**4.1.1 Statistical analysis of experimental results**

256 Sustainable Growth and Applications in Renewable Energy Sources

**Solvent = Hexane**  1 4.27 42.52 65 710 7 2 4.14 41.35 55 710 7 3 4.21 41.98 55 500 7 4 4.23 42.25 65 500 7 5 3.83 38.15 65 500 6 6 3.70 36.92 55 500 6 7 4.30 42.95 65 710 6 8 4.34 43.26 55 710 6 **Solvent = Ethanol**  1 2.57 25.65 75 710 7 2 3.71 27.05 65 710 7 3 2.35 23.45 65 500 7 4 2.77 27.65 75 500 7 5 2.18 21.74 75 500 6 6 2.44 20.34 65 500 6 7 3.45 34.28 75 710 6 8 3.92 38.96 65 710 6 Table 4.2. Oil yield at various conditions from the second run with hexane and ethanol as

Temp (oC)

Volume of ethanol (cm3)

350 25 7.143 300 35 11.667 250 37.5 14.880 150 43 28.667

Particle Size (µm)

Resident Time (hr)

% Ethanol concentration

from rice husk

% wt of oil extracted

S/N

the solvent

Rice husk

Substrate Volume of hydrolysate

(cm3)

Table 4.3. Ethanol production using Zymomonas mobilis from distillation process

Boiling point (oC) 78.15 78.3 Density (g/cm3) 0.789 0.787 Viscosity 1.20 1.34 Flammability Flammable Flammable Flash point (oC) 13 14.5 Refractive index 1.3614-1.3618 1.3626

Table 4.4. Properties of produced ethanol compared to commercial ethanol

Properties Commercial grade ethanol Bio-ethanol produced

Appearance Clear, colourless liquid Clear, colourless liquid

wt of oil extracted (g)

Statistical analyses were conducted with the aim of developing a model to represent the relationship between the factors investigated and the yield of oil from the moringa oleifera seeds with hexane and ethanol as the extraction solvent. Table 4.4 shows an estimation of upper and lower levels of the three factors (temperature, particle size and time). While Tables 4.5 and 4.6 indicates factorial experimental design results with n-hexane and ethanol as the extraction solvent respectively.

The average effect of a factor which is described as the change in response produced by a change in the level of factor response produced by a change in the level of factor averaged over the levels of other factors. This has been calculated and subsequently tabulated in Table 4.7 for n-hexane and ethanol.


Table 4.4. Factors and their coded levels


Table 4.5. 23 Factorial experimental design results using n-hexane as extraction solvent


Table 4.6. 23 Factorial experimental design results using ethanol as extraction solvent

Extraction and Optimization of Oil from

Sum of square

Total 15

Table 4.10. Basic statistical test (n-Hexane)

Table 4.11. Basic statistical test (Ethanol)

Table 4.12. Statistical calculated values for G-test and F-test

Sources of variation

Moringa Oleifera Seed as an Alternative Feedstock for the Production of Biodiesel 259

A 0.5814 1 0.5814 22.9526 1.0561 B 362.6206 1 362.6206 21.8374 658.9508 C 44.8578 1 44.8578 32.4602 81.5152 Ab 36.9372 1 36.9372 22.2200 67.1219 Ac 2.1246 1 2.1246 31.3450 3.8608 Bc 197.4756 1 197.4756 22.5700 358.8508 Abc 3.0625 1 3.0625 32.4602 5.5681

freedom Mean square Expected

mean square

Fo

Degree of

Error 0.5814 8 0.5503 32.0776

Table 4.9. Analysis of variance (ANOVA) for the solvent extraction of oil using ethanol

 Yr1 Yr2 YT Yav Ycal (Yr1-Ycal)2 (Yr2-Yav)2 (Yr1-Yav)2 1 42.03 42.52 84.55 42.28 39.03 9.0000 0.0576 0.0625 2 41.38 41.35 82.73 41.37 39.56 3.3124 0.0004 0.0001 3 42.22 41.98 84.20 42.10 41.40 0.6724 0.0144 0.0144 4 42.81 42.25 85.06 42.53 40.62 4.7961 0.0784 0.3136 5 38.58 38.15 76.73 38.37 41.93 11.2200 0.0484 0.0441 6 37.78 36.92 74.70 37.35 41.15 11.3600 0.1849 0.1849 7 43.01 42.95 85.96 42.98 42.99 0.0004 0.0009 0.0009 8 43.17 43.26 86.43 43.22 43.52 0.0081 0.0016 0.0025 40.3694 0.3866 0.6230

