Rambutan (Nephelium lappaceum) seeds for the treatment of Palm Oil Mill Effluent (POME) and its Feedforward Artificial Neural Network (FANN) modeling
In the present research, local rambutan seed extract was used as a bio-coagulant for the treatment of palm oil mill effluent (POME). Jar test experiments were conducted to find the optimal operating conditions for the removal of turbidity and total suspended solids from POME. At an optimal pH of 3, bio-coagulant dosage of 600 mg/L and room temperature of 28⁰C, an impressive removal of 65% of total suspended solids and 79% of turbidity was achieved. Along with this, a Feedforward Artificial Neural Network (FANN) was used to model the coagulation mechanism. Three different training algorithms were tested on the FANN, namely the Lavenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient methods. The best training algorithm was found to be Bayesian Regularization, based on the fact that it was in closer agreement with the experiment results and gave very low error percentage. The results of this study suggest that rambutan seeds have potential in being used as a bio-coagulant for POME treatment. Treatment efficiencies were reasonably high, and less sludge was produced using this natural treatment method, thus deemed to be more economical and environmentally friendly.
Ahmad, A. L., Ismail, S., & Bhatia, S. (2005). Optimization of coagulation-flocculation process for palm oil mill effluent using response surface methodology. Environmental Science and Technology, 39(8), 2828–2834. https://doi.org/10.1021/es0498080
Albert, R. (2010). Treatment of industrial wastewater by fenton process combine with coagulation (Doctoral dissertation). University Malaysia Pahang.
Amagloh, F. K., & Benang, A. (2009). Effectiveness of Moringa oleifera seed as coagulant for water purification. In African Journal of Agricultural Research (Vol. 4, Issue 1). http://www.academicjournals.org/AJAR
Anderson, M., & Whitcomb, P. (2001). Design of Experiments: Statistical Principles of Research Design and Analysis. Technometrics, 43(2), 236–237. https://doi.org/10.1198/tech.2001.s589
Arai, Y., & Sparks, D. L. (2001). ATR-FTIR spectroscopic investigation on phosphate adsorption mechanisms at the ferrihydrite-water interface. Journal of Colloid and Interface Science, 241(2), 317–326. https://doi.org/10.1006/jcis.2001.7773
Asghar, A., Raman, A. A. A., & Daud, W. M. A. W. (2014). A Comparison of Central Composite Design and Taguchi Method for Optimizing Fenton Process. Scientific World Journal, 2014. https://doi.org/10.1155/2014/869120
Basiron, Y., & Yew, F.-K. (2016). The burden of RSPO certification costs on Malaysian palm oil industry and national economy. Journal of Oil Palm , Environment & Health, 7, 19–27. https://doi.org/10.5366/jope.2016.02
Bhatia, S., Othman, Z., & Ahmad, A. L. (2007a). Coagulation-flocculation process for POME treatment using Moringa oleifera seeds extract: Optimization studies. Chemical Engineering Journal, 133(1–3), 205–212. https://doi.org/10.1016/j.cej.2007.01.034
Bhatia, S., Othman, Z., & Ahmad, A. L. (2007b). Pretreatment of palm oil mill effluent (POME) using Moringa oleifera seeds as natural coagulant. Journal of Hazardous Materials, 145(1–2), 120–126. https://doi.org/10.1016/j.jhazmat.2006.11.003
Chen, J.-F., Do, Q. H., & Hsieh, H.-N. (2015). Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm. Algorithms, 8, 292–308. https://doi.org/10.3390/a8020292
Choong Lek, B. L., Peter, A. P., Qi Chong, K. H., Ragu, P., Sethu, V., Selvarajoo, A., & Arumugasamy, S. K. (2018). Treatment of palm oil mill effluent (POME) using chickpea (Cicer arietinum) as a natural coagulant and flocculant: Evaluation, process optimization and characterization of chickpea powder. Journal of Environmental Chemical Engineering, 6(5), 6243–6255. https://doi.org/10.1016/j.jece.2018.09.038
Chung, C. Y., Selvarajoo, A., Sethu, V., Koyande, A. K., Arputhan, A., & Lim, Z. C. (2018). Treatment of palm oil mill effluent (POME) by coagulation flocculation process using peanut–okra and wheat germ–okra. Clean Technologies and Environmental Policy, 20(9), 1951–1970. https://doi.org/10.1007/s10098-018-1619-y
Demuth, H., Beale, M., & Hagan, M. T. (2007). Neural Network Toolbox. MathWorks Incorporated.
Deshmukh, B. S., Pimpalkar, S. N., Rakhunde, R. M., & Joshi, V. A. (2013). Evaluation performance of natural strychnos potatorum over the synthetic coagulant alum, for the treatment of turbid water. International Journal of Innovative Research in Science, Engineering and Technology, 2(11), 6183–6189.
Flaten, T. P. (2001). Aluminium as a risk factor in Alzheimer’s disease, with emphasis on drinking water. Brain Research Bulletin, 55(2), 187–196. https://doi.org/10.1016/S0361-9230(01)00459-2
Gebbie, P. (2005). A dummy’s guide to coagulants. 68th Annual Water Industry Engineers and Operators’ Conference, 76–83.
Goh, C. W. (2005). Effect of room temperature on coagulation performance of Moringa oleifera seeds (B. Sc. Dissertation). Universiti Putra Malaysia.
Irene Kopytko, M., Eliana Paola Rueda Villamizar, I., & Rincón Picón, I. (2008). Application of Natural Product (Aloe Vera) in Coagulation-Flocculation Procedures, for Water Treatability Study. In Certified International Journal of Engineering Science and Innovative Technology (IJESIT) (Vol. 9001, Issue 3).
