Preliminary investigations of trans-esterification process parameters for biodiesel production

Preliminary investigations of trans-esterification process parameters for biodiesel production Sunil Dhingra Assistant professor, Mechanical Engineering Department, UIET, Kurukshetra University, Kurukshetra, Haryana ABSTRACT The current work is focused on predicting various trans-esterification process parameters and their ranges by studying various contributions of researchers. The preliminary screening of the predicted input process parameters is also observed using one factor a time approach. It is observed that all the parameters considered are significant for fully conversion of biodiesel. Keywords: Preliminary investigations, One factor at a time approach (OFAT), biodiesel production INTRODUCTION The primary aim is to enhance the biodiesel production from various edible and non-edible oils using various optimization techniques as discussed in previous chapter. The various process parameters that affect the biodiesel production have been identified. These parameters are discussed in detail in this section. (i) Ethanol concentration (EC) Alcohol is one of the required additives used in biodiesel production. Methanol is generally utilized in the production of biodiesel because it is easily available in the market. The production of biodiesel using ethanol is found to be in higher side as reported in the literature [James et al., 1996]. Also ethanol can be easily produced from various natural resources like sugar, starch, cellulose etc. In the present work ethanol was used for the production of biodiesel. The amount of ethanol in reference to oils (edible/non-edible) was observed in the range 10-30 % (by weight of oil) by as observed by previous research studies [Demirbas, 2005; Azcan and Danisman, 2007; Berchmans and Hirrata, 2008; Domingos et al., 2008; Abdullah et al., 2009; Dhingra et al., 2013a; Dhingra et al., 2013b; Dhingra et al., 2014a; Dhingra et al., 2014b; Dhingra et al., 2014c; Dhingra et al., 2014d; Dhingra et al., 2015; Dhingra et al., 2016]. (ii) Reaction time (Rt) It is the time for mixing of catalyst, oil and ethanol. The production rate can be enhanced by reducing reaction time. The range of reaction time was found to be 20-70 minutes as observed from previous works [Halim et al., 2009; Jeong and Park, 2009; Jena et al., 2010; Juan et al., 2011; Kilic et al., 2013]. (iii) Reaction temperature (RT) It is the temperature at which trans-esterification process is performed. It is maintained constant to get homogeneous mixture of ethanol, KOH and oil. Its range was found to be 40-70 C based on the various research works reported [Meng et al., 2008; Pal et al., 2010; Lee et al., 2011; Martinez et al., 2014; Ong et al., 2014]. (iv) Catalyst concentration (CC) The various types of catalyst used are KOH, NaOH, H 2 SO 4, lipase based etc. KOH as a catalyst is preferred because of ability to produce homogeneous mixture of oil and alcohol. Hence KOH is selected in the present research work. To increase the speed of trans-esterification process, suitable amount of catalyst is needed. The amount may vary in different oils. The range of KOH catalyst was found to be 0.5-3 % (by weight of oil) as reported by various researchers [Qian et al., 2008; Quintela et al., 2012; Rahman et al., 2013; Patle et al., 2014; Rahimi et al., 2014;]. (v) Mixing speed (MS) The stirrer speed is adjusted by rotating the knob and it is digitally displayed by using digital tachometer. The constant speed is required to get the perfect mixing of ethanol, KOH and oil. The speed range of 150-650 rpm has 104

been suggested by many researchers [Rajendra et al., 2009; Sahoo and Das, 2009; Sathya Selva Bala et al., 2012; Rezaei et al., 2013; Sanjid et al., 2014]. The range of process parameters that have been selected to enhance the biodiesel production using transesterification process are shown in table 1. Table 1: Process parameters selected for production of biodiesel and their ranges S. No. Process parameters Notations Units Range 1. Ethanol concentration EC % by weight of oil 10-30 2. Reaction time Rt minutes 20-70 3. Reaction temperature RT C 40-70 4. Catalyst concentration CC % by weight of oil 0.5-3 5. Mixing speed MS rpm 150-650 4.1.1. SAMPLE CALCULATION FOR BIODIESEL YIELD The biodiesel yield is defined as the ratio of amount of biodiesel produced to the oil sample taken. The calculation of biodiesel yield is as follows: Quantity of oil taken = 100 gram (Assumed) Amount of ethanol used for 100 gram of oil