 Yr1 Yr2 YT Yav Ycal (Yr1-Ycal)2 (Yr2-Yav)2 (Yr1-Yav)2 1 24.75 25.65 50.40 25.20 22.95 3.2400 0.2025 0.2025 2 28.84 27.05 55.89 27.95 21.84 49.0000 0.8100 0.7921 3 22.16 23.45 45.62 22.81 32.46 106.0900 0.4096 0.4225 4 26.67 27.65 54.32 27.16 22.22 19.80000 0.2401 0.2401 5 20.85 21.74 42.56 21.28 31.35 0.8454 0.2116 0.2116 6 19.90 20.34 40.24 20.12 22.57 7.1289 0.0484 0.0484 7 35.32 34.28 69.60 34.80 32.46 8.1796 0.2704 0.2704 8 38.71 38.96 77.67 38.84 32.08 43.9569 0.0144 0.0169 238.24 2.2070 2.2405

Test From Statistical Table Calculated

G-test 0.6800 0.6171 0.5003 F-test 34.8073 35.9825

n-Hexane Ethanol



Variance (ANOVA) analysis, which enables one to examine the magnitude and direction of the factors' effect and determine which variable are likely to be important was also conducted and the results are presented in Table 4.8 and 4.9 respectively for n-hexane and methanol as the extraction solvent. Variance analysis also helps to determine the statistical significance of the regression coefficients (βi ). The level of significance was assumed to be 5% (α = 0.05), which implies that there are about five chances in hundred that reject the hypothesis when it should be accepted: i.e. 95% confidence that right decision is made. Therefore the critical value for each of the F-ratio F {α ,dfr, abc(n -1 )}i.e. F(0.05,1,8 is equal to 5.32 from statistical table is equal to 5.32 from statistical table. The F-ratios were compared with this critical value (5.32) and the null hypothesis using Fcal > F(0.05,1,8) = 5.32. The magnitude of the effects when n-Hexane was used as the extraction solvent indicates that particle size (factor B) is dominant and has a high significant followed by the extraction time (factor C) and the effect of factor A, extraction temperature which is relatively low.


Table 4.8. Analysis of variance (ANOVA) for the solvent extraction of oil using n-hexane

The magnitude of the effects when ethanol was used as the extraction solvent, clearly shows that particle size (factor B) is dominant and has a high significant followed by the interaction of factor A, extraction temperature and factor C, extraction time and the effect of factor A, extraction temperature which is relatively low. Presented in Tables 4.10 and 4.11 are the basic statistical test on the yield of oil from the moringa oleifera seed with n-hexane and ethanol as the extraction solvent respectively. While Table 4.12 present the statistical calculated values of G and F test.


Table 4.9. Analysis of variance (ANOVA) for the solvent extraction of oil using ethanol


Table 4.10. Basic statistical test (n-Hexane)

Factors and interactions Main effects (n- hexane) Main effects (Ethanol) A 0.5300 -0.3813 B 2.4975 9.5213 C 1.5900 -3.3488 Ab -0.1925 -3.0338 Ac 0.1400 0.7288 Bc -2.8675 -7.0263 Abc 0.4325 -0.8750

Table 4.7. Effects and interactions for solvent extraction of oil using n-hexane and ethanol Variance (ANOVA) analysis, which enables one to examine the magnitude and direction of the factors' effect and determine which variable are likely to be important was also conducted and the results are presented in Table 4.8 and 4.9 respectively for n-hexane and methanol as the extraction solvent. Variance analysis also helps to determine the statistical significance of the regression coefficients (βi ). The level of significance was assumed to be 5% (α = 0.05), which implies that there are about five chances in hundred that reject the hypothesis when it should be accepted: i.e. 95% confidence that right decision is made. Therefore the critical value for each of the F-ratio F {α ,dfr, abc(n -1 )}i.e. F(0.05,1,8 is equal to 5.32 from statistical table is equal to 5.32 from statistical table. The F-ratios were compared with this critical value (5.32) and the null hypothesis using Fcal > F(0.05,1,8) = 5.32. The magnitude of the effects when n-Hexane was used as the extraction solvent indicates that particle size (factor B) is dominant and has a high significant followed by the extraction time

