Abstract
The need to provide information on the most suitable degumming process that could be used for the pre-treatment of the oil extracted from Jatropha curcas seed for high-quality biodiesel production necessitated this study. A heterogeneous catalyst for the transesterification process was synthesised from the Jatropha curcas seed husk and characterised using a Fourier Transform Infrared (FTIR) Spectrophotometer, Scanning Electron Microscope (SEM) and X-ray Fluorescence (XRF) Spectrophotometer. The characterisation of the heterogeneous catalyst produced from the Jatropha curcas shell with SEM analysis showed a spongy-like interwoven microstructure surface morphology. The results of FT-IR and XRF confirmed the existence of a high percentage of potassium, which played a pivotal role in the catalytic process. The study showed that the extracted oil contained a substantial amount of FFA, measuring 5%, which, after using ionic, top and acid degumming processes, the FFA reduced to 1.55%, 2.96% and 2.54%, respectively. Process modelling and optimisation indicated that optimal conditions were 4.968 wt% catalyst loading, 17.934:1 molar ratio of methanol to oil, 5.879 h as time of reaction and a corresponding 65.948% yield of biodiesel. From the statistical evaluation, the model had a p value which was lower than 0.0001, and a difference between the adjusted R2 and predicted R2 of <0.2, which confirmed the fact that the model was significant for biodiesel production.
Keywords: Biodiesel, Biofuel, Degumming, Heterogeneous catalysis, Renewable fuel
Introduction
Energy for man utilization is generated in a variety of ways, starting with the harnessing of solar radiation and geothermal energy and progressing to the use of biomass and the eventual substitution of biomass with coal. Much later, the discovery of petroleum products as a source of fuels took centre stage to provide gaseous, liquid, and solid forms of energy, which are essential to the industrial revolution witnessed within the last few decades [1]. However, the use of fuels derived from petroleum has led to serious concerns with regard to the well-being of man and his entire environment. In addition, compared to biofuels made from plants, the burning of petroleum-based products results in higher quantities of harmful pollutants and greenhouse gases being released into the environment [2].
To forestall some of the imminent power shortage and related challenges, scientists must keep searching for substitute fuels that could address some of the emphasised environmental and economic issues [3, 4]. Several scientists have been relentlessly preoccupied with the task of finding alternative renewable energy sources, such as solar, wind power, tidal energy, and biomass, to address this serious issue [5, 6].
It has been noted that vegetable oil-derived biofuels are more environmentally benign, non-toxic, non-explosive, readily renewable, generally accessible, and naturally biodegradable [1, 4, 7]. Additionally, compared to fossil diesel, they have lower aromatic concentrations and higher biodegradability and renewability. Biodiesel, a fatty acid methyl ester (FAME) or fatty acid ethyl ester (FAEE) derived from plant sources or animal fats, has the capacity to combust in standard compression ignition engines, in addition to other benefits. Hence, there is no need to change the transportation and storage infrastructure, which is currently powered by engines, whenever high-quality biodiesel is used [8, 9].
In Africa and other developing continents, the use of oils from consumable seeds as feedstock for the production of biodiesel is not advisable in view of the vast disparity between the supply and demand for such oils and because of the competitive use of such seeds as food. Thus, there is a need to focus more on oils from non-edible sources as possible substitutes for the production of biofuels and other industrial applications. For the purpose of producing biodiesel, oils obtained from vegetables like the kusum plant, palm kernel, and rapeseed have been utilised [10, 11]. Also, algae, microalgae, bacteria and fungi have been explored as oil sources for such purposes [12, 13].
The Jatropha curcas plant is frequently included among the sources of oils that are non-edible and useful in manufacturing biodiesel. Jatropha curcas is presumably the oilseed crop that is currently receiving the most attention worldwide [14]. Oils derived from Jatropha curcas seeds have been tried and proven to be effective as a fuel for basic engines, burning with a clean, smokeless flame [15]. The seed oil content ranges between 25 and 60% [16] and can be used to produce a substantial amount of biodiesel. Some authors have reported significantly higher values ranging from 60 to 80% [17].
Degumming is a process of treating raw oils with salt solutions, dilute acids or water to get rid of phospholipids, waxes, and other impurities. Phospholipids cause several issues with the storage and processing of crude oil, thus necessitating removal [18]. Degumming is carried out in the refinery to improve oil quality, reduce significant refining losses and prevent the oxidation of crude oil so that thermal instability does not cause the oil to discolour [19, 20].
Transesterification is a popular process for processing animal and vegetable fats to biodiesel, which uses less expensive alcohols like methanol or ethanol [21]. Numerous studies have explored biodiesel production from various seed oils and kernels through both acid and base-catalysed transesterification methods. Alkaline catalysts are usually utilised in different synthesis procedures of biodiesel. Potassium and sodium hydroxides (KOH and NaOH), and their corresponding methoxide forms, are the frequently employed homogeneous base catalysts [22, 23].
Homogeneous catalysts in biodiesel production, however, pose a technical challenge because removing the catalyst after the reaction causes the generation of significant wastewater throughout the separation and cleaning processes of the final products. Recognising the drawbacks of homogeneous catalysts, heterogeneous catalysts have been noted to play a pivotal role in the production of biodiesel [24, 25]. The development of heterogeneous catalysts is driven by economic advantages and the need to reduce environmentally harmful waste production. Traditionally, alkali or acidic homogeneous catalysts have been employed in the manufacturing of commercial biodiesel. However, this method has significant limitations. Heterogeneous catalysts have been demonstrated to be excellent compared to homogeneous ones, especially in terms of product separation and purification [26]. Hence, in the present study, a heterogeneous catalyst synthesised from Jatropha curcas seed husk was adopted for the transesterification process, and a mathematical model, response surface methodology (RSM), was used to optimise operating parameters. This was done to maximise biodiesel output, as optimisation of factors influencing the transesterification reaction helps decrease the production cost of biodiesel [27, 28].
