Application of calcined waste cupuaçu (Theobroma grandiflorum) seeds as a low-cost solid catalyst in soybean oil ethanolysis: Statistical optimization

Energy Conversion and Management 200 (2019) 112095

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Application of calcined waste cupuaçu (Theobroma grandiflorum) seeds as a low-cost solid catalyst in soybean oil ethanolysis: Statistical optimization

T

Iasmin Maquiné Mendonçaa, Flavia Lopes Machadoa, Cláudia Cândida Silvaa, Sérgio Duvoisin Juniora, Mitsuo Lopes Takenob, Paulo José de Sousa Maiac, Lizandro Manzatob, ⁎ Flávio Augusto de Freitasb, Universidade do Estado do Amazonas – UEA, Av. Darcy Vargas, 1.200 - Parque Dez de Novembro, Manaus, AM 69050-020, Brazil Instituto Federal do Amazonas – IFAM/CMDI, Av. Gov. Danilo de Matos Areosa, 1731-1975 – Distrito Industrial, Manaus, AM 69075-35, Brazil c Universidade Federal do Rio de Janeiro – UFRJ, Av. Aluízio da Silva Gomes, 50 – Novo Cavaleiro, Macaé, RJ 27930-560, Brazil a

b

A R T I C LE I N FO

A B S T R A C T

Keywords: Cupuaçu seeds Biodiesel Ethanolysis Statistical optimization Central composite design Response surface methodology

In this work, waste cupuaçu seeds were calcined for 4 h at 800 °C and evaluated as a heterogeneous catalyst for the biodiesel synthesis. The catalyst (CCS) was characterized by X-ray powder diffraction (XRD), wavelength dispersive x-ray fluorescence (WDXRF), Fourier transform infrared (FT-IR) spectroscopy, thermogravimetric and differential thermal analysis (TG-DTA) and soluble alkalinity. The catalytic activity was evaluated by CCS-catalyzed ethanolysis of soybean oil and the process was optimized using response surface methodology (RSM) and analysis of variance (ANOVA). The significance of the different process parameters and their combined effects were established through a central composite design (CCD) and the optimum process (catalyst loading of 10% (w/w) relative to oil mass, reaction time 8 h, ethanol:oil molar ratio 10:1 and temperature 80 °C) resulted in a conversion of 98.36% with good agreement with predicted conversion, 97%. The catalyst was recycled, maintaining its great catalytic activity and resulting in conversions close to 98% in the first two cycles. The high potential of CCS as a catalyst for biodiesel production was demonstrated.

1. Introduction Biodiesel has attracted a lot of attention as a promising substitute for conventional diesel because of its attractive features. It is renewable, biodegradable, non-toxic, carbon neutral, emits low exhaust emissions, has a higher flash point and excellent lubricity and is also environmentally acceptable in diesel engines without the need for many engine changes [1,2]. The starting materials for the preparation of biodiesel are vegetable oils or animal fats and alcohol for producing alkyl esters of fatty acids. It is worth noting that most of the studies on biodiesel synthesis prefer to use the methanolysis route, since the size of the alcohol chain greatly influences the conversion percentage [3,4]. However, based on cost and performance considerations, the ethanol can be obtained from renewable agricultural resources, it is non-toxic and it forms stable fatty acid esters [5,6]. Therefore, better suited for the production of this biofuel. Biodiesel synthesis is usually catalyzed by homogeneous, heterogeneous or enzymatic processes, as well as supercritical technology [7,8]. There are several methods for producing the biofuel, but

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transesterification is the easiest and most economical approach to produce it [9,10]. The transesterification process can be carried out in the presence of basic catalysts or acids. In transesterification with homogeneous basic catalysis, the process is usually catalyzed by alkali metal hydroxides, such as sodium or potassium, showing high catalytic activity resulting in high conversion rates of oil or fat into biodiesel. However, homogeneous catalysis systems have many disadvantages, such as difficulties in separating the catalyst from the organic phase, the need to use a large amount of water, which consequently results in the production of a considerable amount of wastewater, as well the production of soaps [11,12]. An alternative method for biodiesel production is the use of solid catalysts. The benefits of heterogeneous catalysis have been emphasized as a solution for homogeneous catalysis especially for feedstocks containing high free fatty acids, since solid basic catalysts prevent or reduce the amount of soap produced by neutralizing free fatty acids or saponification of triglycerides, which simplifies post-treatment (separation and purification) processes and avoids the production of toxic waste water [3,13]. Due to these advantages, the number of studies on

Corresponding author. E-mail address: freitas.fl[email protected] (F.A. de Freitas).

https://doi.org/10.1016/j.enconman.2019.112095 Received 9 July 2019; Received in revised form 20 September 2019; Accepted 21 September 2019 0196-8904/ © 2019 Elsevier Ltd. All rights reserved.

