Oil Extraction From Calophyllum inophyllum L. via Soxhlet Extraction

Transcription

1 Oil Extraction From Calophyllum inophyllum L. via Soxhlet Extraction Optimization Using Response Surface Methodology (RSM) Nur Atiqah Mohd Shamsuddin Bioenergy Programme Forest Research Institute Malaysia Selangor, Malaysia my Suzana Yusup Biomass Processing Cluster, Centre of Biofuel and Biochemical Research (CBBR), Department of Chemical Engineering, Universiti Teknologi PETRONAS Perak, Malaysia m.my * corresponding author Wan Asma Ibrahim Bioenergy Programme Forest Research Institute Malaysia Selangor, Malaysia asma@frim.gov. my Awais Bokhari Biomass Processing Cluster, Centre of Biofuel and Biochemical Research (CBBR), Department of Chemical Engineering, Universiti Teknologi PETRONAS Perak, Malaysia awaisbokhari@ciitlahore.ed u.pk Lai Fatt Chuah Biomass Processing Cluster, Centre of Biofuel and Biochemical Research (CBBR), Department of Chemical Engineering, Universiti Teknologi PETRONAS Perak, Malaysia johnfatt2000@gmail. com Abstract Biodiesel is an alternative biodegradable and nontoxic fuel, which is free of sulfur and aromatics. Several nonedible oils such as Madhuca indica, Jatropha curcas, Pongania pinnata and Calophyllum inophyllum L. has been investigated experimentally, are found to be suitable for biodiesel production. In this study, Response Surface Method (RSM) based on Central Composite Design (CCD) was applied to determine the optimum condition of oil extraction from Calophyllum inophyllum L. oil via Soxhlet extraction. Three variables studied were solvent to seed ratio (ml), extraction time (hr), and drying time (hr) with total of 15 individual experiments conducted for optimizing the combination effects of those variables. The mathematical model developed was found to be adequate and statistically accurate to predict the optimum yield of Calophyllum inophyllum L. oil. From the analysis of variance (ANOVA), the significance of various factors and their influences were identified. The optimum condition identified was solvent to seed ratio of ml/g, extraction time of 2.55 hours, and drying time for the kernel of 6.82 hours. With high percentage of oil yield from Calophyllum inophyllum L. kernel, it serves as a very promising feedstock for biodiesel production Keywords Solvent extraction, Calophyllum inophyllum L. Response Surface Method (RSM), ANOVA I. INTRODUCTION Declining world oil supply from day to day has forced the researchers all around the world to find new alternatives to replace existing petroleum. Increasing industrialization, motorization and modernization has led to the steep rise in the demand for petroleum based fuels. Biofuel is non-renewable fossil fuel which produced from organic material. At present, biofuel are produced from common edible source such as palm oil, soybean, sunflower oil and non-edible sources such as Jathropha curcas, Madhuca indica and Pongania pinnata. However, the utilization of edible oils in the production of biodiesel has created controversy due to food versus fuel issue. Therefore, researchers have begun to study alternative oil sources particularly non-edible oils to replace the edible oils. Calophyllum inophyllum L.is one of the additional plant species that are now gaining importance in the present study. Calophyllum inolphyllum L. also non known as Penaga Laut or Bintangor Laut is a nonedible oilseed ornamental tree which belong to Clusiaceae family. It is easily found in coastal areas, with oil content values of 30-70%, the kernels yields refined, greenish yellow oil, similar to olive oil, with aromatic odor and insipid taste, is the potential substitute of non-edible feedstock for biodiesel. The oil of Calophyllum inophyllum L. can be extracted by mechanical and chemical process. The percentage of extraction using mechanical method is not substantial, being around 20-30%, while using chemical solvents such as hexane, and the percentage yield is reported of 40-68% [10]. Among the several extraction method exist, Soxhlet extraction is one of the most conventional and reliable methods used till nowadays. This method which is commonly known as solidliquid extraction or leaching has some attractive advantages and was originally designed to extract lipid from solid material. Soxhlet extraction of biomass including rubber seed [6] and jojoba meal [2] had been carried out and the effects of the different parameters of the extraction process, /15/$ IEEE