The novelty of this study is reflected in the application of a heterogeneous catalyst synthesised from Jatropha curcas seed husk, a lignocellulosic waste material, often discarded after oil extraction. While many other studies have predominantly focused on the use of homogeneous acid and base catalysts [29–32], these catalysts are known to complicate the separation of catalyst residues, glycerol and biodiesel whilst also causing soap formation when the feedstock contains free fatty acids. However, the utilisation of the Jatropha curcas seed husk as a heterogeneous catalytic precursor remains underreported despite its cost-effectiveness, environmental sustainability, and improved process efficiency, hence this study.
Materials and methods
Materials
The seeds of Jatropha curcas used for the study were collected from a farm along Ede Road and Obafemi Awolowo University research farm, Ile-Ife. Oil was extracted from the prepared seeds by the Soxhlet extraction method with n-Hexane as the extraction solvent. Analytical grade reagents such as Phosphoric acid, Methanol, Glacial acetic acid, Chloroform and other chemicals were obtained from Sigma-Aldrich.
Degumming of oils
Degumming of oil was done following the procedure of Vinayak Kulkarni et al. [33].
Acid degumming
Ten grams (10 g) of Jatropha curcas oil (JCO) was agitated at a temperature of 80 °C for an hour and then cooled to room temperature. Thereafter, 0.05 mL of a 14% by weight solution of phosphoric acid was mixed with this sample for 10 min using a magnetic stirrer. By centrifuging the mixture at 1000 rpm for 15 min, gums generated from the sample were separated out.
Ionic degumming
5 mL of the JCO was combined with 1 mL of 300 μg/mL of NaCl solution and heated at a temperature of 60 °C. Centrifuging of the mixture was carried out for 15 min at1000 rpm and then decantation was done to isolate the colloids from the oil.
TOP degumming
A 10 g JCO was initially water degummed and then mixed with 0.01 mL of 14% by weight solution of phosphoric acid at a temperature of 80 °C. Stirring for about 15 min was done, converting most of the non-hydratable gums into hydratable gums. Subsequently, a 0.1 M NaOH solution of 0.1 wt% was employed to partially neutralise the produced phosphatidic acid. The resulting phosphatides were separated from the oil by centrifuging the degummed sample at 1000 rpm for 10 min [33].
Preparation of heterogeneous catalyst (solid alkali catalyst)
To eliminate any impurities like dirt, soil, and dust, the shell of the Jatropha curcas shell (JCS) was carefully washed three times under running water and given an additional rinse with doubly distilled water. The shell was dried at a constant temperature of 70 °C until it achieved a constant weight. Subsequently, it was broken down, pulverised, and placed in a Muffle furnace set at 450 °C to undergo a 3-h calcination process. The resulting calcined Jatropha curcas shell was crushed and preserved for use as a heterogeneous catalyst in the process of transesterification of Jatropha curcas oil [22].
Catalyst characterisation
The catalyst was characterised using X-ray Fluorescence (XRF), Fourier Transform Infrared Spectroscopy (FT-IR) and Scanning Electron Microscopy (SEM). Morphological investigation was conducted using a Scanning Electron Microscope (Phenom Prox Model). Infrared (IR) spectroscopy was performed on the catalyst samples using the Shimadzu Spectrophotometer 8400 S. The X-ray Fluorescence (XRF) gave the elemental composition of the shell.
Transesterification of degummed JCO
The process of converting the degummed oil to fatty acid methyl esters (FAMEs) involved an esterification process, which was enhanced by an acid catalyst, followed by an alkali-catalysed transesterification reaction.
Acid esterification
Accurately weighed 15 g of ionic degummed JCO was put in a 250 mL flat-bottom flask to which 15:1 methanol: oil and 0.3 g of ferric sulfate were added. The resulting mixture after heating was transferred to a separating funnel and left overnight, leading to a separation of the topmost layer of methanol and water from the lower layer of oil. Filtration of the resulting mixture was done to recover the catalyst [34].
Alkali-catalysed transesterification
The reaction flask was filled with the esterified oil and methanol, and the catalyst was introduced into the flask. The process was conducted at a temperature of 65 °C with varying molar ratios of the methanol and oil, as well as different catalyst amounts depending on the oil volume, as represented in Table 1. This reaction took place over various time intervals under refluxing and continuous stirring. At the specified time of reaction, the content was transferred to a separating funnel and left overnight. The uppermost layer contained the FAME, while the bottom layer contained glycerol.
Table 1.
Experimental design by BBD
| Run | Factor 1 | Factor 2 | Factor 3 |
|---|---|---|---|
| A: Methanol-oil ratio | B: Catalyst loading (wt%) | C: Reaction time (h) | |
| 1 | 15 | 3 | 4 |
| 2 | 18 | 1 | 4 |
| 3 | 15 | 5 | 2 |
| 4 | 18 | 3 | 6 |
| 5 | 18 | 3 | 2 |
| 6 | 18 | 5 | 4 |
| 7 | 12 | 3 | 2 |
| 8 | 12 | 3 | 6 |
| 9 | 15 | 1 | 2 |
| 10 | 12 | 1 | 4 |
| 11 | 15 | 3 | 4 |
| 12 | 15 | 3 | 4 |
| 13 | 15 | 1 | 6 |
| 14 | 15 | 3 | 4 |
| 15 | 15 | 3 | 4 |
| 16 | 12 | 5 | 4 |
| 17 | 15 | 5 | 6 |
Optimisation of the yield of biodiesel using response surface methodology (RSM)
Response surface methodology (RSM) with Box–Behnken Experimental Design (BBD) was utilised to enhance the yield of Jatropha curcas biodiesel through optimisation using Design-Expert software. The studies were conducted to determine the ideal conditions and assess the influence of the parameters, such as catalyst loading, molar ratios of methanol to oil, and time of reaction, on the reaction process. The reaction was investigated at diverse molar ratios of methanol to oil (12:1, 15:1, and 18:1), catalyst loading (1.0, 3.0, and 5.0% weight of the oil), and time of reaction (2, 4, and 6 h). The conditions were varied to optimise the yield of biodiesel. The optimal conditions predicted by the optimisation tools were used to conduct the replicate validation experiments in the laboratory.
