the reaction temperature until it reaches an ideal point of 60°C, with an 89.0 percent biodiesel yield. The transesterification required some thermal energy at first because the reaction was endothermic. Because the reaction mixture is a three-phase system (oil, methanol, and catalyst), enough thermal energy was required to overcome the diffusion barrier between the phases33. The high temperatures, on the other hand, are not ideal. When the temperature rises to the boiling point of methanol, the methanol quickly vaporizes and forms a significant number of bubbles, inhibiting the process at the two-phase interface and lowering the biodiesel yield.
Effect of time on biodiesel yield. Fig.3d illustrates the biodiesel production for waste cooking oil transesterification at various reaction times ranging from 0.5 to 5 hours. Biodiesel generation was rapid in the early phases of the transesterification reaction until the reaction achieved equilibrium. The reaction begins to reverse in the direction of reactants once it has passed the optimum point. The reversibility of the transesterification reaction caused this result34. As a result of the catalyst's ability to absorb the product, a long reaction time limits biodiesel yield. As a result, determining the optimal transesterification reaction time is critical. The best reaction time in this example was 4 hours, with a yield of 89.0%.
Effect of agitation speed on biodiesel yield. During the transesterification reaction of triglycerides, the agitation speed is an important reaction variable that impacts the biodiesel production. Fig.3e shows that as the agitation speed was raised, the biodiesel yield rose, reaching a maximum of 91.0 percent at 350 rpm. However, there was no substantial improvement in biodiesel yield beyond this optimum agitation speed. The current study used a 350 rpm agitation speed to achieve the highest biodiesel output. Furthermore, this demonstrated that a 350rpm agitation speed was sufficient to minimize mass transfer limitations in the transesterification reaction.
Three dimensional response surface and the contour plots
Figures 4b (1), b (2), 4c (1), c (2), 4d (1), and 4d (2) show the three-dimensional response surface and contour plots (2). Each curving contour represents an unlimited number of possible combinations of two test variables, with the other two remaining at zero. It is simple and convenient to comprehend the interactions between two components and to determine their optimum levels using contour plots. Figure 4a, depicts the link between expected and experimental biodiesel yield. It can be seen that the anticipated and experimental biodiesel yields are highly correlated (R2= 0.9956). The predicted and experimental values were reasonably close to one another (R2 value near unity), indicating that the data fit the model well. Figure 4b (1) and 4b (2) demonstrate the response to the interaction between methanol oil ratio and catalyst weight versus yield, as well as the related 3D response surface plot. These graphs show that better biodiesel yields occur when the methanol oil/ratio is 9:1, the catalyst weight is 4%, the reaction temperature is 60° C, and the reaction period is 4 hours. The oil to methanol molar ratio had only a little effect on synthesis at low catalyst concentrations, however at high catalyst concentrations, the oil to methanol molar ratio was significantly important for synthesis enhancement35. However, if the catalyst concentration is higher than the prescribed levels, the product will not separate. In other words, the transesterification reaction would be difficult to complete. As a result, the transesterification reaction was hampered by low catalyst concentrations and a higher methanol-to-oil ratio. When the catalyst was increased to a specific level and the methanol to oil ratio was high, the yield merely improved. The contour plot revealed that a high biodiesel production (> 91.2%) may be achieved by using a combination of intermediate to high catalyst loading (3 to 5 wt.%) and a high methanol to oil molar ratio (6 to 12). The interaction effect of the methanol/oil ratio and temperature is shown in Figures 4c (1) and 4c (2). The maximum yield (91%) was obtained at a temperature of 60°C and a methanol/oil ratio of 9:1 according to the plots. The solubility of methanol in the oil increases as the temperature rises, as does the rate of reaction. In fact, at low temperatures, methanol is not soluble in the oil at all; when stirring begins, an emulsion appears36. On the other hand, a high amount of alcohol (more than 9:1) makes glycerol recovery difficult. This could be due to the stiochiometry of transesterification, which demands a 3:1 molar ratio of alcohol to triglycerides, and because this reaction involves the conversion of one ester and an alcohol into another, an excess of alcohol is utilized to drive the reaction to completion37.The contour and response surface plot of reaction time and methanol to oil ratio on production are shown in Figures 4d (1) and 4d (2). Increases in the methanol/oil ratio over 9:1 and reaction duration above 4 hours result in a better yield when the other parameters remain constant. In other words, as time passes, the methanol/oil ratio rises, resulting in a high yield. When a high molar ratio is used for a specific period of reaction and catalyst weight, higher Methyl ester synthesis is significantly preferred37.