Development of a composite catalyst from anthill and eggshell: an optimization study on biodiesel production from virgin and waste vegetable oils


The primary goal of this study is to develop a composite material from the anthill and chicken eggshell and to use it as a catalyst for the synthesis of biodiesel from virgin and waste vegetable oils. The anthill–eggshell composite (AEC) catalyst was prepared using an incipient wetness impregnation method. Central composite design (CCD) was applied to investigate the effects of catalyst preparation parameters (calcination temperature, calcination time, and anthill proportion in the AEC) on the yields of biodiesel from the two oils. Based on the CCD, two quadratic models were developed to correlate the AEC preparation parameters to the two responses. Analysis of variance (ANOVA) was performed to verify the reliability of the models and also, identify the factor that mostly affects the experimental design responses. Optimization results showed that the predicted values of biodiesel yield from the models for the two oils agreed reasonably well with the experimental values. The optimum conditions for the preparation of AEC catalyst for the transesterification process were calcination temperature of 1000 °C, calcination time of 4 h, and anthill proportion of 20% to achieve 97.13% yield of biodiesel from virgin vegetable oil. At the same optimum parameters, the yield of biodiesel from waste vegetable oil was found to be 70.92%.


Fossil fuel depletion and environmental degradation have been serious concerns in the last decade. To date, about one-fourth of the total pollutant emission results from power generation using fossil fuel [1]. Fossil fuel is a non-biodegradable fuel that is associated with depletion and emission of a large volume of greenhouse gases, and greenhouse gases are regarded as the main contributor to global warming. Thus, shifting from fossil fuel to renewable fuel is an antidote to these menaces. In this view, triglycerides from plant oil or animal fat are reacted with primary alcohol (methanol/ethanol) to obtain biodiesel in the form of (m)ethyl esters but in the presence of a proper catalyst. Many researchers have produced biodiesel from plant oils or animal fat by transesterification process using the conventional homogeneous catalysts such as potassium hydroxide, sodium hydroxide, hydrochloric acid or tetraoxosulphate (VI) acid, but the aftermath of homogeneous processes is not usually desirable as many involve difficulties in the removal of catalysts from the reaction products, subsequently resulting in excess wastewater generation and high cost. As a pivotal approach to conquering such worries, heterogeneous catalysts are used as a low-cost replacement [2], besides several researchers have proven that the use of suitable solid catalysts could provide solutions to those aforementioned problems [3].

Several solid catalysts such as pure metal oxide [4], mixed metal oxide [5], and sulphated/metal oxide [6] have been employed as heterogeneous catalysts for the production of biodiesel. The application of biomass-derived solid catalysts for biodiesel production has also been reported. These include birds’ eggshell [7,8,9], fishbone [10], animal bone [11], solid waste coral fragment [12], alum [13], montmorillonite clay [3], modified peanut husk ash [14] and many more. Most of these materials are cheap sources of calcium oxide (CaO) and other alkaline earth metal oxides and reduce the biodiesel production cost [15]. Eggshell is one of the abundant agricultural wastes, which can be converted into a solid base catalyst (CaO). Recent findings had shown that CaO is the most efficient and easily synthesized catalyst for the transesterification of oil with an alcohol to produce biodiesel [16]. A literature survey reveals that the CaO catalyst leaches during the catalytic reaction. However, the CaO derived from eggshell needs to be supported by thermally stable material, which can prevent leaching of the active ingredient and provide more specific surface area and pores for active species [17]. Various catalyst supports such as alumina (Al2O3), silica (SiO2), and zirconia (ZrO2) have been widely used in the transesterification process due to their thermal and mechanical stability, as well as better textural properties [18]. However, these metal oxides in their pure form are expensive. As reported by Henne [19], anthill contains several metal oxides, including SiO2 and Al2O3, and it is available in abundance. Thus, in this study, anthill was chosen as a catalyst support. An anthill is a composite of clay and other materials formed at the entrances of anthill colonies [19]. It has been used in various applications, including ceramic, cement, bricks, and sand casting making [20] and as an adsorbent for wastewater treatment [21].