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reactions of soybean oil by ethanolysis. In addition, to optimize the biodiesel synthesis process by design of experiment (DOE) and response surface methodology (RSM).

obtaining biodiesel through the heterogeneous catalysis transesterification has increased in the last decade [14,15]. New solid catalysts are being studied in order to overcome the disadvantages of using soluble catalysts (homogeneous catalysis) and reduce the environmental and financial costs of biodiesel production [16–19]. Therefore, the use of residual materials for the synthesis of heterogeneous catalysts or catalytic support has great applicability in the search for a sustainable process. Oysters shells [20,21], eggshells [22–24], animal bones [25–27], fish scales [28] and fruit peels [3,29–31] have been successfully used as effective catalysts in the conversion of oil to biodiesel. Betiku et al. obtained an efficient catalyst from the calcination of banana peels at 700 °C for 4 h, where the catalytic activity was evaluated by transesterification of the Bauhinia monandra seed oil and the process optimized by central composite design (CCD). The optimized conditions (catalyst loading of 2.75% (w/w) relative to oil mass, reaction time 69.02 min, alcohol:oil molar ratio 7.6:1 and temperature 65 °C) yielded conversions higher than 98%, attributed to the high potassium concentration present in the material (99.73% w/w). Mendonça et al. [3] synthesized biodiesel by soybean oil transesterification applying calcined tucuma peels (800 °C por 3 h) as a heterogeneous catalyst, composed mainly of potassium (63.81% w/w). The transesterification conditions were optimized at catalyst loading of 1% (w/w) relative to oil mass, reaction time 4 h, alcohol:oil molar ratio 15:1 and temperature 80 °C, resulting in a high conversion (97.3%). Betiku et al. [30] used Musa paradisiacal peels as a heterogeneous base catalyst and its catalytic activity was evaluated by transesterification of the yellow oleander oil. Applying response surface methodology (RSM), the reactional conditions were optimized: methanol:oil ratio 0.3 (v/v), reaction time 1.5 h and catalyst loading of 3.0% (w/v), resulting in conversions higher than 95%. They also noted that the catalyst mainly consisted of potassium (54.73% w/w). Gohain et al. [31] obtained an effective renewable heterogeneous catalyst for biodiesel production through burning Musa balbisiana Colla peels followed by calcination at 700 °C for 4 h. They observed that the main constituents were K (41.37%) and Ca (36.08%). Reaction conditions were optimized in catalyst loading of 2% (w/w) relative to oil mass reaction time 3 h, methanol:oil molar ratio 6:1 and temperature 60 °C, resulting in 100% conversion of waste cooking oil to biodiesel. Balajii and Niju [32] calcined the peduncle of the Musa acuminate for 4 h at 700 °C and applied as a catalyst to obtain methyl esters from the Ceiba pentandra oil. The process was also optimized by CCD and RSM (2.68% (w/w) of catalyst, methanol:oil molar ratio 11.46:1 at 65 °C in 106 min), where 98.73% biodiesel conversion was achieved and potassium was responsible for the high catalytic activity. To the best of our knowledge, data on the use of heterogeneous catalysts obtained from renewable sources for the ethanolysis process were not found in the literature. The cupuaçu tree (Theobroma grandiflorum) is a native tree of the Amazon, cultivated mainly in the north and northeast of Brazil [33]. It is considered one of the best and most promising fruit trees. The name of its fruit, cupuaçu, comes from the indigenous language Tupi (kupu = similar to cocoa, uasu = big), and its pulp is widely used as an ingredient in products such as juices, ice cream, cream, candies, liqueurs and other culinary processing. Cupuaçu produces a large amount of waste in its industrial production, mainly due to the volume of seeds discarded in the process. In 2006 alone, more than 10 thousand tons of cupuaçu were produced [34], with the seeds constituting about 30% of the weight of the fruit and clearly producing a large amount of waste. This waste is discarded with no viable applications and, in most situations, used by farmers as animal feed [33,35]. The use of these seeds can generate income for poor riverine and northeastern families, as well as providing environmental benefits. Therefore, the objective of this work is to provide a use for cupuaçu seeds, to obtain an efficient and low-cost heterogeneous catalyst by thermal treatment, to characterize it and apply it in transesterification

