04-10-2016, 04:05 PM
Optimization of Mahua Oil Methyl Ester using Response Surface Methodology to Production Biodiesel from Madhuca oil over MgO / ZrO2 Catalyst
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Abstract
The most commonly used methods for biodiesel preparation is via transesterification of Mahua oil using alkaline catalysts. The transesterification process can be affected by differing parameters. The optimization of experimental parameters, such as oil to alcohol molar ratio, catalyst concentration and reaction time, on the transesterification process. The biodiesel production was optimized by response surface methodology. The optimum condition for the predicted condition product yield is 92.7157 ml with 1:12 of methanol to oil molar ratio, 0.4 wt of catalyst concentration and 10 minutes of reaction time. The catalytic properties of the MgO was loaded on ZrO2 using the incipient wetness impregnation method. The catalyst was characterized using FTIR, XRD, XRF and BET. The effects of reaction temperature, reaction time and MgO loading on Mahua oil conversion to biodiesel were investigated. Biodiesel yield was observed to increase with the reaction temperature and the reaction time. The effect of MgO loading in the catalyst on oil conversion was found to be influenced by the reaction temperature.
Index Terms-Biodiesel, response surface methodology, Mahua oil, MgO/ ZrO2, Transesterification reaction.
INTRODUCTION
Biodiesel production is among the most researched topics as it offers a renewable alternative to fossil fuel. However the cost of the feedstock is still among the major factors that make the biodiesel production cost more expensive. There has been renewed interest in the utilization of vegetable oils for making biodiesel due to its less polluting and renewable nature as against the conventional diesel, which is a fossil fuel leading to a potential exhaustion[1]. One of the most interesting alternatives of the renewable fuels, among others, is the Mahua oil fuel for diesel engine. Considerable efforts have been made to develop Mahua oil derivatives that approximate the properties and performance of the hydrocarbon-based diesel fuels. The problems with substituting Mahua oil for diesel fuels are largely associated with their high viscosities, low volatilities and polyunsaturated character [2]. Mahua oil fuel, or biodiesel, is a likely substitute for diesel fuel because it is produced from renewable sources. In the American Society for Testing and Materials (ASTM) biodiesel fuel is defined as mono alkyl esters of long chain fatty acids derived from a renewable lipid feedstock, such as Mahua oil or animal fat [3]. Commonly used feed stocks (vegetable oil) for transesterification include soybean oil, rapeseed oil, etc. In recent years, there exist active researches on biodiesel production from Mahua oil [4, 5]. Response surface methodology (RSM) is a useful statistical technique, which has been used in the research of complex variable processes. Multiple regression and correlation analysis are used as tools to evaluate the effects of two or more independent factors on the dependent variables. Furthermore, the central composite design (CCD) of RSM has been utilized in the optimization of several biotechnological and chemical processes. Its main advantage is the reduction in the number of experimental runs required to generate sufficient data for a statistically acceptable result [6]. RSM has been used successfully for optimization of biodiesel production in fat and oil feedstock, including Mahua oil (Madhuca indica) [1], cotton seed oil jatropha oil [7], palm oil [8].
The objectives of this paper are to investigate the interaction effects among process variables for biodiesel production from Mahua oil and to optimize the process conditions for maximum FAME yield in the presence of various catalysts. The methanol to oil molar ratio, catalyst concentration and reaction time are four variables pertaining to the biodiesel production since these variables significantly affected the process. Response surface methodology (RSM) was used to relate these three process variables with the response (biodiesel yield) and to determine the optimal combination of process variables that would maximize the biodiesel yield [12]. Mahua oil usually contains significant amounts of free fatty acids which lead to the formation of soap during the conversion of mahua oil in an alkaline catalyzed process. This can be avoided by using a solid catalyst. The catalytic properties of various solid catalysts for the transesterification reaction have been reported in literature [13, 14]. In this study the catalytic properties of the MgO/ZrO2 system will be investigated for the conversion of mahua oil into biodiesel. In particular the effect of reaction temperature, reaction time and catalyst loading on the overall mahua oil conversion will be investigated.