In the present study, the aim was to prepare a composite catalyst from anthill and eggshell, optimize the preparation process condition and use it for transesterification of virgin and waste vegetable oils to produce biodiesel. To the best of the authors’ knowledge, no study has been carried out on the optimization of process conditions for the preparation of composite catalyst for transesterification of vegetable oil to biodiesel using response surface methodology (RSM). RSM is one of the experimental design techniques that are commonly used for process analysis and modeling. In this technique, the main objective is to optimize the response surface that is influenced by various process parameters. RSM also estimates the interaction between controlled experimental variables and measured response [22, 23].

Response surface methodology is a combination of mathematical and statistical optimization tools useful for developing, improving, and optimizing processes [24, 25]. The application of statistical experimental design techniques in the process development of catalysts can result in reduced process variability and less resources requirement (time, raw materials, and experimental run) [26, 27]. In using RSM, three steps are necessary: the design of experiments, model equation development and analysis and optimization of parameters [28]. Generally, there are two important optimization methods under RSM, namely, central composite and Box–Behnken designs [28, 29]. However, both of these design methods are exclusive and provide a definite experimental design to address various approaches used in analyzing data [30]. Meanwhile, these two design methods are different in the number of experimental runs required and in the combination of the levels used in the experiments [31]. The central composite design is commonly used to design the experimental procedures to acquire necessary information for examining the lack of fit without necessarily considering numerous design points [28]. Besides, it is a powerful tool for evaluating the output parameter(s) of most of the steady-state processes [32]. Therefore, in this work, the central composite design (CCD) has been applied to the optimization of process conditions for the preparation of the AEC catalyst. The variables considered were the calcination temperature, calcination time, and anthill proportion in AEC. Also, the activity of the prepared catalysts was tested for the transesterification of vegetable oil.

Materials and methods

Raw material

Waste vegetable oil (WVO) and chicken eggshells used for this study were collected from students’ cafeteria 1, Afe Babalola University (ABUAD), Ado-Ekiti, Nigeria. The density, acid value, and saponification value of the WVO are 0.9147 g/cm3, 3.945 mgKOH/g, and 183.1 mgKOH/g, respectively. However, the free fatty acid (FFA) content of the oil was equivalent to 1.973 wt%, and since it is less than 2 wt%, it implies that the WVO could be directly converted to biodiesel via a one-step transesterification process [7, 33]. The type II anthill, used in this study, is situated behind Fidelity Bank, ABUAD, Ado-Ekiti, Nigeria. Synthesis-grade methanol, heptane (solvent), and propylene acetate (internal standard) were all procured from Nizo Chemical Enterprise, Akure, Nigeria. Virgin vegetable oil was purchased from the King’s market, Ado-Ekiti, Nigeria.

AEC catalyst preparation

The harvested anthill was first crushed into powder form and sieved through a sieve mesh of 0.3 mm to obtain particle size lesser than 0.3 mm (< 0.3 mm). The sieved anthill powder was kept in a plastic container and then covered. The eggshells were first soaked for a day and washed thoroughly with tap water to remove all the impurities and inner white membrane, followed by another washing with distilled water. Then, the cleaned eggshells were dried in an oven at 110 °C for 24 h. The dried eggshells were ground by a mechanical grinder to obtain fine powder. The fine powder of eggshells was sieved through an aperture mesh of 0.3 mm to obtain the finest eggshell powder. The obtained eggshell powder was then kept in a soft polythene bag and placed in a sealed plastic container.