2. Experimental 2.1. Reactants All materials were purchased and used without further modifications: Ethanol P.A. (Dinâmica), hexane P.A. (Nuclear), refined soybean oil (Concórdia) (Lot: LR00709; manufacturing: 10/07/2018; validity: 06/01/2019), NaCl (Cromato Produtos Químicos), CDCl3 (Cambridge Isotope Laboratories) and anhydrous Na2SO4 (Dinâmica) were applied in the biodiesel synthesis. Ethyl ether P.A. (Dinâmica), ethyl heptadecanoate (sigma–aldrich) and NaOH (Dinâmica) were used in the characterization of soybean oil by acidity index. Cupuaçu fruits were purchased in the months of March to June 2018 at “Nossa chácara” farm, located at the Ipiranga branch, Industrial District – Extension II (Amazonas, Brazil). 2.2. Catalyst obtained from cupuaçu seeds (CCS) 2.2.1. Preparation The cupuaçu fruits were cut with scissors and the seeds were removed manually. Hereafter, they were washed, dried in an oven with air circulation at 100 °C for 24 h and ground in a knife mill. Then, the dried and ground seeds were calcined for 4 h at 800 °C in a Quimis muffle (Q318M21) with a heating rate of 10 °C.min−1 in airflow [3]. The calcinated seeds yield 2.6% of ash and were named as the CCS (calcined cupuaçu seeds) catalyst, which were applied as a solid catalyst in transesterification reactions by ethanolysis. 2.2.2. Characterization Wavelength dispersive X-ray fluorescence (WDXRF). In order to quantify the elements present in the CCS catalyst, X-ray fluorescence analysis was performed by Wavelength dispersive methodology using Rigaku equipment (Supermini) [36]. Fourier transform infrared spectroscopy – attenuated total reflectance (FTIR-ATR). CCS was characterized by spectroscopy in the infrared region with Fourier transform (FTIR) using a Shimadzu spectrophotometer (IRAffinity – 1S) and IR spectra obtained were recorded in the region of 4000–650 cm−1. X-ray diffraction (XDR). In order to observe the crystalline phases present in the CCS, the X-ray diffraction technique (XRD) was applied on a BRUKER D2 Phaser diffractometer (Bruker AXS, Karls-ruhe, Germany), equipped with a 0.6 mm slit, 3 mm knife, Cu radiation tube (kα 1.5406 Å, 30 kV, 10 mA), scan range of 5–100°. Phases were identified using X'Pert HighScore Plus software. Thermogravimetric analysis (TGA). Thermal stability of calcined CCS was evaluated using TGA 50 equipment (Shimadzu, Japan). Temperature program ranged from 28 to 900 °C using a heating rate of 10 °C/min. The tests were carried out in nitrogen atmosphere (100 mL/min) using ≈10 mg of sample in a platinum crucible. Soluble alkalinity. 0.3 g of the CCS catalyst was weighed into a beaker and 30 mL of distilled water was added, remaining under stirring for 48 h under ambient conditions. The mixture was centrifuged to remove the solid and titrated with 0.1 M HCl solution, the alkalinity being determined by the number of mmol HCl required to neutralize 1 g of the leached material in aqueous medium [37]. Analysis done in triplicate. 2

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2.3. Soybean oil characterization

oil in biodiesel in the investigated experimental range.

2.3.1. Acidity index The acidity index is a very important parameter for the production of biodiesel, since oils with high acidity can neutralize the basic catalysts, forming soaps and emulsions during the purification process. This was quantified by titration, according to Reis et al. [4]. Analysis done in triplicate.

2.5. Catalyst activity evaluation In a typical laboratory-scale transesterification reaction, 1 g of soybean oil was added in a 50 mL flask. Then, the catalyst and the ethanol were added in different catalyst loading and ethanol:oil molar ratio in order to comply with the experimental design CCD (Table 2). The flask was immersed in a constant temperature mineral oil bath equipped with a magnetic stirrer and reflux condenser, maintaining the conditions set by the experimental design. The reaction time was measured from when the oil bath reached the desired temperature. After the transesterification reaction was complete, the catalyst was separated by centrifugation, and the reaction mixture was placed in a separatory funnel to separate the phases using 20% (w/v) NaCl solution (10 mL) for better separation, and hexane (4 mL) as organic phase. Anhydrous sodium sulfate was used to remove any trace of water. Therefore, the reaction medium was filtered, and the hexane was evaporated under reduced pressure, resulting in the biodiesel. The conversion of the soybean oil into ethyl esters was determined by 1H NMR (Bruker Avance III 300 MHz/54 mm), where the direct method uses the areas of the signals corresponding to the methylene groups of the ester (OeCH2) and methylene α to carbonyl (CH2eC]O), present in both unreacted oil and synthesized ester, were measured and the conversion percentage was obtained [39].

2.3.2. Quantification of fatty acid ethyl esters by gas chromatography Gas chromatography (VARIAN, 3800) fitted with a flame ionization detector (280 °C/min) was used for analysis of the fatty acid ethyl esters (FAEE) present in the biodiesel, where ethyl heptadecanoate was used as the internal standard. The inlet temperature was set at 250 °C. The inlet mode used was split ratio of 50:1 while nitrogen and helium were used as make-up gases. The column oven initial temperature was 150 °C, which was then increased to 250 °C at 4 °C/min. A DB5 column (30 m × 0.25 mm × 0.25 µm) was used [38]. 2.4. Experimental design The catalytic activity of CCS was evaluated by the transesterification of soybean oil with ethanol without any catalyst activation. On top of that, the biodiesel synthesis conditions were optimized using central composite design (CCD) combined with response surface methodology (RSM). The design matrix was the factorial 2 k with two levels (−1 and 1) and k = 4 (see Table 1). They were randomized to reduce the variability effect in the observed response. Temperature (XT), reaction time (XRT), ethanol:oil molar ratio (XMR) and catalyst loading (XCAT) were used as independent variables. As a dependent variable and conversion response, the conversion of the oil into biodiesel (% C) was applied. In this matrix a factorial 24 was employed with the central point in triplicate to test the repeatability of the method, bringing forth 19 experimental conditions. In Table 2, it is possible to visualize the complete matrix of the experiments performed, together with the experimental and predicted results. In order to correlate the response variable with the independent variables, multiple regressions were used to fit the coefficient of the second-order polynomial model of the response. The quality of this fit to the mathematical model was evaluated using a test of significance and analysis of variance (ANOVA). The response model in relation to the studied parameters is given by Eq. (1).

