98 .Creative Commons Attribution 4.0 International License This work is licensed under a Optimization of Process Parameters for Biodiesel Production from Three Indigenous Vegetable Oils Abstract Optimization procedures using a variety of input parameters have gotten a lot of attention, but using three non-edible seed oils of Jatropha (Jatropha curcas), Sesame (Sesamum indicum), and Sweet Almond (Prunusamygdalus dulcis) has a few advantages, including availability and non-food competitiveness. Optimizing a two-stage trans-esterification process using a sodium hydroxide-based catalyst at a fixed catalyst (1.0wt %) and temperature (60 oC) while varying molar ratio (1:3, 1:6, 1:12), time (20–60 min), and mixing speed (500–1000 rpm), to produce optimal responses of yields were studied using response surface methodology (RSM). The optimization solution of molar ratio (1:3), time (40.9 min.), and speed (500 rpm) resulted in an 86.9 % for refined jatropha biodiesel (RJB), the optimization for refined sesame biodiesel (RJB) with molar ratio (1:6), time (41.7 min.), and speed (619 rpm) resulted in an 88.5 %, and the optimization for refined sweet almond biodiesel (RSAB) with the molar ratio (1:3), time (49.359 min.), and speed (500 rpm) resulted in an 88.7 % at the conditions. RJO, RJB, and RSAB had predicted biodiesel yields of 86.9 %, 88.5 %, and 88.7 %, with less than 0.2 % variation, respectively. The characteristics of biodiesel were studied, and the results were Ibn Al-Haitham Journal for Pure and Applied Sciences http://jih.uobaghdad.edu.iq/index.php/j/index: Journal homepage Doi: 10.30526/35.3.2810 Article history: Received 6 February 2022, Accepted 17 April, 2022, Published in July 2022. 1Aliru Olajide Mustapha aliru.mustapha@kwasu.edu.ng aliru.mustapha@kwasu.edu.ng Yemisi Tokunbo Afolabi8 ytokunbo.afolabi@gmail.com Jesutomisin Oladele6 oladelejesutomisin@gmail.com 2Kudirat Afolashade Usman usmankudirat1995@gmail.com usmankudirat1995@gmail.com Oluwaseyi Mojibade Adekoya4 adekoyaoluwaseyi656@gmail.com Toheeba Aramide Zakariyah3 toheebahzakariyah98@yahoo.com 5Joseph Phillip Tosin joephil20166@gmail.com Toluwalase Solomon Amao7 amaotoluwalasesolomon@gmail.com , Kwara State SciencesDepartment of Chemical, Geological & Physical Sciences, College of Pure and Applied 8-1 University Malete, PMB 1530, Ilorin, Kwara State, Nigeria. https://creativecommons.org/licenses/by/4.0/ file:///F:/العدد%20الثاني%202022/:%20http:/jih.uobaghdad.edu.iq/index.php/j/index mailto:aliru.mustapha@kwasu.edu.ng mailto:ytokunbo.afolabi@gmail.com mailto:oladelejesutomisin@gmail.com mailto:usmankudirat1995@gmail.com mailto:adekoyaoluwaseyi656@gmail.com mailto:toheebahzakariyah98@yahoo.com mailto:joephil20166@gmail.com mailto:amaotoluwalasesolomon@gmail.com IHJPAS. 53 (3)2022 99 determined to meet both ASTM D6751 and EN14214 criteria. The effects of molar ratio, and time on biodiesel yield from their respective oils were important parameters that greatly influenced the yields, but speed only changed the yields marginally. This work has addressed important difficulties influencing mass production of biodiesel such as the utilization of low- cost feedstock such as non-edible vegetable oils, boosting production efficiency through variable optimization of process parameters, and lowering catalyst dosages through catalyst regeneration. Keywords: vegetable-oils; biodiesel, optimization, yield; non-edible . 