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Article

Macauba Kernel Oil: Refining, Transesterification, and Density/Viscosity of Blends B15 to B20 with Mineral Diesel

by
Bruna Sousa
1,
Igor Figueredo
2,
Débora Brito
3,
Mauricio Dorneles
1,
Eva Sousa
1,
Tassio Nascimento
4,
Francisco Assis Cunha
5,
Francisco Murilo T. Luna
2,
Célio L. Cavalcante, Jr.
2 and
Maria Rios
1,*
1
Grupo de Inovações Tecnológicas e Especialidades Químicas, Departamento de Engenharia Mecânica, Universidade Federal do Ceará, Campus do Pici, Bl. 714, Fortaleza 60440-554, Brazil
2
Núcleo de Pesquisas em Lubrificantes Ícaro de Souza Moreira (NPL), Departamento de Engenharia Química, Universidade Federal do Ceará, Campus do Pici, Bl. 1010, Fortaleza 60020-181, Brazil
3
Centro Nordestino de Aplicação e Uso da Ressonância Magnética Nuclear, Universidade Federal do Ceará, Campus do Pici, Fortaleza 60020-181, Brazil
4
Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte (IFRN), Apodi 59700-000, Brazil
5
Laboratório de Bioprospecção do Semiárido e Métodos Alternativos—LABSEMA, do Universidade Regional Cariri—URCA, Rua Cel. Antônio Luíz, 1161-Pimenta, Crato 63105-010, Brazil
*
Author to whom correspondence should be addressed.
Processes 2025, 13(8), 2637; https://doi.org/10.3390/pr13082637
Submission received: 15 July 2025 / Revised: 10 August 2025 / Accepted: 14 August 2025 / Published: 20 August 2025
(This article belongs to the Special Issue Bioenergy, Green Chemistry and Biorefinery: Challenges and Perspectives on Circular Economy and Sustainability)
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Abstract

Macauba is a versatile palm and has been explored in various sectors due to its ability to produce oils, proteins, energy, and biofuels. This paper presents the extraction, refining, and characterization of the macauba kernel oil, the synthesis of biodiesel, and an evaluation of the density and viscosity of its blends with mineral diesel, ranging from B15 to B20. Conversion was determined using the integral areas of the 1H NMR spectrum for the FAME methyl ester (3.62, -CH3) and FAME carbonyl (2.26, -COOCH2). Predictions of the key inputs required for the extraction and degumming of the macauba kernel oil, as well as for the biodiesel production, are also presented. These results provide valuable insights into diesel-biodiesel blends exceeding 14% (vol.) of biodiesel, thereby contributing to the expansion of the biofuels industry.

Graphical Abstract

1. Introduction

The macauba (Acrocomia aculeata) is a tropical American palm with a wide distribution in Brazil. It may be found in the Cerrado, Atlantic Forest, Amazon Rainforest, and Pantanal regions [1,2,3]. This versatile palm has been strategically implemented in different industrial sectors due to its multifaceted nature, a crucial attribute in the pursuit of a low-carbon economy, given its capacity to produce oils, proteins, energy, and biofuels [4,5,6,7,8].
Its seed oil shows high potential for the development of biofuels, with the pulp and kernel oils used for biodiesel and sustainable aviation fuel (SAF) production [9,10]. Moreover, certain traditional rural communities utilize the endocarp for charcoal production and the kernel for cooking oil extraction, and soap production [5,11,12,13].
For the synthesis of biodiesel, good-quality vegetable oil is essential [14,15,16]. Consequently, vegetable oils characterized by elevated acidity and impurity levels necessitate a previous refining process before their industrial utilization [17,18,19,20,21]. Degumming and neutralization have been applied to remove undesirable components of the macauba kernel oil before the transesterification reaction [22,23,24,25,26]. Given the increase in biodiesel percentage in commercial diesel and its significance in the energy matrix transition, it has been essential to evaluate issues related to density and viscosity that have the potential to interfere with its performance in diesel cycle engines [27,28,29,30]. Hernández et al. [31] reported on the density and viscosity of mineral diesel and various biodiesel samples, as well as kinematic viscosity ranges of mineral diesel–biodiesel blends (see Table 1). However, the report did not include any information on biodiesel derived from macauba oil and its blends with mineral diesel.
Presently, the mandatory percentage of biodiesel in commercial diesel in Brazil is 14% (vol., B14), and its quality is specified by Resolution No. 920/2023 of the Brazilian National Agency for Petroleum, Natural Gas and Biofuels (ANP) [32,33]. Additionally, the specifications for the blend of diesel and biodiesel are delineated in Resolutions No. 909/2022 and No. 968/2024, with the blend ranging from BX to B30, where X denotes the percentage of biodiesel [34,35]. Densities and viscosities (dynamic and kinematic) of diesel-biodiesel blends outside of specifications may cause technical problems in cycle Diesel engines, so, in this study, these properties were evaluated for the biodiesel obtained from a refined macauba kernel oil, for its blends B15 (15% vol.: 85% vol. diesel) and for B20 (20% vol. biodiesel: 80% vol. diesel) at 20–100 °C. Furthermore, the densities and viscosities of the blends B16, B17, B18, and B19 were predicted. Biodiesel conversion was determined using 1H NMR spectrum. Moreover, predictions for key inputs to the extraction and degumming of macauba kernel oil, as well as the production of biodiesel, are provided, with valuable insights about diesel-biodiesel blends at levels exceeding 14% vol., contributing to the expansion of the biofuels industry.