2.6. Leaching and catalyst reuse A stable catalyst allows its reuse by reducing biodiesel production costs. Thus, the stability of CCS was verified through its reuse, where it was used in successive reactions under optimized conditions. Therefore, the CCS stability was verified by a leaching test according to the methodology applied by Sharma et al. [37]. Under optimized reaction conditions, only the CCS catalyst and ethanol were added in a 50 mL flask, remaining under constant stirring and temperature (80 °C) for 8 h. Then, the catalyst was separated by centrifugation and the applied ethanol was added in another 50 mL flask with soybean oil (ethanol:oil molar ratio 10:1) in order to observe the existence of a catalyst leached in the ethanol, which would result in a conversion influenced by homogeneous catalysis. In this new step, the alcoholic solution and the oil remained for 8 h under constant stirring and a temperature of 80 °C and after purification, the obtained oil was analyzed by 1H NMR. In order to measure the CCS reusability, the catalyst was recovered by centrifugation after the reactions, washed with ethanol, and successively applied to the transesterification of the soybean oil under optimized reaction conditions: ethanol:oil molar ratio 10:1, catalyst loading of 10% (w/w), reaction time 8 h and 80 °C.

Y= β0 + β1 XT + β2 XMR + β3 XRT + β4 X CAT + β12 XT XMR + β13 XT XRT + β14 XT X CAT + β23 XMR XRT + β24 XMR X CAT

(1)

where Y is the predicted response (% conversion), β0 is the intercept term, β1, β2, β3 e β4 are the linear coefficients, β12, β13, β14, β23, β24 e β34 are the interactive coefficients, and XT, XMR, XTR e XCAT are the coded independent variables. Statistical analysis of the collected data was conducted using the 10 software (StatSoft Inc., Tulsa, OK, EUA) and Minitab 17 software after running the design of experiments. The ANOVA was applied in order to obtain the individual influence of each of the parameters, as well as the influence of the relation of two or more of them, taking into account the interactions on the conversion of the

3. Results and discussions 3.1. Soybean oil characterization Oils with high acidity can neutralize catalysts such as the CCS. Therefore, the soybean oil used in this work had a low acidity index (0.37 ± 0.08 mg KOH/g), being within the norms established by the National Petroleum Agency – ANP (Brazil). According to Zagonel [40], the ideal oil for the production of biodiesel must have acidity below 2 mg KOH/g, otherwise it will need a pretreatment to eliminate the free fatty acids, since they can neutralize basic active sites or produce carboxylates. The fatty acid composition of the commercial soybean oil was determined after complete conversion of oil into their corresponding fatty acid ethyl esters. According to the gas chromatography analysis, the fatty acid composition profile of the soybean oil is given in Table 3, where it can be observed that soybean oil is mainly composed of unsaturated chains and the profile is similar to that presented by other

Table 1 Experimental design matrix of the soybean oil ethanolysis. Variables

Temperature (XT) Reaction time (XRT) Molar ratio ethanol:oil (XMR) Mass ratio of catalyst to oil (XCAT)

Level −1

0

1

40 °C 4h 10:1 1%

60 °C 6h 15:1 5.5%

80 °C 8h 20:1 10%

3

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Table 2 CCD experimental design and experimental and predicted results for soybean oil transesterification with CCS as catalyst. Runs

XT (°C)

XRT (h)

XMR (mol/mol)

XCAT (% w/w)

Conversion (%)

Predicted (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

80 40 80 40 80 40 80 40 60 60 60 80 40 80 40 80 40 80 40

8 8 4 4 8 8 4 4 6 6 6 8 8 4 4 8 8 4 4

20:1 20:1 20:1 20:1 10:1 10:1 10:1 10:1 15:1 15:1 15:1 20:1 20:1 20:1 20:1 10:1 10:1 10:1 10:1

10 (1) 10 (1) 10 (1) 10 (1) 10 (1) 10 (1) 10 (1) 10 (1) 5.5 (0) 5.5 (0) 5.5 (0) 1 (−1) 1 (−1) 1 (−1) 1 (−1) 1 (−1) 1 (−1) 1 (−1) 1 (−1)

98.36 65.49 89.36 71.72 96.82 73.59 91.21 65.75 72.58 71.95 72.10 81.74 69.23 72.50 67.05 75.86 59.76 71.20 52.50

94.58 69.47 89.74 69.00 98.73 69.87 91.09 66.60 74.59 74.59 74.59 80.93 67.43 74.31 65.18 76.73 59.48 67.32 54.43

(1) (−1) (1) (−1) (1) (−1) (1) (−1) (0) (0) (0) (1) (−1) (1) (−1) (1) (−1) (1) (−1)

(1) (1) (−1) (−1) (1) (1) (−1) (−1) (0) (0) (0) (1) (1) (−1) (−1) (1) (1) (−1) (−1)

(1) (1) (1) (1) (−1) (−1) (−1) (−1) (0) (0) (0) (1) (1) (1) (1) (−1) (−1) (−1) (−1)

Table 3 Fatty acid composition of soybean oil. Fatty Acid

Composition (wt.%)

Myristic (C14:0) Palmitic (C16:0) Stearic (C18:0) Oleic (C18:1) Linoleic (C18:2) Linolenic (C18:3) Behenic (C22:0) Others