2 including the solvent to seed ratio, extraction time, and drying time were studied. RSM is a collection of mathematical and statistical techniques for empirical model building, which helps in the process of modeling and analyzing engineering problem, at the same time useful for optimizing the response surface that is influenced by various parameters [9]. Originally, RSM was developed to model experimental responses, and then migrated into the modeling of numerical experiments. As an effective tool for optimizing the process, RSM is usually employed when many factors and interactions affect the desired response [1]. The main advantage of RSM is the reduced number of experimental trials needed to evaluate multiple parameters and their interactions. Through the use of optimization techniques, the optimum model corresponding to the experimental that will presumably produce the best result can thus be found [8]. This research focused on the optimization of oil extraction from Calophyllum Inophyllum kernel via Soxhlet extraction, based on Response Surface Methodology (RSM). Parameters involve in this study are drying time, solvent to seed ratio, and extraction time. The effects of these parameters on the yield of the oil (response) were studied concurrently in a rotatable Central Composite Design (CCD) and subsequently an empirical mathematical model correlating the response to the variables was developed and presented. II. EXPERIMENTAL PROCEDURE A. Soxhlet extraction and principle Soxhlet was named after its invertor Franz von Soxhlet in the year of The Soxhlet apparatus was heated according to the type of solvent used, and basically it works on polarity of solvent and sample. For example, oil is dissolved in certain solvent such as hexane and thus hexane is used to extract the oil. Figure 1 shows the Soxhlet extraction apparatus. When the solvent was boiling, the vapour rises through the vertical tube into the condenser at the top so the liquid condensate drips into the filter paper thimble in the centre, which contains the solid sample to be extracted. The extract moves through the pores of the thimble and fills the siphon tube, where it flows back down into the round bottom flask. This process continued, according to the parameters set. Salisova et.al. also investigate the difference between conventional and modern techniques of extraction, but somehow Soxhlet extraction is still reliable and being used in the industry. Sudradjat et.al reported that in extraction of Calophylum Inophylum L. oil, the percentage yield of oil by mechanical pressing is was small, 20-30% compared to the theoretical value. Meanwhile at laboratory scale, the yield of oil reaches up to 40-50% of oil. Pre-treatment in processing Calophyllum inophyllum L. notably gives effect to the Soxhlet extraction.thus, Soxhlet extraction has been chosen to be the best and economical method to extract the Calophyllum inophyllum L. oil. Figure 1: Soxhlet extraction apparatus B. Response Surface Methodology The application of RSM for optimization is aimed at reducing the number of cost and analysis methods, in the range that is appropriate and meet the quality set. An important aspect of RSM is the design of experiments usually abbreviated as DoE. Most of the optimal design of experiments are associated with the mathematical process that is polynomial with unknown structure, thus the experiments are only designed only for certain circumstances. Examples of DoE are Full Factorial Design (FFD), Central Composite Design (CCD), D-optimal Design, Taguchi Method and etc. The CCD, is a useful methodology that is employed for sequential experimentation, provides reasonable amount of information for testing the goodness of fit from fewer number of experiments, therefore reducing the overall cost associated with the analysis [3]. III. MATERIALS AND METHOD A. Materials Calophyllum inophyllum L. seeds were collected from Lumut, Perak. The sample was dried and the kernel was removed from the shell. Then the kernel was steamed for two hours at temperature of 100 C to remove the unsaponified oil or gum. It was next dried in the oven at 105 C at different interval of time to remove any moisture present before being crushed using a steel blender. The hexane used in the present investigation was an analytical grade reagent. Physical characteristics of Calophyllum inophyllum L. seed and kernel is shown in Table 1.