by considering 25 % of ethanol concentration = 25 % (by weight of oil) = (25 / 100) 100 = 25 gram Catalyst (KOH) taken = 1 % by weight of oil = (1/100) 100 = 1 gram Quantity of biodiesel produced = 90 gram (say). Biodiesel yield = (Quantity of biodiesel produced/quantity of oil taken) 100 = (90/100) 100 = 90 % 4.1.2. PRELIMINARY SCREENING OF PROCESS PARAMETERS FOR BIODIESEL PRODUCTION Preliminary screening is carried out to find the significant parameters affecting the response by performing the experiments using one-factor-at-a-time (OFAT) approach. It is applied by incrementing one input parameter while others are kept at central values of their available ranges. This approach is found to be useful for analyzing the effect of each input parameter on the response parameters and is widely used [Tarng et al., 1995; Parameswar Rao and Sarkar, 2010; Rafiqul and Sakinah, 2012; Simonoska Crcarevska et al., 2013; Irfan et al., 2014]. The significant process parameters among ethanol concentration, reaction temperature, reaction time, catalyst concentration and mixing speed affecting biodiesel yield have been found using this approach. The jatropha oil has been taken for preliminary investigations. Also the significant parameters affecting biodiesel yield will be same for all the oils considered. Figure 1 (a) shows the effect of ethanol concentration on jatropha biodiesel yield by keeping remaining parameters at middle values of the selected range as shown in table 1. It is observed from figure 1 (a) that in general biodiesel yield of jatropha oil increases with increase in ethanol concentration. Though the biodiesel yield increases with increase in ethanol concentration the rate of increase of biodiesel yield is observed to be maximum for 15-25 % of ethanol concentration. The effect of reaction time on jatropha biodiesel yield is depicted in figure 1 (b). Biodiesel yield of jatropha oil increases with increase in reaction time. It is also observed that after 60 minutes of reaction time, the increase in biodiesel yield of jatropha oil is negligible. Figure 1 (c) shows the effect of reaction temperature on jatropha biodiesel yield. The biodiesel yield of jatropha oil increases sharply when reaction temperature is increased from 40 to 60 C. However, after 60 C of reaction temperature, the increase in biodiesel yield is negligible. Figure 1 (d) shows the effect of catalyst concentration on jatropha biodiesel yield. The biodiesel yield of jatropha oil increases with an increase in catalyst concentration from 0.5 to 2.5 in a step of 0.5. However no further enhancement in biodiesel tield is observed beyond 2.5 % of catalyst concentration. Figure 1 (e) shows the effect of mixing speed on jatropha biodiesel yield. The biodiesel yield of jatropha oil is observed to increase with increase in mixing speed. However, increase in speed beyond 550 rpm does not increase biodiesel yield. 105

Figure 4.3 (a): Effect of ethanol concentration (EC) on jatropha biodiesel yield (Rt = 45 minutes, RT = 55 C, CC = 1.75 % by weight of oil, MS = 400 rpm) Figure 4.3 (b): Effect of reaction time on jatropha biodiesel yield (EC = 20 % by weight of oil, RT = 55 C, CC = 1.75 % by weight of oil, MS = 400 rpm) Figure 4.3 (c): Effect of reaction temperature on jatropha biodiesel yield (EC = 20 % by weight of oil, Rt = 45 minutes, CC = 1.75 % by weight of oil, MS = 400 rpm) 106

Figure 4.3 (d): Effect of catalyst concentration on jatropha biodiesel yield (EC = 20 % by weight of oil, Rt = 45 minutes, RT = 55 C, MS = 400 rpm) Figure 4.3 (e): Effect of mixing speed on jatropha biodiesel yield (EC = 20 % by weight of oil, Rt = 45 minutes, RT = 55 C, CC = 1.75 % by weight of oil) Hence it is concluded from the above discussion that all the parameters are equally significant for biodiesel production. Thus all these parameters are selected for predicting optimum biodiesel production using CCRD of RSM. Further the range of these significant parameters selected for main experimentation is shown in table 2. Table 2: Range of process parameters selected for main experimentation S. No. Process parameters Notations Units Range 1. Ethanol concentration EC % by weight of oil 15-25 2. Reaction time Rt Minutes 20-60 3. Reaction temperature RT C 40-60 4. Catalyst concentration CC % by weight of oil 0.5-2.5 5. Mixing speed MS rpm 150-550 CONCLUSION One factor at a time approach is an effective method for predicting significant input parameters before main experimentation is conducted. 107