(factor C) and the effect of factor A, extraction temperature which is relatively low.

freedom Mean square Expected

A 1.1236 1 1.1236 39.5575 2.2940 B 22.5150 1 22.5150 41.4025 45.9677 C 10.1124 1 10.1124 40.6175 20.6460 Ab 0.1482 1 0.1482 41.9325 0.3026 Ac 0.0784 1 0.0784 41.1475 0.1601 Bc 32.8902 1 32.8902 42.9925 67.1503 Abc 0.7482 1 0.7482 43.5225 1.5276

Table 4.8. Analysis of variance (ANOVA) for the solvent extraction of oil using n-hexane

The magnitude of the effects when ethanol was used as the extraction solvent, clearly shows that particle size (factor B) is dominant and has a high significant followed by the interaction of factor A, extraction temperature and factor C, extraction time and the effect of factor A, extraction temperature which is relatively low. Presented in Tables 4.10 and 4.11 are the basic statistical test on the yield of oil from the moringa oleifera seed with n-hexane and ethanol as the extraction solvent respectively. While Table 4.12 present the statistical

mean square

Fo

Degree of

Error 3.9182 8 0.4898

Sources of variation

Sum of square

Total 15

calculated values of G and F test.


Table 4.11. Basic statistical test (Ethanol)


Table 4.12. Statistical calculated values for G-test and F-test

Extraction and Optimization of Oil from

Moringa Oleifera Seed as an Alternative Feedstock for the Production of Biodiesel 261

oil such as oil from moringa oleifera seed which is the focus of this study. In order to depend fully on less expensive materials for the production of biodiesel, this study also investigates production bio-ethanol from rice husk. The results of the effects of various parameters, such as extraction time, particle size, temperatures and types of extraction solvents influence the rate extraction of oil from moringa oleifera. In this study, effect of extraction on the yield of oil from moringa oleifera seed was investigated using n-hexane and ethanol as the extraction solvent. It has been reported that the entrainer chosen has to be a good selective solvent with sufficient low viscosity for it to circulate freely. A relatively pure solvent will initially increase the extraction rate, but as the extraction proceeds, the concentration of solute will increase and the extraction rate will progressively decrease, firstly because the concentration gradient will reduce and secondly, the solvent will

The results of analysis yield a model equation presented in Equation 7, which was not only used to obtain the effect of one factor on the other but also their interaction. From the analysis it was discovered that when n-hexane was used as the extraction solvent, the effect of factor B (particle size), has the highest magnitude of 2.4975 followed by the effect of factor C (extraction time) with a magnitude of 1.5900, the effect of factor A, extraction temperature is relatively low with a magnitude value of 0.5300. A "– "in a 2k model equation implies an inverse proportionality, while "+" implies a direct proportionality. This means that the extraction temperature, X1, the particle size X2 and the extraction time X3 are all directly proportional to the oil yield (Averill and Kelton, 1996 and Onifade, 2001). Results obtained on the effect of extraction temperature on the yield of oil as expected indicate that rate of extraction of oil from moringa oleifera seed is increases with increase in temperature. Increase in temperature positively affects the diffusivity of the solvent into the inner part of the seed and consequently aid the solubility of the oil in the solvent which increase the rate of extraction of oil from the seed. Though results obtained shows that increase in temperature favoured the extortion of oil, care must be taken not to exceed the limit, which is the boiling point of the solvent n-hexane. Exceeding the boiling temperature of the solvent could result into the evaporation of the solvent consequence of which is the quick usage of the solvent, which is not economical. Also investigated in this research is the effect of particle size on the rate of extraction of oil from the seed and from the model equation, the result reveal that the oil yield is directly proportional to the particle size i.e. oil yield increases with a increase in the particle size. It has been reported that the size of particle could influence the extraction rate and the yield of oil in a number of ways. For instance, the smaller the size of the particle the higher the interfacial area between the solid and the solvent, the higher the rate of transfer of the solute (oil), and the smaller the distance the solute must be diffused within the solid particle, hence the higher the rate of extraction of oil. It is therefore desirable that the range of the particle size should be small so that each particle will require approximately the same time of extraction. It is therefore important that particles size are well selected not to exist a critical particle size at which oil yield will no longer be optimum. Since above the optimum particle size, there will be a reduction in the surface area of the oil molecules exposed to the solvent for dissolution. The effect of time on the extraction of oil from moringa oleifera seed was studied using n-hexane as the solvent. Results obtained as presented in Table 4.1 and the statistical model (Equation 7) indicates that the oil yield has a direct proportion effect with the extraction time. This means that increasing the extraction time will bring about a high yield of oil, however, there is the need to optimized extraction time to save cost of production of oil from the seed. This is