3 Table 1 Physical characteristics of Callophyllum inophyllum L. seed and kernel Physical characteristics Unit Data Mass of CI seed g 8.89 Mass of CI kernel g 3.66 Diameter of CI seed mm Diameter of CI kernel mm Moisture content of CI kernel % Table 2 Selected variables and coded levels Variables Symbol Coded Levels Solvent to seed ratio (ml/g) A Extraction time (hr) B Drying time (hr) C Dried fruits Fruit shells Seed kernels Soxhlet extraction of Calophyllum inophyllum L. kernel was carried out with n-hexane as solvent. 4g of dried sample was placed into a thimble and the thimble was placed in the Soxhlet extraction apparatus. The sample was extracted with hexane at 65 C. After certain interval of the time, the experiment was stopped and the trapped oil was separated using rotary evaporator under vacuum at temperature of 70 C. The percentage of oil yield was calculated using equation (1) below. All experiments were run in triplicates. Oil yield (wt %) = Mass of extracted oil x 100 (1) Mass of sample fed B. Experimental Design RSM by Design Expert Version (Stat Ease, USA) was used to model the multiple process parameters and for the optimum operating condition of the extraction. A design tool known as Central Composite Design (CCD) were used for experiment as it provides much information with less number of experiments [9]. Three identified independent variables are A: solvent to seed ratio (ml/g), B: extraction time (hr) and C: drying time (hr). Each variables in the experiment were coded into levels -1, 0 and +1 as show in Table 2. The optimization condition of the variables was obtained using the data from statistical analysis. IV. ESULT AND DISCUSSION A. Development of regression model equation Regression analysis is a general approach to fit the empirical model with the collected response variable data [10]. The detail of the outcome is shown in Table 3, where the response ranges from 23.04% to 45.84% depending on the conditions of experiments. These results can be fitted into a second order quadratic model of coded units as given in Eq. (2). Positive sign in front of the terms indicates synergistic effect while, negative sign indicates antagonistic effect. Oil yield = A B C AB AC BC A B C 2 (2) An analysis of variance (ANOVA) is then carried out for the response in order to test the significant of the suitability as presented in Table 4. The model p-value (0.0105) indicates that the model terms are significant, as the probability>f is less than 0.05.The smaller the p-value, the bigger the significance of the corresponding coefficient [4]. It is also implied that the model was significant at 95% confidence level. The p-value (probability of error value) is used as a tool to check the significance of each regression coefficient, which also indicates the interaction effect of each cross product [4]. From the ANOVA results, the main model terms suggested that variables with significant influence on oil yield was drying time (C).

4 Table 3 Experimental design with process data and the response for the Soxhlet extraction process Run Factor 1: Solvent To Seed Factor 2: Extraction Factor 3: Drying Response 1: % Oil Yield Ratio Time Time B. Statistical Analysis Table 4 Analysis of Variances (ANOVA) for model regression Source Sum of p-value Df Mean square F value Squares Prob>F Model A-Solvent to seed ratio B-Extraction time C-Drying time AB AC BC A^ B^ C^ Residual Lack of Fit Pure Error Cor Total Table 5 Response of the model Std. Dev. a R-Squared Mean Adj R-Squared b C.V. % c Pred R-Squared d a Standard of Deviation b Adjusted R2 c Coefficient of variation d Predicted R2 e Adequate precision Adeq precision e The Model F-value of 9.96 also reveals that the model is significant. There is only a 1.05% chance that a "Model F-Value" this large could occur due to noise. In this model, factor C, which is the drying time of the kernel is the most important model term with a value of However, the other factors have lesser effect on the oil yield. Table 5 presented the table of response of the model. Subsequently, coefficient of determination or R 2 is a measure of how well the regression line represents the data. If the regression line passes exactly through every point on the scatter plot, it would be able to explain all of the variation. The further the line is away from the points, higher deviation is predicted. The model was found to have R 2 of can attributed to the independent variables investigated and only 5.28% of the total variation could not be explained by the model. Figure 2

5 illustrates the correlation between experimental values versus predicted values by using the developed model. Figure 3: Perturbation plot of Soxhlet extraction model Figure 2: Predicted vs actual of oil yield C. Interactions Between Process Variable The effect of all factors is illustrated in perturbation plot in which it compares the factors at a particular point in the design space. Figure 3 is the perturbation plot for the oil yield based on the three factors involved. The steep slope or curvature in this model is factor C; drying time of the kernel, and shows it is more sensitive to that factor. Also, this plot strengthen that factor A and B which each representing solvent to seed ratio and the extraction time respectively give lesser effect on the oil yield. Instead of studying a single variable as in conventional method the interactions is investigated to know its significance and importance for a comprehensive optimization study [7]. This is revealed by Figure 4 a, b and c showing that the 3D plot of each factors with respects to the response. From Figure 4b and 4c, the drying time is shown to be the most important factor affecting to the response. Figure 4c shows that the 3D response surface illustrated that the increment of drying time from low level (3hr) to high level (8hr) leads to an increase of oil yield with reduced extraction time. While 4b shows that the 3D response surface gives information that increasing drying time increased the oil yield compared to solvent to seed ratio influence. The drying time for the kernel is very important factor, as the moisture and volatile is reduced by drying, which gives higher oil yield. The moisture content of the kernel is quite high that is 51.46%. When the kernel is changed from greenish-white to brown color in the drying process, it shows that most of the moisture is absence and results in the high oil percentage. Figure 4a: Effect of extraction time on solvent to seed ratio

