Production Process and Optimization of Solid Bioethanol from Empty Fruit Bunches of Palm Oil Using Response Surface Methodology

This study aimed to observe the potential of solid bioethanol as an alternative fuel with high caloric value. The solid bioethanol was produced from liquid bioethanol, which was obtained from the synthesis of oil palm empty fruit bunches (PEFBs) through the delignification process by using organosolv pretreatment and enzymatic hydrolysis. Enzymatic hydrolysis was conducted using enzyme (60 FPUg−1 of cellulose) at a variety of temperatures (35 ◦C, 70 ◦C, and 90 ◦C) and reaction times (2, 6, 12, 18, and 24 h) in order to obtain a high sugar yield. The highest sugars were yielded at the temperature of 90 ◦C for 48 h (152.51 mg/L). Furthermore, fermentation was conducted using Saccharomyces cerevisiae. The bioethanol yield after fermentation was 62.29 mg/L. Bioethanol was extracted by distillation process to obtain solid bioethanol. The solid bioethanol was produced by using stearic acid as the additive. In order to get high-quality solid bioethanol, the calorific value was optimized using the response surface methodology (RSM) model. This model provided the factor variables of bioethanol concentration (vol %), stearic acid (g), and bioethanol (mL) with a minus result error. The highest calorific value was obtained with 7 g stearic acid and 5 mL bioethanol (43.17 MJ/kg). Burning time was tested to observe the quality of the solid bioethanol. The highest calorific value resulted in the longest burning time. The solid bioethanol has a potential as solid fuel due to the significantly higher calorific value compared to the liquid bioethanol.


Introduction
Greenhouse gas emissions, due to the impact of excessively used fossil fuel [1], have created negative impacts on the environment [2,3]. The primary emission of greenhouse gas is carbon dioxide [4], ethanol 55% was employed as the organic solvent [29]. Enzymatic hydrolysis was conducted in this study to hydrolyze cellulose and hemicellulose into reducing sugars. This hydrolysis has the advantage of producing more sugar yield compared to acid hydrolysis. However, the cost of the enzymatic hydrolysis is significantly higher than acid hydrolysis [30,31]. Moreover, studies about enzymatic hydrolysis have been successfully done using various forms of treated biomass such as corn stover, rice straw [32], rice hull [33], sugarcane bagasse [34][35][36], and palm empty fruit bunch (PEFB) [37][38][39]. Different researchers have used different pretreatment processes and optimization to produce bioethanol from different feedstocks. For example, Sebayang et al. [40] used Manihot glaziovii starch as a feedstock to produce bioethanol through the optimization of enzymatic hydrolysis. Kim et al. [41] used a sequential pretreatment process with diluted acid and then alkali to treat empty palm fruit bunch fiber biomass for producing bioethanol. However, there is not much research found on the optimization of the organosolv pretreatment process to produce bioethanol from PEFB through enzymatic catalyzation. The novelty of this study is to highlight the EFBs as a less expensive second-generation feedstock for bioethanol production using the organosolv pretreatment process and enzymatic hydrolysis in Malaysia. In this study, the production and optimization of solid bioethanol were carried out using stearic acid as an additive. Liquid bioethanol was produced from palm empty fruit bunches (PEFBs) via organosolv pretreatment and enzymatic hydrolysis. The optimization of solid bioethanol production was determined by response surface methodology (RSM) to get the highest calorific value. Table 1. Pros and cons of organosolv pretreatment process compared to other processes [42].

Pretreatment Method Pros Cons
Milling -Reduces cellulose crystallinity -Reduces polymerization degree -Reduces particle size -Increases specific surface area and pore size -Significantly increases power and energy consumption

Materials
The palm empty fruit bunch (PEFB) biomass, which was supplied from palm plantations in Indonesia, was used as a feedstock for bioethanol production. Aqueous ethanol with a concentration of 55 vol % was proposed as a solvent in order to break lignin content, known as the delignification process; this aqueous ethanol was supplied from Fisher Scientific (Waltham, MA, USA). Enzyme cellulose (cellulose from Trichoderma reesei ATCC 26921) and β-glucosidase (β-glucosidase from Caldocellum saccharolyticum) for the hydrolysis process, and the yeast extract, peptone, and glucose as a medium for the fermentation process, were supplied from Sigma-Aldrich (St. Louis, MO, USA). Furthermore, the bioethanol was solidified with stearic acid (Sigma-Aldrich, St. Louis, MO, USA), which has a melting point of 67-72 • C.

