OPTIMIZATION OF MICROWAVE HYDRODISTILLATION OF DRIED PATCHOULI LEAVES BY RESPONSE SURFACE METHODOLOGY

Similar documents
Optimization of cyclone geometry for maximum collection efficiency

Study of the effect of machining parameters on material removal rate and electrode wear during Electric Discharge Machining of mild steel

Optimization of extraction parameters for dye from Pterocarpus santalinus L.f. (red sanders) wood using response surface methodology

3.4. A computer ANOVA output is shown below. Fill in the blanks. You may give bounds on the P-value.

Optimization of Extraction Parameters for Natural Dye from Pterocarpus santalinus by using Response Surface Methodology

Research Article. Fabrication and evaluation of citrate stabilized zinc oxide quantum dots

Available online Research Article

Chapter 5 Introduction to Factorial Designs Solutions

Box Behnken modelling of phenol removal from aqueous solution using Emulsion Liquid Membrane

CHAPTER 4 EXPERIMENTAL DESIGN. 4.1 Introduction. Experimentation plays an important role in new product design, manufacturing

Chapter 6 The 2 k Factorial Design Solutions

Measurement and Prediction of Speed of Sound in Petroleum Fluids

Boiling Point ( C) Boiling Point ( F)

20g g g Analyze the residuals from this experiment and comment on the model adequacy.

CHAPTER 6 A STUDY ON DISC BRAKE SQUEAL USING DESIGN OF EXPERIMENTS

CHAPTER 5 NON-LINEAR SURROGATE MODEL

Preparation of Aliphatic Amines by the Leuckart Reaction

Working with Hazardous Chemicals

Modeling of the Extraction of Oil from Neem Seed using Minitab 14 Software

A STATISTICAL INVESTIGATION OF THE RHEOLOGICAL PROPERTIES OF MAGNESIUM PHOSPHATE CEMENT

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS)

4023 Synthesis of cyclopentanone-2-carboxylic acid ethyl ester from adipic acid diethyl ester

Drilling Mathematical Models Using the Response Surface Methodology

2015 Stat-Ease, Inc. Practical Strategies for Model Verification

Addition of Center Points to a 2 k Designs Section 6-6 page 271

Sizing Mixture (RSM) Designs for Adequate Precision via Fraction of Design Space (FDS)

Influence of cutting parameters on thrust force and torque in drilling of E-glass/polyester composites

Experimental techniques

ANALYSIS OF PARAMETRIC INFLUENCE ON DRILLING USING CAD BASED SIMULATION AND DESIGN OF EXPERIMENTS

Design of Engineering Experiments Chapter 5 Introduction to Factorials

Statistical Modeling and Differential Evolution Optimization of Reactive Extraction of Glycolic Acid

C. C. TAY*, M. I. A. KHOSHAR-KHAN, N. S. MD-DESA, Z. AB-GHANI, S. ABDUL-TALIB

Working with Hazardous Chemicals

Optimization and modeling of microwaveassisted extraction of active compounds from cocoa leaves

The 2 k Factorial Design. Dr. Mohammad Abuhaiba 1

CHAPTER 6 MACHINABILITY MODELS WITH THREE INDEPENDENT VARIABLES

VOL. 11, NO. 2, JANUARY 2016 ISSN

7. Response Surface Methodology (Ch.10. Regression Modeling Ch. 11. Response Surface Methodology)

Available online at ScienceDirect. Procedia Engineering 148 (2016 )

Optimum Configuration of a Double Jet Mixer Using DOE

EFFECT OF TEMPERATURE ON THE VOLUMETRIC STUDIES OF SOME THIOCYANATES IN WATER

SIMULTANEOUS DETERMINATION OF PROCAINE AND BENZOIC ACID BY DERIVATIVE SPECTROMETRY

Associate Professor, Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Chapter 4 - Mathematical model

OPTIMIZATION OF SESAME SEEDS OIL EXTRACTION OPERATING CONDITIONS USING THE RESPONSE SURFACE DESIGN METHODOLOGY

Solvent-free and aqueous Knoevenagel condensation of aromatic ketones with malononitrile

Chapter 6 The 2 k Factorial Design Solutions

NUMERICAL INVESTIGATION AND STATISTICAL ANALYSIS OF LASER BENDING OF TITANIUM SHEETS

Optimization of Methane Conversion to Liquid Fuels over W-Cu/ZSM5 Catalysts by Response Surface Methodology

Response Surface Methodology

PREDICTION OF PHYSICAL PROPERTIES OF FOODS FOR UNIT OPERATIONS

CHEM 304 Experiment Prelab Coversheet

What If There Are More Than. Two Factor Levels?