1. Introduction Several important variables influence the transesterification reaction. To achieve the largest biodiesel production, these variables must be at their peak since the rate of reaction is influenced by the reaction temperature. A higher reaction temperature can reduce the viscosity of oils, increasing the reaction rate as more energy was supplied for the reaction to occur. The reaction temperature must be lower than the alcohol's boiling point (60–70 oC at normal atmospheric pressure for methanol) to prevent the alcohol from vaporizing. As a result, raising the reaction temperature over its optimum range reduces biodiesel yield since the saponification reaction was quickened, resulting in less biodiesel. Temperatures between 60 and 80 oC generate the best production, depending on the type of oil [1-2]. The stoichiometric ratio of the trans-esterification reaction in 3 moles of alcohol and 1 mole of triglyceride produced 3 moles of fatty acid ester and 1 mole of glycerol. The forward reaction was more favorable, during trans-esterification, more alcohol was used to ensure that the oils were completely converted to ester [3-5]. A larger alcohol-to-triglyceride ratio can also lead to faster ester conversion. The molar ratio is strongly influenced by the type of catalyst used. In base-catalyzed biodiesel production, a molar ratio of 5:1 or 6:1 of methanol to oil is sufficient to convert Jatropha oil to biodiesel if free fatty acids after pretreatment are less than 1% [6-9]. If the fraction of free fatty acids in oils was large, a molar ratio of 20:1 or 24:1 was necessary when using acid-catalyzed trans- esterification [10-13]. The amount of catalyst used can alter the yield of biodiesel produced as basic catalysts are often favored over acid catalysts due to their better reactivity and reduced process temperature requirements [14]. Freedman et al. and Ifeoluwa et al. [15-16] discovered that sodium methoxide was more effective than sodium hydroxide due to the lesser amount of water produced when mixing sodium hydroxide with methanol. As the catalyst concentration was increased, the conversion of triglycerides and the generation of biodiesel both increased. It has been demonstrated that a concentration of NaOH in the range of 1.0–1.4 percent (w/w) converts jatropha oil to methyl ester by 90–98%. [17-19]. About 95–99 % of jatropha biodiesel has been obtained with KOH concentrations ranging from 0.55 to 2.0 percent (w/w) [20-21]. However, if the alkali catalysts were used at higher concentrations than their optimum, the generation of biodiesel was reduced because more soap was generated [22] According to the literature, as the reaction time reduces, the conversion rate increases. Because the alcohol was blended and spread into the oil, the reaction was first delayed. After a period, the reaction picks up pace until it reaches its maximum yield. For base-catalyzed trans- esterification, the output of biodiesel peaks at 120 minutes or less [1]. Acid catalyzed trans- IHJPAS. 53 (3)2022 100 esterification takes much longer than base catalyzed trans-esterification because base catalysts are frequently more reactive than acid catalysts [14]. According to previous studies [12-13], the reaction time needed to convert triglycerides to biodiesel might be anywhere from 18 to 24 hours. On the other hand, excessive reaction time would limit the product yield due to the reverse reaction of trans-esterification, which causes more fatty acids to be produced in the form of soaps [20]. Before biodiesel can be developed and optimized on a large scale, many factors and difficulties must be resolved. The key issues include the utilization of low-cost feedstock such as non-edible vegetable oils, increasing production efficiency through optimization of process parameters, lowering catalyst costs through catalyst regeneration, and the optimization of process parameters to maximize the biodiesel yield. This work seeks to address these issues. 