EXERGY ANALYSIS OF THIN LAYER DRYING OF PHYSIC NUTS (Jatropha curcas) USING RESPONSE SURFACE METHODOLOGY

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1 Proceedings of the OAU Faculty of Technology Conference 2015 EXERGY ANALYSIS OF THIN LAYER DRYING OF PHYSIC NUTS (Jatropha curcas) USING RESPONSE SURFACE METHODOLOGY T. B. Onifade 1,, S. O. Jekayinfa 1 and F. Uthman 2 1 Department of Agricultural Engineering, Ladoke Akintola University of Technology, P.M.B.4000, Ogbomoso, Nigeria 2 Department of Agricultural Engineering, Kwara State Polytechnic, Ilorin, Nigeria * of Corresponding Author: tbonifade@lautech.edu.ng ABSTRACT Physic nut (Jatropha curcas) is a bio-material that requires effective drying for oil and biofuel production. However, natural method of drying is ineffective due to prolonged drying time leading to low quality control, which can be addressed by exergy analyses. Therefore, this work studied the exergy efficiency of drying process of physic nuts using a crop dryer. A 2 5 factorial (2 factors and 5 levels) with three replications was used for the drying kinetics. The drying kinetics of physic nut was conducted at selected temperatures of 40, 50, 60, 70 and 80 C; and varying air velocities of 1, 2, 3, 4 and 5 m/s. The data obtained from the drying kinetics were fitted into existing exergy equations to obtain exergy inflow, exergy outflow, exergy loss and exergy efficiency. Response surface methodology was used to generate mathematical models for the exergy responses. The exergy inflow increased from to J/s as drying conditions increased. The values of exergy outflow decreased from to kj/s, exergy loss decreased from to J/s and exergy efficiency increased from to The models were able to predict well the values of the responses when the optimum variable parameters were validated as proven by the generally acceptable values of the residual percentages. The values of regression coefficient (R 2 ) and Adj. R 2 of the responses are; Ex inflow (0.8558, ), Ex outflow (0.9435, ), Ex loss (0.9564, ), Ex eff. (0.8668, ) respectively. Exergy analysis in the drying process was established. Keywords: Temperature, air velocity, exergy analysis, physic nut INTRODUCTION Physic nut (Jatropha curcas) is a multipurpose drought resistant large shrub (Belewu et al., 2010). Physic nut is classified as one of the plant oil similar to palm oil. The plant that is generally cultivated for the purpose of extracting jatropha oil is physic nut (Parawira, 2010). Jatropha plant has potential as a renewable energy crop as its oil may be used directly with slow speed diesel engine or upgraded via transesterification to conventional biodiesel. In addition to biodiesel production, the by-product of physic nuts transesterification process can be used to make a wide range of products including high quality paper, energy pellets, soap, cosmetics, and tooth paste, embalming fluid pipe, joint cement, and cough medicine and as a moistening agent in tobacco (Parawira, 2010). Physic nut is unusual among tree crops, it is known as a diesel fuel plant as the seed produce substantial quantity of oil which may not be refined (Becker and Makkar, 2009). The extracted oil from the seed has similar properties as diesel but some properties such as kinematic viscosity, solidifying point, flash point and ignition point are very high in jatropha oil (Belewu et al., 2010) and (Parawira, 2010). Drying is one of the oldest methods of preserving agricultural products, like physic nuts, which has a wide production potential in the world (Minaei and Motevali, 2012). Drying rates could be controlled by monitoring either the heat supply or the relative humidity of the surrounding air or both (Onifade et al., 2012). Drying process is therefore, controlled by the properties of the 321 drying air, often known as external parameters, and the properties of the material to be dried that is, internal parameters (Minaei and Motevali, 2012). The objective of a dryer is to supply the product with more heat than that available under ambient conditions, thus increasing sufficiently the vapour pressure of the moisture held within the crop and decreasing sufficiently the relative humidity of the drying air, thus increasing the moisture carrying capacity and ensuring sufficiently low equilibrium moisture content (Saeed et al., 2008). Natural method of drying makes use of exposure of the wet farm produce to the sun and wind. The drying of agricultural materials by the use of heated air has advantages on quality control, achievement of hygienic conditions, and reduction of product loss (Methakhup et al., 2005). Thin layer drying is the process of removal of moisture from