The procedure employed to synthesize the AEC catalyst referred to our previous work [34]. The AEC catalyst was prepared, by mixing different proportions of the anthill, and eggshell powders, as suggested by central composite design (Table 1). Typically, 20 g of the mixture of anthill and eggshell powders was formulated by varying their mixing proportions. The mixture of anthill and eggshell powders was poured into a beaker while an adequate amount of distilled water was added, and stirred for 2 h on a hot plate to homogenize the mixtures. The obtained slurry was heated up at 125 °C in an oven overnight to remove excess water. Finally, raw AEC samples at various mixing ratios of anthill to eggshell were calcined at various temperatures (600–1000 °C) and corresponding time in the range of 1–4 h using a muffle furnace with a heating rate of 10 °C/min. The calcined AEC samples were kept in a desiccator containing silica pellets to prevent moisture contamination.

Table 1 Experimental ranges and level of the independent test variables

Design of experiment

In the present study, central composite design, which is a form of RSM, was used for the optimization of process conditions for the preparation of the AEC catalyst. To evaluate the effect of operating variables on the performance of the AEC catalyst, three main factors were considered: calcination temperature, x1 °C, calcination time, x2 h, and anthill proportion in AEC, x3. A total of twenty experiments were conducted, 23 = 8 cube points, 6 replications at the center point, and 6 axial points. Experimental data were analyzed using the Design-Expert software version 7.0.0 (STAT-EASE Inc., Minneapolis, USA). Table 1 presents the experimental ranges and the level of the chosen input variables for biodiesel yield (Y).

The main goal is to establish the best variables for the catalyst preparation process, from the developed models using experimental data. The desired goal in terms of biodiesel yields (responses) was described as “maximization” to determine the optimum process parameters for the maximum biodiesel yields. The responses were obtained via the transesterification process and used to develop mathematical relations, which correlate the responses (biodiesel yields) and catalyst preparation process parameters studied according to the second-order polynomial response equation given in Eq. 1.

$$Y_{i} = b_{0} + bx_{1} + b_{2} x_{2} + b_{3} x_{3} + b_{12} x_{1} x_{2} + b_{13} x_{1} x_{3} + b_{23} x_{2} x_{3} + b_{11} x_{1}^{2} + b_{22} x_{2}^{2} + b_{33} x_{3}^{2} ,$$

where Yi is the response variable of biodiesel yield. The bis are regression coefficients for linear effects; bik the regression coefficients for interaction effects; bii the regression coefficients for quadratic effects and xi represent coded experimental level of the variables.

Activity study

The biodiesel was produced via the transesterification of oil with methanol using a batch reactor (a 250 mL three-neck round bottom flask). One of the side necks was used to insert a thermometer for regular temperature monitoring; the other side neck was fitted with a reflux condenser to minimize loss of methanol and required quantities of reactants and catalyst were fed through the middle neck. The reactor was then placed on a temperature-controlled magnetic stirrer to maintain the required reaction temperature and a fixed 300 rpm stirring rate. The reactor content was heated to 60 °C before proper mixing commenced. The reaction parameters were fixed at a catalyst loading of 5 wt%, methanol to oil molar ratio of 6:1, reaction temperature of 60 °C, and reaction time of 2 h for all the experiments [35]. At the end of a reaction, the reactor contents were cooled to room temperature, and the catalyst was removed from the product mixture by centrifugation. The methyl ester contents were analyzed using gas chromatography–mass spectrometry (Varian 4000 GC/MS/MS system). The GC column was an Agilent J&W capillary column (DB-624, length: 30 mm, diameter: 0.320 mm and film thickness: 1.8 µm) with helium as the carrier gas.

The yield (Yi) of biodiesel produced was calculated by the following equation:

$${\text{Biodiesel yield }}\left( {Y_{i} } \right),\% = \frac{{M_{is} A_{b} }}{{M_{b} A_{is} }} \times 100$$

where Mis is the mass of internal standard added to the biodiesel sample, Ais is the peak area of internal standard, Mb is the mass of biodiesel sample and Ab is the peak area of biodiesel sample [7].