0.4 13.9 6.9 25.5 43.5 6.4 0.3 4.1

studies [41]. 3.2. CCS catalyst characterization 3.2.1. WDXRF The elemental analysis of the CCS catalyst was obtained by WDXRF and the results are shown in Table 4, where this analysis showed that this material consists mainly of alkaline and alkaline earth metals with potassium (K), phosphorus (P) and magnesium (Mg) the main elements. Based on these results, it is fair to speculate that K has a critical influence on catalyst activity. Some studies also observed that K was the main element in the catalysts obtained from renewable resources, being credited as responsible for the excellent activity in the transesterification reactions with methanol [3,29,32]. 3.2.2. FTIR-ATR In order to characterize the catalyst obtained after calcination at 800 °C (CCS), the spectroscopy in the infrared region was employed (Fig. 1). The reused CCS was also analyzed with the objective of verifying changes in the material composition. In the infrared spectrum of CCS (Fig. 1) the presence of an intense band around 1032 cm−1 can be seen, characteristic of sulfate, carbonate and phosphate groups [3,42–44]. In 1393 and 1512 cm−1 bands related to C-O bond are found [45], which could be derived from CO32− [3] or CO2 released during calcination, which may be chemisorbed on the alkali compounds Table 4 Elemental composition of CCS obtained by WDXRF. Elements

K

P

Mg

Ca

S

Na

Outros

% (w/w)

54.76

18.80

17.57

3.61

2.97

1.52

< 1

Fig. 1. a) FTIR spectra of obtained catalysts (CCS). b) Enlargement of the region from 1600 to 650 cm−1 to observe changes in CCS reuse.

4

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Fig. 3. Thermogravimetric analysis of CCS.

ashes at 500, 800, 1000 e 1200 °C and they also observed a higher alkalinity for the ash obtained at 800 °C (2.6 mmol g−1 of catalyst). Hence, there is an indication of low leaching of CCS in protic solvents at ambient conditions.

Fig. 2. X-ray diffraction of CCS after calcination for 4 h at 800 °C.

present in the catalyst surface [46]. The small band present at 729 cm−1 is characteristic of the Mg-O bond [47].

3.3. Catalytic activity evaluation

3.2.3. XRD The X-ray diffraction that characterizes the structural properties of the crystalline phases formed by the calcination of cupuaçu seeds is presented in Fig. 2, where the phases observed in the CCS were compared with the functional groups observed by FTIR (Fig. 1) and elemental analysis (Table 4). Analyzing the most intense peaks to determine the phases formed in the calcination of the material, it was possible to observe the presence of the main elements of the catalyst identified in the WDXRF analysis, where potassium is present in the following forms KCaPO4, K2SO4 e KNaSO4 (Fig. 2). These groups were also confirmed by FTIR-ATR (Fig. 1). According to Wu et al. [48], during calcination of K and P-rich biomass KPO3 formation may occur at low temperatures. When calcium compounds are present, KPO3 reacts with them to form calcium phosphate (KCaPO4), as observed by XRD. The MgO phase was observed by XRD and also confirmed by infrared analysis, with magnesium being one of the main elements in CCS. Using the X'Pert HighScore Plus software the presence of carbonates was also observed. However, the relative peaks are less intense in the diffractogram and are superimposed. Several studies have reported the use of MgO and K, P and Ca rich compounds as heterogeneous catalysts [3,32,47].

In order to evaluate the catalytic activity of CCS, as well as to obtain the best reaction conditions for a higher catalytic activity and, consequently, higher conversion of soybean oil to biodiesel, the parameters ethanol:oil molar ratio, temperature, catalyst loading and reaction time were varied. The conversion obtained for the productivity response at the experimental design points is shown in Table 2. The conversion of soybean oil to biodiesel ranged from 52.50 to 98.36%, where CCS catalytic activity is probably related to high potassium concentration (Table 4). The conversion values are separated by a difference of 45.86%, which clearly demonstrates that the conditions chosen for carrying out the transesterification reactions, such as the catalyst loading and temperature, were decisive for the performance of this process and influencing it considerably. Evaluating each parameter separately, it can be seen in the Pareto graph (Fig. S2) that temperature (XT) was the most influential parameter for this transesterification process using CCS as a catalyst. Studies show that reaction temperatures lower than the boiling point of ethanol (78 °C) result in low conversions [50]. By analyzing runs 1 and 2 (Table 2), it can be seen that the temperature change from 80 to 40 °C resulted in a conversion reduction of approximately 33%. Mendonça et al. [3] also observed a significant increase in conversion by varying the temperature from 40 to 80 °C when applying calcined tucumã peels in transesterification reactions. This parameter was also highly significant in sunflower oil ethanolysis CaO-catalyzed, where temperatures higher than 75 °C showed a better response [51]. The catalyst loading (XCat) also proved quite relevant to the process presented in our study. Comparing runs 6 and 17 (Table 2), where the catalyst loading was varied from 10 to 1% (w/w) relative to oil mass, respectively, a considerable increase in conversion is observed. Calcined banana peels, composed mainly of potassium, were applied as a catalyst in the transesterification of Bauhinia monandra seed oil. Increasing catalyst mass was found to contribute significantly to the conversion [29]. For calcined tucumã peels, also with high potassium concentration, there was a slight reduction in conversion with increasing catalyst loading from 1 to 10%, probably caused by the increase in the viscosity of the reaction medium [3]. Other studies show that even applying amounts greater than 10% of basic solid catalyst and methanol do not result in similar conversions to those presented by CCS [41]. The reaction time (XTR) showed a slight significance (Fig. 3), where increasing from 4 to 8 h in most experiments resulted in a conversion increase of