a porous media by evaporation, in which excess drying air is passed through a thin layer of the material until the equilibrium moisture content is reached (Amer et al., 2003). Drying processes are very important for easy extraction of oil from seeds. Exergy balance, derived from both the first and second laws of thermodynamics, provides a measure of energy quality which complements the efficiency measure of energy usage (Minaei and Motevali, 2012). Exergy can be defined as a measure of maximum capacity of an energy system to perform useful work as it proceeds to a specified final state in equilibrium within the surroundings (Ahamed et al., 2011) and (Sulaiman et al., 2010). It can also be expressed as the maximum amount of work that can be extracted from a physical system in a reference state by

2 exchanging matter and energy with large reservoirs. The greater the temperature differences between an energy source and its surroundings, the greater the capacity to extract work from the system. In any real process (irreversible), exergy can be consumed or destroyed or lost since exergy cannot be conserved while energy is conserved. When there is a finite amount of energy in the universe, the amount of exergy is constantly decreasing with every physical process. The ability of energy to do work is measured by the quality of the energy contained in a substance (Ahamed et al., 2011) and (Yunardi et al., 2011). The ratio of exergy to energy substance can be considered a measure of energy quality. An exergy analysis can distinguish between high-quality and low quality energy sources and give an opportunity to match high-quality energy sources with high-temperature applications to acquire higher efficiencies (Akpinar, 2004). This work studied exergy efficiency in the drying process of the physic nut. The relationships between temperature, air velocity and drying time and the responses (exergy analysis) were determined using response surface methodology (RSM) to generate mathematical models. The RSM has the advantage to predict responses on few sets of experimental data in which all factors are varied within a chosen range (Gratuito, 2008) and (Abnisa et al., 2011). The exergy efficiency is high when exergy loss is minimized in the system. The analysis also illustrates the fact that exergy balance is a powerful diagnostic tool for energy use optimization (Abdullah and Aydin, 2010). The aim of this study is to determine exergy efficiency in the drying process of physic nut using response surface methodology. RESEARCH METHODOLOGY Sample preparation Fresh matured physic nuts (Jatropha curcas) were used for the study and the fruits were harvested from bushes around Ladoke Akintola University of Technology campus, Ogbomoso (8 o 07 N, 4 o 16 E) in Nigeria. Samples selected were sorted, peeled and washed. This preparation was done to remove impurities and foreign materials harvested with the fruits. The water on surface the physic nuts, was allowed to drain and then the samples were put in wire mesh baskets and placed in the dryer. The initial weight of the sample was measured using an electronic weighing balance, Mettler Toledo P.B 153 with an accuracy of 0.01 g; the moisture content of physic nut is 84.3% (wb) and 16.4 % (db). Each experiment was done in triplicates. Drying Tests 5 kg of the samples Jatropha curcas were put in each of the three wire mesh baskets, the initial weight was recorded and placed on the drying trays and dried as thin layer inside a locally made electric dryer. The temperature control of the electric dryer (Figure 1) was set to desired level and the required air velocity for drying was kept constant by turning the fan regulator. The experiments were carried out in the cabinet dryer operated at temperatures 40, 50, 60, 70 and 80 o C and air flow velocity of 1, 2, 3, 4 and 5 m/s. Each sample was weighed hourly using electronic balance, Mettler Toledo P.B 153 of 0.01 g accuracy and the reduction in weight was monitored until constant weight was attained. The samples were replicated three times and average reading was recorded at different temperatures and air velocity with varying drying periods. In this research work, the effects of drying conditions on the drying behavior of physic nuts, seventy-five thin-layers drying experiments, were studied. The data obtained from drying kinetics were used to carry out exergy analysis. Exergy Analysis The data obtained from drying kinetics were fitted into exergy equations such as; exergy inflow, exergy outflow, exergy loss and exergy efficiency. Thermo-hygro meter was used to measure the ambient temperature and relative humidity. The instrument was also used to read the outlet temperature and relative humidity values at each drying (inlet) temperature and air velocity. Figure 1: Pictorial view of the electric crop dryer 322