Results and discussion
Physicochemical properties of raw and degummed Jatropha curcas oil
Physicochemical properties, such as acid value, iodine value, saponification number, peroxide value, specific gravity, surface tension, cetane number, viscosity, and density of the raw and variously degummed oils are shown in Table 2.
Table 2.
Values of physicochemical parameters of the raw and degummed oils
| Parameters | Raw oil | Top degummed oil | Ionic degummed oil | Acid degummed oil |
|---|---|---|---|---|
| Acid value (mg KOH/g) | 10.34 ± 0.01 | 5.89 ± 0.03 | 3.09 ± 0.15 | 5.05 ± 0.00 |
| %FFA | 5.00 ± 0.02 | 2.96 ± 0.02 | 1.55 ± 0.02 | 2.54 ± 0.05 |
| Saponification value (mg KOH/g) | 208.97 ± 0.03 | 112.20 ± 0.05 | 119.21 ± 0.08 | 197.75 ± 0.06 |
| Iodine value (gI2/100 g oil) | 101.52 ± 0.05 | 98.98 ± 0.10 | 50.76 ± 0.03 | 96.44 ± 0.06 |
| DBE | 1.07 ± 0.01 | 1.95 ± 0.02 | 0.94 ± 0.00 | 1.08 ± 0.00 |
| Peroxide value (meq/kg) | 5.00 ± 0.01 | 4.00 ± 0.00 | 6.60 ± 0.05 | 5.20 ± 0 0.01 |
| Surface tension (N/m) | 0.0310 ± 0.0001 | 0.0404 ± 0.0015 | 0.0393 ± 0.0001 | 0.0404 ± 0.0020 |
| Viscosity (Cp) | 42.00 ± 0.10 | 38.69 ± 0.00 | 19.01 ± 0.10 | 27.93 ± 0.15 |
| Refractive index | 1.4966 ± 0.0010 | 1.5162 ± 0.0015 | 1.5163 ± 0.0010 | 1.5155 ± 0.0000 |
Acid value
The quality and freshness of oils are indicated by their FFA content, which is twice the acid value. The raw JCO had a value of 10 mg KOH/g; degumming of the oil was essential. This value was higher than the 4.77 mg KOH/g determined by Ahmed et al. [35], but less than 35.8 mg KOH/g and 27.78 mg KOH/g, respectively, reported by Aransiola et al. [36] and Aigba et al. [37]. The %FFA content of the raw JCO before degumming was 5%. It has been observed that a %FFA value higher than 5% in oil often leads to excessive soap formation during the usage of oils in the manufacturing of biodiesel [38]. Thus, the high FFA content obtained in the current study was not favourable for the intended production of the biodiesel. The process of degumming resulted in a reduction of the acid value. The high % decrease in acid value and the FFA of all the degumming types would confer better stability on the oil.
Saponification value
The saponification value of the raw JCO was 208.97 mg KOH/g. This value was higher than 115.83 mg KOH/g obtained by Ahmed et al. [35] and the 193 mg KOH/g reported by Aransiola et al. [36]. The high value obtained was an indication of the soap-making potential of the oil. According to the Codex standard, the permissibility level of the saponification value of oil meant for soap making should be between 190 and 209 mg KOH/g (CODEX-STAN210-1999). The value obtained in the present study suggests that the oil contained largely shorter-chain fatty acid which may be suitable for soap formation [39].
Iodine value
The iodine value is a property which quantifies the unsaturation level and thus a direct reflection of the stability against oxidation. A higher value signifies the vulnerability of the oil to stability against oxidation. The observed iodine value of the raw Jatropha curcas oil was 101.52 g I2/100 g oil. The crude Jatropha oil had 1.07 number of double bonds. Biodiesel containing double bonds is highly reactive with oxygen, especially when exposed to air, sunlight, or water directly [40, 41], which afterwards has adverse effects on the motor engine. This is because lipid oxidation will usually occur at the double bond sites. The observed decrease in the iodine value after degumming with the different degumming types implies a decrease in unsaturation of the oil. This is of great benefit since the lower the unsaturation of oil, the greater the oxidative stability [18]. The lowest iodine value observed in the ionic degummed type implies the oil had a low degree of unsaturation, which made it a high-quality biodiesel feedstock and a more efficient source of fuel.
Peroxide value
The most common chemical technique for measuring the oxidative degradation of oils is the peroxide value. In the current study, the raw oil had a peroxide value of 5.00. This value was less than that obtained by Belewu et al. [39] for Nigerian Jatropha curcas oil obtained by chemical and mechanical extraction methods (56 and 44, respectively). Oils with higher peroxide values are liable to greater rancid development. Although the ionic degumming process led to an increase in peroxide value, which may suggest the susceptibility of the oil to oxidative degradation compared to other degummed oils, the effect of this is considered temporary because the degumming process had removed a great level of peroxide deterioration, implying that no serious problem would be posed by using the degummed oil as biodiesel feedstock.