MATERIALS AND METHODS
Materials
Madhuca longifolia, commonly known as mahwa or Mahua, is an Indian tropical tree found largely in the central and north Indian plains and forests. The two major species of genus Madhuca found in India are Madhuca Indica (latifolia) and Madhuca longifolia (longifolia). The seed potential of this tree in India is 500,000 tons and oil potential is 180,000 tons. It is a fast-growing tree that grows to approximately 20 meters in height, possesses evergreen or semi-evergreen foliage, and belongs to the family Sapotaceae. It is found in India in the states of Chhattisgarh, Jharkhand, Uttar Pradesh, Bihar, Maharashtra, Madhya Pradesh, Kerala, Gujarat and Orissa. Oil content in latifolia is 46% and 52% in longifolia. In seeds oil content is 35% and protein at 16% [9].
Production of Mahua Oil Methyl Ester
The set of experiments was constructed and randomly conducted to evaluate the three factors such as oil to alcohol molar ratio, catalyst concentration and reaction time. Oscillatory baffled reactors are used in biodiesel production, it consisting of tube containing equally spaced orifice plate baffles. An oscillatory motion is superimposed upon the net flow of the process fluid, creating flow patterns conducive to efficiently heat and mass transfer, whilst maintaining plug flow [10]. The reactions were carried out in Oscillatory Baffled Reactor at room temperature 250C to 300C. The speed of the oscillation was fixed at 138 r/min for all experiments.
1) Step – 1: Esterification of Mahua Oil (Catalyst preparation, Characterization , Testing and Pretreatment)
Catalyst Preparation
The catalyst preparation was performed in two steps: i) ZrO2 pre-treatment and ii) MgO stabilization on ZrO2 support. ZrO2 from Sigma Aldrich (Aldrich, < 5 μm powder) was pre-treated by mixing it with distilled water in a 1:1 weight ratio, dried in air at 120ºC overnight and calcined in air at 500ºC for 10 hours. The pre-treated ZrO2 was subsequently crushed and sieved to retain the particles with sizes between 50 and 150 μm for use as catalyst support. MgO was stabilized on the support by incipient wetness impregnation of the ZrO2 support with an aqueous solution of Mg (NO3)2.6H2O (Sigma Aldrich) followed by drying in air overnight at 120oC and calcination in air at 500ºC for 8 hours. Catalyst samples with MgO loadings of 10 and 20 wt. % were successfully prepared by using an appropriate amount of Mg (NO3)2.6H2O.
Catalyst Characterization
MgO loading in the prepared catalyst samples was verified by using XRF analysis that was performed using a MAGIX PRO XRF spectrometer. Fourier Transform Infrared spectroscopy was used to determine functional groups in the catalyst samples after drying and after calcination. The analysis was performed using a Bruker Tensor 27 with a Pike Golden Gate ATR attachment in the range of 4000-400 cm-1. The surface area and pore size distribution of the prepared catalyst samples were measured using BET analysis by N2 adsorption at a temperature of 77 K on a Micromeritics ASAP 2000 apparatus. Finally x-ray diffraction (XRD) analysis was used to determine the structure of the catalyst system on a Philips PW 3040/60 X-ray diffraction apparatus with a CuKα (λ = 1.54) radiation. Samples were scanned over a 2θ range of 4–130° with a 0.02° step size and a scan speed of 0.04 s/step.
Catalyst Testing
The waste vegetable oil that was used in this study was obtained from local frying houses and had an acid value of 11.1 mg of KOH/g and a moisture content of 0.12 wt.%. It was reacted with methanol (methanol to oil molar ratio of 18:1) in a 300 cm3 stainless steel batch reactor (PARR 4842 series reactor) fitted with a pitched blade stirrer in presence of varying masses of catalyst. The reaction was performed at 60, 150, 175, 200 and 225ºC under appropriate N2 pressure to keep methanol in liquid form. A sample was collected from the reactor after every 15 min up to 1h of reaction. The product sample was cooled to room temperature and the catalyst was removed from the products by centrifugation. Methanol was removed from the sample by evaporation at 65 °C under vacuum. The products that remained after evaporation were glycerol, unreacted oil and methyl esters. These products separate into two phases: glycerol as the bottom phase and oil and methyl esters as the top phase. The top phase was used to determine the oil conversion to methyl esters using NMR data and the following equation:
Where AME is the integration value of the protons of the methyl esters; Aα-CH2 is the integration value of the methylene protons.
The pretreatment process comprised of two steps. In each step, different methanol-to-oil ratios and MgO/ZrO2 as a catalyst (5 v/v %) by varying reaction time at room temperature i.e. 25ºC - 30ºC were used to investigate their influence on the acid value of crude Mahua oil. Then, the mixture was left overnight to settle into two layers. The lower layer was removed while the upper layer which contained fatty acid methyl ester and un-reacted triglycerides were subjected to the second step of transesterification process.