Preparation of Bioethanol
The palm empty fruit bunch (PEFB) biomass was dried under the sun and crushed to a small size of approximately 1 cm width. Furthermore, it was dried in an oven at a temperature of 105 • C for 24 h in order to evaporate all the water content. The dried biomass was then ground for the further homogenous processing of the material, into the 710-500 µm size range, and then this biomass was stored in a desiccator (NS 24/29 Duran, Darmstadt, Germany). The same procedure of drying and grinding was conducted in the previous study [29].
The pretreatment method, used to synthesize sugars from the dried biomass, was organosolv pretreatment and further enzymatic hydrolysis processes. The organosolv pretreatment method was performed chemically as a delignification process by dissolving dried PEFB with aqueous ethanol at a solid-liquid ratio of 1:10 (10 g in 100 mL) in an Erlenmeyer flask. The condition was settled at 55 vol % ethanol, a temperature of 120 • C, and a reaction time of 60 min [29]. The units of enzymes were found as an adequate amount to obtain a high sugar yield [43]. The temperature and reaction time were varied at 35 • C, 70 • C, and 90 • C for 2, 6, 12, 18, and 24 h, respectively, in a water bath shaker. The hydrolyzed PEFBs were carried out to measure total sugar yield using the phenol-sulfuric acid dinitrosalicylic (DNS) colorimetric method on the obtained samples of acid pretreatment, as well as on the hydrolyzed PEFBs after and without acid pretreatment. The test was conducted by mixing 1 mL of 0.1% DNS reagent solution with 1 mL diluted solution of treated PEFB. The mixed solutions were then put into a water bath at 90 • C for 5 min and allowed to cool down to room temperature before spectrophotometer reading at 540 nm.
The optimum sugar yield of hydrolysate was selected to be fermented into bioethanol. The fermentation was carried out with media consisting of 1% (w/v) yeast extract and 2% (w/v) peptone and 3% (w/v) glucose. The media was poured into the hydrolysate and was autoclaved in 121 • C for 30 min in order to sterilize it. After the medium was cooled down to room temperature, the yeast culture was added to be inoculated in a shaker for 24 h in order to activate the yeast cultures' enzymatic activity. After the inoculation, treated PEFB was added into media containing 1% (w/v) yeast extract, 2% (w/v) peptone, and 10% (v/v) yeast cultures at a solid-to-liquid ratio of 2:5 in a 250 mL volume Erlenmeyer flask. The fermentation was performed in a shaker incubator at 35 • C and 150 rpm for 84 h. Ethanol yield was determined every 12 h. Bioethanol was distilled using a control rotary evaporator (230 VAC, IKA, Staufen, Germany) at 79 • C in order to remove the water content. The concentration of bioethanol was determined using a density meter (DMA 35, Anton Paar, Graz, Austria).

Solid Ethanol Production and Optimization of Solid Bioethanol Calorific Value
Solid ethanol production was carried out by mixing melted stearic acid with ethanol. Calorific value was determined using a calorimeter (Oxygen Bomb Calorimeter 6200, Paar, MO, USA) for optimization of the solid ethanol quality. The composition of 70%, 80%, and 90% (v/v) ethanol and stearic acid was varied in order to optimize the calorific value of the solid ethanol.
Response surface methodology (RSM) with Box-Behnken experimental design was used to optimize the calorific value of the solid bioethanol [44,45]. The design is available in Design-Expert software 9.0.4.1 (Stat-Ease Inc., Minneapolis, MN, USA). The independent variables chosen were bioethanol concentration (%) represented as A, stearic acid (g) represented as B, and bioethanol (mL) represented as C. The coded levels of independent variables resulting from the Box-Behnken experimental design are presented in Table 2. Furthermore, the combustion test was carried out to observe the burning time of the solid bioethanol ( Figure 1). The four highest calorific value samples were selected to be tested. The diameter of each sample to be burned was 3 in with a length of 4 cm.