Surface tension
The surface tension is a measure of an oil force resistance associated with the fuel break-up, such that the surface of the fuel contracts into a possible smaller surface area [42, 43]. From the literature, the established minimum surface tension standard value of lubricating oil is 29.5 dynes/cm (0.0295 N/m) [1]. The surface tension of the raw oil was 31 dynes/cm (0.0310 N/m). Although the raw JCO had higher surface tension than the minimum standard for lubricating oil, the difference was not significant (at p ≤ 0.05), which makes the raw JCO a very good lubricant. After degumming, the increase in surface tension which was observed could be a result of FFA removal during degumming, which enhances the lubricating property of the oil.
Viscosity
According to Mishra and Goswami [41], the viscosity of an oil indicates the internal friction or barrier to its free flow at a given temperature. The determination of the oil lubricating property is aided by its Viscosity. The values obtained for the raw and degummed oils implied that the oil would have high lubricating properties [39]. Viscosity of oils also influences their injection and atomization process in internal combustion engines [44].
Refractive index
The refractive index is an optical characteristic that can be employed to estimate the level of saturation of esters in oils [45]. At room temperature, the values obtained for raw JCO was 1.4966 which supported the findings of earlier researcher that vegetable oils have higher refractive index than that of water which is 1.330 [46, 47]. The value obtained in the current study was closely related to the 1.47 obtained by Aigba et al. [37]. Values of refractive index of the degummed oils were of the order: 1.5163 (ionic degummed oil) > 1.5162 (top degummed oil) > 1.5155 (acid degummed oil). The increase in refractive index values obtained after degumming is comparable with several reports in the literature. The refractive index increases with the unsaturation level from the acid degummed oil compared to the ionic degummed oil [18].
Specific gravity (sg)
The relative density of oil to an equivalent volume of water is measured by its specific gravity [48]. The specific gravities of the raw and degummed oils at different temperatures are shown in Table 3. The values obtained for the raw oil at varying temperatures were less than 1.0, which indicates good floating ability. This also suggested good lubricating properties of the oil. According to literature, the specific gravity of diesel oil should range from 0.85 to 0.87. The values obtained for raw J. curcas oil in this study ranged between 0.8698 and 0.8813, implying that the raw J. curcas was suitable for fuel application. The values obtained for the raw and degummed oils were also temperature dependent.
Table 3.
Specific gravities of the raw and degummed oils
| Oil type | Temperature (°C) | |||||||
|---|---|---|---|---|---|---|---|---|
| 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 | |
| Raw oil | 0.8813 | 0.8790 | 0.8782 | 0.8756 | 0.8752 | 0.8747 | 0.8735 | 0.8698 |
| Top degummed oil | 0.9147 | 0.9131 | 0.9123 | 0.9115 | 0.9107 | 0.9146 | 0.9085 | 0.9065 |
| Ionic degummed oil | 0.9149 | 0.9117 | 0.9110 | 0.9132 | 0.9114 | 0.9140 | 0.9111 | 0.9065 |
| Acid degummed oil | 0.9134 | 0.9124 | 0.9104 | 0.9122 | 0.9096 | 0.9126 | 0.9096 | 0.9056 |
Catalyst characterisation
Elemental composition of the Jatropha curcas shell (JCS)
The results of elemental composition of the JCS analysed using XRF are presented in Table 4, in which K, Sn, Ca, Fe, Rb, Mn, Zn, Al, P, Si, Ni, Cu, Nb, and Mo have been identified as the elemental components of the catalyst. In the result obtained, K had the highest intensity, followed by Ca and Sn. Nath et al. [49] also observed a significant amount of K in the waste biomass-derived heterogeneous catalyst from Brassica nigra plant. This high concentration of K and the other metals played a pivotal role in enhancing the catalytic activity of the calcined JCS used in the production of biodiesel. This was a result of better adsorptive activities at the surface of the catalyst, which was enhanced by these elements.
Table 4.
Elemental composition of catalyst obtained from JCS
| Element | Intensity | Content |
|---|---|---|
| Al | 0.0007 | 0.2070 |
| Si | 0.0017 | 0.0743 |
| P | 0.0034 | 0.1589 |
| S | 0.0095 | 0.7331 |
| K | 0.0695 | 5.6592 |
| Ca | 0.0450 | 3.3993 |
| Mn | 0.0007 | 0.0477 |
| Fe | 0.0015 | 0.2247 |
| Ni | 0.0013 | 0.0773 |
| Cu | 0.0032 | 0.0785 |
| Zn | 0.0051 | 0.1760 |
| Pb | 0.0002 | 0.0038 |
| Rb | 0.0078 | 0.0795 |
| Nb | 0.0054 | 0.0632 |
| Mo | 0.0059 | 0.1147 |
| Sn | 0.0222 | 4.6213 |
| Sb | 0.0301 | 3.8000 |
Catalyst morphology
The SEM image in Fig. 1 shows the morphology of the catalyst with regular pores, shapes and size. The SEM image shows a full array of uniformly shaped interwoven microstructures in a large number. It is characterised by well-aligned structures embedded on the large particle surface, which possibly influences its high catalytic activity. A comparable structure was seen in the examination of the morphological characteristics of calcined orange ash according to Sharikh et al. [50].
Fig. 1.
SEM image of catalyst obtained from JCS
Fourier transform infrared spectroscopy (FTIR)
The FTIR spectra of the calcined JCS are presented in Fig. 2. The band at 3022 cm−1 could be due to free OH in H2O adsorbed on the catalyst surface [51]. The existence of the bands at 1400–1300 cm−1 caused by OH deformation often supports the inclusion of alcohols and phenol groups. The existence of the CO3 group-bending mode was indicated by the band at 872 cm−1 [34]. Bands at 1401 and 1461 cm−1 could be a result of basic carbonates when there is adsorption of CO2 gas from the surrounding environment [52]. This might account for the presence of K2CO3 and CaCO3 in the catalyst. This is also similar to the investigations of Basumatary et al. [51]. The bands at 1461 cm−1 could also be due to C–H bending and stretching vibrations of CH2.