generally become more viscous and thereby decreases its penetration power.

Based on the statistical analysis of experimental results, the regression model for the 23 design analysis is therefore given by Equation 5, i.e.

$$\mathbf{Y} = \mathbf{q}\_0 + \mathbf{q}\_1 \mathbf{x}\_1 + \mathbf{q}\_2 \mathbf{x}\_2 + \mathbf{q}\_3 \mathbf{x}\_3 + \mathbf{q}\_{12} \mathbf{x}\_1 \mathbf{x}\_2 + \mathbf{q}\_{13} \mathbf{x}\_1 \mathbf{x}\_3 + \mathbf{q}\_{23} \mathbf{x}\_2 \mathbf{x}\_3 + \mathbf{q}\_{123} \mathbf{x}\_1 \mathbf{x}\_2 \mathbf{x}\_3 \tag{5}$$

The Residual for 23 designs for the yield of oil from moringa oleifera seed kernel using n-Hexane can now be obtained by considering only the three largest effects, which are B, C and A. Equation 5 therefore reduced to;

$$\mathbf{Y} = \mathbf{a}\_0 + \mathbf{a}\_1 \mathbf{x}\_1 + \mathbf{a}\_2 \mathbf{x}\_2 + \mathbf{a}\_3 \mathbf{x}\_3 \tag{6}$$

$$a\_0 = \frac{1}{8} \sum \mathbf{(Y\_l)}$$

$$\mathbf{1} = \mathbf{1}$$

$$a\_{\boldsymbol{\jmath}} = \frac{1}{8} \sum \left( Y\_{\boldsymbol{\jmath}} \mathcal{S}\_{\boldsymbol{\jmath}} \right)$$

Where αj is the coefficient of factor j and Si is the sign of eight factor combinations from the design matrix table. Thus

$$\mathbf{Y} = 41.27\mathbf{\tilde{5}} + 0.265\mathbf{\tilde{X}}\_1 + 1.187\mathbf{\tilde{5}}\mathbf{\tilde{X}}\_2 + 0.795\mathbf{\tilde{X}}\_3\tag{7}$$

Similarly, the residual for 23 designs for the yield of oil from moringa oleifera seed with ethanol as the extraction solvent can be obtained by considering only the three largest main effects, which are B, AC and A. The regression equation can therefore reduced to

$$\mathbf{Y} = \mathbf{q}\_0 + \mathbf{q}\_1 \mathbf{x}\_1 + \mathbf{q}\_2 \mathbf{x}\_2 + \mathbf{q}\_{1,3} \mathbf{x}\_1 \mathbf{x}\_3 \tag{8}$$

Thus

$$\mathbf{Y} = 27.1488 - 0.1913\mathbf{X}\_1 + 4.7538\mathbf{X}\_2 + 0.3663\mathbf{X}\_1\mathbf{X}\_3\tag{9}$$

### **4.2 Discussion of results**

The world is presently on the brinks of an environmental disaster owing to the build-up of harmful materials from the use of fossil oil as base oil for lubricants. Coupled with the prediction that the fossil oil will ultimately run out sometime in the future, there is therefore, the urgent need to source for replaceable and environmentally friendly base oil for lubricants. Biodiesels which is the product of transesterification of vegetables oil is considered as perfect alternative and sustainable energy sources, due to less emission and availability. The Promotion of Biomass faces an increasing rate of awareness, research and adoption. One way of increasing the adoption rate is to promote the utilization of the product from plants such as the leaves, fruits, stem, flowers and the roots of the trees. Presently, the alternative way of utilizing the fruit is to extract oil from the seeds, most of which are edible oil which is a source of concern. Despite the wide acceptance of biofuel as alternative energy to supplement or replace the fossil fuel, it will be wise to recognise the consequences of the new technology on the society. For instance, the production of biodiesel from edible oil could result in pressure on farmers, consequence of which is food shortage and environmental problem as a result of deforestation. Hence the need to produce the biodiesel from non-edible oil or from the sources that are not sources of production of edible