2. Materials and Methods

The macauba fruit (Acrocomia aculeata) and the pretreatment steps are displayed in Figure 1. The fruits were harvested in Cariri, Ceará, Brazil, at 7°14′28″ S, 39°25′00″ W, ground and stored in plastic containers.
The oil was extracted using a Soxhlet apparatus, with hexane as the solvent (60 ± 5 °C). Each procedure was performed for one hour, corresponding to 6–7 cycles. The hexane was recovered using a rotary evaporator (Fisatom, model 802, São Paulo, Brazil) set to 65 °C and 300 mmHg and subsequently returned to the oil extraction process. The recovery efficiency was ca. 70%. This step is essential due to the toxicity of hexane to the environment and human health. Nevertheless, hexane is still widely used in vegetable oil production due to its miscibility, low boiling point, ease of recycling, and low cost [36]. The acidity index was determined using a methodology from the Adolfo Lutz Institute (325/IV, Chapter XVI, p. 591, 2008) [37], and the saponification index (SI) was determined using the AOCS CD3-25 methodology of the American Oil Chemists’ Society [38]. The macauba kernel oil was refined using the degumming and neutralization processes, following the method described by Nunes et al. [23], with modifications. The degumming process was performed using a proportion of 100 g of oil to 50 g of distilled water. Both oil and water were heated to 70 ± 5 °C, then mixed and stirred for 10 min.
The biodiesel synthesis involved two steps. In the first one, a 1:6 ratio of macauba kernel oil to methanol, along with 1.5% (wt.) KOH based on the oil weight, was used. In the second step, 15% (wt.) methanol, calculated from the weight of methyl esters produced in the first step, was combined with 0.5% (wt.) KOH. Equations (1) and (2) [39] were used to determine the quantities of methanol and KOH.
M K O H = ( M o i l · % K O H 100 + A I 1000 ) / ( K O H   p u r i t y )
M M e O H = 0.2 · M o i l
where:
  • MKOH = weight of potassium hydroxide (g),
  • Moil = weight of macauba kernel oil (g),
  • %KOH = 1.5% (wt.) based on the macauba kernel oil mass,
  • IA = Acidity index of the macauba kernel oil mass (mg NaOH/g),
  • KOH purity = purity of the KOH provided by the supplier (85%), and
  • MMeOH = weight of methanol (g).
After transesterification, the solution was transferred to a separating funnel to generate two phases due to density differences: methyl esters in the upper phase and glycerol in the lower phase (Figure 2). Then, glycerol was removed, and the methyl ester phase was washed with distilled water until a pH of 7.0 was achieved. In the final step, the methyl ester mixture was distilled and stored.
For evaluating the density and viscosity of the biodiesel-diesel blends, two samples at concentrations of 15% biodiesel and 85% diesel, and 20% biodiesel and 80% diesel (vol.: vol.) were prepared. The mineral diesel was supplied by Petrobras (Brazil). Densities, as well as kinematic and dynamic viscosities, were determined using a digital viscodensimeter, Anton Paar model SVM 3000-Stabinger (Anton Paar GmbH, Graz, Austria), following ASTM D7042-21a [40], with experiments performed in triplicate. Equations (3) and (4) [35] were used to calculate the densities and viscosities of the blends.
DensityBX (T) = ((X·densityBiodiesel(T)) + ((1 − X)·densityDiesel(T))
ViscosityBX (T) = ((X·viscosityBiodiesel(T)) + ((1 − X)·viscosityDiesel(T))
where:
  • X = 15% and 20%,
  • DensityBX (T) = density of the blend composed by X biodiesel: (1 − X) mineral diesel (vol.:vol.) at the determined Temperature (g.cm−3),
  • ViscosityBX (T) = viscosity of the blend composed by X biodiesel: (1 − X) mineral diesel (vol.:vol.) at the determined Temperature (if dynamic (mPa·s), if kinematic (mm2/s)).
The densities and viscosities for B16, B17, B18, and B19 were calculated using the same method presented in Equations (3) and (4).
The infrared spectrum was obtained using a Shimadzu IR-Tracer100 Spectrometer (USA) with an Attenuated Total Reflection (ATR), range of 4000 to 600 cm−1, and a resolution of 4 cm−1.
Nuclear Magnetic Resonance (NMR) spectra were recorded on a Bruker Avance DRX-500 spectrometer (USA) using deuterated chloroform (CDCl3), and the data processed on Bruker Top Spin software (version 3.6.2). The biodiesel yield was predicted by Equation (5) [14] with the integral areas of the signals of the methyl esters (-CH3) at 3.7 ppm and α-carbonyl methylene of the fatty ester derivatives (-COOCH2-) at 2.2–2.3 ppm.
Yield   ( % ) = 100 · 2 A ( m e t h y l   e s t e r s ) 3 A ( α − c a r b o n y l   m e t h y l e n e )
The ester content was also determined by gas chromatography/mass spectrometry (Varian, GC 450 model, Palo Alto, CA, USA), following the standard method EN 14103:2020.

3. Results

The extraction yields (in mL oil/100 g dry biomass) for the macauba kernel oil and its acidity index, saponification index (SI), density (20 °C), and kinematic viscosity (40 °C) are presented in Table 2 and Table 3, respectively.
The degumming and neutralization processes were applied to refining the macauba kernel oil [23]. As shown in Table 4, the degumming promoted a significant reduction of 62.2% in the acidity index and the neutralization of 98.3%.
The FTIR spectrum of the refined macauba kernel oil is presented in Figure 3, with the absorption bands and their assignments detailed in Table 5.
The 1H and 13C NMR spectra of the refined macauba kernel oil are shown in Figure 4 and Figure 5, respectively.
The 1H and 13C NMR spectra of the biodiesel obtained from the refined macauba kernel oil are presented in Figure 6 and Figure 7, respectively.
The fatty acid methyl esters (FAME) and their contents (%), with a prevalence of lauric acid methyl ester and oleic acid methyl ester, are shown in Table 6.
The prediction of key inputs for extracting and degumming the macauba kernel oil and the obtained biodiesel, based on 1142 g of crushed macauba kernel, extrapolated to 1.5 t, are presented in Table 7. The extrapolated values are representative of industrial metrics, such as productivity, vegetable oil extraction, and the estimated cost per hectare or ton for other feedstocks [47,48]. The viscosities and densities of macauba kernel oil at 20, 40, and 100 °C are presented in Table 8.
Values of density (g/cm3) at different temperatures (°C) are plotted in Figure 8 for the pure biodiesel obtained from refined macauba kernel oil and for the pure mineral diesel, including regression line equations and R2 values. For the blends of mineral diesel and biodiesel, B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel), temperature versus density are shown in Figure 9, at the same temperature range (20–100 °C).
Values predicted for the density versus experimental density for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C are plotted in Figure 10. On Table 9 densities predicted for B16, B17, B18, and B19 between 20–100 °C, using Equation (3), are reported.
Dynamic (η) and kinematic (ν) viscosities for the biodiesel obtained from refined macauba kernel oil, mineral diesel, B15, and B20, at temperatures between 20–100 °C, are shown in Table 10.
Plots of prediction dynamic viscosity versus experimental dynamic viscosity and prediction kinematic viscosity versus experimental kinematic viscosity are presented in Figure 11 and Figure 12, respectively, for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C, including the regression line equations and R2. On Table 11 and Table 12, predicted viscosities for B16, B17, B18, and B19 between 20–100 °C, using Equation (4), are reported.