Characterization of the prepared AEC catalyst

The optimal composite catalyst was characterized for its morphological structure, porosity development, and elemental composition, using a scanning electron microscope equipped with an energy-dispersive X-ray (EDX) analyzer (SEM–EDX, JEOL-JSM 7600F). Fourier transform infrared (FTIR) spectrophotometer (IRAffinity-1S, Shimadzu, Japan) was used on the prepared AEC catalyst to determine various surface functional groups. The spectra were recorded from 4000 to 500 cm−1. Also, the Brunauer–Emmett–Teller (BET) surface area and pore size distribution of fresh and spent AEC catalysts were determined by the surface area analyzer (Quantachrome instrument, NOVA station A model, 11:03, USA) at the temperature of 77 K.

Results and discussion

Development of regression model equation

The complete design matrix, as generated by the Design-Expert software and the experimental results obtained during activity evaluation of AEC samples, are presented in Table 2, and second-order polynomial models (Eqs. 3 and 4) were used to correlate the dependent and independent variables. The final models in terms of coded factors for the virgin oil biodiesel (VOB) yield, (Y1) and waste oil biodiesel (WOB) yield, (Y2) are shown in Eqs. 3 and 4, respectively.

Table 2 Experimental design matrix and expected responses
$$Y_{1} = 76.55 + 9.41x_{1} + 1.78x_{2} - 5.66x_{3} - 2.64x_{1} x_{2} - 1.65x_{1} x_{3} - 0.33x_{2} x_{3} + 2.299x_{1}^{2} + 2.73x_{2}^{2} - 1.29x_{3}^{2}$$
$$Y_{2} = 42.14 + 8.23x_{1} + 1.14x_{2} - 2.07x_{3} - 0.27x_{1} x_{2} - 1.80x_{1} x_{3} - 3.42x_{2} x_{3} + 5.48x_{1}^{2} + 7.11x_{2}^{2} - 1.56x_{3}^{2}$$

The yields of biodiesel produced from the virgin and waste oils have been predicted by Eq. 2 and presented in Table 2. The adequacy of the models obtained was evaluated based on the values of the correlation coefficient (R2) and adjusted R2 (Adj. R2). The R2 value quantitatively evaluates the correlation between the predicted and the observed output values. The experimental and predicted biodiesel yields obtained from the two models (Eqs. 3 and 4) were compared. Generally, there were good agreements between the predicted and observed values of biodiesel yield with R2 = 0.9584 for VOB yield and R2 = 0.9806 for WOB yield. This indicates that only 95.84% and 98.06% of the whole variations for VOB and WOB yields, respectively, are explained by the process variables considered, and this also means that 4.16% of the variation for VOB yield and 1.94% of the variation for WOB yield are not explained by the corresponding models. Adj-R2, which measures the goodness of a model fit, was also used to correct R2 value for the sample size and the number of terms in the model using the degrees of freedom on its computations. If there are many terms in a model and not a very large size, Adj-R2 may be visibly smaller than R2 [27]. In the current study, Adj-R2 values for the WOB and VOB models were very close to their corresponding R2 values (Tables 3 and 4). Thus, this observation indicated that the experimental responses agreed reasonably well with the predicted values from the two models.

Table 3 Analysis of variance (ANOVA) for response surface quadratic model for VOB yield
Table 4 Analysis of variance (ANOVA) for response surface quadratic model for WOB yield

Furthermore, the fitness of the models was examined by analysis of variance (ANOVA—Type III). The ANOVA for VOB and WOB yields are presented in Tables 3 and 4, respectively. As indicated in Tables 3 and 4, the model F values for the VOB and WOB yields were found to be 25.59 and 56.21, respectively, which indicated that the two models were significant. According to the ANOVA, values of “Prob > F” less than 0.0500 indicate model terms are significant. In this case, \(x_{1}\), \(x_{2}\), \(x_{3},\)\(x_{1} x_{2}\), \(x_{1}^{2}\), and \(x_{2}^{2}\) were the significant model terms to the VOB yield, whereas \(x_{1}\), \(x_{3}\), \(x_{1} x_{3}\), \(x_{2} x_{3}\), \(x_{1}^{2}\), \(x_{2}^{2}\), and \(x_{3}^{2}\) were significant model terms to the WOB yield. It can be observed that the two models were adequate to predict the two responses (VOB and WOB yields) within the range of parameters considered in this study.