3.2.4. TGA The thermal stability of the CCS was verified by the TGA technique. The thermogravimetric (TG) and first derivative thermogravimetry (DTG) curves can be observed in Fig. 3, where it is possible to observe that the CCS was stable up to 360 °C, a temperature much higher than those used in the transesterification process. TGA and DTG curves show a small mass loss in the range from 30 to 360 °C, corresponding to 5.65% of the sample weight and is related to water evaporation [49]. The second thermal event occurs between 360 and 886 °C, and may be related to oxidation of carbonaceous materials and CO2 release [3]. 3.2.5. Soluble alkalinity In order to quantify the soluble alkalinity of the CCS, the catalyst was placed in contact with water, showing a low solubility of alkaline compounds (1.05 mmol g−1 of catalyst). Mendonça et al. [3] found alkalinity of 3.7 mmol g−1 for the tucumã peels calcined at 800 °C. The higher alkalinity presented by this catalyst is probably associated with the higher concentration of alkali and alkaline earth metals in the form of carbonates and phosphates. Sharma et al. [37] obtained the wood 5

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a statistically significant model at a 95% confidence level. In this context, the linear terms (i.e. XT, XTR, XRM and XCAT) are all significant, as well as the interactions between them and the quadratic term (XT. XT). Between the studied variables, XT (temperature) and XCAT (catalyst) and their interaction were the most significant (Fig. S2 – Supplementary Information). The model was also evaluated through the coefficient of determination (R2) associated with Eq. (2), being a measure of the degree of fit, where a good model fit should result in an R2 of at least 0.8 [60]. This means that the response model evaluated in this study can explain the conversion of the oil into biodiesel very well (R2 = 0.9996), showing that the deduced model is able to explain 99.96% of the behavior of the conversion of the oil into biodiesel within the range of the factors adopted. With respect to the adjusted R2, which exclude non-significant terms of the model, also showed a good model fit (Adj-R2 = 0.9980). Many works on biodiesel process optimization do not mention or omit the lack of fit of the applied model, where it would be necessary to adjust the levels used. For our work, this factor was not significant (Table 5), showing that the levels used are consistent. It is very important to state that these values were obtained after removing 4 outliers (see Supplementary Information). The diagnostic plots were used in order to examine the quality of the regression model developed in this work and they are shown in Fig. 4. At Fig. 4a, it is possible to observe the correspondence plot of the experimental and predicted values of biodiesel conversions, where the linear regression of fit shows good agreement between the values. This shows the existence of a good estimated response in relation to the changes in the independent variables, such as ethanol:oil molar ratio, catalyst loading, temperature and reaction time. The acceptability of the regression model can also be validated when the residuals follow a normal distribution. The residuals show how well the model satisfies the assumptions of the ANOVA whereas the internally studentized residual is the residual divided by the estimated standard deviation of the residual that measures the number of standard deviations separating the experimental and predicted values. It observed that the studentized residuals follow a normal distribution, as evidenced from the straight line in Fig. 4b [61,62]. The effect of residues summarized and conversion of the oil into biodiesel was predicted (Fig. 4c). The residuals scatter randomly in the plot instead of a funnel standard, which indicates an original variance observation response and any problem with the predicted conversion value. After standardization of the residues, if the errors are normally distributed approximately 95% of them will fall

approximately 7% (Table 2). However, the best reaction time was 8 h, similar to the optimal reaction time presented for sunflower oil ethanolysis catalyzed by CaO, where they applied catalyst loading greater than 10% [51]. The ethanol:oil molar ratio (XRM) showed a low significance, explicit when comparing runs 1 and 5 (Table 2). A difference in conversion of only 1.54% was observed when varying ethanol:oil molar ratio from 20:1 to 10:1. This low significance can be explained by the presence of esters and intermediates in the reaction medium, leading to an increase in the liquid/liquid interfacial area, thus minimizing the importance of oil solubility. [51]. Rubio-Caballero et al. [52] varied the ethanol:oil molar ratio from 15:1 to 50:1 at 78 °C in the sunflower ethanolysis. They observed that the production of FAEE was negligible at molar ratios less than 20:1. Bouaid et al. [53] also reported a low significant impact of the ethanol:oil molar ratio lower than 10:1 in similar reactions. Most studies used ethanol:oil molar ratios higher than 10:1 [52,54–57]. It is important to note that used CCS can be applied to alkalize acidic soils such as those found in the Amazon region (pH ranging from 3.75 to 5.5) [58]. This would also help in increasing the concentration of elements of major importance to plants, especially potassium and phosphorus [59]. 3.4. Adequacy of the regression model The mathematical model that provides the best description of the system response to the parameter variations in the range of the analyzed levels and the final empirical model in terms of the predicted response (Y) are shown in Eq. (2).