2) Step – 2: Transesterification
In the second stage, treated Mahua oil obtained from first stage was transesterified in an Oscillatory Baffled Reactor by varying the oil–methanol molar ratio with various catalysts (MgO/ZrO2) having reaction time of 10min and temperature 25ºC to produce methyl ester. Eventually, the mixture was left overnight to settle into two distinct layers i.e. glycerol layer and methyl ester Layer. Upper layer of methyl ester is separated and is preserved for analysis. To optimize the above transesterification process, a two level three factor (23) fractional factorial experimental design was employed (Table I).
Table. 1: Design Experiments, With Four Parameters at Three-Level, for the Production of Mahua Methyl Esters
Analysis
The composition of methyl ester in the Pre Treatment and transterification was analyzed by using gas chromatography by using GCMS-MS Test method. The capillary column is TR Wax MS (30m × 0.25mm × 0.25um) and helium was used as carrier gas. The operating oven temperature is 280o C. The other properties checked were the acid value, density, viscosity in laboratory and calorific value by using IS: 1350(Part 2): 1970 Test method. The optimal condition was analyzed by using LAB FIT software and Design Expert 7.0 Software.
RESULT AND DISCUSSION
Optimization process
The optimization of a chemical process i.e. biodiesel using Response Surface Methodology follows the statistical approach which involves three major steps. They are selected of designing experiments, estimation of coefficients based on mathematical models and response prediction along with the conformation of mathematical model adequacy [11].
Experimental Design
Three level variables were employed in this study, requiring 27 experiments. Methanol-to-oil ratio, catalyst concentration and reaction time were the independent variables selected to optimize the yield of Mahua oil after transesterification. A response surface methodology (RSM) was used to examine the influence of these three process variables on the fatty acid methyl esters (FAMEs) content. This methodology is a sequential process that usually originates at one reasonable operating condition, and then requires three points to achieve a set of “better” conditions as rapidly and efficiently as possible.
First of all we are working on software LAB FIT for finding the LINEAR, CROSS and SQUARE VALUE for Natural variables. Now we will proceed in determining the value of Yield on the basis of linear equation. Firstly select the number of independent variable i.e. Molar Ratio (X1), Catalyst Concentration (X2) and Reaction Time (X3). Put the value of independent variable when the software provides the data sheet. Next assign the value of Y i.e. Yield. The value of X1, X2, X3 and Y is saved by the software. Now we will select the toolbar of curve fitting in the software and develop the equation for Linear Equation as below. This all value are Developed by RSM software by using the independent equation for finding the value of Y i.e. Yield for all Runs. i.e. 27 number Runs (shown in Table II).
Further we need the square equation model to find the best 20 Runs in Design Expert 7 software. The best 20 runs are the developed by the software which provide the same optimum condition and 3D graph which is tantamount to the same condition and graph which can be obtained by using the 27 number of practical experimental runs. We have to supply the data, Molar Ratio (X1), catalyst Concentration (X2) and Reaction Time (X3). The next software
predicts the value of Y from the equation i.e. Yield. The software will develop the best 20 Runs suitable for the above parameters. The best 20 runs final sheet provided by the software is demonstrated in Table III
CONCLUSION
The Response surface methodology and central composite design optimization tool is effective to determine the optimum condition for the process and the interconnected relationship. Also a second order model was obtained to predict yield as a function of methanol-to-oil ratio, catalyst concentration and reaction time. The conversion of mahua oil to biodiesel over MgO/ZrO2 catalyst has been assessed at 60, 150, 175 and 200 and 225ºC and at reaction times of 15, 30, 45and 60 min. It has been found that the conversion increases with the reaction temperature and the reaction time. The effect of MgO loading in the catalyst on the oil conversion has also been assessed and found to also be influenced by the reaction temperature. Below 175ºC the reaction was more mass transfer-controlled and the effect of MgO loading was not significant. Higher oil conversions were measured on the catalyst with the highest MgO loading for the reactions performed at 175 and 200ºC. At 225ºC the effect of MgO loading could not be evaluated because of a significant contribution of the blank ZrO2 to the oil conversion. The model was found to describe adequately the experimental range studied. The optimum condition for the predicted condition product yield is 92.7157 ml with 1:12 of methanol to oil molar ratio, 0.4 wt of catalyst concentration and 10 minutes of reaction time.