2. Materials and Procedures The three seeds of Jatropha (Jatropha curcas), sesame (Sesamum indicum) and sweet almond (Prunusamygdalus dulcis), and were collected in Ilorin markets in Kwara State, Nigeria. Sigma Aldrich provided the chemicals and equipment (Gillingham, Dorset, UK). Cold oil extraction was used to extract refined Jatropha oil (RJO), Sweet Almond oil (RSAO), and sesame oil (CSO) from crude Jatropha oil (CJO), Sweet Almond oil (CSAO), and sesame oil (CSO) (RSO). Following refinement, the oils were transesterified to obtain refined jatropha biodiesel (RJB), Sweet Almond biodiesel (RSAB), and sesame biodiesel (RSB) using the two-step method recommended by the American Standard for Testing Materials, the Association of Official Analytical Chemists and Mustapha et al. [22-26]. 2.1The Response Surface Method for Optimization of Biodiesel The Response Surface Method (RSM) is a technique for calculating the number of parameters it takes for optimal response. Correlations between independent and response variables are established using the RSM approach. Although Box and Wilson [27] were the first to create a model or optimal response using experimental data, various techniques to process optimization have expanded its practical use. The p-value for each of the models can be determined using ANOVA. When the values were less than 0.05, the 0.05 p-value for most process variables was favorable, indicating that model terms were significant. Design Expert II was chosen as the statistical tool because it includes the three minimal categories of input and response variables, as well as anticipated and experimental values, which are required for the adequacy assessment. 2.2. Design of Experiments To produce reliable ANOVA models, the RSM must create a design of experiments (DoE) using the smallest amount of data possible. Because Box–Behnken Design (BBD) designs do not contain axial points, all design points must fall between operating restrictions, and a design matrix (inputs) must be constructed using a BBD. It necessitates a decrease in the number of treatment options. The input components (molar ratio, time and speed) in fixed catalyst and temperature were chosen in a variety of combinations to give yield as an output. A fixed IHJPAS. 53 (3)2022 101 sodium hydoxide dose of 1.0 wt. %, a molar ratio of 1:3, 1:6, 1:12, and a temperature of 60 oC were randomly tuned with variable time (20, 40, 60 min), and speed (500, 750, 1000 rpm) [26]. Table1. Design levels with multiple independent variables. 3. Biodiesel optimization test matrices were developed using a fixed sodium hydroxide dose and time. 3.1. Biodiesel derived from refined jatropha biodiesel (RJB) Table 2. Experimental matrix with a variety of molar ratios, times, and speeds Factor 1 Factor 2 Factor 3 Response Run A:Molar ratio B:Time C:Speed Yield (%) s rpm Actual Predicted 1 7.5 45 750 80.00 80.00 2 7.5 45 750 80.00 80.00 3 12 60 750 73.30 75.64 4 7.5 60 500 86.67 82.34 5 12 45 1000 96.00 89.32 6 7.5 45 750 80.00 80.00 7 7.5 30 1000 66.70 71.03 8 3 60 750 80.00 77.66 9 3 45 500 82.67 89.35 10 12 45 500 85.30 87.30 11 7.5 45 750 80.00 80.00 12 12 30 750 73.30 75.65 13 3 30 750 66.70 64.36 14 7.5 60 1000 73.30 77.64 15 7.5 45 750 80.00 80.00 16 7.5 30 500 80.00 75.66 17 3 45 1000 80.00 78.00 Based on the three levels of inputs, the Design Expert program generated the most number of runs possible. Figure 1 depicts the link between the actual values acquired experimentally (Table 2) and the yield values predicted by various models. Independent factors to production Molar ratio 1:3, 1:6, 1:12 NaOH (%) 1 Speed (rpm) 500, 750, 1000 Temperature (oC) 60 Time (min) 20, 40, 60 IHJPAS. 