Fig. 2.
FTIR spectrum of JCS
Two-step transesterification
Acid esterification
Table 5 shows how Ferric sulfate, an acid catalyst, was employed to lower the FFA content of degummed (JCO) from an initial level of 1.55%. To produce a suitable raw material for the subsequent transesterification procedure, the esterification process had to occur. During the initial esterification phase, the %FFA concentration was lowered to 0.42%. The resulting esterified oil possessed an acid value of 0.84 mg KOH/g, indicating its suitability for alkali-based transesterification.
Table 5.
Esterification of degummed oil
| Ionic degummed oil | Esterified oil | |
|---|---|---|
| Acid value mg KOH (g) | 3.09 | 0.84 |
| % Free fatty acid | 1.55 | 0.42 |
Alkali-based transesterification
Due to higher conversion rates and efficiencies, alkali catalysts are often preferred to acid catalysts in the synthesis of biodiesel. As shown in Table 6, the dependent variable for this study was biodiesel yield, while the independent factors were the molar ratio of methanol to oil, catalyst amount, and reaction time. A total of 17 runs, including four centre points, were the outcomes. Using a factorial design and Analysis of Variance (ANOVA), the yield of the biodiesel from the best degummed JCO was examined. This investigation was done to see whether the model could produce biodiesel effectively utilising JCO. Results of the investigation of how operating parameters affected the transesterification process are shown in Table 7. Statistically, individual parameters had a substantial impact on the biodiesel production, as stated in the results of the ANOVA.
Table 6.
Biodiesel yield from the experimental design
| Run | Factor 1 | Factor 2 | Factor 3 | Response 1 |
|---|---|---|---|---|
| A: Methanol-oil ratio | B: Catalyst loading (wt%) | C: Reaction time (h) | Biodiesel yield (%) | |
| 1 | 15 | 3 | 4 | 47.5 |
| 2 | 18 | 1 | 4 | 55.0 |
| 3 | 15 | 5 | 2 | 48.5 |
| 4 | 18 | 3 | 6 | 62.5 |
| 5 | 18 | 3 | 2 | 56.3 |
| 6 | 18 | 5 | 4 | 65.0 |
| 7 | 12 | 3 | 2 | 39.5 |
| 8 | 12 | 3 | 6 | 40.0 |
| 9 | 15 | 1 | 2 | 45.0 |
| 10 | 12 | 1 | 4 | 38.0 |
| 11 | 15 | 3 | 4 | 47.5 |
| 12 | 15 | 3 | 4 | 47.5 |
| 13 | 15 | 1 | 6 | 45.5 |
| 14 | 15 | 3 | 4 | 47.5 |
| 15 | 15 | 3 | 4 | 47.5 |
| 16 | 12 | 5 | 4 | 42.5 |
| 17 | 15 | 5 | 6 | 50.0 |
Table 7.
ANOVA for quadratic model
| Source | Sum of squares | Dof | Mean square | F value | p value |
|---|---|---|---|---|---|
| Model | 890.42 | 9 | 98.94 | 85.40 | <0.0001 |
| A: Methanol-oil ratio | 775.20 | 1 | 775.20 | 669.15 | <0.0001 |
| B: Catalyst loading | 63.28 | 1 | 63.28 | 54.62 | 0.0002 |
| C: Reaction time | 9.57 | 1 | 9.57 | 8.26 | 0.0238 |
| AB | 7.56 | 1 | 7.56 | 6.53 | 0.0378 |
| AC | 8.27 | 1 | 8.27 | 7.13 | 0.0319 |
| BC | 0.2500 | 1 | 0.2500 | 0.2158 | 0.6564 |
| A2 | 25.66 | 1 | 25.66 | 22.15 | 0.0022 |
| B2 | 0.1028 | 1 | 0.1028 | 0.0887 | 0.7744 |
| C2 | 0.6949 | 1 | 0.6949 | 0.5998 | 0.4640 |
| Residual | 8.11 | 7 | 1.16 | ||
| Lack of fit | 8.11 | 3 | 2.70 | ||
| Pure error | 0.0000 | 4 | 0.0000 | ||
| Corrected total | 898.53 | 16 |
By analysing variances in the data and identifying specific causes of variation, this statistical technique, which uses the least squares method, evaluated the model’s fitness. The ANOVA showed a strong fit, which was supported by a high multiple correlation coefficient (R2) of 0.8556 and an adjusted R2 of 0.9794. The importance of the model was further established by the minimal difference (<0.2) between the adjusted and predicted R2. The mutual interactions and variable importance, as well as the model’s statistical significance, were established at a level of confidence above 85% [53].