Sugars and Bioethanol Yield of PEFB
The organosolv pretreatment was used as a delignification method in this study. Aqueous ethanol was used as the solvent solution to degrade lignin in the palm empty fruit bunch (PEFB); this method was successfully used in a previous work, which was able to break down the lignin content in the biomass by producing less residue [29], hence this will not be a major problem for further processes of bioethanol production [45,46]. However, the organosolv pretreatment has been commonly used in softwood and hardwood synthesis processes, which are able to obtain 90% of the sugars' efficiency by incorporating an enzymatic hydrolysis method [47][48][49]. In the present study, enzymatic hydrolysis was performed by using the enzyme 60 FPUg −1 of cellulose to derive the sugar yield from the source biomass with varying temperature conditions of 35, 70, and 90 °C, where the enzyme cellulose 60 FPU was the standard unit of cellulose used for the enzymatic hydrolysis process in bioethanol production [50][51][52][53].
Moreover, several enzymes have been used in hydrolysis processes and are categorized based on their activities' temperature range, that is, 20-50 °C, 50-80 °C, and above 80 °C, which are known as mesozyme, thermozyme, and hyperthermozyme, respectively [54,55]. Hence, the cellulose enzyme used in this work was thermozyme, which has an activation temperature of about 39-90 °C. For industrial purposes such as pulp and paper processes, the thermozyme type of enzyme is used due to the low possibility of contamination within a short, extended process.

Sugars and Bioethanol Yield of PEFB
The organosolv pretreatment was used as a delignification method in this study. Aqueous ethanol was used as the solvent solution to degrade lignin in the palm empty fruit bunch (PEFB); this method was successfully used in a previous work, which was able to break down the lignin content in the biomass by producing less residue [29], hence this will not be a major problem for further processes of bioethanol production [45,46]. However, the organosolv pretreatment has been commonly used in softwood and hardwood synthesis processes, which are able to obtain 90% of the sugars' efficiency by incorporating an enzymatic hydrolysis method [47][48][49]. In the present study, enzymatic hydrolysis was performed by using the enzyme 60 FPUg −1 of cellulose to derive the sugar yield from the source biomass with varying temperature conditions of 35, 70, and 90 • C, where the enzyme cellulose 60 FPU was the standard unit of cellulose used for the enzymatic hydrolysis process in bioethanol production [50][51][52][53].
Moreover, several enzymes have been used in hydrolysis processes and are categorized based on their activities' temperature range, that is, 20-50 • C, 50-80 • C, and above 80 • C, which are known as mesozyme, thermozyme, and hyperthermozyme, respectively [54,55]. Hence, the cellulose enzyme used in this work was thermozyme, which has an activation temperature of about 39-90 • C. For industrial purposes such as pulp and paper processes, the thermozyme type of enzyme is used due to the low possibility of contamination within a short, extended process. Figure 2 shows the rate of sugar yields obtained from the enzymatic hydrolysis process with the enzyme 60 FPUg −1 of cellulose in different combinations of temperature, namely, 35 • C, 70 • C, and 90 • C. The result shows that the optimum sugar yield at the first 2 h of the hydrolysis process is obtained at a temperature of 90 • C as 72.81 mg·L −1 , which is much higher than the sugar yield achieved at temperatures of 35 • C and 70 • C as 7.45 mg·L −1 and 10.92 mg·L −1 , respectively. Subsequently, these sugar yields at 35 • C and 70 • C presented a significant increment once 6 h of the fermentation process was reached, and afterwards simultaneously increased as the fermentation times were increased. On the other hand, the sugar yield at 90 • C increased from 2 h of fermentation time onward, which can be attributed to the higher temperature. The increasing sugar yield occurred because of the high temperature that affected the enzyme's activity. The optimal temperature for enzyme activity is 35-37 • C. However, the higher temperature possibly increased the kinetic energy of the enzyme. At the temperature of 90 • C, the enzyme works better for a certain time before enzyme denaturation. However, overall, and once the 48 h fermentation processes ended, the optimum yield of sugars reaching 152.51 mg·L −1 was obtained at a temperature of 90 • C. This optimum yield was due to the increasing kinetic energy, which increased the interaction between the substrate and enzyme, thereby achieving a better reaction rate for a higher sugar yield [56].