Five input process parameters (Ethanol concentration, reaction time, reaction temperature, catalyst concentration and mixing speed) are responsible for the conversion of biodiesel as studied by preliminary investigations. The parameters and their significant ranges are shown in table 2 REFERENCES [1] Abdullah, A. Z., Razali, N., & Lee, K. T. (2009). Optimization of mesoporous K/SBA-15 catalyzed transesterification of palm oil using response surface methodology. Fuel Processing Technology, 90(7 8), 958-964. [2] Azcan, N., & Danisman, A. (2007). Alkali catalyzed transesterification of cottonseed oil by microwave irradiation. Fuel, 86(17 18), 2639-2644. [3] Berchmans, H. J., & Hirata, S. (2008). Biodiesel production from crude Jatropha curcas L. seed oil with a high content of free fatty acids. Bioresource Technology, 99(6), 1716-1721. [4] Demirbas, A. (2005). Biodiesel production from vegetable oils via catalytic and non-catalytic supercritical methanol transesterification methods. Progress in Energy and Combustion Science, 31(5 6), 466-487. [5] Dhingra, S., Bhushan G., & Dubey, K. K. (2013a). Development of a combined approach for improvement and optimization of karanja biodiesel using response surface methodology and genetic algorithm. Frontiers in Energy, 7(5), 495 505 [6] Dhingra, S., Bhushan G., & Dubey, K. K. (2013b). Performance and emission parameters optimization of mahua (madhuca indica) based biodiesel in direct injection diesel engine using response surface methodology. Journal of Renewable and Sustainable Energy, 5, 063117, DOI: 10.1063/1.4840155. [7] Dhingra, S., Bhushan G., & Dubey, K. K. (2014a). Understanding the interactions and evaluation of process factors for biodiesel production from waste cooking cottonseed oil by design of experiments through statistical approach. Frontiers in Energy (in press). [8] Dhingra, S., Bhushan G., & Dubey, K. K. (2014b). Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-ii. Frontiers of Mechanical Engineering,9(1), 81-94 [9] Dhingra, S., Bhushan G., & Dubey, K. K. (2015). Comparative performance analysis of jatropha, karanja, mahua and polanga based biodiesel engine using hubrid genetic algorithm. Journal of Renewable and Sustainable Energy, (in press). [10] Dhingra, S., Bhushan G., & Dubey, K. K. (2016). Validation and enhancement of waste cooking sunflower oil based biodiesel production by the trans-esterification process. Energy Sources, part A, 38(10), 1448-1454. [11] Dhingra, S., Dubey, K. K., & Bhushan, G. (2014c). A Polymath Approach for the Prediction of Optimized Transesterification Process Variables of Polanga Biodiesel. Journal of the American oil Chemist s Society, 91(4), 641-653 [12] Dhingra, S., Dubey, K. K., & Bhushan, G. (2014d). Enhancement in Jatropha-based biodiesel yield by process optimization using design of experiment approach. International Journal of Sustainable Energy, 33 (4), 842-853. [13] Domingos, A. K., Saad, E. B., Wilhelm, H. M., & Ramos, L. P. (2008). Optimization of the ethanolysis of Raphanus sativus (L. Var.) crude oil applying the response surface methodology. Bioresource Technology, 99(6), 1837-1845. [14] Halim, S. F. A., Kamaruddin, A. H., & Fernando, W. J. N. (2009). Continuous biosynthesis of biodiesel from waste cooking palm oil in a packed bed reactor: Optimization using response surface methodology (RSM) and mass transfer studies. Bioresource Technology, 100(2), 710-716. [15] Irfan, M., Nadeem, M., & Syed, Q. (2014). One-factor-at-a-time (OFAT) optimization of xylanase production from Trichoderma viride-ir05 in solid-state fermentation. Journal of Radiation Research and Applied Sciences. DOI: 10.1016/j.jrras.2014.04.004 [16] James, G. R., Richards, P. T., Schaefer, W. E., & Wilmes, S. A. (1996). Methanol vs ethanol--`96. Journal Name: Preprints of Papers, American Chemical Society, Division of Fuel Chemistry; Journal Volume: 41; Journal Issue: 3; Conference: 212. national meeting of the American Chemical Society (ACS), Orlando, FL (United States), 25-30 Aug 1996; Other Information: PBD: 1996, Medium: X; Size: pp. 880-889. [17] Jena, P. C., Raheman, H., Prasanna Kumar, G. V., & Machavaram, R. (2010). Biodiesel production from mixture of mahua and simarouba oils with high free fatty acids. Biomass and Bioenergy, 34(8), 1108-1116. [18] Jeong, G.-T., & Park, D.-H. (2009). Optimization of Biodiesel Production from Castor Oil Using Response Surface Methodology. Applied Biochemistry and Biotechnology, 156(1-3), 1-11. [19] Juan, J. C., Kartika, D. A., Wu, T. Y., & Hin, T.