Based on the statistical analysis of experimental results, the regression model for the 23

 Y = αo + α1 x1 + α2 x2 + α3 x3 + α1 2x1 x2 + α1 3 x1 x3 + α2 3 x2 x3 + α1 2 3 x1 x2 x3 (5) The Residual for 23 designs for the yield of oil from moringa oleifera seed kernel using n-Hexane can now be obtained by considering only the three largest effects, which are B, C

8�����

8������� Where αj is the coefficient of factor j and Si is the sign of eight factor combinations from the

Similarly, the residual for 23 designs for the yield of oil from moringa oleifera seed with ethanol as the extraction solvent can be obtained by considering only the three largest main

The world is presently on the brinks of an environmental disaster owing to the build-up of harmful materials from the use of fossil oil as base oil for lubricants. Coupled with the prediction that the fossil oil will ultimately run out sometime in the future, there is therefore, the urgent need to source for replaceable and environmentally friendly base oil for lubricants. Biodiesels which is the product of transesterification of vegetables oil is considered as perfect alternative and sustainable energy sources, due to less emission and availability. The Promotion of Biomass faces an increasing rate of awareness, research and adoption. One way of increasing the adoption rate is to promote the utilization of the product from plants such as the leaves, fruits, stem, flowers and the roots of the trees. Presently, the alternative way of utilizing the fruit is to extract oil from the seeds, most of which are edible oil which is a source of concern. Despite the wide acceptance of biofuel as alternative energy to supplement or replace the fossil fuel, it will be wise to recognise the consequences of the new technology on the society. For instance, the production of biodiesel from edible oil could result in pressure on farmers, consequence of which is food shortage and environmental problem as a result of deforestation. Hence the need to produce the biodiesel from non-edible oil or from the sources that are not sources of production of edible

�� � <sup>1</sup>

�� � <sup>1</sup>

effects, which are B, AC and A. The regression equation can therefore reduced to

Y = αo + α1 x1 + α2 x2 + α3 x3 (6)

Y = 41.275 + 0.265X1 + 1.1875X2 + 0.795X3 (7)

Y = αo + α1 x1 + α2 x2 + α1 3 x1 x3 (8)

Y = 27.1488 – 0.1913X1 +4.7538X2 + 0.3663X1 X3 (9)

design analysis is therefore given by Equation 5, i.e.

and A. Equation 5 therefore reduced to;

design matrix table. Thus

**4.2 Discussion of results** 

Thus

oil such as oil from moringa oleifera seed which is the focus of this study. In order to depend fully on less expensive materials for the production of biodiesel, this study also investigates production bio-ethanol from rice husk. The results of the effects of various parameters, such as extraction time, particle size, temperatures and types of extraction solvents influence the rate extraction of oil from moringa oleifera. In this study, effect of extraction on the yield of oil from moringa oleifera seed was investigated using n-hexane and ethanol as the extraction solvent. It has been reported that the entrainer chosen has to be a good selective solvent with sufficient low viscosity for it to circulate freely. A relatively pure solvent will initially increase the extraction rate, but as the extraction proceeds, the concentration of solute will increase and the extraction rate will progressively decrease, firstly because the concentration gradient will reduce and secondly, the solvent will generally become more viscous and thereby decreases its penetration power.