3 Exergy values The general form of exergy equation applicable for steady flow systems was employed using Eq (1) as stated by [12] and [9]. The sum of inlet and outlet air exergy of wet and dried product is calculated by second law of thermodynamics (Minaei and Motevali, 2012). dependent variables were the exergy inflow, exergy outflow, exergy loss and exergy efficiency of the drying process. The design of three levels low, medium and high are coded as -1, 0 and +1 was applied to this study. The level values of each variable and code investigated in this study is presented as shown in Table 1. = [ ] (1) = ( / ) = h T = fixed temperatures, = temperature, Exergy loss The inflow and outflow air exergy could be determined depending on the inlet or outlet temperatures of the drying chamber. The exergy loss was determined by applying Eq. (2) as used by Akpinar (2004) and Akpinar et al. (2006). = (2) Exergy Efficiency The exergy efficiency could be defined as the ratio of exergy use (investment) in the drying of the product to exergy of the drying air supplied to the system. This can be calculated using equation 3 as employed by Abdullah and Aydin (2010). = =1 (3) Statistical analysis Design expert software was applied to analyze the exergy analysis parameters. The experimental data obtained in the above procedure were analyzed by the response surface regression to generate mathematical models for exergy responses using the following secondorder polynomial Eq. (5) as applied by (Yunardi et al., 2011), (Gratuito, 2008) and (Abnisa et al., 2011). = (5) Where y is the predicted response (,, and ); and are the coded independent variables corresponding to temperature and air velocity and,,, and are intercept, linear, quadratic and interaction constant coefficients respectively. RSM package was also used for regression analysis and analysis of variance (ANOVA). Response surfaces, normal probability and plots were developed using the fitted quadratic polynomial equation obtained from regression, holding one of the independent variables at a constant value corresponding to the stationary point and changing the other variable. The independent variables being studied were temperature and air velocity. The (4) 323 Table 1: Experimental range and levels of independent variables Coded level and range Independent variables Temperature, o C Air velocity, m/s RESULTS AND DISCUSSIONS Table 2 presented the design matrix in the coded units in conjunction with experimental data and predicted values of the response variables, the exergy inflow, exergy outflow, exergy loss and exergy efficiency of the drying process of physic nut. The predicted values of the responses were calculated from quadratic model fitting techniques utilized from the Design expert software. The experimental data were utilized to generate the statistical model using multiple regression analysis method to fit the response function in accordance to Eq. 5. The results of the relationship between the independent factors, temperature and air velocity and each response are presented in Table 3, where Y 1, Y 2, Y 3, Y 4 are exergy inflow, exergy outflow, exergy loss and exergy efficiency, respectively. In Table 4, the analysis of variance (ANOVA) reveals a significant model for all response variables, where significant effect of independent variables is presented. The value of Prob.>F less than < 0.05 indicates that the quadratic model of the response variable is significant at 95% confidence level and each coefficient of determination, R 2 is presented in Table 4. Effect of drying temperature and air velocity on exergy inflow The actual values of the independent factors and the response from the application of the Design-Expert software for exergy inflow of physic nut were used for the prediction of the model equations. The ANOVA reveals a significant model for exergy inflow with P- value < 0.05 and F-value of at 95% confidence level and a coefficient of determination, R 2 of as shown in Table 4. The lack-of-fit as determined by the ANOVA P-value < 0.05 was not significant indicating that the response quadratic model represented the actual relationships of the experimental factors well within the ranges of experimental study. The model equation obtained from the analysis in terms of coded value is presented Table 3. The effect of two factors on exergy inflow, = was found out using two dimensional and normal probability plots. The ANOVA showed that the two factors can directly or indirectly influence the exergy inflow.