Figure 1a and b depict the graphs of the predicted values against the experimental values for VOB and WOB yields, respectively. It was found in both cases that the predicted response values agreed reasonably well with the corresponding experimental values within the range of the operating conditions. However, Fig. 1b revealed that the predicted WOB yield values were nearly close to experimental values, indicating that the relationship between the catalyst preparation variables and WOB yield was best described by the model developed. Also, Fig. 1a displayed that the model (Eq. 3) captured the correlation between the composite catalyst preparation variables and the VOB yield. These observations implied that the independent variables considered in this study had effects on both VOB and WOB yields.

Fig. 1

Predicted vs. experimental yield of a VOB and b WOB

Virgin oil biodiesel (VOB) yield

The ANOVA analysis (Table 3) revealed that all three composite catalyst preparation variables showed influential effects on the VOB yield. The calcination temperature (\(x_{1}\)), calcination time (\(x_{2}\)), and composition of an anthill in the composite catalyst (\(x_{3} )\) exhibited F values of 140.74, 5.02, and 50.84, respectively. However, the calcination temperature is the most influential factor on VOB yield, followed by mixing proportion, as they exhibited the largest F values compared to that of calcination time. Meanwhile, only the quadratic effects of calcination temperature and calcination time on the VOB yield were significant. Figure 2 illustrates the effect of calcination temperature and calcination time on virgin oil conversion to biodiesel for anthill proportion in the AEC catalyst of 30%. As it is obvious from the figure, the VOB yield increased with increasing temperature and time. The reason for this observation is thought to be the fact that calcination of raw catalysts at elevated temperature and time resulted in complete removal of adsorbed gases and creates cavities on its surface, which paved way for the adsorption of methanol [36]. In other words, higher temperatures coupled with prolonging calcination caused solid rearrangement and pore opening on the surface of the solid, which enhanced the adsorption of methanol. However, it can also be observed that the calcination temperature showed more effect on the catalyst performance in the conversion of virgin oil to biodiesel than calcination time, and this was confirmed by the large F value (Table 3) obtained for calcination temperature. The result obtained agreed with the work performed by Olutoye et al. [2] which reported that calcination temperature and time had a significant effect on the morphological property of heterogeneous catalyst prepared from barium-modified clay.

Fig. 2

Three-dimensional response surface plot of VOB yield (effect of calcination temperature and time)

Waste oil biodiesel (WOB) yield

Based on the F value (Table 4), calcination temperature (\(x_{1}\)) showed the highest F value of 204.69, indicating that it had the most significant effect on the conversion of waste oil to biodiesel, compared to other parameters. The composition of an anthill in AEC catalyst (\(x_{3}\)) was the second significant parameter among the variables studied with an F value of 12.91 and a p value of less than 0.05. However, the quadratic effects of the three variables on the WOB yield were all significant. Figure 3a depicts the combined effect of calcination temperature and calcination time on the WOB yield at fixed anthill proportion in the AEC catalyst of 30%. As it is obvious from Fig. 3a, a slight improvement in WOB yield with increasing calcination time was observed, whereas the same figure showed a sharp increase in WOB yield with an increase in calcination temperature. The presumed reason is that prolonged or short period calcination could reduce the surface area and performance of the prepared catalyst, as a longer calcination period might result in agglomeration of catalyst particles (sintering), whereas shorter calcination time could not guarantee the formation of pores on the catalyst surface [3].