The results of analysis yield a model equation presented in Equation 7, which was not only used to obtain the effect of one factor on the other but also their interaction. From the analysis it was discovered that when n-hexane was used as the extraction solvent, the effect of factor B (particle size), has the highest magnitude of 2.4975 followed by the effect of factor C (extraction time) with a magnitude of 1.5900, the effect of factor A, extraction temperature is relatively low with a magnitude value of 0.5300. A "– "in a 2k model equation implies an inverse proportionality, while "+" implies a direct proportionality. This means that the extraction temperature, X1, the particle size X2 and the extraction time X3 are all directly proportional to the oil yield (Averill and Kelton, 1996 and Onifade, 2001). Results obtained on the effect of extraction temperature on the yield of oil as expected indicate that rate of extraction of oil from moringa oleifera seed is increases with increase in temperature. Increase in temperature positively affects the diffusivity of the solvent into the inner part of the seed and consequently aid the solubility of the oil in the solvent which increase the rate of extraction of oil from the seed. Though results obtained shows that increase in temperature favoured the extortion of oil, care must be taken not to exceed the limit, which is the boiling point of the solvent n-hexane. Exceeding the boiling temperature of the solvent could result into the evaporation of the solvent consequence of which is the quick usage of the solvent, which is not economical. Also investigated in this research is the effect of particle size on the rate of extraction of oil from the seed and from the model equation, the result reveal that the oil yield is directly proportional to the particle size i.e. oil yield increases with a increase in the particle size. It has been reported that the size of particle could influence the extraction rate and the yield of oil in a number of ways. For instance, the smaller the size of the particle the higher the interfacial area between the solid and the solvent, the higher the rate of transfer of the solute (oil), and the smaller the distance the solute must be diffused within the solid particle, hence the higher the rate of extraction of oil. It is therefore desirable that the range of the particle size should be small so that each particle will require approximately the same time of extraction. It is therefore important that particles size are well selected not to exist a critical particle size at which oil yield will no longer be optimum. Since above the optimum particle size, there will be a reduction in the surface area of the oil molecules exposed to the solvent for dissolution. The effect of time on the extraction of oil from moringa oleifera seed was studied using n-hexane as the solvent. Results obtained as presented in Table 4.1 and the statistical model (Equation 7) indicates that the oil yield has a direct proportion effect with the extraction time. This means that increasing the extraction time will bring about a high yield of oil, however, there is the need to optimized extraction time to save cost of production of oil from the seed. This is

Extraction and Optimization of Oil from

**5. Conclusion** 

**6. Acknowledgment** 

**7. References** 

2516.

0233.

Technology, Minna, Nigeria is also appreciated.

Moringa Oleifera Seed as an Alternative Feedstock for the Production of Biodiesel 263

The need for alternative sources of energy other than fossil fuel gained momentum recently, and biofuel is considered perfect alternative sources of energy that is sustainable and reliable. However, the possibility of producing biofuel in commercial quantities is not certain; this is blame on the consequence effects of producing the biofuel from vegetable oil, as this can lead to food shortage. To achieve commercial availability of biofuel, it is therefore important to produce biofuel from non-edible oil or from the sources that are not popular sources of edible oil. To achieve commercial realisation of biodiesel production, this work focuses on the extraction and optimization of oil from moringa oleifera seed as an alternative feedstock for the production of biodiesel. Analysis of results indicates that when n-hexane was employed as the extraction solvent, the effect of particle size has the highest effect with magnitude of 2.50, followed by the extraction temperature with magnitude of 1.59, while the effects of extraction time was the lowest with the magnitude value of 0.53. With ethanol as the extraction solvent, particle size also has the highest dominance of 9.52, while the interaction of temperature and time has an effect of 0.73, while the extraction temperature was -0.3813. Based on these results it can be deduce that for an appreciable yield of oil to be achieved with ethanol as the solvent, the particle size and interaction of temperature and time are the factors which have high significance. Results obtained from the production of bio-ethanol from husk indicate that, it is possible to produced bio-ethanol

from rice husk, which is also a major a feedstock in the production of biodiesel.

National research foundation (NRF), South Africa (Grant BS 123456) and Faculty of Science, Engineering and Technology are highly appreciated for their support. Federal University of

Abdulakreem, A. S & Odigure, J.O. (2002). Radiative Heat Evaluation from Gas Flaring By

Abdulkareem, A. S. (2005). Evaluation of ground level concentration of pollutant due to gas

Abdulkareem, A. S.(2005). Urban Air Pollution Evaluation by Computer Simulation: A Case

Abdulkareem, A.S & Odigure, J.O. (2006). Deterministic Model for Noise Dispersion from

Cluj - Napoca Romania. Issue 6, pp 29 – 42, ISSN 1583 - 1078.