Y=72.21 + 9.20937XT + 2.23062XRT + 2.18812XMR + 5.84547X Cat + 1.37031X2T + 1.48187XT. XRT− 1.18062XT. XMR + 3.60953XT. X Cat − 0.98688XRT. XMR − 1.68203XRT. X Cat − 2.70703XMR . X Cat (2) where Y is the efficiency of conversion, XT, XRT, XMR and XCat are corresponding coded variables of temperature, reaction time, ethanol:oil molar ratio and catalyst loading, respectively. Table 5 shows the ANOVA of the experimental data used to evaluate the conversion, obtained with the full factorial experiment 24. It can be observed that the F value for the quadratic regression model applied in this work was much higher than 4.0 (636.31) and p < 0.05, indicating Table 5 Test of significance for every regression coefficients and ANOVA. Source

Sum of squares

Degrees of freedom

Mean square

F-value

p-value

Characteristics*

Model XT XTR XRM XCAT XT.XT XT.XTR XT.XRM XT.XCAT XTR.XRM XTR.XCAT XRM.XCAT Residual Lack of fit Pure error Total SS R2 Adjusted R2 Predicted R2 S-value

2001.54 1678.50 39.81 38.30 291.58 4.40 17.57 11.15 111.18 7.79 24.14 62.53 0.86 0.64 0.22 2002.40 0.9996 0.9980 0.9166 0.534752

11 1 1 1 1 1 1 1 1 1 1 1 3 1 2 14

181.958 678.501 39.806 38.303 291.580 4.397 17.568 11.151 111.178 7.791 24.143 62.532 0.286 0.641 0.108 –

636.31 2372.72 139.20 133.95 1019.65 15.38 61.43 39.00 388.79 27.25 84.43 218.68 – 5.92 – –

0.000 0.000 0.001 0.001 0.000 0.030 0.004 0.008 0.000 0.014 0.003 0.001 – 0.135 – –

Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant

* Significant at < 0.05%. 6

Not significant

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Fig. 4. All diagnostic plots of optimization process using CCD; (a) actual and predicted conversion, (b) normality, (c) examined residuals and (d) outlier T.

within the range −3.5 to +3.5 [63]. If standardized residues are out of this range, they will be characterized as outliers [64]. In this study, the optimization was less than ± 0.5 after removal of 4 outliers (see Supplementary Information), explaining a considerable adjustment in the prediction, showing a significant data distribution in Fig. 4c. The data points lying on the straight line in Fig. 4d indicate that there was no apparent problem with the normality [65]. Regarding the repeatability of the method applied in this study, the triplicate center point was added, obtaining an average conversion (response) of 72.21 ± 0.27% with a low variation coefficient (0.4%), showing that the method has a good repeatability [66].

loading had the most significant effects on biodiesel production. However, all analyzed parameters and their interactions showed to be significant, similar to NaOH-catalyzed ethanolysis of sunflower oil [67]. In Fig. 5 the three-dimensional response surfaces, showing the effects of the interactions between the variables on conversion to biodiesel are presented. Fig. 5a shows the effect of the interaction between temperature (XT) and catalyst loading (XCat), where higher values result in higher conversion. This is also confirmed in Eq. (2), which has a positive value for this interaction. The interaction between temperature (XT) and reaction time (XRT) is shown at Fig. 5b, where it is possible to observe that temperature is most influential in the transesterification process. This interaction indicates that at 100 °C it is possible to obtain high conversions within 4 h of reaction. The interactions of ethanol:oil molar ratio (XMR) with catalyst loading (XCat), temperature (XT) and reaction

3.5. Parameter interactions Based on the ANOVA results, the temperature and the catalyst 7

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Fig. 4. (continued)

conversion of the oil into biodiesel. Fig. 6 shows the best condition to increase the percent conversion: catalyst loading of 10% (w/w) relative to oil mass, 8 h, ethanol:oil molar ratio 10:1 and 80 °C. The conversion efficiency was around 96.96%. To confirm the validity of predicted optimum response, three experiments were carried out using optimum conditions, where the conversion average between the experiments was 98.36 ± 0.54%, showing once again the validity of the correlations and the repeatability of the experimental procedure. The optimum conditions for KOH-catalyzed ethanolysis of mixed castor and soybean oils was reported with approximately 1% of KOH (w/w) relative to oil mass, 4 h, ethanol:oil molar ratio 18:1 and 78 °C, obtaining 97% of biodiesel [70]. KOH-catalyzed ethanolysis of refined soybean oil was also optimized with KOH (1% w/w), 1 h, 70 °C and ethanol:oil molar ratio 12:1, resulting in a conversion of 95.6% . However, both studies reported about the production of soap when increasing catalyst loading. Few studies on biodiesel production by ethanolysis use solid catalysts. SrO-catalyzed ethanolysis of palm oil have shown that under optimum reaction conditions (catalyst loading of 5% (w/w), ethanol:oil molar ratio 12:1, 3 h and 80 °C) is possible to achieve high conversion (98.2%) [71]. The optimal conditions for calcium ethoxide-catalyzed