53 (3)2022 102 Figure 1. shows a scatter diagram with the 3D surfaces that correspond to it. The Variance Analysis (ANOVA) The equation represents the second polynomial functions in terms of the actual components used to describe yield In terms of actual factors, the following is the final equation: Yield =+46.78389-0.478426Molar ratio+3.79436Time-0.143413Speed-0.049259Molar ratio * Time+0.002971 Molar ratio * Speed-4.66667E-06Time * Speed+0.065432Molar ratio²-0.035556 Time²+0.000075Speed² (1) Table 3.ANOVA Quadratic model "RJB Yield" Source Sum of Squares df Mean Square F-value p-value Model 614.62 9 68.29 2.46 0.1243 not significant A-Molar ratio 42.92 1 42.92 1.55 0.2538 B-Time 88.25 1 88.25 3.18 0.1178 C-Speed 43.43 1 43.43 1.56 0.2512 AB 44.22 1 44.22 1.59 0.2474 AC 44.69 1 44.69 1.61 0.2451 BC 0.0012 1 0.0012 0.0000 0.9949 A² 7.39 1 7.39 0.2662 0.6218 B² 269.47 1 269.47 9.71 0.0170 C² 91.73 1 91.73 3.30 0.1120 Residual 194.36 7 27.77 Lack of Fit 194.36 3 64.79 Pure Error 0.0000 4 0.0000 Cor Total 808.99 16 IHJPAS. 53 (3)2022 103 Table 4: Constraints for RJB biodiesel optimization Name Goal Lower Limit Upper Limit Lower Weight Upper Weight Importance A:Molar ratio minimize 3 12 1 1 3 B:Time minimize 30 60 1 1 3 C:Speed minimize 500 1000 1 1 3 Yield maximize 66.7 96 1 1 3 Table 5. Results discovered based on the RSAB biodiesel optimization scenario Number Molar ratio Time Speed Yield Desirability 1 3.000 40.910 500.000 86.937 0.843 Selected 2 3.000 41.024 500.000 87.021 0.843 3 3.000 41.102 500.000 87.077 0.843 4 3.000 40.609 500.000 86.712 0.843 5 3.000 40.372 500.000 86.531 0.843 Tables 2–4 show desirability functions for three different criteria using varied input components (molar ratio, time and speed) for constant NaOH, temperature, and the combination of processes that were examined. The optimization strategies identified based on the biodiesel optimization scenario is shown in Table 5. Using a fixed catalyst of 1.0 wt. %, temperature 60 oC and a molar ratio (1:3, 1:6, 1:12), the optimization solution with the molar ratio (1:3), time (40.910) and speed (500.00 rpm) yielded biodiesel (RJB) of 86.937 %, with the stipulated overall desirability of 0.843. Molar ratio, time, and speed were all important variables in biodiesel synthesis, according to the results of the analysis of variance (ANOVA). 3.1.2 Biodiesel derived from refined sesame biodiesel (RSB) Table 6. Experimental matrix with a variety of molar ratios, times, and speeds Factor 1 Factor 2 Factor 3 Response Run A:Molar ratio B:Time C:Speed Yield (%) s rpm Actual Predicted 1 7.5 45 750 90.00 90.00 2 7.5 45 750 90.00 90.00 3 12 60 750 83.30 84.56 4 7.5 60 500 90.00 90.42 5 12 45 1000 80.00 79.14 6 7.5 45 750 90.00 90.00 7 7.5 30 1000 86.67 86.25 8 3 60 750 86.67 85.40 9 3 45 500 78.30 79.16 10 12 45 500 90.00 88.32 11 7.5 45 750 90.00 90.00 12 12 30 750 83.30 84.57 13 3 30 750 81.67 80.41 14 7.5 60 1000 83.30 82.89 15 7.5 45 750 90.00 90.00 16 7.5 30 500 81.67 82.08 17 3 45 1000 83.30 84.98 IHJPAS. 