The signal-to-noise ratio is deemed to have adequate precision when it exceeds 4 [54]. This model in this study demonstrated that the model has a good level of adaptability and dependability with a sufficient accuracy value of 30.66 and a % coefficient of variation (CV) of 2.22, indicating a strong performance. The model aptitude for navigating the design space was confirmed by the commendable figures and indication of sufficient signal strength. The overall significance of the model was further highlighted by its substantial F value of 85.40, which highlighted how well it could reflect the data. The lack of fit term is insignificant when compared with the pure error, which also denotes the model goodness-of-fit. Examining each factor in detail revealed that the methanol-oil ratio, with its high F value of 669.15, had the greatest influence on the yield of biodiesel and in addition, catalyst loading with a value of 54.62. Reaction time also made a contribution with a value of 8.26. The significance of the methanol: oil ratio (A), catalyst loading (B), and reaction time (C) in determining biodiesel outcome was thus underscored by the ANOVA analysis. Quadratic terms (A2, B2, C2) and interaction terms (AB, AC, BC) also contributed significantly to the process. A polynomial equation was generated as a result of using advanced multiple regression analysis for a precise understanding. This thorough regression model’s coefficients were established, confirming its statistical importance for the evaluation procedure. The significant terms equation derived by the model is as follows:
 |
Diagnostic plots and residual analysis
Figure 3 is a plot of the predictions of values from RSM. The experimental values were juxtaposed with the predicted values in order to test the fitness of the model, as well as to confirm the efficacy of the predicted model derived using the model equation. The plot consists of a slope generated by the line of perfect fit, whose points correspond to the zero error between predicted and experimental values. The closeness of the points to the slope is an indication of the degree of proper agreement between the experimental and predicted values [55]. Furthermore, an analysis of the regression coefficient (R2) gave 0.8556, while the adjusted regression coefficient gave 0.9794 in the quadratic model. These values indicated a proper correlation between the actual and predicted results. The CV of 2.22 and standard deviation of 1.08 also indicated that there was only a little variation between the actual and predicted %biodiesel yield. The results showed that the equation of the regression model effectively matched the experimental data, indicating its efficacy in evaluating the relationship between the three operation parameters on biodiesel yield. As a result, using a small number of experiments, the RSM is effective in identifying the relationship between operating parameters and responses.
Fig. 3.
Predicted versus actual% biodiesel yield
Figure 4 is a plot of the expected studentized residuals compared with the observed studentized residuals. If an S-curve was formed then the residues would be said not to follow normal distribution. However, the residual would be said to follow normal distribution if a straight line were formed. From Fig. 4, a straight line was formed which indicated that the residues followed a normal distribution. Also, the predictions of the model were correct as the real values were based on the expected values. The normal distribution curve also showed that the residuals were additional pointers to the distribution of the normal residual. From the plot, the closeness of the points to the traditional line approves the normal distribution of errors with a mean zero [56].
Fig. 4.
Normal plot of internally studentized residuals
The plot of internally studentized residuals against predicted %biodiesel yield is represented by Fig. 5. The arbitrary scattering of the residuals as shown in the figure is an indication that there was a variance of the original observation which was also constant for the response of each run.
Fig. 5.
Residual versus predicted %biodiesel yield
The perturbation plot represented in Fig. 6 further describes the operating parameters significance as indicated by the steepness of the slope. Methanol-oil ratio (A) was the most significant parameter since it had the highest slope. Catalyst loading (B) was less significant than the methanol-oil ratio since it had a lesser slope while the reaction time was the least significant since it had the least slope.
Fig. 6.
Perturbation plot of individual process variables
Effect of process variables on biodiesel yield using the optimised yield model
Interaction between methanol-oil ratio and the catalyst loading
Figure 7 shows that there was a considerable relationship between the methanol-oil ratio and catalyst loading. By increasing the methanol-oil ratio and catalyst loading, the yield of the biodiesel increased substantially. The equilibrium typically shifted in favour of a high production of biodiesel by an increased methanol-oil ratio [57]. The use of adequate catalyst loading was expected to favour the forward reaction, which in turn would result in a high biodiesel yield. Increased catalyst loading led to the availability of more active sites for the transesterification process [58]. For example, an increase in the methanol-oil ratio from 12:1 to 15:1 while maintaining a constant catalyst weight of 3% significantly increased the biodiesel yield from 40 to 47.5%. A simultaneous increase in the methanol-oil ratio and catalyst loading from 12:1 to15:1 and 3 to 5 wt% also favoured increment in the yield of biodiesel from 40 to 48.5%.
Fig. 7.
Surface plots of the interactive impact of catalyst loading and methanol-oil ratio on biodiesel yield
Low biodiesel yield at lower amount of catalyst may be attributed to limited availability of catalytic sites for the reactants [59]. A significant increase was also observed from an increase in the catalyst loading from 3 to 5 wt% maintaining constant methanol-oil ratio of 18:1. The yield of the biodiesel was observed to increase from 56.3 to 65.0%. Both the contour and the surface plots showed that the %yield of biodiesel increased as both the methanol-oil ratio and catalyst loading increased. It can therefore be inferred that these variables played pivotal roles during the process of transesterification. The highest %yield of biodiesel was observed from the plots at highest methanol-oil ratio and catalyst loading.
Interaction between the methanol-oil ratio and the reaction time
The interactive effect of methanol-oil ratio and reaction time on percent biodiesel conversion, as shown in Fig. 8, is an indication that there was a substantial interaction between the two variables. This observation further reinforced the observation of Fadhil et al. [60] that one of the most crucial factors that determine biodiesel production is the methanol-to-oil ratio.
Fig. 8.
Surface plots of the interactive impact of reaction time and methanol-oil ratio on biodiesel yield
At an increased methanol-oil ratio and longer reaction time, a high %biodiesel yield was observed, but with a lower methanol-oil ratio less %biodiesel yield was observed. For instance, at a constant catalyst loading of 3 wt%, increasing the methanol-oil ratio from 15 to 18 alongside a corresponding increment in reaction time from 4 to 6 h resulted in increased biodiesel yield from 47.5 to 62.5%. Likewise, increasing the methanol-oil ratio from 12 to 15 alongside a corresponding increase in reaction time from 2 to 4 h resulted in increased biodiesel yield from 39.5 to 47.5% at a constant catalyst loading of 3 wt%. Evidently, the transesterification reaction cannot be favoured by a lower methanol-oil ratio. However, the cost of the alcohol recovery process was increased owing to excess amount of alcohol. Another difficulty associated with an increased methanol-oil ratio was the segregation of glycerol from the biodiesel, a situation also observed by Yesilyurt [61].
