Drying Process of Senna alata Medicinal Leave: Comparative Empirical and Artificial Neural Networks Modelling of Mass Transfer Kinetics with Energy Analysis
Main Article Content
Abstract
This study investigated the microwave drying of Senna alata leaves (SAL) for sustainable utilization. The effect of SAL form (un-chopped and chopped) and microwave power (200, 400 and 600 W) on the drying characteristics and energy utilization with comparative semi-empirical and artificial neural network (ANN) modelling was investigated. SAL was dried at the selected drying factors (leaf form and microwave power); and moisture transport characteristics including moisture content, moisture ratio, effective moisture diffusivity, activation energy, energy consumption, specific energy consumption and energy efficiency were determined gravimetrically and empirically. In addition, models were utilized to represent the experimental observations and compared statistically. Results showed that un-chopped SAL had a drying time of 10, 8.87, 7.34 s while chopped SAL had a drying time of 8.34, 5.45, 3.5 s at 200, 400 and 600 W, respectively. The effective moisture diffusivity of un-chopped and chopped SAL ranged between 1.40e-6 - 1.94e-6 m2/s and 1.99e-6 – 3.79e-6 m2/s at 200, 400 and 600 W, respectively; while activation energy was 1.79 and 3.64 W/g, respectively. The un-chopped SAL has energy efficiency of 47.38, 26.71 and 21.52% while chopped SAL has energy efficiency of 56.47, 43.49 and 45.14 KJ/kWs at 200, 400 and 600 W. The range of coefficient of determination (R2) of empirical models was 0.9963 – 0.9994 while R2 value of ANN model was 0.9996. It was generally observed that the form of SAL and selected microwave power affected the drying and energy indicators, where size alteration (chopping) and increment in microwave power reduced the drying time and improved the energy indicators. The semi-empirical and ANN models performed well in representing the drying process with ANN having a marginal edge. These results are useful in conservation of SAL, control and commercialization of the microwave drying process.
Article Details
References
Duke, J.A. (1981). Handbook of legumes of world economic importance. Springer Science & Business Media. DOI: https://doi.org/10.1007/978-1-4684-8151-8
Grieve, M.M. (1984). A modern herbal. Tiger Books International.
Owolabi, O.J., Aderibigbe, A. A., & Adebayo, A. H. (2013). Wound healing and antibacterial properties of Senna alata leaf extract. Journal of Complementary and Integrative Medicine, 10(2), 10.
El-Mahmood, A.M., Doughari, J. H., & Egwari, L. O. (2008). Traditional medicine practice among the Fulani ethnic group in north-eastern Nigeria. Journal of Medicinal Plants Research, 2(5), 347-354.
Nair, R., Basile, G., Roberta, P., Calignano, A., and Zingarelli, C. (2005). In vitro screening for antiprotozoal activity of extracts and fractions from endophytic and rhizospheric fungi of Azadirachta indica A. Juss. Journal of Antimicrobial Chemotherapy, 55(5), 726-731.
Oladeji, O.S., Adelowo, F.E., Oluyori, A.P. & Bankole, D.T. (2020). Ethnobotanical Description and Biological Activities of Senna alata. Evidence-Based Complementary and Alternative Medicine, 2020 (13), 1-12. https://doi.org/10.1155/2020/2580259 DOI: https://doi.org/10.1155/2020/2580259
Singh, P., Singh, B.R., Singh, S., Chauhan, N., Kumar, R., Kumar, R. & Singh, Y. (2019). Study of Drying Kinetics of Cauliflower (Brassica oleracea) using Tray Dryer. Int.J.Curr.Microbiol.App.Sci, 8 (9): 2487- 2491. DOI: https://doi.org/10.20546/ijcmas.2019.809.288
Kenghe, R.N., Jadhav, M.S., Nimbalkar, C.A. & Kamble, T.M. (2015). Effect of Drying Methods on Quality Characteristics of Curry (Murraya koenigii) Leaves. International Journal of Environmental & Agriculture Research, 1(5), 8 – 12. https://extension://ngphehpfehdmjellohmlojkplilekadg.