4. Discussion

The mean yield of macauba kernel oil extraction was 50 mL oil/100 g dry biomass, calculated from the results shown in Table 2. These findings align with the existing literature on the oil extraction of the Acrocomia sp. genus. Previous studies have reported oil extractions using solvent (hexane) showing yields between 25 and 56 mL oil/100 g dry biomass (Lieb et al. [49], Magalhães et al. [50], Evaristo et al. [51] and Trentini et al. [52]). The variability in yield can be attributed to the extraction method and time, moisture content, particle size, and storage conditions [53]. Trentini et al. [52] reported that the high moisture content of the macauba kernel hindered the oil extraction process. Indeed, previous studies into the effect of moisture content on the efficiency of vegetable oil extraction through the Soxhlet method have demonstrated that a high moisture content has a detrimental effect on solvent penetration and oil diffusion, resulting in a decrease in the extraction yield [54,55]. The studies also indicated that the surface area of oilseeds is increased by reducing their size before solvent extraction, which in turn facilitates solvent permeability and oil diffusion, thereby maximizing the oil extraction yield [54].
The extracted oil exhibited a high acidity index (Table 3). In previous published reports, Prado et al. [56] and Nunes et al. [2] showed values of 9.7 and 0.57 mg KOH/g, respectively, demonstrating a substantial variation in this parameter. For biodiesel production, an oil with a high acidity index affects the efficiency of the transesterification reaction due to hydroxides. These act as catalysts for transesterification but may also promote the hydration reaction, in which a free hydroxide breaks down the bonds between the fatty acids and the glycerol. Therefore, the saponification process is critical for biodiesel production, especially when using feedstocks of low quality [50].
The saponification index (SI) increases with free fatty acids and water content, and a high SI value indicates soap formation harming the biodiesel production efficiency [57,58]. Thus, the high SI of the macauba kernel oil may be related to the high free fatty acid content, as indicated by the acidity index (Table 3), so its pretreatment would be required. As Del Río et al. [11] reported, the SI may also provide supplementary insights into the fatty acid profiles of vegetable oils. They demonstrated similarity in the distribution of free fatty acids in kernel and pulp macauba oils, with oleic acid being the most prevalent (27.69% in kernel oil and 54.79% in pulp oil), followed by palmitic and stearic acids.
The degumming and neutralization processes were applied to refine the macauba kernel oil [23]. As shown in Table 4, the degumming step promoted a significant reduction of 62.2% in the acidity index. After that, the neutralization process further decreased the acidity in index to 0.13 (an overall reduction of 98.3% from the initial value).
The FTIR spectrum profile of the refined macauba kernel oil is consistent with the characteristic bands of vegetable oils [11,41,43]. Del Río et al. [11] reported that the kernel oil is enriched in saturated triglycerides, as indicated by signals showing higher levels of saturated acids in comparison to the FTIR spectrum of the pulp oil. Nascimento et al. [43] presented the FTIR spectra of andiroba, babassu, baru, and sweet almond oils obtained through different extraction methods, demonstrating comparable spectral profiles to those of macauba kernel oil, notably babassu oil, which exhibited characteristic signals attributable to its high content of saturated fatty acids. As illustrated in Figure 3, the asymmetrical and symmetrical stretching vibrations attributed to C-H bonds appear at 2956, 2925, and 2854 cm−1. Furthermore, the stretching vibrations of C=O (1742 cm−1) and C-O (1232 and 1110 cm−1), attributed to the esters, as well as the bending vibrations of CH2 (1464 cm−1), CH3 (1376 cm−1), and CH2 (1158 cm−1), are also presented. Finally, the bands at 964, 888, and 721 cm−1 are attributed to carbon-hydrogen (C-H) bending, =CH wagging vibration, and (CH2) in-plane bending or rocking [11,41,42,59].
The 1H NMR measurements were performed in duplicate (Sample 1 and Sample 2) to evaluate potential differences in combining the nine samples (Table 2). As demonstrated in Figure 4, both spectra exhibited chemical similarity. Thus, subsequent measurements were performed using only a representative sample from the nine extractions. The signals exhibited a high degree of similarity to those of vegetable oils, characterized by a predominance of saturated compounds, with lauric acid being the most prevalent [11,43].
Additionally, chemical shifts between 4.15 and 4.30 ppm, corresponding to the methylene group of the glycerol, and at 5.34 ppm, indicative of olefinic protons, were observed [14]. This chemical profile is appropriate for biodiesel production because saturated esters tend to be more stable [60,61,62]. The 13C NMR spectrum was utilized to supplement the identification of the regions of saturated and unsaturated compounds, acyl chains, and triacylglycerols [44]. The signal of acyl groups appears at 173.29 ppm, unsaturated fatty acids are between 129.88 and 130.21 ppm, and glycerol is at 62.31 ppm [43]. The signals were found to be in corroboration with the results previously documented by del Río et al. [11], who utilized CG-MS to investigate the chemical composition of the kernel and pulp oils from Acrocomia aculeata (macauba palm fruit). They identified a preponderance of saturated triglycerides ranging from C6 to C24, with a prevalence of C12 (lauric acid) and minor amounts of C18:1 (oleic acid) and C18:2 (linoleic acid) in the kernel oil.
The biodiesel sample was obtained by transesterification. Its acidity index was 0.33 mg NaOH/g, which complies with the ANP Resolution Nº 920/2023 limit (≤ 0.50 mg NaOH/g) [33]. The yield of 85.1% was evaluated using the 1H NMR spectroscopy. For the calculation, the integral areas of the FAME-methyl ester (3.62, -CH3) and FAME-carbonyl (2.26, -COOCH2-) were considered (Figure 6). This methodology has been extensively applied [45], with results showing good agreement with gas chromatography (standard method EN 14103:2020) [63], as related by Braga et al. [14], showing deviations of 0.4% and 2% for safflower biodiesel and 1.9% for babassu biodiesel. Navarro-Díaz et al. [64] reported a yield of 78.5% when using Brazilian macauba oil with supercritical methanol under the following conditions: 648 K, 15 MPa, a molar ratio of 1:30 (oil: methanol), 5 wt.% water, and a flow rate of 2.5 mL/min. The percentages and identification of fatty acid methyl esters (FAME) were also determined using gas chromatography/mass spectrometry, revealing a predominance of saturated esters (77.45%). Mekonnen et al. [65] and Sajjadi et al. [66] reported that the low percentage of unsaturated esters enhances certain properties of biodiesel, including its cetane number and oxidative stability. As for the cold flow properties, these are demonstrated to be critically influenced by the presence of long-chain saturated esters. The cold filter plugging point is typically associated with the chain length saturated factor [67]. Rodrigues et al. [68] reported that linear esters have relatively high crystallization temperatures due to the strong van der Waals attraction and the efficient packing into crystals. In their study, the methyl esters from babassu coconut oil demonstrated a low onset crystallization temperature attributed to the weaker molecular interaction of the lauric acid esters [68].