Processes 2019, 7, x FOR PEER REVIEW 7 of 16 Figure 2 shows the rate of sugar yields obtained from the enzymatic hydrolysis process with the enzyme 60 FPUg −1 of cellulose in different combinations of temperature, namely, 35 °C, 70 °C, and 90 °C. The result shows that the optimum sugar yield at the first 2 h of the hydrolysis process is obtained at a temperature of 90 °C as 72.81 mg·L −1 , which is much higher than the sugar yield achieved at temperatures of 35 °C and 70 °C as 7.45 mg·L −1 and 10.92 mg·L −1 , respectively. Subsequently, these sugar yields at 35 °C and 70 °C presented a significant increment once 6 h of the fermentation process was reached, and afterwards simultaneously increased as the fermentation times were increased. On the other hand, the sugar yield at 90 °C increased from 2 h of fermentation time onward, which can be attributed to the higher temperature. The increasing sugar yield occurred because of the high temperature that affected the enzyme's activity. The optimal temperature for enzyme activity is 35-37 °C. However, the higher temperature possibly increased the kinetic energy of the enzyme. At the temperature of 90 °C, the enzyme works better for a certain time before enzyme denaturation. However, overall, and once the 48 h fermentation processes ended, the optimum yield of sugars reaching 152.51 mg·L −1 was obtained at a temperature of 90 °C. This optimum yield was due to the increasing kinetic energy, which increased the interaction between the substrate and enzyme, thereby achieving a better reaction rate for a higher sugar yield [56]. This sugar yield was thus carried out for the further fermentation process using Saccharomyces cerevisiae in order to produce bioethanol over a period of 84 h, and was then analyzed every 12 h to determine the yield of bioethanol that was converted from the sugars. Figure 3 presents the comparison of rate yield ethanol production and sugar decomposition from an analysis carried out every 12 h. These results show that the rate of ethanol yield obtained increased when the fermentation time increased, where a significant increment appears at 24 h and shows a continuous slight increase afterwards, whereas it is vice versa for the sugar decomposition. However, sugars acted as a carbon (C) source to grow the S. cerevisiae cells within the fermentation processes [57].
Meanwhile, as the boiling point of the bioethanol (78.3 °C) was lower than the boiling point of water (100 °C), the remaining water content in the bioethanol solution was not removed completely and formed an azeotropic solution (i.e., a mixture of 95% ethanol and 5% water), which had a boiling point of 78.15 °C [58]. However, the azeotropic percentage between bioethanol and water obtained in this study was 90% ethanol and 10% water. This sugar yield was thus carried out for the further fermentation process using Saccharomyces cerevisiae in order to produce bioethanol over a period of 84 h, and was then analyzed every 12 h to determine the yield of bioethanol that was converted from the sugars. Figure 3 presents the comparison of rate yield ethanol production and sugar decomposition from an analysis carried out every 12 h. These results show that the rate of ethanol yield obtained increased when the fermentation time increased, where a significant increment appears at 24 h and shows a continuous slight increase afterwards, whereas it is vice versa for the sugar decomposition. However, sugars acted as a carbon (C) source to grow the S. cerevisiae cells within the fermentation processes [57].
Meanwhile, as the boiling point of the bioethanol (78.3 • C) was lower than the boiling point of water (100 • C), the remaining water content in the bioethanol solution was not removed completely and formed an azeotropic solution (i.e., a mixture of 95% ethanol and 5% water), which had a boiling point of 78.15 • C [58]. However, the azeotropic percentage between bioethanol and water obtained in this study was 90% ethanol and 10% water.

Quadratic Regression Model for Solid Bioethanol Optimization
Optimization was performed using the response surface methodology (RSM) model. This method is a technique based on the design of experiments (DOE). RSM is used to analyze the changes of the dependent variable and has been widely used to optimize the experiment process due to the advantage that it can predict the maximum result, which is not carried out in the experiment [44,45,59]. The variation of data was estimated by Box-Behnken experimental design ( The polynomial equation above is used to predict the calorific value, and the results of the prediction are presented in Table 2, where CV represents the calorific value, A represents bioethanol concentration, B represents stearic acid, and C represents bioethanol. Analysis of variance (ANOVA) was conducted to calculate the statistical significance of the quadratic regression model and the effects of significant individual correlation of the chosen responses. The results are presented in Table 3. The p-value corresponds to the probability of error and is expressed to determine whether each regression coefficient is significant. A p-value less than 0.0001 indicates that the model is significant at the 95% level of confidence. The model F-value of 95.5% implies that the model is significant. There is only a 0.01% chance that an F-value this large can occur due to noise. The value of "Prob>" less than 0.0500 shows the model terms are significant. For this case, A, B, C, and A 2 are significant model terms. If the value exceeds 0.1000, it indicates that the model terms are insignificant. In addition, if there are many insignificant model terms (not counting those required to support hierarchy), model reduction by reducing the parameter range may improve the model.