Maskan, M. (2001). Drying, shrinkage and rehydration characteristics of kiwifruits during hot air and microwave drying. Journal of Food Engineering. 48(2). 177-182, https://doi.org/10.1016/S0260-8774(00)00155-2. DOI: https://doi.org/10.1016/S0260-8774(00)00155-2
Adeyi, O., Adeyi, A.J., Oke, E.O., & Ajayi, O.K., Oyelami, S., Otolorin, J.A., Areghan, S.E. & Isola, B.F. (2022). Adaptive Neuro Fuzzy Inference System modeling of Synsepalum dulcificum L. drying characteristics and sensitivity analysis of the drying factors. Scientific Reports. 12(1), 1 – 16, DOI: 10.1038/s41598-022-17705-y. DOI: https://doi.org/10.1038/s41598-022-17705-y
Adeyi, A.J., Adeyi, O., Oke, E.O., Okwokwo, E. & Ogunsola, A.D. (2021). Effective Moisture Diffusivity of Sierrathrissa leonensis Cracker: Optimization, Sensitivity and Uncertainty Analyses. Scientific Africa Journal, 12(2), 1 - 10. https://doi.org/10.1016/j.sciaf.2021.e00807. DOI: https://doi.org/10.1016/j.sciaf.2021.e00807
Liu, H., Liu, H., Liu, H., Zhang, X., Hong, Q., Chen, W. & Zeng, X. (2021). Microwave drying characteristics and drying quality analysis of corn in China, Processes, 9(9):15-11. https://doi.org/10.3390/pr9091511. DOI: https://doi.org/10.3390/pr9091511
Motevali, A., Minaei, S., Banakar, A., Ghobadian, B., & Darvishi, H. (2014). Energy analyses and drying kinetics of chamomile leaves in microwave-convective dryer. Journal of the Saudi Society of Agricultural Sciences. 26(2), 1 - 10. http://dx.doi.org/10.1016/j.jssas.2014.11.003. DOI: https://doi.org/10.1016/j.jssas.2014.11.003
Okonkwo, C.E., Olaniran, A.F, Adeyi, A.J.,Adeyi, O., Ojediran, J.O., Erinle, O.C., Mary, I.Y., & Taiwo, A.E. (2022). Neural network and adaptive neuro fuzzy inference system modeling of the hot air drying process of orange –fleshed sweet potato. Journal of food processing and preservation. 46(2), 1 -13, https://doi.org/10.1111/jfpp.16312. DOI: https://doi.org/10.1111/jfpp.16312
Okonkwo, C.E., Olaniran, A.F, Adeyi, O. and Adeyi, A.J., Ojediran, J.O., Adewumi, A.D., Iranloye, Y.M & Erinle, O.C. (2021). Drying characteristics of fermented – cooked cassava chips used in the production of complementary foods: Mathematical and Gaussian process regression modeling approach. Journal of Food Process Engineering. Vol. 44 (1), 1 – 10,. https://doi.org/10.1111/jfpe.13715. DOI: https://doi.org/10.1111/jfpe.13715
Onwude, D.I., Hashim, N., Janius, R.B., Nawi, N. & Abdan, K. 2016. Modelling the Convective Drying Process of Pumpkin (Cucurbitamoschata) Using an Artificial Neural Network. International Food Research Journal. 23(2), 237-243
Akter, F., Muhury, R., Sultana, A., & Deb, U.K. (2022). A comprehensive review of mathematical modelling for drying processes of fruits and vegetables. Int J Food Sci. 2022(8):1-10. doi: 10.1155/2022/6195257. DOI: https://doi.org/10.1155/2022/6195257
Zarein, M., Samadi, S.H., & Ghobadian, B. (2013). Investigation of microwave dryer effect on energy efficiency during drying of apple slices. Journal of the Saudi Society of Agricultural Sciences 14 (1), 41–47. DOI: https://doi.org/10.1016/j.jssas.2013.06.002
Loghmanieh, I. & Bakhoda, H. (2013). Dehydration characteristics and mathematical modelling of thyme leaves using the microwave process. Global Journal of Science Frontier Research Physics and Space Science. 13(8), 1 – 8, extension://ngphehpfehdmjellohmlojkplilekadg. .
Ojediran, J.O, Okwonkwo, C.E., Adeyi, A.J, Adeyi, O., Olaniran, A.F, George, N.E, & Olayanju, A.T. (2020).
Drying Characteristics of Yam Slices (Dioscorea rotundata) in a Convective Hot Air Dryer: Application of ANFIS in the Prediction of Drying Kinetics. Heliyon. 6(3): 1 – 12. DOI: 10.1016/j.heliyon.2020.e03555 DOI: https://doi.org/10.1016/j.heliyon.2020.e03555
Hassan, M.M.A, (2016). Energy consumption and mathematical modelling of microwave drying of date.
Process Engineering. Misr J. Ag. Eng., 33(1), 151 – 164. DOI: 10.21608/mjae.2016.98173 DOI: https://doi.org/10.21608/mjae.2016.98173
Brabanter, J.B., Vergote, I.M.D., Verrelst, H., Timmerma, D.M.D., Huffel, S., & Vandewalle, J. (1999). Univariate and multivariate regression analysis: some basic statistical principles. Book Chapter, CME Journal of Gynecologie Oncology, 226 -270. (8) (PDF) Univariate and multivariate regression analysis: Some basic statistical principles (researchgate.net).
Soysal, Y., Oztekin, S., & Eren, O. (2006). Microwave Drying of Parsley: Modelling, Kinetics, and Energy Aspects. Biosystems Engineering. 93(4):403-413, Doi:10.1016/j.biosystemseng.2006.01.017 DOI: https://doi.org/10.1016/j.biosystemseng.2006.01.017
Darvishi, H., Zarein, M., Minaei, S., & Khafajeh, H. (2014). Exergy and energy analysis, drying kinetics and mathematical modeling of white mulberry drying process. International Journal of Food Engineering. 10(2), 1 -8, DOI: 10.1515/ijfe-2013-0065 DOI: https://doi.org/10.1515/ijfe-2013-0065
Chokphoemphun, S. & Chokphoemphun, S. (2018). Moisture content prediction of paddy drying in a fluidized-bed drier with a vortex flow generator using an artificial neural network. Applied Thermal Engineering. 145 (1). 630-636, https://doi.org/10.1016/j.applthermaleng.2018.09.087. DOI: https://doi.org/10.1016/j.applthermaleng.2018.09.087