According to Fernández-Coppel et al. [69], the Acrocomia aculeata exhibits a productivity of 25 t of fruit and 6200 kg of oil per hectare per year, which places it as one of the main agroenergy crops when compared to the African palm oil (Elaeis guineensis Jacq.). Colombo et al. [70] report that commercial plantations produce 5 t pulp oil and 1.5 t kernel oil per hectare per year, which makes macauba a promising biofuel feedstock. Considering oil productivity, predictions may be made for the production of macauba biodiesel and the key inputs for the extraction, degumming, and transesterification processes, as presented in Table 7. The factors that influence the biodiesel costs include vegetable oil, taxes, demand, market structure, and competitiveness. In addition to vegetable oil, production costs encompass inputs such as alcohol and catalyst, processing, energy, maintenance, employees, and equipment depreciation [71]. Consequently, it is imperative to incorporate the prediction of the key inputs per ton produced. Lopes et al. [72] conducted a study on the economic viability of biodiesel production from Macauba in Brazil, and it was determined that all eight simulated scenarios showed potential for profitability. However, only two of these scenarios were considered competitive based on biodiesel selling prices, making them worthwhile projects. It was considered as feedstock the seed macauba oil and the alkaline transesterification process. The authors posit that the macauba is a promising biomass for utilization in biodiesel production, citing its high yield, capacity for adaptation to different ecosystems, cost-effectiveness, and the favorable valuation of its coproducts.
The viscosities and densities of macauba kernel oil are shown in Table 8, with results similar to other vegetable oils such as soybean, sunflower, and rapeseed [73,74,75,76]. Understanding how these properties change with temperature is important for using vegetable oils in biodiesel production and other fuel applications, e.g., high viscosity adversely impacts the oil flow in the system and the atomization efficiency. Thus, studying vegetable oils’ density and viscosity helps predict trends in atomization, droplet size, spray jet penetration, mixing with other fuels, combustion efficiency, and emissions [73,77].
The biodiesel composition influences its properties, so it is essential to evaluate the viscosities and densities of both the pure sample and its blends with mineral diesel for use in internal combustion engines. The densities of biodiesel and mineral diesel are regulated by ANP Resolutions Nº 920/2023 and Nº 968/2024, which set respective limits of 0.850 to 0.900 g.cm−3 and 0.815 to 0.850 g.cm−3, respectively. All results shown in Figure 8 met the established limits, with differences between 2.02% and 2.66%. Due to the influence of density on the fuel injection systems of Diesel engines, the accuracy in determining and estimating is essential. As reported by Suh and Lee [78], the density of liquid fuels, including biodiesel, diesel, and their blends, influences the initiation and pressure of injection, spray behavior, and droplet breakup in a diesel engine, affecting combustion and emissions. Generally, the mass flow rate through the injector nozzle varies with the square root of biodiesel density. Mishra et al. [79] observed that biodiesel density decreases with straight chain saturation factor and increases with unsaturation degree. Esteban et al. [73] showed that commercial biodiesel density decreases with temperature from 10 °C (0.8859 g.cm−3) to 140 °C (0.7912 g.cm−3).
ANP regulation establishes that the density range for B15 and B20 is 0.8203–0.8575 g.cm−3 and 0.8220–0.8600 g.cm−3, respectively. This is calculated using: Density_minimum at T °C = (Density_minimum (biodiesel) x (%) biodiesel) + (Density_minimum (mineral diesel) x (%) mineral diesel) and Density_maximum at T °C = (Density_maximum (biodiesel) x (%) biodiesel) + (Density_maximum (mineral diesel) x (%) mineral diesel). Both samples met the ANP limits (B15: 0.8514 g.cm−3 and B20: 0.8540 g.cm−3, Figure 9). The measurements for experimental densities, shown in Figure 10, were performed in triplicate, with R2 values of 0.9989 for B15 and 0.9996 for B20, indicating a good fit. Thus, the predictions of densities for B16, B17, B18, and B19 were calculated using the experimental values for diesel and biodiesel, and Equation (3), Table 9. The proposed method for predicting the densities of diesel-biodiesel blends eliminates the need for measurements, especially in exploratory studies.
Regarding viscosity, assessing the impact of temperature and fuel mixture on dynamic (η) and kinematic (ν) viscosities is crucial for automotive engines [80]. High viscosities result in poor atomization, longer spray penetration, and a narrow spray cone angle, whereas low viscosity contributes to wear on fuel pumps and higher leakage losses [81]. The kinematic viscosity of biodiesel and diesel, also regulated by ANP Resolutions Nº 920/2023 and Nº 968/2024, have limits of 3.0 to 5.0 mm2·s−1 (40 °C) and 2.0 to 4.5 mm2·s−1 (40 °C), respectively. All viscosities shown in Table 10 met the established limits.
Previous reports [82,83,84,85,86,87] indicate that biodiesel viscosity is influenced by the chemical characteristics of the alkyl radical, the unsaturation degree, and temperature. Knothe et al. [88] reported an increase in the biodiesel kinematic viscosity with decreasing temperature. Nogueira et al. [89] evaluated the dynamic viscosity of biodiesels from soybean, linseed, babassu, and corn, showing a decrease in viscosity with increasing temperature. The babassu biodiesel had the lowest viscosity, probably due to the high lauric acid ester (C12) content. The biodiesel from macauba kernel oil at 20 °C had a dynamic viscosity value similar to that of babassu biodiesel at the same temperature. This is probably due to their similarity in chemical profile, with macauba kernel oil also containing high content of lauric acid. As stated by Suh and Lee [78], an elevated viscosity is linked to coarser atomization. In the case of biodiesel, the presence of viscous losses during the disintegration process reduces the energy available for atomization, leading to a coarse spray pattern. Furthermore, increased viscosity of biodiesel results in carbon deposit buildup and lowers the Reynolds number in the spray zone, which hinders the development of instability during the atomization process.
The results for dynamic kinematic viscosities (see Figure 11 and Figure 12) presented very good fit, similar to what was observed for the density measurements. Consequently, the viscosities predicted for B16, B17, B18, and B19, applying the experimental values for diesel and biodiesel and Equation (4), are displayed in Table 11 and Table 12. A significant contribution of this study is the prediction of densities and viscosities of B15 to B20 blends over a wide range of temperatures. This is a notable advance since most existing models in the literature for estimating the viscosity of biodiesel samples are only applicable at a single temperature [28].