Kamyab, H., Chelliapan, S., Din, M. F. M., Shahbazian-Yassar, R., Rezania, S., Khademi, T., Kumar, A., & Azimi, M. (2017). Evaluation of Lemna minor and Chlamydomonas to treat palm oil mill effluent and fertilizer production. Journal of Water Process Engineering, 17, 229–236. https://doi.org/10.1016/j.jwpe.2017.04.007
Kamyab, H., Din, M. F. M., Keyvanfar, A., Majid, M. Z. A., Talaiekhozani, A., Shafaghat, A., Lee, C. T., Shiun, L. J., & Ismail, H. H. (2015). Efficiency of Microalgae Chlamydomonas on the Removal of Pollutants from Palm Oil Mill Effluent (POME). Energy Procedia, 75, 2400–2408. https://doi.org/10.1016/j.egypro.2015.07.190
Kamyab, H., Md Din, M. F., Tin, C. L., Ponraj, M., Soltani, M., Mohamad, S. E., & Roudi, A. M. (2014). Micro-macro algal mixture as a promising agent for treating POME discharge and its potential use as animal feed stock enhancer. Jurnal Teknologi (Sciences and Engineering), 68(5), 1–4. https://doi.org/10.11113/jt.v68.3021
Keijsers, N. L. W. (2010). Neural Networks. In Encyclopedia of Movement Disorders (pp. 257–259). Elsevier Inc. https://doi.org/10.1016/B978-0-12-374105-9.00493-7
Lee, C. S., Robinson, J., & Chong, M. F. (2014). A review on application of flocculants in wastewater treatment. In Process Safety and Environmental Protection (Vol. 92, Issue 6, pp. 489–508). Institution of Chemical Engineers. https://doi.org/10.1016/j.psep.2014.04.010
León-Luque, A. J., Barajas, C. L., & Peña-Guzmán, C. A. (2016). Determination of the Optimal Dosage of Aluminum Sulfate in the Coagulation-Flocculation Process Using an Artificial Neural Network. International Journal of Environmental Science and Development, 7(5), 346–350. https://doi.org/10.7763/IJESD.2016.V7.797
Leone, A., Spada, A., Battezzati, A., Schiraldi, A., Aristil, J., & Bertoli, S. (2016). Moringa oleifera Seeds and Oil: Characteristics and Uses for Human Health. International Journal of Molecular Sciences, 17(12), 2141. https://doi.org/10.3390/ijms17122141
Madaki, Y. S., & Seng, L. (2013). Palm Oil Mill Effluent (POME) From Malaysia Palm Oil Mills : Waste or Resource. International Journal of Science, Environment and Technology, 2(6), 1138–1155.
Malakahmad, A., & Chuan, S. Y. (2013). Application of response surface methodology to optimize coagulation-flocculation treatment of anaerobically digested palm oil mill effluent using alum. Desalination and Water Treatment, 51(34–36), 6729–6735. https://doi.org/10.1080/19443994.2013.791778
Ramavandi, B. (2014). Treatment of water turbidity and bacteria by using a coagulant extracted from Plantago ovata. Water Resources and Industry, 6, 36–50. https://doi.org/10.1016/j.wri.2014.07.001
Saifuddin, N., & Dinara, S. (2011). Pretreatment of palm oil mill effluent (pome) using magnetic chitosan. Journal of Chemistry, 8. https://doi.org/10.1155/2011/427532
Sapari, N., Chew, T. Y., & Yaziz, M. I. (1996). Treatment of Palm Oil Mill Effluent by Mesophilic Anaerobic Digestion with Flocculant Addition. In Pertanika J. Sci. & Technol (Vol. 4, Issue 2).
Sarpong, G., & Richardson, C. P. (2010). Coagulation efficiency of Moringa oleifera for removal of turbidity and reduction of total coliform as compared to aluminum sulfate. African Journal of Agricultural Research, 5(21), 2939–2944.
Schouten, G., & Glasbergen, P. (2011). Creating legitimacy in global private governance: The case of the Roundtable on Sustainable Palm Oil. Ecological Economics, 70(11), 1891–1899. https://doi.org/10.1016/j.ecolecon.2011.03.012
Selcuk, H., Kaptan, D., & Meric, S. (2004). Coagulation of textile finishing industry wastewater using alum and Fe (III) salts. Fresenius Environmental Bulletin, 13(10), 1045–1048.
Selvanathan, M., Yann, K. T., Chung, C. H., Selvarajoo, A., Arumugasamy, S. K., & Sethu, V. (2017). Adsorption of Copper(II) Ion from Aqueous Solution Using Biochar Derived from Rambutan (Nepheliumlappaceum) Peel: Feedforward Neural Network Modelling Study. Water, Air, and Soil Pollution, 228(8), 1–19. https://doi.org/10.1007/s11270-017-3472-8
Sethu, V., Mendis, A., Rajiv, R., Chimbayo, S., & Vejayan, S. (2015). Fenugreek seeds for the treatment of Palm Oil Mill Effluent (POME). International Journal of Chemical and Environmental Engineering, 6(2), 126–130.
Wu, G. De, & Lo, S. L. (2008). Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system. Engineering Applications of Artificial Intelligence, 21(8), 1189–1195. https://doi.org/10.1016/j.engappai.2008.03.015
Zainal Abidin, Z., Mohd Fadzli, M., & Liew Abdullah, A. G. (2014). Preliminary Study of Rambutan (Nephelium lappaceum) Seed as Potential Biocoagulant for Turbidity Removal | Scientific.Net. Advanced Material Research, 917, 96–105. https://doi.org/https://doi.org/10.4028/www.scientific.net/AMR.917.96
Copyright (c) 2020 Vasanthi Sethu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.
The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.