-Y. Y. (2011). Biodiesel production from jatropha oil by catalytic and non-catalytic approaches: An overview. Bioresource Technology, 102(2), 452-460. [20] Kılıç, M., Uzun, B. B., Pütün, E., & Pütün, A. E. (2013). Optimization of biodiesel production from castor oil using factorial design. Fuel Processing Technology, 111(0), 105-110. [21] Lee, H. V., Yunus, R., Juan, J. C., & Taufiq-Yap, Y. H. (2011). Process optimization design for jatropha-based biodiesel production using response surface methodology. Fuel Processing Technology, 92(12), 2420-2428. [22] Martínez, S. L., Romero, R., Natividad, R., & González, J. (2014). Optimization of biodiesel production from sunflower oil by transesterification using Na2O/NaX and methanol. Catalysis Today, 220 222, 12-20. [23] Meng, X., Chen, G., & Wang, Y. (2008). Biodiesel production from waste cooking oil via alkali catalyst and its engine test. Fuel Processing Technology, 89(9), 851-857. 108

[24] Ong, H. C., Masjuki, H. H., Mahlia, T. M. I., Silitonga, A. S., Chong, W. T., & Leong, K. Y. (2014). Optimization of biodiesel production and engine performance from high free fatty acid Calophyllum inophyllum oil in CI diesel engine. Energy Conversion and Management, 81, 30-40. [25] Pal, A., Verma, A., Kachhwaha, S. S., & Maji, S. (2010). Biodiesel production through hydrodynamic cavitation and performance testing. Renewable Energy, 35(3), 619-624. [26] Parameswara Rao, C. V. S., & Sarkar, M. M. M. (2009). Evaluation of optimal parameters for machining brass with wire cut EDM. Journal of Scientific & Industrial Research, 68(1), 32-35. [27] Patle, D. S., Sharma, S., Ahmad, Z., & Rangaiah, G. P. (2014). Multi-objective optimization of two alkali catalyzed processes for biodiesel from waste cooking oil. Energy Conversion and Management, 85, 361-372. [28] Qian, J., Wang, F., Liu, S., & Yun, Z. (2008). In situ alkaline transesterification of cottonseed oil for production of biodiesel and nontoxic cottonseed meal. Bioresource Technology, 99(18), 9009-9012. [29] Quintella, S. A., Saboya, R. M. A., Salmin, D. C., Novaes, D. S., Araújo, A. S., Albuquerque, M. C. G., & Cavalcante Jr, C. L. (2012). Transesterificarion of soybean oil using ethanol and mesoporous silica catalyst. Renewable Energy, 38(1), 136-140. [30] Rafiqul, I. S. M., & Sakinah, A. M. M. (2012). Design of process parameters for the production of xylose from wood sawdust. Chemical Engineering Research and Design, 90(9), 1307-1312. [31] Rahimi, M., Aghel, B., Alitabar, M., Sepahvand, A., & Ghasempour, H. R. (2014). Optimization of biodiesel production from soybean oil in a microreactor. Energy Conversion and Management, 79, 599-605. [32] Rahman, S. M. A., Masjuki, H. H., Kalam, M. A., Abedin, M. J., Sanjid, A., & Sajjad, H. (2013). Production of palm and Calophyllum inophyllum based biodiesel and investigation of blend performance and exhaust emission in an unmodified diesel engine at high idling conditions. Energy Conversion and Management, 76, 362-367. [33] Rajendra, M., Jena, P. C., & Raheman, H. (2009). Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA. Fuel, 88(5), 868-875. [34] Rezaei, R., Mohadesi, M., & Moradi, G. R. (2013). Optimization of biodiesel production using waste mussel shell catalyst. Fuel, 109, 534-541. [35] Sahoo, P. K., & Das, L. M. (2009). Process optimization for biodiesel production from Jatropha, Karanja and Polanga oils. Fuel, 88(9), 1588-1594. [36] Sanjid, A., Masjuki, H. H., Kalam, M. A., Rahman, S. M. A., Abedin, M. J., & Palash, S. M. (2014). Production of palm and jatropha based biodiesel and investigation of palm-jatropha combined blend properties, performance, exhaust emission and noise in an unmodified diesel engine. Journal of Cleaner Production, 65, 295-303. [37] Sathya Selva Bala, V., Thiruvengadaravi, K. V., Senthil Kumar, P., Premkumar, M. P., Vinoth kumar, V., Subash sankar, S., Sivanesan, S. (2012). Removal of free fatty acids in Pongamia Pinnata (Karanja) oil using divinylbenzenestyrene copolymer resins for biodiesel production. Biomass and Bioenergy, 37, 335-341. [38] Simonoska Crcarevska, M., Geskovski, N., Calis, S., Dimchevska, S., Kuzmanovska, S., Petruševski, G., Goracinova, K. (2013). Definition of formulation design space, in vitro bioactivity and in vivo biodistribution for hydrophilic drug loaded PLGA/PEO PPO PEO nanoparticles using OFAT experiments. European Journal of Pharmaceutical Sciences, 49(1), 65-80. [39] Tarng, Y. S., Ma, S. C., & Chung, L. K. (1995). Determination of optimal cutting parameters in wire electrical discharge machining. International Journal of Machine Tools and Manufacture, 35(12), 1693-1701. 109