5. Conclusions

In this study, macauba kernel oil was extracted, refined, and its chemical profile evaluated. After the degumming and neutralization process, the refined oil acquired adequate quality to be used in biodiesel production. Macauba biodiesel was obtained by transesterification, and its yield was calculated using NMR spectroscopy and the signals of FAME-methyl ester (-CH3) and FAME-carbonyl (-COOCH2-). Considering macauba oil productivity, an extrapolation was presented for 1.5 tons of crushed macauba kernel applying similar yields of bench scale experiments (using 1142 g of macauba kernel) for the oil extraction, degumming, and transesterification processes. Two binary blends containing macauba biodiesel (MB) and mineral diesel (D) were formulated; one comprised of 15% (vol.) MB and 85% (vol.) D, and the other 20% (vol.) MB and 80% (vol.) D. Density, kinematic viscosity, and dynamic viscosity were measured for biodiesel, diesel, B15, and B20 across a temperature range of 20 °C to 100 °C. The density and viscosities of the binary blends B16, B17, B18, and B19 were predicted using the experimental data for diesel and biodiesel. All R2 values greater than 0.98 are indicative of optimal predictive performance regarding density and viscosity values. The proposed method for predicting biodiesel-diesel blend densities and viscosities eliminates the need for numerous measurements in exploratory studies. Furthermore, the prediction of density and viscosity of the B15 to B20 blends, including macauba biodiesel, over a wide range of temperatures, is also presented. This is a significant contribution of this study since available models for estimating the physical properties of biodiesel are only applied at a single temperature. Empirical models to determine the physicochemical properties of other binary fuel blends (biodiesel and mineral diesel) can be developed in the future. Together with appropriate government policies, these results may encourage the industry to invest in macauba oil as a biofuel feedstock.