The coefficient of determination (R 2 ) represents the variety of dependent variables (calorific value) and its relationship with the predicted variables. A high R 2 indicates the higher viability of the data due to the accordance of model-predicted data and experimental data. The relationship of those variables is presented in Figure 4. It shows that the value of R 2 is 0.9919, which indicates that 99.19% of the variability in experimental calorific value is explained by the quadratic regression model.

Quadratic Regression Model for Solid Bioethanol Optimization
Optimization was performed using the response surface methodology (RSM) model. This method is a technique based on the design of experiments (DOE). RSM is used to analyze the changes of the dependent variable and has been widely used to optimize the experiment process due to the advantage that it can predict the maximum result, which is not carried out in the experiment [44,45,59]. The variation of data was estimated by Box-Behnken experimental design (Table 2).
The polynomial equation above is used to predict the calorific value, and the results of the prediction are presented in Table 2, where CV represents the calorific value, A represents bioethanol concentration, B represents stearic acid, and C represents bioethanol.
Analysis of variance (ANOVA) was conducted to calculate the statistical significance of the quadratic regression model and the effects of significant individual correlation of the chosen responses. The results are presented in Table 3. The p-value corresponds to the probability of error and is expressed to determine whether each regression coefficient is significant. A p-value less than 0.0001 indicates that the model is significant at the 95% level of confidence. The model F-value of 95.5% implies that the model is significant. There is only a 0.01% chance that an F-value this large can occur due to noise. The value of "Prob>" less than 0.0500 shows the model terms are significant. For this case, A, B, C, and A 2 are significant model terms. If the value exceeds 0.1000, it indicates that the model terms are insignificant. In addition, if there are many insignificant model terms (not counting those required to support hierarchy), model reduction by reducing the parameter range may improve the model.
The coefficient of determination (R 2 ) represents the variety of dependent variables (calorific value) and its relationship with the predicted variables. A high R 2 indicates the higher viability of the data due to the accordance of model-predicted data and experimental data. The relationship of those variables is presented in Figure 4. It shows that the value of R 2 is 0.9919, which indicates that 99.19% of the variability in experimental calorific value is explained by the quadratic regression model.   The signal-to-noise ratio is determined by statistical parameter adequate precision. A preferable adequate precision is higher than 4. The adequate precision in this study was 36.330, as presented in Table 3. It indicates that the signal is adequate, and the quadratic regression model is viable to be used to navigate the design space. The standard deviation of experimental and predicted data is determined as residual. If residuals follow a normal distribution, it means that the experimental errors are random. Therefore, the residuals were normalized with the estimate of standard deviations, which presented as externally studentized residuals. The studentized residuals should be The signal-to-noise ratio is determined by statistical parameter adequate precision. A preferable adequate precision is higher than 4. The adequate precision in this study was 36.330, as presented in Table 3. It indicates that the signal is adequate, and the quadratic regression model is viable to be used to navigate the design space. The standard deviation of experimental and predicted data is determined as residual. If residuals follow a normal distribution, it means that the experimental errors are random. Therefore, the residuals were normalized with the estimate of standard deviations, which presented as externally studentized residuals. The studentized residuals should be according to the normal distribution function [44]. The standard deviation for the runs is within ±3.00 interval, which shows the approximation of model prediction and experimental results.

Effects of Independent Variables on Calorific Value
Solid bioethanol is an alternative for easily transported fuel. It was produced by adding stearic acid as the additive. The addition of stearic acid into different concentrations of bioethanol (70 vol %, 80 vol %, 90 vol %) was studied in order to optimize the calorific value of the solid bioethanol. Calorific value is one of the most important properties for fuel. Bioethanol in a liquid state has significantly lower calorific value compared to the solid state. The theoretical calorific value of ethanol is 29.3 MJ/kg. The liquid bioethanol in this study had different calorific values in each concentration. Bioethanol 70 vol %, 80 vol %, and 90 vol % had calorific values of 26.6 MJ/kg, 27.5 MJ/kg, and 28.3 MJ/kg, respectively. After the addition of stearic acid, the calorific value significantly increased above 30 MJ/kg due to the ratio of stearic acid and bioethanol and the concentration of bioethanol. The highest calorific value was 43.17 MJ/kg at a solid-liquid ratio of stearic acid and bioethanol 5:7 for 90% of bioethanol concentration. The increasing calorific value was due to the addition of stearic acid, which has a higher calorific value than liquid bioethanol (40.08 MJ/kg). The calorific value is affected by the number of carbonyl atoms (C) in molecules. Stearic acid (CH 3 (CH 2 ) 16 COOH) has 18 carbon atoms, whereas bioethanol (C 2 H 5 OH) has 2 carbon atoms. Hydrocarbon chains of those molecules reacted with oxygen (O 2 ) with heat/flame.