4 Table 2: The design matrix with experimental data predicted results Temperature Air Einflow (kj/s) Eoutflow (kj/s) Eloss (J/s) Eeff (%) velocity Run ( o C) (m/s) Exp. Pred. Exp. Pred. Exp. Pred. Exp. Pred Table 3: The fitted model equations = = = = = =0.50= = = =Exergy inflow; = Exergy outflow; = Exergy loss and = Exergy efficiency Table 4: Analysis of variance (ANOVA) of the fitted Models Source SS DF MS F-value Prob.>F For Y1 Model Residual Lack of fit Pure error Cor total R 2 =0.8558; Adj. R 2 =0.7529; Pred. R 2 = ; adeq. Prec.= For Y2 Model Residual x10-3 Lack of fit x10-3 Pure Error Cor Total R 2 = ; Adj. R 2 =0.9252; Pred. R 2 = ; Adeq. Prec. = For Y3 Model Residual Lack of fit Pure Error Cor Total R 2 = ; Adj R 2 =0.9031; Pred. R 2 = ; Adeq. Prec. = For Y Model Residual Lack of fit Pure Error Cor Total R 2 = ; Adj R 2 =0.7716; Pred. R 2 =0.0527; Adeq. Prec. = It is revealed that the linear and quadratic effects of temperature (A) and air velocity (B) were the primary determining factor of the responses followed by interaction effect of AB. As a single factor, the heating temperature was the most influential factor due to its higher F-value than that of air velocity. This is always the case for exergy and is due to the fact that the exergy value of heat is often much lower than its energy value, particularly at temperature close to reference temperature (Fadare et al. 2010). The response surface plot for the exergy inflow as shown in Figure 2 depicts the interaction between the drying temperature and air velocity from the response model. The normal probability plot in Figure 2a shows the distribution of residual value defined as the difference between the predicted and experimental data for all response variables of exergy inflow; temperatures and air velocity are forming a straight line. The results of the predicted and experimental data for all response variables are presented by design matrix in Table 2. It is observed that increases with increasing temperature and fairly constant with increase in air velocity. This indicates that effects of air velocity on were lower than those by air temperature. It is clearly seen that the residual values are normally distributed on both sides of the line indicating that the experimental data are in excellent agreement with the outstanding adequacy of the proposed quadratic model to represent the variable responses of exergy inflow in the range of temperature and air velocity: o C and m/s respectively. The "Pred R-Squared" is while the "Adj R-Squared" is as shown in Table 3. A negative "Pred R-Squared" implies that the overall mean is a better predictor of the response than the current model. "Adequate Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of indicates an adequate signal. This model can be used to navigate the design space. Figure 2b shows the 3D response surface of the interaction between the two variables; temperature and air velocity, both factors have positive effect on as shown in the model equation 6. It is also observed that increases with increasing temperature and slightly with increase in air velocity. The interaction effects between drying temperature and air velocity is as shown in Figure 2b.

5 Figure 2a: Normal probability plots for exergy inflow Figure 2b: Response surface plots for exergy inflow of air velocity on were lower than those by air temperature. The response surface plot for the exergy outflow as shown in Figure 3 depicts the interaction between the drying temperature and air velocity from the response model. The normal probability plot in Figure 3a shows the distribution of residual value defined as the difference between the predicted and experimental data for all response variables of exergy outflow; temperatures and air velocity are forming a straight line. The results of the predicted and experimental data for all response variables are presented by design matrix in Table 2. It is observed that E out increases with increasing temperature and fairly constant with increase in air velocity, where effect of temperature is more significant at P<0.05. It is clearly seen that the residual values are normally distributed on both sides of the line indicating that the experimental data are in excellent agreement with the outstanding adequacy of the proposed quadratic model to represent the variable responses of energy utilization in the range of temperature and air velocity: o C and m/s respectively. The results of the analysis are presented in Table 3 above. The "Pred R-Squared" is while the "Adj R-Squared" is "Adequate Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of indicates an adequate signal. This model can be used to navigate the design space. Figure 3b shows