Author Contributions

Conceptualization, B.S. and M.R.; methodology, B.S., I.F., M.D., D.B. and E.S.; software, B.S. and I.F.; validation, B.S., I.F., F.M.T.L. and M.R.; formal analysis, B.S., I.F., M.D., D.B. and E.S.; investigation, B.S., and I.F.; resources, F.A.C., T.N., F.M.T.L., C.L.C.J. and M.R.; data curation, F.A.C., T.N., F.M.T.L., C.L.C.J. and M.R.; writing—original draft preparation, B.S., I.F., M.D., D.B. and E.S.; writing—review and editing, M.R.; visualization, F.A.C., T.N., F.M.T.L., C.L.C.J. and M.R.; supervision, F.A.C., T.N., F.M.T.L., C.L.C.J. and M.R.; project administration, T.N. and M.R.; funding acquisition, F.A.C., T.N., F.M.T.L., C.L.C.J. and M.R. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by CNPq (313647/2020-8; 402757/2023-8; 310037/2023-9); FUNCAP (PS1-00186-00255.01.00/21; Research and Innovation Network on Renewable Energy, Rede VERDES, 07548003/2023); FINEP; and CAPES (Finance Code 001).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Macauba fruit and pretreatment steps.
Figure 1. Macauba fruit and pretreatment steps.
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Figure 2. Images of the apparatus and funnel used in the transesterification process.
Figure 2. Images of the apparatus and funnel used in the transesterification process.
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Figure 3. FTIR spectrum of the refined macauba kernel oil.
Figure 3. FTIR spectrum of the refined macauba kernel oil.
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Figure 4. 1H NMR spectra of the refined macauba kernel oil and assignments (CDCl3, 500 MHz). Assignments (δ, ppm): δ = 0.87 (-CH3); δ = 1.26–1.30 (-CH2)n; δ = 1.61 (CH2-CH2-COOH); δ = 2.01 (CH2-CH=CH); δ = 2.31 (CH2-COOH); δ = 4.15 and 4.30 (CH2-OCOR); δ = 5.27 (CH-OCOR); δ = 5.34 (CH=CH) [43].
Figure 4. 1H NMR spectra of the refined macauba kernel oil and assignments (CDCl3, 500 MHz). Assignments (δ, ppm): δ = 0.87 (-CH3); δ = 1.26–1.30 (-CH2)n; δ = 1.61 (CH2-CH2-COOH); δ = 2.01 (CH2-CH=CH); δ = 2.31 (CH2-COOH); δ = 4.15 and 4.30 (CH2-OCOR); δ = 5.27 (CH-OCOR); δ = 5.34 (CH=CH) [43].
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Figure 5. 13C NMR spectrum of the refined macauba kernel oil and chemical shifts (CDCl3, 125 MHz). Signals (δ, ppm): δ = 25.11–34.42 (Acyl chains: saturated, monounsaturated, and polyunsaturated); δ = 62.31 (Glycerol (Triacylglycerols)); δ = 69.08 (Glycerol (Triacylglycerols)); δ = 129.88–130.21 (double bonds region, oleyl and linoleyl); δ = 173.29 (Triacylglycerols) [11,44].
Figure 5. 13C NMR spectrum of the refined macauba kernel oil and chemical shifts (CDCl3, 125 MHz). Signals (δ, ppm): δ = 25.11–34.42 (Acyl chains: saturated, monounsaturated, and polyunsaturated); δ = 62.31 (Glycerol (Triacylglycerols)); δ = 69.08 (Glycerol (Triacylglycerols)); δ = 129.88–130.21 (double bonds region, oleyl and linoleyl); δ = 173.29 (Triacylglycerols) [11,44].
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Figure 6. 1H NMR spectrum of the biodiesel from the macauba kernel oil and assignments (CDCl3, 500 MHz). Assignments (δ, ppm): 1. δ = 0.87 (-CH3, terminal); 2. δ = 1.25 (-CH2)n; 3. δ = 1.59 (CH2-CH2-COO-); 4. δ = 1.98 (CH2-CH=CH); 5. δ = 2.26 (-COOCH2-); 6. δ = 3.62 (-CH3, methyl ester); 7. δ = 5.30 (-CH=CH-) [45].
Figure 6. 1H NMR spectrum of the biodiesel from the macauba kernel oil and assignments (CDCl3, 500 MHz). Assignments (δ, ppm): 1. δ = 0.87 (-CH3, terminal); 2. δ = 1.25 (-CH2)n; 3. δ = 1.59 (CH2-CH2-COO-); 4. δ = 1.98 (CH2-CH=CH); 5. δ = 2.26 (-COOCH2-); 6. δ = 3.62 (-CH3, methyl ester); 7. δ = 5.30 (-CH=CH-) [45].
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Figure 7. 13C NMR spectrum of the biodiesel from the macauba kernel oil and chemical shifts (CDCl3, 125 MHz). Signals (δ, ppm): δ = 29.28.11–34.22 (Aliphatic carbon (long-chain ester), -CH2-); δ = 51.47 (Methyl ester carbon, -O-CH3); δ = 129.84–130.09 (Olefin, -CH=CH-); δ = 174.36 (Carbonyl carbon, -C=O-) [45,46].
Figure 7. 13C NMR spectrum of the biodiesel from the macauba kernel oil and chemical shifts (CDCl3, 125 MHz). Signals (δ, ppm): δ = 29.28.11–34.22 (Aliphatic carbon (long-chain ester), -CH2-); δ = 51.47 (Methyl ester carbon, -O-CH3); δ = 129.84–130.09 (Olefin, -CH=CH-); δ = 174.36 (Carbonyl carbon, -C=O-) [45,46].
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Figure 8. Density (g/cm3) vs. temperature (°C) for biodiesel obtained from refined macauba kernel oil and for mineral diesel, including the regression line equations and R2.
Figure 8. Density (g/cm3) vs. temperature (°C) for biodiesel obtained from refined macauba kernel oil and for mineral diesel, including the regression line equations and R2.
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Figure 9. Temperature vs. density graph for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C.
Figure 9. Temperature vs. density graph for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C.
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Figure 10. Graph of prediction density versus experimental density for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C, including the regression line equations and R2.
Figure 10. Graph of prediction density versus experimental density for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C, including the regression line equations and R2.
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Figure 11. Graph of prediction dynamic viscosity vs. experimental dynamic viscosity for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C, including the regression line equations and R2.
Figure 11. Graph of prediction dynamic viscosity vs. experimental dynamic viscosity for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C, including the regression line equations and R2.
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Figure 12. Graph of prediction kinematic viscosity vs. experimental kinematic viscosity for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C, including the regression line equations and R2.
Figure 12. Graph of prediction kinematic viscosity vs. experimental kinematic viscosity for B15 (15% biodiesel: 85% diesel) and B20 (20% biodiesel: 80% diesel) at 20–100 °C, including the regression line equations and R2.
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Table 1. Density and viscosity of mineral diesel, biodiesel, and the kinematic viscosity ranges of mineral diesel–biodiesel blends. Adapted from [31].
Table 1. Density and viscosity of mineral diesel, biodiesel, and the kinematic viscosity ranges of mineral diesel–biodiesel blends. Adapted from [31].
FuelDensity (g/cm3), 40 °C Viscosity (cSt), 40 °CKinematic Viscosity Range of Blends (cSt), 40 °C
Mineral diesel0.81722.8821-
Sunflower oil biodiesel0.86724.03033.4322–3.7926 (2–75 vol.% biodiesel) 1
Soybean oil biodiesel0.86773.97133.4315–3.7786 (2–75 vol.% biodiesel) 2
Corn oil biodiesel0.86724.17693.4589–3.9091 (2–75 vol.% biodiesel) 3
Canola oil biodiesel0.86584.34013.4368–4.0318 (2–75 vol.% biodiesel) 4
Cottonseed oil
biodiesel		0.86684.05683.4384–3.8250 (2–75 vol.% biodiesel) 5
Waste palm oil
biodiesel		0.85764.28023.4501–4.0717 (2–75 vol.% biodiesel) 6
1 Blend = Sunflower oil biodiesel: Mineral diesel. 2 Blend = Soybean oil biodiesel: Mineral diesel. 3 Blend = Corn oil biodiesel: Mineral diesel. 4 Blend = Canola oil biodiesel: Mineral diesel. 5 Blend = Cottonseed oil biodiesel: Mineral diesel. 6 Blend = Waste palm oil biodiesel: Mineral diesel.