53 (3)2022 104 Based on the three levels of inputs, the Design Expert program generated the most number of runs possible. Figure 2 depicts the link between the actual values acquired experimentally (Table 6) and the yield values predicted by various models. Figure 2. shows a scatter diagram with the 3D surfaces that correspond to it. The Variance Analysis (ANOVA) The equation represents the second polynomial functions in terms of the actual components used to describe yield In terms of actual factors, the following is the final equation: Yield=-17.43250+6.76833Molar ratio+1.55789Time+0.121850Speed-0.018519Molar ratio * Time- 0.003333Molar ratio * Speed-0.000780 Time * Speed-0.216667 Molar ratio²-0.008344Time²- 0.000043Speed² (2) Table 7. ANOVA Quadratic model "RSB Yield" Source Sum of Squares df Mean Square F-value p-value Model 259.77 9 28.86 14.21 0.0010 significant A-Molar ratio 5.54 1 5.54 2.73 0.1424 B-Time 12.40 1 12.40 6.11 0.0428 C-Speed 5.61 1 5.61 2.76 0.1404 AB 6.25 1 6.25 3.08 0.1228 AC 56.25 1 56.25 27.70 0.0012 BC 34.22 1 34.22 16.85 0.0045 A² 81.05 1 81.05 39.92 0.0004 B² 14.84 1 14.84 7.31 0.0305 C² 30.98 1 30.98 15.26 0.0059 Residual 14.21 7 2.03 Lack of Fit 14.21 3 4.74 Pure Error 0.0000 4 0.0000 Cor Total 273.99 16 IHJPAS. 53 (3)2022 105 Table 8. Constraints for RSB biodiesel optimization Name Goal Lower Limit Upper Limit Lower Weight Upper Weight Importance A:Molar ratio minimize 3 12 1 1 3 B:Time minimize 30 60 1 1 3 C:Speed minimize 500 1000 1 1 3 Yield maximize 78.3 90 1 1 3 Table 9. Results discovered based on the RSB biodiesel optimization scenario Number Molar ratio Time Speed Yield Desirability 1 6.930 41.734 619.262 88.545 0.967 Selected 2 6.920 41.738 621.375 88.562 0.967 Tables 6–8 show desirability functions for three different criteria using varied input components (molar ratio, time and speed) for constant NaOH, temperature, and the combination of processes that were examined. The optimization strategies identified based on the biodiesel optimization scenario are shown in Table 6. Using a fixed catalyst of 1.0 wt. %, temperature 60 oC and a molar ratio (1:3, 1:6, 1:12), the optimization solution with the molar ratio (1:6), time (41.734) and speed (619.262 rpm) yielded biodiesel (RSB) of 88.545 %, with the stipulated overall desirability of 0.967. Molar ratio, time, and speed were all important variables in biodiesel synthesis, according to the results of the analysis of variance (ANOVA). 3.1.3 Biodiesel derived from refined sweet almond biodiesel (RSAB) Table 10. Experimental matrix with a variety of molar ratios, times, and speeds Factor 1 Factor 2 Factor 3 Response Run A:Molar ratio B:Time C:Speed Yield (%) s rpm Actual Predicted 1 7.5 45 750 81.40 82.63 2 7.5 45 750 81.40 82.63 3 12 60 750 90.00 79.04 4 7.5 60 500 89.50 86.13 5 12 45 1000 85.80 88.67 6 7.5 45 750 81.40 82.63 7 7.5 30 1000 88.50 90.28 8 3 60 750 85.70 83.64 9 3 45 500 82.80 86.69 10 12 45 500 64.20 76.99 11 7.5 45 750 81.40 82.63 12 12 30 750 92.80 86.62 13 3 30 750 78.50 81.22 14 7.5 60 1000 70.00 76.55 15 7.5 45 750 81.40 82.63 16 7.5 30 500 85.70 77.55 17 3 45 1000 84.20 78.17 IHJPAS. 53 (3)2022 106 Based on the three levels of inputs, the Design Expert program generated the most number of runs possible. Figure 3 depicts the link between the actual values acquired experimentally (Table 10) and the yield values predicted by various models. Figure 3. shows a scatter diagram of with the 3D surfaces that correspond to it The Variance Analysis (ANOVA) The equation represents the second polynomial function in terms of the actual components used to describe the yield In terms of actual factors, the following is the final equation: Yield=+46.37108-1.65556Molar ratio+1.30694Time+0.036383Speed-0.037037Molar ratio * Time +0.004489Molar ratio * Speed-0.001487Time * Speed (3) Table 11. ANOVA Linear