the 3D response surface of the interaction between the two variables; temperature and air velocity, both factors have positive effect on E out in the model equation as shown Table 3. It is also observed that E out increased with increasing temperature and slightly with increase in air velocity. The interaction effects between drying temperature and air velocity is as shown in Figure 3b. Effect of drying temperature and air velocity on exergy outflow The actual values of the independent factors and the response from the application of the Design-Expert software for exergy outflow of physic nut were used for the prediction of the model equations. The ANOVA reveals a significant model for exergy outflow with P- value < 0.05 at 95% confidence level and a coefficient of determination, R 2 of as shown in Table 4. The lack-of-fit as determined by the ANOVA P-value < 0.05 was not significant indicating that the response quadratic model represented the actual relationships of the experimental factors well within the ranges of experimental study. The model equation obtained from the analysis in terms of coded value is presented in Table 3. The effect of two factors on ( ) was found out using three dimensional and normal probability plots. The ANOVA showed that the two factors can directly or indirectly influence the energy outflow from Table 4. It is revealed that the linear and quadratic effects of temperature (A) and air velocity (B) were the primary determining factor of the responses followed by interaction effect of A 2 and AB. As a single factor, the heating temperature was the most influential factor due to its higher F-value higher than that of air velocity. The temperature at which was conducted was highly significant (p<0.05) (Table 4). This indicates that effects 325 Figure 3a: Normal probability plots for exergy outflow Effect of drying temperature and air velocity on exergy loss The actual values of the independent factors and the response from the application of the Design-Expert software for exergy loss of physic nut were used for the prediction of the model equations.

6 Figure 3b: Normal probability plots for exergy outflow The ANOVA reveals a significant model for exergy loss with P-value < 0.05 at 95% confidence level and a coefficient of determination, R 2 of as shown in Table 4 The lack-of-fit as determined by the ANOVA P- value < 0.05 was not significant indicating that the response quadratic model represented the actual relationships of the experimental factors well within the ranges of experimental study. The model equation obtained from the analysis in terms of coded value is presented in Table 3. The effect of two factors on (E loss) was found out using three dimensional and normal probability plots. The ANOVA showed that the two factors can directly or indirectly influence the exergy loss Table 4. It is revealed that the linear and quadratic effects of temperature (A) and air velocity (B) were the primary determining factor of the responses followed by interaction effect of A 2 and AB. As a single factor, the heating temperature was the most influential factor due to its higher F-value than that of air velocity. The temperature at which the E loss was conducted was highly significant (P<0.05) with F-value of (Table 4). This result is similar to the findings of Fadare et al. (2010); Corzo et al. (2008) and Mortaza et al. (2010). There are relatively low exergy losses in the production of malt drink, thin layer drying of coroba fruit and potato slices respectively which may be due to the fact that heat loss in the drying process was low. The response surface plot for the exergy loss as shown in Figure 4 depicts the interaction between the drying temperature and air velocity from the response model. The normal probability plot in Figure 4a shows the distribution of residual value defined as the difference between the predicted and experimental data for all response variables of exergy loss; temperatures and air velocity are forming a straight line. The results of the predicted and experimental data for all response variables are presented by design matrix in Table 2. It is observed that E loss increases with increasing temperature and fairly constant with increase in air velocity. This indicates that effects of air velocity on E loss were lower than those by air temperature. It is clearly seen that the residual values are normally distributed on both sides of the line indicating that the experimental data are in reasonable agreement with the outstanding adequacy of the proposed quadratic model to represent the variable responses of exergy loss in the range of temperature and air velocity: 40, 50, 60, 70 and 80 o C and 1.0, 2.0, 3.0, 4.0 