Table 2. Yields from macauba kernel oil extraction processes.
Table 2. Yields from macauba kernel oil extraction processes.
Kernel Ground (g)Extracted Oil (mL)Yield 1
7037.954
10569.066
7048.469
3515.745
3515.544
58.6422.839
145.3876.753
61.6224.640
64.6224.137
1 mL oil/100 g dry biomass.
Table 3. Results of the physicochemical parameters of the macauba kernel oil 1.
Table 3. Results of the physicochemical parameters of the macauba kernel oil 1.
ParameterResult
Acidity index (mg NaOH/g)7.49
Saponification index (mg KOH/g) 2565.44
Density at 20 °C (kg/m3)919.9
Kinematic viscosity at 40 °C (mm2/s)27.72
1 The oil samples from the nine extractions were mixed. 2 AOCS CD3-25 methodology of the American Oil Chemists’ Society [38].
Table 4. Acidity indexes after the refining process (degumming and neutralization).
Table 4. Acidity indexes after the refining process (degumming and neutralization).
Macauba Kernel OilAcidity Index (mg NaOH/g)
Before the refining process 7.49
After the degumming process2.83
After the neutralization process0.13
Table 5. Absorption bands and assignments of the refined macauba kernel oil FTIR spectrum.
Table 5. Absorption bands and assignments of the refined macauba kernel oil FTIR spectrum.
Wavenumber (cm−1)Assignments
2956Asymmetrical stretching (νas CH2 and CH3)
C-H stretching [11]
2925Asymmetrical stretching (νas CH2)
(C-H) cis bonds [11,41,42]
2854Symmetrical stretching (νs CH2) [11,41,42]
1742Ester carbonyl (-C-C=O-) group [11,41,42]
1464In-plane bending or scissoring (δs CH2) [11,41,42]
1376Symmetrical bending vibration δs CH3 [11,41,42]
1232C–O stretching in O–C(=O)–CH2 of ester
C–O stretching [11]
1158Out-of-plane bending or wagging (ω CH2) or
Out-of-plane bending or twisting (τ CH2) [11,41,42]
1110ν C-O ester [11,41,42]
964C–H bending of a trans double bond
C–H bending [11]
888=CH wagging vibration in the plan
=CH wagging vibration [11]
721In-plane bending or rocking (ρ CH2) [11,41,42]
Table 6. Fatty acid methyl esters (FAME) from macauba kernel oil and their contents (%).
Table 6. Fatty acid methyl esters (FAME) from macauba kernel oil and their contents (%).
Fatty Acid Methyl Esters Content (%)
Caprylic acid methyl esterCH3(CH2)6COOCH35.57
Capric acid methyl esterCH3(CH2)8COOCH34.36
Lauric acid methyl esterCH3(CH2)10COOCH344.45
Myristic acid methyl esterCH3(CH2)12COOCH311.87
Palmitic acid methyl esterCH3(CH2)14COOCH38.32
Stearic acid methyl esterCH3(CH2)16COOCH32.88
Oleic acid methyl esterCH3(CH2)7CH=CH(CH2)7COOCH319.84
Linoleic Acid Methyl EsterCH3(CH2)3(CH2CH=CH)2(CH2)7COOCH32.71
Table 7. Prediction of key inputs for extracting and degumming macauba kernel oil and biodiesel production.
Table 7. Prediction of key inputs for extracting and degumming macauba kernel oil and biodiesel production.
Oil Extraction Process
Macauba kernel crushed (mass)Oil extracted (volume)Hexane (volume) a
1142 g689 mL5519 mL
1.5 ton * 0.91 m3 *7.25 m3 *
Degumming Process
Oil extracted (volume)Water (volume) bOil degummed (volume)
0.000689 m30.000345 m30.000416 m3
0.91 m3 *0.45 m3 *0.55 m3 *
Transesterification Process
Oil degummed (volume)MeOH (volume)KOH (mass)Water (volume) cBiodiesel (volume)
0.000416 m30.000203 m30.000013 kg0.000063 m30.000373 m3
0.55 m3 *0.27 m3 *0.02 kg *0.08 m3 *0.49 m3 *
a Hexane is recovered by distillation. b Water volume used in the degumming process. c Water volume used in the transesterification process. * Predictions for 1.5 t were based on experiment using 1142 g of macauba kernel.
Table 8. Refined macauba kernel oil viscosities and densities at 20, 40, and 100 °C.
Table 8. Refined macauba kernel oil viscosities and densities at 20, 40, and 100 °C.
Temperature (°C)η (mPa·s) 1ν (mm2/s) 2ρ (g/cm3)
2059.8165.020.9199
4025.0727.720.9041
1005.316.160.8590
1 Dynamic viscosity. 2 Kinematic viscosity.
Table 9. Densities predicted for B16, B17, B18, and B19 between 20–100 °C using Equation (3).
Table 9. Densities predicted for B16, B17, B18, and B19 between 20–100 °C using Equation (3).
SampleTemperature
20 °C30 °C40 °C50 °C60 °C80 °C90 °C100 °C
ρ (g/cm3)ρ (g/cm3)ρ (g/cm3)ρ (g/cm3)ρ (g/cm3)ρ (g/cm3)ρ (g/cm3)ρ (g/cm3)
B160.85190.84440.83780.83030.82400.81040.80380.7966
B170.85210.84460.83800.83050.82420.81060.80400.7967
B180.85240.84480.83820.83070.82440.81080.80410.7969
B190.85260.84500.83840.83090.82460.81100.80430.7971
Table 10. Dynamic and Kinematic viscosities for biodiesel from refined macauba kernel oil, diesel, B15, and B20.
Table 10. Dynamic and Kinematic viscosities for biodiesel from refined macauba kernel oil, diesel, B15, and B20.
SampleResultTemperature
20 °C30 °C40 °C50 °C
η c (mPa·s)ν d
(mm2/s)		η (mPa·s)ν
(mm2/s)		η (mPa·s)ν
(mm2/s)		η (mPa·s)ν
(mm2/s)
BiodieselAverage3.75884.31612.97013.44052.40142.80651.98902.3533
SD *0.24790.27700.18580.20930.16380.18580.13360.1366
DieselAverage3.56054.19382.78743.31192.25142.69821.86682.2689
SD *0.00060.00620.00200.00050.01170.01270.00080.0222
B15 aAverage3.55044.17022.86783.39082.29992.74311.88052.2634
SD *0.00240.00290.00170.00210.01910.02150.00050.0006
B20 bAverage3.53704.14882.77722.95192.26922.70581.88422.2655
SD *0.00200.00230.00110.57630.02340.02620.00040.0005
SampleResultTemperature
60 °C80 °C90 °C100 °C
η
(mPa·s)		ν
(mm2/s)		η (mPa·s)ν
(mm2/s)		η (mPa·s)ν
(mm2/s)		η (mPa·s)ν
(mm2/s)
BiodieselAverage1.71652.03891.27571.53761.13801.39130.99711.2310
SD *0.03510.04550.02910.05030.01560.01860.00520.0062
DieselAverage1.56931.91241.18561.46821.04651.30640.92241.1617
SD *0.00010.00020.02250.02590.00020.00020.00020.0003
B15Average1.65252.00091.21821.53131.06431.32350.93981.1789
SD *0.00090.00110.00010.05200.00010.00010.00030.0004
B20Average1.58571.92351.20111.48071.05201.30610.93591.1724
SD *0.00020.00010.00030.00040.00350.00020.01160.0141
* Standard deviation. a 15% biodiesel + 85% diesel. b 20% biodiesel + 80% diesel. c Dynamic viscosity. d Kinematic viscosity.
Table 11. Dynamic viscosities predicted for B16, B17, B18, and B19 samples across 20–100 °C using Equation (5).
Table 11. Dynamic viscosities predicted for B16, B17, B18, and B19 samples across 20–100 °C using Equation (5).
SampleTemperature
20 °C30 °C40 °C50 °C60 °C80 °C90 °C100 °C
η (mPa·s)η (mPa·s)η (mPa·s)η (mPa·s)η (mPa·s)η (mPa·s)η (mPa·s)η (mPa·s)
B163.59222.81672.27541.88631.59291.20001.06120.9344
B173.59422.81852.27691.88761.59431.20091.06210.9351
B183.59622.82032.27841.88881.59581.20181.06300.9359
B193.59812.82212.27991.89001.59731.20271.06390.9366
Table 12. Kinematic viscosities predicted for B16, B17, B18, and B19 across 20–100 °C using Equation (5).
Table 12. Kinematic viscosities predicted for B16, B17, B18, and B19 across 20–100 °C using Equation (5).
SampleTemperature
20 °C30 °C40 °C50 °C60 °C80 °C90 °C100 °C
ν (mm2/s)ν (mm2/s)ν (mm2/s)ν (mm2/s)ν (mm2/s)ν (mm2/s)ν (mm2/s)ν (mm2/s)
B164.21343.33252.71552.28241.93261.47931.32001.1728
B174.21463.33382.71662.28331.93391.48001.32081.1735
B184.21583.33502.71772.28411.93521.48071.32171.1742
B194.21703.33632.71882.28501.93641.48141.32251.1748
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Sousa, B.; Figueredo, I.; Brito, D.; Dorneles, M.; Sousa, E.; Nascimento, T.; Cunha, F.A.; Luna, F.M.T.; Cavalcante, C.L., Jr.; Rios, M. Macauba Kernel Oil: Refining, Transesterification, and Density/Viscosity of Blends B15 to B20 with Mineral Diesel. Processes 2025, 13, 2637. https://doi.org/10.3390/pr13082637