Optimization of EDM process parameters using Response Surface Methodology for AISI D3 Steel

Response Surface Methodology Optimization of Methylene Blue Removal by Activated Carbon Derived from Foxtail Palm Tree Empty Fruit Bunch

2017 Reaction of cinnamic acid chloride with ammonia to cinnamic acid amide

Statistical and regression analysis of Material Removal Rate for wire cut Electro Discharge Machining of SS 304L using design of experiments

EXPERIMENTAL PROCEDURE

Working with Hazardous Chemicals

CHAPTER 5 EXPERIMENTAL AND STATISTICAL METHODS FOR DRILLING

ORGANIC SYNTHESIS: MICROWAVE-ASSISTED FISCHER ESTERIFICATION

Response Surface Methodology

Extraction of Phenol from Industrial Water Using Different Solvents

Experiment 9 Dehydration of Methylcyclohexanol Friday/Monday 1

Using Design of Experiments to Optimize Chiral Separation

THE CATHOLIC UNIVERSITY OF EASTERN AFRICA A. M. E. C. E. A

RESPONSE SURFACE MODELLING, RSM

Chemistry 262 Laboratory Experiment 3: Derivatization of the Grignard Product Formation of Ethyl and Propyl Benzoate 2/01/18, 2/08/18

Product Information. Silicon Carbide Well Plates

THE STUDY OF EFFECT OF METAL ION Fe(III) ON THE CHLOROPHYLL AS POTENTIAL PHOTOSENSITIZER ON DYE SENSITIZED SOLAR CELL

Available online Journal of Chemical and Pharmaceutical Research, 2012, 4(6): Research Article

Optimization of Machining Process Parameters in Drilling of

S-GSTAR-SUR Model for Seasonal Spatio Temporal Data Forecasting ABSTRACT

Design of Engineering Experiments Part 5 The 2 k Factorial Design

Response Surface Methodology:

Design and Analysis of

EXPERIMENTAL DESIGN TECHNIQUES APPLIED TO STUDY OF OXYGEN CONSUMPTION IN A FERMENTER 1

The Optimal Milling Condition of the Quartz Rice Polishing Cylinder Using Response Surface Methodology

Biogasoline from Catalytic Cracking of Refined Palm Oil using H-ZSM-5 Catalyst

Modeling and Optimization of Milling Process by using RSM and ANN Methods

A supramolecular approach for fabrication of photo- responsive block-controllable supramolecular polymers

Numerical Simulation and Parameters Optimization of Laser Brazing of Galvanized Steel

Application of Response Surface Methodology in Characterization of Biolubricant with Titanium Oxide Nanoparticles

2 nd exam of the 1 st term for 2 nd ESO G. 1. Look at the following picture:

RESPONSE SURFACE ANALYSIS OF EDMED SURFACES OF AISI D2 STEEL

Contents. 2 2 factorial design 4

SYNTHESIS OF 1-BROMOBUTANE Experimental procedure at macroscale (adapted from Williamson, Minard & Masters 1 )

DYEING BEHAVIOR PREDICTION OF COTTON FABRICS IN SUPERCRITICAL CO 2

OPTIMIZATION OF FREEZE DRYING CONDITION OF PREDNISOLONEACETATE SOLID LIPID NANPARTICLES USING BOX-BEHNKEN DESIGN

Response Surface Methodology in Application of Optimal Manufacturing Process of Axial-Flow Fans Adopted by Modern Industries

Experiment DE: Part II Fisher Esterification and Identification of an Unknown Alcohol

Supporting Information

Working with Hazardous Chemicals

Shortcut Distillation. Agung Ari Wibowo, S.T., M.Sc Politeknik Negeri Malang Malang - Indonesia

Institutionen för matematik och matematisk statistik Umeå universitet November 7, Inlämningsuppgift 3. Mariam Shirdel

Chemometrics Unit 4 Response Surface Methodology

Simultaneous Estimation of Residual Solvents (Isopropyl Alcohol and Dichloromethane) in Dosage Form by GC-HS-FID

Since the publication of the ISO Guide to the Expression

Development and Evaluation of Calibration Procedure for a Force- Moment Balance Using Design of Experiments

Songklanakarin Journal of Science and Technology SJST R1 Sifriyani

Transcription:

ISSN: 0974-1496 e-issn: 0976-0083 CODEN: RJCABP http://www.rasayanjournal.com http://www.rasayanjournal.co.in OPTIMIZATION OF MICROWAVE HYDRODISTILLATION OF DRIED PATCHOULI LEAVES BY RESPONSE SURFACE METHODOLOGY H. S. Kusuma*, M.E. Syahputra, D. Parasandi, A. Altway and M. Mahfud* Department of Chemical Engineering, Faculty of Industrial Engineering, Institut Teknologi Sepuluh Nopember, 60111, Indonesia *E-mail: heriseptyakusuma@gmail.com; mahfud@chem-eng.its.ac.id ABSTRACT This paper deals with the optimization of patchouli oil yield in microwave hydrodistillation using response surface methodology (RSM). The factors considered were microwave power (A), feed to the solvent ratio (B) and extraction time (C). These parameters were varied at two levels. The conditions of optimum patchouli oil yield predicted were microwave power of 467.517 W, feed to solvent ratio of 0.432 g/ml and extraction time of 152.122 min. These factors gave an optimum patchouli oil yield of 4.862%. The significant model terms are extraction time. Analysis of variance (ANOVA) indicates that the model was significant as evidenced from R 2 of 0.9036 and the model F-value of 7.29. The patchouli oil yield predicted by the model was closed to the experimentally determined values (1.85 % and 1.87 % respectively); hence the model can be used for prediction of patchouli oil yield in essential oil extraction from dried patchouli leaves using microwave hydrodistillation method. Keywords: Box-Behnken design; microwave hydrodistillation; patchouli oil. RASĀYAN. All rights reserved INTRODUCTION Patchouli is one of the essential oil producing plants which is quite important as Indonesia's export commodity and contributes about 60% of the total export of Indonesian essential oil. Indonesia is the world's largest supplier of patchouli oil with the contribution of 90% 1. Currently, the extraction of patchouli oil is still done using conventional methods that generally have a smaller yield, takes relatively long time and requires a large cost. This is supported by research data that the extraction of basil oil using hydrodistillation method with feed to solvent ratio of 0.5 g/ml for 1 hour obtained yield as much as 0.95 ± 0.08% (v/w) 2. Therefore, consideration should be given to using a new "green technique" in the extraction of essential oils with minimum energy, solvent, and time usage. Until now it has developed new methods to extract essential oils, one of which is using the microwave (microwave-assisted extraction). Previous research has shown that the extraction using the microwave is an alternative that could be developed than the conventional methods, because of the high levels of product purity, lack of solvent usage, and processing time is short 3. Some extraction using the microwave that has been successfully developed is microwave hydrodistillation method which is a combination of hydrodistillation with microwave heating 4. This is supported by previous studies that had been conducted by Asghari et al. 5 which showed that the extraction of essential oil from Ferulago angulata using microwave hydrodistillation method is more effective and efficient when compared with hydrodistillation method. Based on these research, the extraction of essential oil from Ferulago angulata using microwave hydrodistillation method with feed to solvent ratio of 0.067 g/ml and microwave power of 650 W for 70 minutes obtained a yield of 3.8%. While for the extraction of essential oil from Ferulago angulata using hydrodistillation method with feed to solvent ratio of 0.083 g/ml for 3 hours obtained a yield of 1.7% 5. Therefore, in this study conducted extraction of patchouli oil using microwave hydrodistillation method. Rasayan J. Chem., 10(3), 861-865(2017) http://dx.doi.org/10.7324/rjc.2017.1031763