6 VI. ACKNOWLEDGEMENT Author would like to acknowledge Forest Research Institute Malaysia (FRIM), Universiti Teknologi PETRONAS and PRGS (0153AB-K19) for funds provided. REFERENCES Figure 4b: Effect of drying time on solvent to seed ratio [1] Badwaik, L.S., Prasad, K. Deka, S.C. (2012) Optimization of extraction conditions by response surface methodology for preparing partially defatted peanut. International Food Research Journal.Vol 19(1). pp [2] Ferial A. Zaher, Omayma S El Kinawy, Dalia E.El Haron. (2004). Solvent extraction of jojoba oil from pre-pressed jojoba meal. Grasas Y. Aceites. Vol 55, Fasc 2, pp [3] Giuliano dragone, Bruno D. Fernandes, Ana P.Abreu, Antonio A. Vicente, Jose A. Teixeira. (2011) Nutrient limitation as a strategy for increasing starch accumulation in microalgae. Applied Energy. Pp [4] H.V. Lee, R.Yunus, J.C.Juan, Y.H.Taufiq-Yap (2011) Process optimization design for jathropha-based biodiesel production using response surface methodology. Fuel Processing Technology. Pp [5] Jibrail Kansedo, Keat Teong Lee (2013) Process optimization and kinetic study for biodiesel production from non-edible sea mango (Cerbera odollam) oil using response surface methodology. Chemical Engineering Journal. Vol 214. Pp [6] K. Modhar and Y. Suzana. (2009). Solvent extraction and characterization of rubber seed oil. International Journal of Postharvest Technology and Innovation, 2009, vol 1, No. 4, pp [7] Kok Tat Tan, Keat Teong Lee, Abdul Rahman Mohamed. (2010) A glycerol free process to produce biodiesel by supercritical methyl acetate technology: An optimization study via Response Surface Methodology. Bioresource Technology. Pp [8] Ming-ju Chen, Kun-Nan Chen, Chin-Wen Lin. (2005) Optimization on response surface models for the optimal manufacturing conditions of dairy tofu. Journal of Food Engineering. Vol 68. pp [9] Muhammad Azim Jamaluddin, Khudzir Ismail, Mohd Azlan Mohd Ishak, Zaidi Ab Ghani, Mohd Fauzi Abdullah, Muhammad Taqi-uddeen Safian, Siti Sawalliah Idris, Shawaluddin Tahiruddin, Mohammed Faisal Mohammed Yunus, Noor Irma nazashida Mohd Hakimi. (2013). Microwave-assisted pyrolisi of palm kernel shell: Optimization using response surface methodology (RSM). Renewable Energy. Vol 55. pp [10] DC M. Design and analysis experiment. 5 ed. New York: John Wiley and Sons; 2001 Figure 4c: Effect of drying time on extraction time D. Optimization Analysis All the factors and responses with satisfactory high and low limit experimental region have to satisfy the creation as presented in Table 2. The optimum condition identified is solvent to seed ratio of ml/g, extraction time of 2.55 hours, and drying time for the kernel of 6.82 hours. V. CONCLUSION The high percentage of oil yield from Calophyllum Innophyllum kernel showed that it is a promising feedstock for biodiesel production. On the other hand, optimization of Soxhlet extraction of oil yield was successfully carried out by utilizing RSM analysis with optimum yield of 46.22%. The mathematical model developed was proven to be statistically adequate to predict the optimal yield of oil.