Interaction between catalyst loading and reaction time
The response surface plot (Fig. 9) illustrates how catalyst loading and reaction time interact. The plots demonstrated that a rise in catalyst loading and a parallel rise in reaction time increased biodiesel yield. For example, at a constant methanol-oil ratio of 18:1, an increment in the catalyst loading from 3 to 5 wt% and reaction time from 2 to 4 h caused an increase in the biodiesel yield from 56.25 to 65%. Similar to this, an increment in catalyst loading from 1 to 3 wt% and in reaction time from 2 to 4 h led to an increase in the biodiesel output from 45 to 47.5%, maintaining a constant methanol-oil ratio of 15.
Fig. 9.
Surface plots of the interactive impact of reaction time and catalyst loading on biodiesel yield
According to Rao et al. [62], it is anticipated that a catalyst will accelerate the reaction by lowering the energy barrier that must be overcome to kick-start the reaction. As a result, increasing the catalyst loading would cause the reaction to move even more toward the product’s side. For instance, a catalyst loading of 3 wt%, a reaction time of 4 h and a constant methanol-oil ratio of 15:1 produced a yield of 47.5% biodiesel. However, a 1 wt% catalyst loading and a reaction time of 6 h, maintaining a constant methanol-oil ratio, resulted in a yield of 45.5% biodiesel. This is an indication that a reduced catalyst loading would require longer reaction times to attain the highest biodiesel yield possible. In contrast, increasing the catalyst loading reduced the reaction time required for maximum biodiesel yield. For example, at a constant methanol-oil ratio of 18, a catalyst loading of 3 wt% and a reaction time of 6 h gave a biodiesel yield of 62.5%. However, an increment in the catalyst loading to 5 wt% with an accompanying reduction in reaction time to 4 h gave a relatively increased biodiesel yield of 65.0%. This occurrence could be ascribed to the catalyst’s high activity, which might have emanated from the presence of hydrophilic surface functional groups such as –OH and –COOH [60].
The impact of reaction time on the yield of biodiesel has economic significance because a longer reaction time would translate to higher production cost [63]. The surge in biodiesel yield with increased reaction time, as observed in the present study, agreed with the observation of other researchers [58, 63].
Optimisation and model validation
The transesterification process of the degummed JCO using the JCS as catalyst was significantly influenced by three operating parameters, namely: methanol-oil ratio, catalyst loading and reaction time. The process was optimised using the RSM, and the optimised conditions for JCO transesterification are presented in Table 8. The model predicted an optimum %biodiesel yield of 65.948% at a methanol-oil ratio of 17.994:1, catalyst loading of 4.963 wt% and reaction time of 5.384. The model was validated by conducting a replicate run using the optimum parameters. An actual %FFA conversion of 65.0% was obtained, giving an error margin of 1.44% from the predicted value.
Table 8.
Optimised conditions for JCO transesterification
| Time (h) | Temp (°C) | MeOH-JCO molar ratio | Catalyst loading (wt%) | Predicted %biodiesel yield | Actual %biodiesel yield | Margin error (%) |
|---|---|---|---|---|---|---|
| 5.87 | 65 | 18:1 | 5 | 65.948 | 65 | 1.44 |
Physicochemical properties of biodiesel
The physical and chemical properties of the transesterified oil (biodiesel) obtained at the optimum reaction condition are presented in Table 9. The acid value of the biodiesel was 0.28 mg KOH/g. In the alkali-catalysed transesterification process, the base catalysts usually strip the oil of available free fatty acid, thereby resulting in a low acid value. The acid value obtained met the specification of the European and American Standards of a maximum value of 0.5 mg of KOH/g and 0.8 mg KOH/g in biodiesel [64]. The value corresponds to 0.14% FFA, which was less than the limit of 1% required for biodiesel [65].
Table 9.
Physicochemical properties of biodiesel
| Properties | ASTM D 6751 | EN 14214 | This work | Basumatary et al. [51] | Zulfiqar et al. [66] |
|---|---|---|---|---|---|
| Acid value (mg KOH/g) | 0.8 | – | 0.28 | – | – |
| %FFA | – | – | 0.14 | – | – |
| Saponification value (mg KOH/g) | 185–196 | NS | 188 | 191.64 | – |
| Density (g/cm3) | NS | 0.86–0.90 | 0.90 | 0.878 | 0.873 |
| Specific gravity | 0.85–0.90 | 0.90 | – | – | |
| Refractive index | 1.20–1.80 | – | 1.5142 | – | – |
| Pour point (°C) | −15 to 10 | NS | −2 | – | −6 |
| Cloud point (°C) | −3 to 12 | – | 10 | – | 11 |
| Smoke point (°C) | – | – | 78 | – | – |
| Flash point | >130 | – | 245 | – | 132 ± 1 |
| Viscosity (mm2/s) | 1.9–6.0 | NS | 2.9 | ||
| Fire point | – | 360 | – | 148 | |
| Higher heating value (HHV) | – | – | 38.08 | – | – |
NS not stated
The flash point, which is the lowest temperature at which an inflammable air can be produced, was 245 °C. The biodiesel produced is safe for use and handling as fuels with lower flash points often appear to catch fire at lower temperatures if not properly handled or stored. This value was higher than the 200 °C obtained by Belewu et al. [39]. The fire point was also determined at 360 °C. The pour point of biodiesel, which refers to the lowest temperature at which oils continue to flow when undergoing cooling in the absence of stirring, for this study was −2 °C. This value fell within the range of biodiesel standard of −15 to 10 (ASTM D6751). The value was higher than the −6 °C that was obtained by Zulfiqar et al. [66]. The pour point value implies that the biodiesel produced can be useful as a good fuel during cold temperatures. The cloud point refers to the cooling temperature at which the oil components begin to precipitate or crystallise [67]. The cloud point was measured to be 10 °C which also fell within the range for biodiesel standard (−3 to 12) according to ASTM D6751 standard. The value obtained for the cloud point might be a result of the maximum decrease in the degree of unsaturation of the oils.