Fig. 3

Three-dimensional response surface plot of WOB yield: a effect of calcination time and temperature; b effect of anthill proportion in AEC catalyst and calcination time

To study the combined effect of calcination temperature and anthill proportion in AEC catalyst on WOB yield, the experiments were conducted with calcination time varying from 2 to 4 h and anthill proportion varying from 20 to 40% at a constant calcination temperature of 800 °C. The result is depicted in Fig. 3b. The figure shows that the WOB yield increases with an increase in anthill proportion in the AEC catalyst. The anthill/eggshell impregnation ratio played a significant role in the performance of the AEC catalyst. Anthill comprises mixed metal oxides which can serve as a catalyst support [37]. However, when anthill is mixed with eggshell powder in appropriate quantity and calcined at high temperature, they tend to degrade as a result of the evolvement of adsorbed gases (CO2, SO2, and others) on the solid. The thermal treatment opens up the pores on the catalyst surface for improved activity and forms synergetic mixed metal oxides that are most basic [2]. This observation is corroborated by the EDX analysis.

Optimization of process variables

The optimum values of the process parameters for the preparation of the AEC catalyst were 1000 °C, 4 h, and 20% for calcination temperature (\(x_{1} )\), calcination time (\(x_{2}\)), and anthill proportion in AEC catalyst (\(x_{3} )\), respectively, as it can also be seen in Table 5. At these optimum values, the experimental values of VOB and WOB yields were found to be 97.13% and 70.92%, respectively. It was found that the experimental values obtained were closed to those values predicted by the models, with slight errors between the predicted and the experimental values, which was calculated as 0.67% and 0.50% for VOB and WOB yields, respectively. This implies that the strategy to optimize the AEC catalyst preparation conditions and to obtain maximal biodiesel yields by RSM in this study is successful.

Table 5 Optimum conditions and model validation for AEC catalyst preparation

Also, the results obtained indicate that the performance of the AEC catalyst for the conversion of virgin and waste oils to biodiesel was satisfactory, with the maximum triglyceride conversion close to 100% for virgin oil. As reflected in the literature, Xie et al. [38] reported that 87% of virgin vegetable oil was converted to biodiesel by ZnO/KF catalyst. This implies that the composite catalyst developed in this work was effective for the virgin vegetable oil conversion to biodiesel. However, the low biodiesel yield recorded in the case of waste vegetable oil was due to high FFA content, which was slightly less than 2 wt%. As reported in the literature, transesterification would not occur if FFA content in the biodiesel feedstock were higher than 2 wt%, except high FFA content is first reduced by acid esterification [35]. Nevertheless, the optimal AEC catalyst proved to be effective for the production of biodiesel from high FFA feedstock via a single-step transesterification process. However, further research such as optimization of the transesterification process is recommended to investigate the activity of the optimal catalyst and also, enhancing the biodiesel yield by considering a two-step transesterification process. Moreover, investigating the stability of the spent catalyst during reuse is necessary. The process/method adopted to develop the catalyst was considered to be cost effective, and practicable as both anthill and waste chicken eggshells were available in abundance, so an effective composite catalyst with excellent activity could be derived from their combinations.

Characterization of AEC catalyst prepared under optimum conditions

The SEM image depicted in Fig. 4 reveals the surface morphology of the AEC catalyst prepared under optimum conditions. The prepared catalyst surface has rough, irregular, and larger particles. More so, pores of different shapes and sizes are observed on its surface, thus there is a better possibility for the methanol to be adsorbed. However, the presence of the pores on the optimal AEC catalyst might be attributed to the elimination of adsorbed gases, organic matter, and moisture content [39].