The effects of bioethanol concentration and the ratio of stearic acid-bioethanol are presented in three-dimensional surface plots (see Figure 5a-c). Figure 5a,b illustrates that bioethanol concentration had a more significant influence on the results of calorific value compared with bioethanol volume and stearic acid. Furthermore, for a fixed bioethanol concentration, the increase of either bioethanol volume or stearic acid only increases the calorific value by a slight amount. In both cases, the highest calorific value was achieved for 90% ethanol. The higher bioethanol concentration regularly increased calorific value due to the volatility of bioethanol, which allows it conveniently to be burned. However, the solid-liquid ratio of stearic acid and bioethanol support the effect of bioethanol concentration. Figure 5c illustrates that stearic acid significantly affects the calorific value compared to bioethanol volume. For a fixed stearic acid, a variation of bioethanol volume has an insignificant effect on calorific value. The lack of stearic acid inhibits the formation of solid bioethanol, whereas the high amount inhibits the bioethanol vapor to be burned by heat. In this study, the highest calorific value was obtained at 90 vol % bioethanol concentration with 5 mL bioethanol and 7 g stearic acid.
However, the high calorific value of the solid bioethanol should be supported by shape and solidity for an efficient solid fuel. The solidity was observed before the optimization of the calorific value. The variables for solidity used are the same as the calorific value optimization variables using the Box-Behnken design. According to the study results, the solidity of the solid bioethanol was affected by the bioethanol concentration and the ratio of bioethanol and stearic acid. The best solidity form as well as the highest calorific value were obtained at a concentration of 90% bioethanol and at the solid-liquid ratio of 5:7 of stearic acid and bioethanol. The lowest solidity is shown in number 17, with a concentration of 70% bioethanol. It formed a water layer at the bottom with the ratio of stearic acid higher than bioethanol (3:5). The water layer seems to come from the higher water content bioethanol azeotrope in 70% of the bioethanol concentration.
The solid-liquid ratio of stearic acid and bioethanol also affected the solidity of the solid bioethanol. Sample numbers 1, 2, 3, 8, and 16 were produced using 80% bioethanol concentration in the different solid-liquid ratio of stearic acid and bioethanol, as presented in the experimental design in Table 2. The higher stearic acid content of sample 8 resulted in a too dense form which inhibited the vaporization of bioethanol to be burned out [60]. Sample number 3 containing 7:7 stearic acid and bioethanol produced the calorific value of 42.53 MJ/kg. It is insignificantly different from the highest calorific value (43.171 MJ/kg), which was obtained from sample number 11. The burning test was performed in order to prove the feasibility of the solid bioethanol as solid fuel. The four highest calorific value samples were selected to be investigated because of the reference that calorific value is the main factor of flame temperature. The samples to be burned had a diameter of 3 in and a length of 4 cm. Table 4 shows that the longest burning time of the solid bioethanol, namely 177 min, was obtained from sample number 11, which had the highest calorific value. This The burning test was performed in order to prove the feasibility of the solid bioethanol as solid fuel. The four highest calorific value samples were selected to be investigated because of the reference that calorific value is the main factor of flame temperature. The samples to be burned had a diameter of 3 in and a length of 4 cm. Table 4 shows that the longest burning time of the solid bioethanol, namely 177 min, was obtained from sample number 11, which had the highest calorific value. This shows that the highest calorific value profoundly influences the burning time of solid bioethanol. However, concentrations of 80% and 90% bioethanol seem to have insignificant burning time with different bioethanol-stearic acid ratios. Nonetheless, the higher concentration of bioethanol has a better effect on the flame as well as the addition of stearic acid. According to its chemical reaction, one mole of bioethanol produces less CO 2 than LPG (that mainly contains propane and butane) in combustion due to the lower carbon content per mole [61]. Those gases produce high carbon dioxide during the combustion. Burning bioethanol produced 2 moles of carbon dioxide in every reaction. Meanwhile, combustion reactions of the propane and butane content in LPG produce significantly higher carbon dioxide [62].