and 5.0 m/s respectively. From the resulted analysis presented in Table 3, the "Pred R-Squared" is while the "Adj R- Squared" is "Adequate Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of indicates an adequate signal. This model can be used to navigate the design space. Figure 4b shows the 3D response surface of the interaction between the two variables; temperature and air velocity, both factors have positive effect on E loss in the model equation in shown Table 3. It is also observed from 3D plot that E loss increases with increasing temperature and slightly with increase in air velocity. The interaction effects between drying temperature and air velocity is as shown in Figure 4b. Figure 4a: Normal probability plots for energy loss Figure 4b: Response surface plots for exergy loss 326

7 Effect of drying temperature and air velocity on exergy efficiency The actual values of the independent factors and the response from the application of the Design-Expert software for exergy efficiency of physic nut were used for the prediction of the model equations. The ANOVA reveals a significant model for exergy efficiency with P- value < 0.05 at 95% confidence level and a coefficient of determination, R 2 of as shown in Table 4. The lack-of-fit as determined by the ANOVA P-value < 0.05 was not significant indicating that the response quadratic model represented the actual relationships of the experimental factors well within the ranges of experimental study. The model equation obtained from the analysis in terms of coded value is presented in Table 3. The effect of two factors on (E eff) was found out using three dimensional and normal probability plots. The ANOVA showed that the two factors can directly or indirectly influence the exergy efficiency from Table 4. It is revealed that the linear and quadratic effects of temperature (A) and air velocity (B) were the primary determining factor of the responses followed by interaction effect of A 2. As a single factor, the heating temperature was the most influential factor due to its higher F-value than that of air velocity. The temperature at which the E eff was conducted was highly significant (p<0.05) (Table 4). This result is in agreement with the findings of Minaei and Motevali (2012) and Abdullah and Aydin (2010) that the energetic efficiency increased as drying conditions increased in the thin layer drying of promegalis and mulberry fruits respectively. This is dissimilar in thin layer drying of potato and coroba fruit slices as exergetic efficiency decreased at high temperature in the findings of Corzo et al. (2008) and Mortaza et al. (2010). The response surface plot for the exergy efficiency as shown in Figure 5 depicts the interaction between the drying temperature and air velocity from the response model. The normal probability plot in Figure 5a shows the distribution of residual value defined as the difference between the predicted and experimental data for all response variables of exergy efficiency; temperatures and air velocity are forming a straight line. Table 2. It is observed that E eff increases with increasing temperature and fairly constant with increase in air velocity. It is clearly seen that the residual values are normally distributed on both sides of the line indicating that the experimental data are in reasonable agreement with the outstanding adequacy of the proposed quadratic model to represent the variable responses of exergy efficiency in the range of temperature and air velocity: o C and m/s respectively. The "Pred R-Squared" is while the "Adj R-Squared" is this shows that "Adequate Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of indicates an adequate signal. This model can be used to navigate the design space. Figure 5 b shows the 3D response surface of the interaction between the two variables; temperature and air velocity, both factors have positive effect on E eff as shown in the model equation 9. It is also observed from 3D plot that E eff increases with increasing temperature and slightly with increase in air velocity. The interaction effects between drying temperature and air velocity is as shown in Figure 5b, though the effect of drying temperature is higher than that of air velocity. This indicates that exergy efficiency is high at the peak of quadratic shape shown in Figure 5b. This means if the temperature is slightly increased, the exergy efficiency will drop. Figure 5b: Response surface plots for exergy efficiency Figure 5a: Normal probability plots for exergy efficiency The results of the predicted and experimental data for all response variables are presented by design matrix in 327 CONCLUSION The effect of process variables (temperature and air velocity) on