AMA Style

Sousa B, Figueredo I, Brito D, Dorneles M, Sousa E, Nascimento T, Cunha FA, Luna FMT, Cavalcante CL Jr., Rios M. Macauba Kernel Oil: Refining, Transesterification, and Density/Viscosity of Blends B15 to B20 with Mineral Diesel. Processes. 2025; 13(8):2637. https://doi.org/10.3390/pr13082637

Chicago/Turabian Style

Sousa, Bruna, Igor Figueredo, Débora Brito, Mauricio Dorneles, Eva Sousa, Tassio Nascimento, Francisco Assis Cunha, Francisco Murilo T. Luna, Célio L. Cavalcante, Jr., and Maria Rios. 2025. "Macauba Kernel Oil: Refining, Transesterification, and Density/Viscosity of Blends B15 to B20 with Mineral Diesel" Processes 13, no. 8: 2637. https://doi.org/10.3390/pr13082637

APA Style

Sousa, B., Figueredo, I., Brito, D., Dorneles, M., Sousa, E., Nascimento, T., Cunha, F. A., Luna, F. M. T., Cavalcante, C. L., Jr., & Rios, M. (2025). Macauba Kernel Oil: Refining, Transesterification, and Density/Viscosity of Blends B15 to B20 with Mineral Diesel. Processes, 13(8), 2637. https://doi.org/10.3390/pr13082637

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