Model "RSAB Yield" Source Sum of Squares df Mean Square F-value p-value Model 269.87 6 44.98 0.8572 0.5561 not significant A-Molar ratio 0.3200 1 0.3200 0.0061 0.9393 B-Time 13.26 1 13.26 0.2527 0.6260 C-Speed 4.96 1 4.96 0.0946 0.7648 AB 25.00 1 25.00 0.4764 0.5057 AC 102.01 1 102.01 1.94 0.1934 BC 124.32 1 124.32 2.37 0.1548 Residual 524.72 10 52.47 Lack of Fit 524.72 6 87.45 Pure Error 0.0000 4 0.0000 Cor Total 794.60 16 Table 12. Constraints for RSAB biodiesel optimization Name Goal Lower Limit Upper Limit Lower Weight Upper Weight Importance A:Molar ratio minimize 3 12 1 1 3 B:Time minimize 30 60 1 1 3 IHJPAS. 53 (3)2022 107 C:Speed minimize 500 1000 1 1 3 Yield maximize 64.2 92.8 1 1 3 Table 13. Results discovered based on the RSAB biodiesel optimization scenario Number Molar ratio Time Speed Yield Desirability 1 3.000 49.359 500.000 88.664 0.892 Selected 2 3.000 49.461 500.000 88.710 0.892 3 3.000 49.186 500.001 88.586 0.892 4 3.000 49.629 500.001 88.786 0.892 5 3.000 48.729 500.000 88.379 0.892 Tables 10–12 show desirability functions for three different criteria using varied input components (molar ratio, time and speed) for constant NaOH, temperature, and the combination of processes that were examined. The optimization strategies identified based on the biodiesel optimization scenario is shown in Table 5. Using a fixed catalyst of 1.0 wt.%, temperature 60 oC and a molar ratio (1:3, 1:6, 1:12), the optimization solution with the molar ratio (1:3), time (49.359) and speed (500.00 rpm) yielded biodiesel (RSAB) of 88.664 %, with the stipulated overall desirability of 0.892. Molar ratio, time, and speed, were all important variables in biodiesel synthesis, according to the results of the analysis of variance (ANOVA). Table 14. Optimization solutions for the three biodiesel optimizations (RJB, RSB and RSAB) Number Molar ratio Time Speed Yield Desirability RJB 3.000 40.910 500.000 86.937 0.995 Selected RSB 6.930 41.734 619.262 88.545 0.931 Selected RSAB 3.000 49.359 500.000 88.664 0.892 Selected 4.Conclusions The optimal parameters for biodiesel were studied in this study using the Surface Response Methodology of Box-Behnken Design. It demonstrated and compared the desirability package's ability to combine production factors to produce three optimal biodiesel productions with a fixed catalyst, temperature and under diverse molar ratio, time, and speed for the optimization scenarios. The correctness of the projected technique was tested using the biodiesel data obtained from the three sets of combination variable testing. Biodiesel yields of 86.937 %, 88.545 %, and 88.664 % were predicted by RJO, RJB, and RSAB, respectively, with less than 0.2 % variation. Biodiesel properties were investigated, and the results were found to meet both ASTM D6751 and EN14214 standards. The optimal yield outputs for each of these biodiesels were obtained, and the effects of molar ratio, and time on biodiesel yield from the RJO, RSO, and RSAO were major parameters that greatly influenced the yield, although speed altered only a little. Finally, the use of low-cost feedstock, such as non-edible vegetable oils, increasing production efficiency through process parameters and variable optimization, and lowering catalyst prices through catalyst regeneration are all major issues affecting mass production that this work addressed. References IHJPAS. 53 (3)2022 108 1. Sahoo, P.K.; Das, L.M. Process optimization for biodiesel production from Jatropha, Karanja and Polanga oils, Fuel. 2009, 88, 94-1588. 2. 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