Computer Simulation. Journal of Association for the advancement of Modelling and simulation in enterprises, Lyon France. Vol.71, No 2, pp 19 – 35, ISSN 0761 –

flaring by computer simulation: A case study of Niger – Delta area of Nigeria. Leonardo Electronic Journal of Practices and Technologies, Technical University of

study of Petroleum Refining Company, Nigeria. Leonardo Journal of Science Technical University of Cluj - Napoca Romania. Issue 6, pp 17 – 28, ISSN: 1583 -

gas Flaring: A case study of Niger – Delta area of Nigeria. Journal of Chemical and Biochemical Engineering, Croatia Q 20, No 2, pp 139 – 146, ISSN 0352 – 9568.

because higher extraction time above the optimum time cannot yield oil more than the maximum oil content in the seed kernel.

With ethanol as extraction solvent, the effect of factor B (particle size) has the highest dominance of 9.5213. The interaction of factor AC, temperature and extraction time has an effect of 0.7288, while temperature has the least effect of -0.3813. The 23 factorial analyses, give a model equation (Equation 8). From the statistical model, it can be seen that the extraction temperature, X1, is inversely proportional to the oil yield, the particle size X2 and the extraction time X3 are all directly proportional to the oil yield. The inverse proportion effect of temperature on the extraction of oil from moringa oleifera seed is an indication that a range of 65 – 70oC is adequate to give a better yield of oil. Above this temperature range, the effect of temperature on the oil yield is negative. Thus a reduction in the extraction temperature from the maximum will result in an increase in the yield of oil. Results obtained also shows that the oil yield is directly proportional to the particle size. Hence the effect of particle size on oil yield increases with an increase in the particle size this is because greater surface area of the oil molecules exposed to solvent for dissolution. In the same vein, increase in the extraction time leads to increase in the yield of from moringa oleifera seed with ethanol as the solvent.

Production of ethanol from starch or sugar based feedstock is among man's earliest ventures into value added processing, while the basic steps remain the same, the process has been considerably refined in recent years, leading to a very efficient process. Bio-ethanol is an alcohol made by fermenting sugar components of biomass (Bailey and Ollis, 1986; Elba and Antenieta, 1996). Apart from food and pharmaceutical uses, bio-ethanol is finding alternative uses as motor fuel and fuel additive, ethanol as motor fuel is preferred to fossil fuel in that, it is environmentally friendly, comes from a renewable source and has a higher performance in engine (Eurasia, 2009).It can be mass-produced by fermentation of sugars or by hydration of ethylene from petroleum and other sources (Eurasia, 2009). Hence the need to produce bio-ethanol from relatively inexpensive and readily available raw materials like rice husks. In this study, rice husks were used to produce ethanol through hydrolysis and fermentation with Zymomonas mobilis. In the process of fermentation, the organism fermented the substrate (rice husk) to produce ethanol, Zymomonas mobilis possesses alcohol dehydrogenase (ADH) and pyruvate decarboxylase (PDC) which is key enzymes in ethanol fermentation from organic substrate as stated by Gunasegaram and Chandra (1998). Results obtained of bio-ethanol from rice husks as presented in Table 4.3 indicates that the volume of bio-ethanol is influence by the volume of hydrolysate. The maximum volume of bio-ethanol produced was 43cm3 from 150 cm3 of hydrolysate, while 25cm3 of bio-ethanol was produced from 350cm3 of the hydrolysate. The high yield of bio-ethanol from rice husk may be due to high carbohydrates contents of rice husk or the high ethanol tolerance of Zymomonas mobilis and the presence of alcohol dehydrogenase in Zymomonas mobilis which appears to facilitate ethanol formation even at high ethanol concentration. Presented in Table 4.2 are the properties of oil, such as viscosity, refractive index, density and flash point of the bio-ethanol produced from rice husk, which compared favorably with those of the commercially available methanol. The slight variation between the values of properties of bio-ethanol and that of the commercially available methanol can be attributed the sources of production and experimental methods employed. It can therefore be inferred that the bioethanol produced from rice husk cab be used as an alternative feedstock for the production of biodiesel base on the properties of bio-ethanol presented in table 4.2.