In the extraction of patchouli oil using microwave hydrodistillation, the extraction parameters need to be optimized. Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques useful for modeling and analyzing of problems in which a response of interest is influenced by some quantitative variables with the objective of optimizing the response 6. In this study, an attempt was made at optimizing microwave hydrodistillation of patchouli oil using response surface methodology. EXPERIMENTAL Material and Chemicals The dried patchouli leaves used in this study were obtained from Trenggalek, East Java, Indonesia and have a size of 2.45±0.56 cm. The distilled water and anhydrous sodium sulfate used in the study were of analytical grade. Microwave Hydrodistillation Method In employing microwave hydrodistillation, we used a domestic microwave oven (EMM2308X, Electrolux, maximum delivered power of 800 W) with a wave frequency of 2450 MHz. The dimensions of the PTFEcoated cavity of the microwave oven were 48.5 cm x 37.0 cm x 29.25 cm. The microwave oven was modified by drilling a hole at the top. A round bottom flask with a capacity of 1000 ml was placed inside the oven and was connected to the three-way adapter and liebig condenser through the hole. Then, the hole was closed with PTFE to prevent any loss of the heat inside. Some feed to solvent ratio (0.3; 0.4; 0.5 g/ml) were placed in the reaction flask and heated by microwave irradiation with various power (300; 450; 600 W) and various extraction time (60; 120; 180 min). The different densities and their immiscibility required that the water and patchouli oil be separated from each other by separating funnel and the excess water be refluxed to the extraction vessel in order to provide uniform conditions of solid-to-liquid ratios for extraction. The patchouli oil was collected in amber vials, dried under anhydrous sodium sulfate and stored at 4 o C. The extraction yield of patchouli oil was calculated according to the equation given: Extraction yield %, w/w =!"#$% %$ %!"#$% $& '() ** x 100 (1) Fig.-1: Schematic representation of the microwave hydrodistillation apparatus used in this study Design of Experiment A 2 3 Box-Behnken Design (BBD) was employed resulting in a total of 17 experiments using the Design- Expert version 9.0.4.1 (State-Ease Inc., Minneapolis, MN, USA) to optimize the chosen key factors namely microwave power (A), feed to the solvent ratio (B) and extraction time (C). These variables each at two levels, low and high: A (300 600 W), B (0.3 0.5 g/ml) and C (60 180 min) are presented in Table-1. 862

These levels were chosen based on the capacity of the experimental set up for variables A and B, while C was selected based on experimental run time. The experimental design is shown in Table-2. Table-1: Design summary Factors Name Units Low Actual High Actual Low Coded High Coded A Microwave power W 300 600-1 1 B Feed to solvent ratio g/ml 0.3 0.5-1 1 C Extraction time min 60 180-1 1 The regression analysis was performed to estimate the response function as a second order polynomial 5 4 5(' 5 -=. / + 26'. 22 3 2 + 26' 764. 27 3 2 3 7 (2) where Y is the predicted response, X i is the uncoded value of the i-th test variable, β 0, β j and β ij are coefficients estimated from the regression according to Rajasimman et al. 7 A statistical program package Design-Expert version 9.0.4.1 (State-Ease Inc., Minneapolis, MN, USA) was used for regression analysis of the data and for estimating the coefficient of the regression equation. The equations were validated by the analysis of variance (ANOVA) test. Model and regression coefficients were considered significant when the p-value was lower than 0.05 8. Table-2: Box-Behnken Design matrix Run Actual variables Yield (%) A (W) B (g/ml) C (min) Experimental Predicted Residue 1 300 0.3 120 3.0221 2.7454 0.2767 2 300 0.4 60 2.3617 2.2383 0.1234 3 300 0.4 180 3.7482 3.7070 0.0412 4 300 0.5 120 2.7831 3.2250-0.4419 5 450 0.3 60 2.4343 2.8349-0.4006 6 450 0.3 180 3.3429 3.6613-0.3185 7 450 0.4 120 4.6493 4.6495-0.0002 8 450 0.4 120 4.6493 4.6495-0.0002 9 450 0.4 120 4.6493 4.6495-0.0002 10 450 0.4 120 4.6493 4.6495-0.0002 11 450 0.4 120 4.6493 4.6495-0.0002 12 450 0.5 60 3.3471 3.0291 0.3180 13 450 0.5 180 4.9240 4.5239 0.4001 14 600 0.3 120 3.6771 3.2356 0.4415 15 600 0.4 60 3.0438 3.0855-0.0416 16 600 0.4 180 3.8141 3.9379-0.1238 17 600 0.5 120 3.5357 3.8128-0.2771 RESULTS AND DISCUSSION Table-2 presents the Box-Behnken Design matrix and the patchouli oil yield obtained for each experimental run. Analysis of variance for the response surface model is presented in Table-3. The analysis indicates that the model F-value of 7.29 implies the model is significant. There is only a 0.79% chance that an F-value this large could occur due to noise, the model also has a satisfactory level of adequacy (R 2 ). In this study C, A 2, B 2 and C 2 are significant model terms (p < 0.05). Table-3: Analysis of variance (ANOVA) for response surface quadratic model to identify significant factors affecting the patchouli oil yield Source Sum of Squares df Mean Square F Value p-value Model 10.31 9 1.15 7.29 0.0079* A 0.58 1 0.58 3.69 0.0960 B 0.56 1 0.56 3.55 0.1015 863