the exergy response variables has been established. The exergy inflow and exergy efficiency increased from 0.18 to 1.74 J/s and 77.4 % to 99.6 %, respectively while exergy outflow and exergy loss decreased from 0.73 to 0.03 kj/s and 0.10 to J/s, respectively as drying conditions increased. The respective values of coefficient of determination (R 2 ) and adjusted R 2 of the exergy responses are; exergy inflow (0.856, 0.753); exergy outflow (0.944, 0.903); exergy loss (0.956, 0.903) and exergy efficiency (0.867, 0.772). The models were able to predict well the values of the responses when the optimum variable parameters were validated as proven by the generally acceptable values of the residual percentages. The optimum conditions to produce desired quality products were obtained at 80 o C

8 and 5 m/s; hence exergy efficiency in the drying process was established. REFERENCES Abdullah, A. and Aydin, D. Energy and exergy analyses of the thin layer drying of mulberry in a forced solar dryer. Journal of Energy, 35, , Abnisa, F., Daud, W. M. and Sahu, J.N. Optimization and characterization studies on bio-oil production from palm shell by pyrolysis using response surface methodology. Journal of Biomass and Bio- energy, 35, , Akpinar, E. K. Exergy and exergy analyses of drying red pepper slices in a convective type dryer. International Journal of Heat and Mass Transfer, 31, , Akpina, E. K., Midilli, A. and Bicer, Y. The first and second law analyses of thermodynamics of pumpkin drying process. Journal of Food Engineering, 72, , Ahamed, J. U., Saidur, R., Masjuki, H. H., Mekhilef, S., Ali, M. B., Furqon, M. H. An application of energy and exergy analysis in agricultural sector of Malaysia. Journal of Energy Policy, 39, , Amer, B. M., Morcos, M. A. and Sabbah, M. A. New method for the determination of drying rates of fig fruits depending on empirical data under conditions suiting solar drying. The International Conference Institute of Agricultural Engineering LUA Raudondvaris, 18-19, Becker, K. and Makkar, H. Physic nut (Jatropha curcas): A potential source for tomorrow s oil and biodiesel. Journal of Lipid Toxicology, 20, , Belewu, M. A., Adekola, F. A., Adebayo, G. B., Ameen, O. M., Muhammed, N. O., Olaniyan, A.M. Adekola, O. F. and Musa, A.K. Physico-chemical characteristics of oil and biodiesel from Nigerian and Indian Jatropha curcas seeds. International Journal of Biological and Chemical Sciences, 4, , Corzo, O. Nelson, B. Alberto, V. and Angel, P. Energy and Exergy Analyses of the thin layer of drying of coroba slices. Journal of Food Engineering, 86, , Fadare, D. A., Nkpubre, D. O., Oni, A. O., Falana, A., Waheed, M. A. and Bamiro, O. A. Energy and Exergy Analyses of Malt Drink Production in Nigeria. Journal of Energy, 35, (12): , Gratuito, M. K. B., Panyathanmaporn, T., Chumnanklang, R. A., Sirinuntawittaya, N. and Dutta, A. Production of activated carbon from coconut shell: Optimization using response surface methodology. Journal of Bioresource Technology; 99, , Midilli, A. and Kucuk, H. Mathematical modeling of thin layer drying of pistach solar energy. Energy Conversion and Management, 44, , Methakhup, S., Chiewchan, N. and Devahastin, S. Effects of drying methods and conditions on drying kinetics and quality of Indian goose berry flake. Journal of Swiss Society of Food Science and Technology, 38, , Minaei, S. and Motevali, A. Effects of microwave and pretreatment on the energy and exergy utilization in thin layer drying of sour pomegranate arils. Scientific Paper UDC Chemical Industry and Chemical Engineering Quarterly, CICEQ: 18, 63-73, Mortaza, A. Mohammad, H. K., Akbar, A Energy and exergy analyses of thinlayer drying of potato slices in semi-industrial continuous band dryer. International Journal of Drying Technology, 26, , Onifade, T. B., Akande, F. B., Idowu, D. O. and Bello,I.A. Desorption isotherms for Local Variety of Tomatoes (Lycopersicum lycopersicum). International Journal of Biotechnology and Biochemistry, 8, , Parawira, W. Biodiesel production from Jatropha curcas. Science Research and Essays, Academic Journals, 5, , Saeed, I.E., Sopian, K. and Abidin, Z. Thin-Layer Drying of Roselle (I): Mathematical Modeling and Drying Experiments. International Journal of Agricultural Engineering the CIGR E-journal, 10, 15, Sulaiman, M.A. Oni, A.O. and Fadare, D.A. Energy and exergy analysis of a vegetable oil refinery. Journal of Energy and Power Engineering, 4, , Yergin, D. Stobaugh, R. and Weeks, J. World Energy Supply. Microsoft student Encarta Yunardi, Zulkifli, and Masrianto. Response Surface Methodology Approach to optimizing process variables for densification of rice straw as a rural alternative solid fuel. Journal of Applied Sciences, 11, ,