The refractive index is a measure of the degree of conjugation of oil. The refractive index for the biodiesel was 1.5142, which was within the comparable range (1.20–1.80) for biodiesel (ASTM D 6751). The density and specific gravity of the biodiesel were found to be 0.90 g/cm3. The values obtained for these parameters were within the range (0.86–0.90) prescribed by EN 14214 and higher than the value (0.878) obtained by Basumatary et al. [51]. Specific gravity plays a crucial role in diesel fuel injection systems and for thorough combustion [68]. The kinematic viscosity of the produced biodiesel at 40 °C was 2.9 mm2/s. This value is appropriate as it was within the ASTM standards (1.9–6 mm2/s), as well as high combustibility and non-deposition in the engine due to its lower value fuels with lower value as reported by Nath et al. [49]. The number of saponifiable units present per unit weight in the biodiesel, denoted by saponification number (mg KOH/g), was found to be 188, also within the ASTM range of 185–196 mg KOH/g. This shows that the biodiesel has a well-balanced ester content, which promotes good combustion and fluidity. Biodiesel with this saponification number often has a consistent molecular structure, which improves combustion efficiency [61]. Fatty acid esters with lower molecular weights increase combustion qualities by increasing volatility and atomisation, which improves engine performance and fuel efficiency [69]. The saponification number also indirectly indicates the quantity of double bonds in fatty acid chains. Biodiesel made from oils with longer-chain fatty acids may have reduced oxidation stability, making it more likely to degrade over time. A saponification number of 188 suggests a fairly stable biodiesel composition; additional antioxidants may be required for long-term storage [70].
The higher heating value (HHV) of a fuel, which is a measure of the fuel’s heat of combustion, can be estimated from its physicochemical parameters, including molecular weights, density, viscosity, etc. In the present study, the HHV was estimated using a mathematical relationship involving the HHV and the fuel’s viscosity [71, 72]. The estimated HHV of the biodiesel was 38.08 MJ/kg. HHV directly affects fuel economy in compression ignition engines. A biodiesel with an energy content of 38.08 MJ/kg delivers adequate energy for efficient combustion, guaranteeing performance parity with diesel while diminishing dependence on fossil fuels.
Conclusion
This study investigated the physicochemical parameters of biodiesel produced from multitechnically degummed oil using a heterogeneous catalytic pathway. The physical and chemical properties of the raw oil suggested that the raw oil contained impurities. However, on pre-treatment of the raw oil with the various degumming processes (acid, ionic, water and TOP), the ionic degumming process produced the oil with the best physical and chemical properties for biodiesel production. Husks of Jatropha curcas seed were utilised as a cheap biomass source for the effective synthesis of the heterogeneous catalyst. The inclusion of the biomass-based catalyst made available potassium, which served as an active component that was crucial to the catalytic activities involved in the production of biodiesel. The manufacturing of biodiesel uses a two-step transesterification process. Response surface methodology (RSM) was helpful in efficiently identifying the correlations between process variables and responses with the least amount of experimentation. The impacts of the individual process variables and their interactions on the %biodiesel production were examined, and gave an indication that the variables were significant. The optimal condition established for %biodiesel yield was 4.968 wt% catalyst loading, 17.934:1 methanol-to-oil ratio, 5.879 h reaction time, with a corresponding %biodiesel yield of 65.948%. This study established the fact that there is a need to ensure the degumming process that would yield the maximum biodiesel whenever a given oil is being used for biodiesel production. In particular, the study concluded that the ionic degummed Jatropha curcas oil could serve as an excellent feedstock for biodiesel production using a heterogeneous catalyst synthesised from the Jatropha curcas seed husk. However, these experiments were conducted on a laboratory-scale and, as such, could limit their direct scalability to industrial processes. In addition, the produced biodiesel was not directly evaluated for engine compatibility, emissions, and combustion performance. In light of the aforementioned limitations, our future study will incorporate pilot-scale transesterification as well as the use of a single-cylinder diesel engine in the testing of the produced biodiesel in order to validate its emission characteristics, combustion behaviour, and operational performance.
Acknowledgements
The authors sincerely acknowledge the management of the Obafemi Awolowo University, Ile-Ife, Nigeria, for providing an enabling environment for the research.
Abbreviations
- ANOVA
Analysis of variance
- BBD
Box–Behnken Design
- FAEE
Fatty acid ethyl ester
- FAME
Fatty acid methyl ester
- FFA
Free fatty acid
- FTIR
Fourier Transform Infrared Spectrophotometer
- HHV
Higher heating value
- JCO
Jatropha curcas oil
- JCS
Jatropha curcas shell
- RSM
Response surface methodology
- SEM
Scanning electron microscope
- XRF
X-ray fluorescence
Author contributions
JAOO and EFA conceived and designed the experiment, ODA conducted the experiment, ODA and OTO wrote the first manuscript draft, ASA carried out data analysis and visualization. All authors reviewed the manuscript.
Funding
The study did not receive any funding.
Data availability
The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
All authors have approved the final version of the manuscript for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Olufunmilola D. Adeagbo, Email: adeagboolufunmilola@gmail.com
Odunayo T. Ore, Email: oreodunayo@yahoo.com
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Data Availability Statement
The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.