C 2.69 1 2.69 17.13 0.0044* AB 0.002385 1 0.002385 0.015 0.9054 AC 0.095 1 0.095 0.60 0.4625 BC 0.11 1 0.11 0.71 0.4272 A 2 2.92 1 2.92 18.56 0.0035* B 2 1.33 1 1.33 8.47 0.0227* C 2 1.39 1 1.39 8.85 0.0207* Residual 1.10 7 0.16 Lack of Fit 1.10 3 0.37 Pure Error 0.000 4 0.000 Cor Total 11.41 16 Standard deviation: 0.40; R 2 : 0.9036; Adj R 2 : 0.7796; Pred R 2 : -0.5427; Adeq precision: 7.928 *Significant variable The Pred R 2 of -0.5427 implies that the overall mean is a better predictor of the response than the current model. Adeq Precision measures the signal to noise ratio; the ratio of 7.928 obtained indicates an adequate signal (a ratio greater than 4 is desirable). Therefore the model can be used to navigate the design space. The response surface curves are plotted to understand the interaction of the variables and the optimum level of each variable for maximum response 7. The response surface curves for extraction of patchouli oil using microwave hydrodistillation are presented in Fig.-2. Fig.-2(a) shows the interaction of microwave power and feed to solvent ratio on the yield, while Fig.-2(b) and 2(c) indicates interaction of microwave power and extraction time on the yield as well as that of feed to solvent ratio and extraction time on the yield, respectively. Final equation in terms of actual variables is given by Equation-3. Yield = -16.46046 + 0.036499 A + 43.55528 B + 0.044561 C + 1.62794.10-3 AB 1.71199.10-5 AC + 0.027848 BC 3.69989.10-5 A 2 56.23451 B 2 1.59685.10-4 C 2 (3) (a) (b) (c) Fig.-2: Response surface showing effect of (a) microwave power and feed to solvent (F/S) ratio on the yield at constant extraction time (120 min); (b) microwave power and extraction time on the yield at constant feed to solvent (F/S) ratio (0.4 g/ml); (c) feed to solvent (F/S) ratio and extraction time on the yield at constant microwave power (450 W) 864

Experimental data generated in Table-2 in terms of actual values were substituted into Equation-3 and the predicted patchouli oil yield was obtained. Actual patchouli oil yield and the predicted patchouli oil yield are presented in Table-2; Comparison of these values indicates that the 2 sets of values are in close agreement. This suggests good reliability of the model as also evidenced from the statistical parameters of the model such as standard deviation of 0.40, R 2 of 0.9036 and F-value of 7.29. This shows fitness of the data for the model. Numerical optimization method was used to predict optimum condition for the response using the Design- Expert version 9.0.4.1 (State-Ease Inc., Minneapolis, MN, USA). The microwave power of 467.517 W, feed to solvent ratio of 0.432 g/ml and extraction time of 152.122 min were obtained among the solutions for the optimum conditions for the yield. This condition gives a yield of 4.862%. CONCLUSION The optimization model was developed using response surface methodology technique for prediction of patchouli oil yield in microwave hydrodistillation of dried patchouli leaves. The model fits experimental data. The extraction time is the major process parameters found to significantly influence the patchouli oil yield. The conditions of optimum patchouli oil yield predicted were microwave power of 467.517 W, feed to solvent ratio of 0.432 g/ml and extraction time of 152.122 min, which gives patchouli oil yield of 4.862%. REFERENCES 1. H.S. Kusuma and M. Mahfud, RSC Advances, 7, 1336(2017a). 2. D.J. Charles and J.E. Simon, Journal of the American Society for Horticultural Science, 115(3), 458(1990). 3. H.S. Kusuma and M. Mahfud, Periodica Polytechnica Chemical Engineering, 61(2), 82 (2017b). 4. H.S. Kusuma and M. Mahfud, Journal of Applied Research on Medicinal and Aromatic Plants, 4, 55(2017c). 5. J. Asghari, C.K. Touli, M. Mazaheritehrani and M. Aghdasi, European Journal of Medicinal Plants, 2(4), 324(2012). 6. R.H. Myers and R.C. Montgomery, Response Surface Methodology, Process and Product Optimization using Design Experiment, Wiley, New York (2002). 7. M. Rajasimman, R. Sangeetha and P. Karthik, Chemical Engineering Journal, 150, 275 (2009). 8. S. Liu, F. Yang, C. Zhang, H. Ji, P. Hong and C. Deng, The Journal of Supercritical Fluids, 48, 9(2009). [RJC-1763/2017] 865