Fig. 4

Scanning electron micrograph of AEC catalyst prepared under optimum conditions (calcination temperature = 1000 °C, calcination time = 4 h, and anthill/eggshell percentage composition = 20%/80%)

Figure 5 shows the FTIR spectrum of the AEC catalyst prepared under optimum conditions. The sharp and broad absorption bands, respectively, at 3644 cm−1 and 3450 cm−1 are assignable to hydroxyl bond from adsorbed moisture. The absorption band observed at 2359 cm−1 is due to the \(C \equiv C\) stretching. The bands at 1479 cm−1 and 1417 cm−1 are attributed to the CH3 antisymmetric deformation and C–O asymmetric stretching modes, respectively. The optimal AEC catalyst also shows another set of bands at 1057 cm−1, 767 cm−1 and 459 cm−1 and can be, respectively, attributed to the O-Si–O stretching, Al–Mg–OH and Si–O–Al vibration of the clay sheet [3]. These detected functional groups are essential to the activity of the catalyst as they provide sufficient adsorptive sites for reactants [16].

Fig. 5

FTIR spectrum of AEC catalyst prepared under optimum conditions (calcination temperature = 1000 °C, calcination time = 4 h, and anthill/eggshell percentage composition = 20%/80%)

Table 6 shows the result of EDX analysis and it is found that calcium is the main component in the prepared catalyst, no carbon element is, however, detected in the AEC sample and this indicates that CaCO3 contained in the chicken eggshell was completely decomposed into CaO and CO2. The table also shows that Si, Al, Fe, and O have a high content, and the result from EDX analysis indicates that the mineral compositions in the study sample are CaO, SiO2, Al2O3, and Fe2O3. Due to the high Ca content, the prepared AEC catalyst could be regarded as a heterogeneous base catalyst. The oxygen atom in the CaO after calcination represents Lewis base site and the calcium ion is Lewis acid site whereby good activity exhibited by the AEC catalyst during the transesterification reaction was attributed to high basicity. Moreover, the prepared catalyst could be referred to as a supported catalyst as it contains SiO2, Al2O3, which are good catalyst supports [18, 37].

Table 6 Elemental analysis for optimal AEC catalyst

The BET surface area and pore size distribution analyses were conducted to determine the textural properties of the fresh and spent AEC catalysts, and the results are presented in Table 7. The surface area of the fresh catalyst was determined to be 48.12 m2/g by the BET analysis. However, the surface area of the used catalyst was found to be 7.03 m2/g. Additionally, a distinct decrease in the pore volume and the average pore radius of the catalyst was observed. The reason for the observation was attributed to the agglomeration of the catalyst particles by the glycerol and unreacted oil blockage on the catalyst surface [7]. The studied composite is found to have a relatively large specific surface area (48.12 m2/g) and this indicates that it could be considered as an effective catalytic material for the transesterification of vegetable oil with alcohol to produce fatty acid alkyl esters, mostly when compared with lithium-based chicken bone (8.62 m2/g) [40], barium-modified montmorillonite K10 (13.114 m2/g) [41] and KOH-modified zinc oxide (4.35 m2/g) [42].

Table 7 Analysis of BET and pore size distribution of the fresh and spent AEC catalysts


The preparation process condition for the AEC catalyst was optimized, and the optimum values of the process variables were 1000 °C, 4 h, and 20% for calcination temperature, calcination time, and anthill proportion in the AEC catalyst, respectively. At these optimum values, the yields of VOB and WOB were 97.13% and 70.92%, respectively. Analysis of variance showed high correlation coefficients for the two responses (R2 = 0.9584 for VOB and R2 = 0.9806 for WOB), thus indicating better agreement between the predicted and the experimental values. A detailed characterization of the AEC sample prepared under optimum conditions revealed that the catalyst was of good quality and contained components that were actively involved in the transesterification process.


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Correspondence to Adeyinka Sikiru Yusuff.

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Yusuff, A.S. Development of a composite catalyst from anthill and eggshell: an optimization study on biodiesel production from virgin and waste vegetable oils. Waste Dispos. Sustain. Energy 1, 279–288 (2019) doi:10.1007/s42768-019-00015-x

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  • Anthill
  • Chicken eggshell
  • Conversion of vegetable oil to biodiesel
  • Heterogeneous catalyst
  • Central composite design