The combustion of solid bioethanol has the same reaction as liquid bioethanol. However, stearic acid increases the calorific value as well as the burning time of bioethanol in the solid state. According to the burning time test results in Table 4, samples 8 and 15, which have slightly different calorific value as compared to samples 4 and 11, generate a significantly lower burning time. It is due to the disproportionate amount of stearic acid content in the sample. As for samples 8 and 15, the stearic acid and bioethanol ratio is 7:3 and 5:7, respectively. A stearic acid content higher than bioethanol provided a better solid shape of the solid bioethanol. This affects the rate of bioethanol vaporization due to the tight polymerization of stearic acid, which inhibits the vaporization of bioethanol. Inversely, the lack of stearic acid leads to a shorter burning time due to the easy vaporization of bioethanol. In addition, adding a proper amount of stearic acid is an essential factor in obtaining a quality solid bioethanol. In this study, sample 11 generated the longest burning time as well as the highest calorific value. The burning of the solid bioethanol is shown in Figure 6. shows that the highest calorific value profoundly influences the burning time of solid bioethanol. However, concentrations of 80% and 90% bioethanol seem to have insignificant burning time with different bioethanol-stearic acid ratios. Nonetheless, the higher concentration of bioethanol has a better effect on the flame as well as the addition of stearic acid. According to its chemical reaction, one mole of bioethanol produces less CO2 than LPG (that mainly contains propane and butane) in combustion due to the lower carbon content per mole [61]. Those gases produce high carbon dioxide during the combustion. Burning bioethanol produced 2 moles of carbon dioxide in every reaction. Meanwhile, combustion reactions of the propane and butane content in LPG produce significantly higher carbon dioxide [62].
The combustion of solid bioethanol has the same reaction as liquid bioethanol. However, stearic acid increases the calorific value as well as the burning time of bioethanol in the solid state. According to the burning time test results in Table 4, samples 8 and 15, which have slightly different calorific value as compared to samples 4 and 11, generate a significantly lower burning time. It is due to the disproportionate amount of stearic acid content in the sample. As for samples 8 and 15, the stearic acid and bioethanol ratio is 7:3 and 5:7, respectively. A stearic acid content higher than bioethanol provided a better solid shape of the solid bioethanol. This affects the rate of bioethanol vaporization due to the tight polymerization of stearic acid, which inhibits the vaporization of bioethanol. Inversely, the lack of stearic acid leads to a shorter burning time due to the easy vaporization of bioethanol. In addition, adding a proper amount of stearic acid is an essential factor in obtaining a quality solid bioethanol. In this study, sample 11 generated the longest burning time as well as the highest calorific value. The burning of the solid bioethanol is shown in Figure 6.

Conclusions
This study was aimed at producing solid bioethanol from liquid bioethanol obtained from palm empty fruit bunches (PEFBs). The production of liquid bioethanol was carried out by organosolv pretreatment, enzymatic hydrolysis, and fermentation. Cellulose was employed to hydrolyze treated PEFBs into sugars. It resulted in 152.51 mg/L of sugar yield. Furthermore, the fermentation process resulted in 62.29 mg/L of bioethanol yield. The bioethanol was distilled by rotary evaporator 4 or 5 times at 70 • C to obtain concentrated bioethanol. The bioethanol was collected to produce solid bioethanol using stearic acid. The solid bioethanol was successfully produced and its calorific value was optimized by RSM. The optimization resulted in the highest calorific value of 43.17 MJ/kg at 5 mL bioethanol and 7 g stearic acid for a bioethanol concentration of 90 vol %. This value is significantly higher than the highest liquid bioethanol calorific value (28.3 MJ/kg). The viability of solid bioethanol was also proven by a burning test. The highest calorific value and the proper shape of the solid bioethanol affect its quality. Therefore, it can be concluded that solid bioethanol using stearic acid has the potential to be solid fuel with a high calorific value. However, further study is needed to determine and optimize the storage time of solid bioethanol.