time (XRT) (Fig. 5c, d and e) showed a negative influence in the biodiesel conversion at Eq. (2), which means that these three parameters have a huge significance at low XMR. These results indicate that it is possible to obtain high conversion in ethanol:oil molar ratio lower than 10:1 when applying high catalyst loadings. The same conclusion is valid for interaction between catalyst loading (XCat) and reaction time (XRT), with the effect also negative (Eq. (2)), but significant. This negative effect is related to the possibility of obtaining high conversion in shorter reaction times when applying 10% (w/w) of CCS. It is worth noting that, in some cases, increasing the catalyst amount leads to increased viscosity of the reaction medium. As a consequence, the mass transfer rate of the reactants to the catalyst surface decreases, resulting in lower conversions [68,69]. However, this effect was not observed when applying CCS as catalyst, since there was no reduction in conversion with increasing catalyst concentration. Of the quadratic relations, XT2 was the only one obtained by the model, showing to be slightly significant. The same was observed by Stamenković et al. [67]. Validation is an important step to evaluate the prediction stability of the factors selected in this study. Maximum option of interval for all was selected in Minitab software numerical optimization to obtain the 8

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Fig. 5. (continued) Fig. 5. Three-dimensional surface plot showing the combined effects between: a) ethanol:oil molar ratio (XMR) and reaction time (XRT), b) reaction time (XRT) and temperature (XT), c) catalyst loading (XCat) and reaction time (XRT), d) catalyst loading (XCat) and temperature (XT), e) catalyst loading (XCat) and ethanol:oil molar ratio (XMR), f) ethanol:oil molar ratio (XMR) and temperature (XT).

ethanolysis of various oils were an ethanol:oil molar ratio of 12:1, catalyst loading of 3.5% w/w, 3 h and 80 °C, reaching maximum conversions of approximately 80% [72]. 3.6. CCS stability Through the conversions obtained in the reuse of the CCS it is possible to observe that the CCS can be used twice, maintaining its 9

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Fig. 6. Predicted conditions to produce higher conversion obtained by RSM.

According to Sharma et al. [37], calcination at high temperatures leads to the sintering of small mineral aggregates and prevents the leaching of the potassium, allowing the reuse of the catalyst. Therefore, calcination of cupuaçu seeds at temperatures above 800 °C and washing the catalyst with an aprotic solvent, hexane, for example, can probably lead to a more stable catalyst. 4. Conclusions This study shows that cupuaçu seeds (CCS) have proven to be a promising new heterogeneous catalyst for the transesterification of soybean oil with ethanol, being a low cost catalyst obtained from wastes. The catalytic activity of CCS, mainly attributed to potassium (54.76%), was evaluated by soybean ethanolysis, where CCD was applied for simultaneous study of parametric effects, achieving conversions higher than 98% at optimum conditions. It was observed by ANOVA that temperature, catalyst mass and the interaction between these parameters showed to have greater influence on the transesterification process. The CCS was reused, maintaining oil conversion into biodiesel close to 98% for 2 cycles. However, catalyst leaching was observed, resulting in a partial conversion from homogeneous catalysis. Despite this, the study revealed the possibility of producing a low-cost and efficient catalyst from the cupuaçu seeds for biodiesel synthesis, giving a use for this waste and proposes the use of catalytic residues in the correction of acidic soils.

Fig. 7. CCS reusability. Reaction conditions: ethanol:oil molar ratio 10:1, 10% w/w of catalyst, 8 h, and 80 °C.

excellent performance (≈98%). In the third cycle, a considerable decrease of the catalytic activity was observed, resulting in a low conversion (≈22%) even after washing and activating, which indicates a possible leaching of the material. It was also found that only washing the catalyst with methanol was not sufficient to recover it, obtaining a conversion of approximately 45%. Therefore, activation at 400 °C for 2 h was necessary (Fig. 7). The leaching of the catalyst can cause biodiesel contamination, making this oxide faster, besides reducing the life time of the biofuel and reducing the possibility of reuse of the catalyst [3]. In this context, the possibility of leaching was verified following the methodology proposed by Sharma et al. [37], where the CCS was left in contact with the ethanol under optimized reaction conditions obtained by CCD. The catalyst was separated by centrifugation and the ethanol with supposed leached material was placed back into the flask with soybean oil in a new reaction. It has been verified that part of the conversion of the oil into biodiesel comes from homogeneous leached active species, since a conversion of 60.38% was obtained. This same behavior is presented by catalysts composed of potassium [45], phosphorus [73] and magnesium [74], major constituents of the CCS, where these compounds are leached, forming ethoxide and, consequently, contributing to the conversion with a homogeneous catalytic effect. Leaching was also observed when the catalyst was analyzed by FTIR after reuse. In Fig. 1b, it is possible to observe an intense decrease of the bands near 1000 cm−1, as well as of the bands 729, 875, 1393 and 1513 cm−1, confirming the leaching of the main groups observed by FTIR and XRD (phosphate, oxides and carbonates) when reusing the CCS.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Acknowledgements The authors would like to thank to “Nossa Chácara” farm for providing the cupuaçu fruits, to NMR-LAB (UFAM) for all NMR analysis and to my love, the designer Ana Emilia M. de Freitas, for create my graphical abstracts. This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.enconman.2019.112095. References [1] Salvi BL, Panwar NL. Biodiesel resources and production technologies – a review.

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