Optimization of colour pigments removal from palm oil using activated ogbunike kaolinite

Yıl 2020, Cilt: 38 Sayı: 1, 253 - 262, 27.03.2020

Ikechukwu Abuchi Nnanwube Vincent Nwoye Okafor John Ifeanyi Obıbuenyı Okechukwu Dominic Onukwulı Regina Obiageli Ajemba

Öz

Response surface methodology (RSM) and genetic algorithm (GA) were employed to determine the optimum conditions for the removal of pigments from palm oil using Ogbunike clay activated with hydrochloric acid. The physicochemical characterization of the clay showed that it exists mainly as kaolinite. The process variables and ranges used in the experimental design were 75 – 150 oC bleaching temperature, 1.50 – 3.00 hours bleaching time, 1.25 -5.50 g clay dosage and 0.05 – 0.40 mm particle size. The analysis of variance (ANOVA) showed that a second order polynomial regression equation was adequate for fitting the experimental data. The model statistical tests carried out showed a good correlation between the experimental and predicted values (R2 = 0.9964). About 73.35 % pigments were removed using RSM while 71.34% pigments were removed using genetic algorithm at the optimum conditions. Hence, Ogbunike kaolinite proved to be a good adsorbent for pigments removal from palm oil.

Anahtar Kelimeler

Optimization, ANOVA, bleaching, genetic algorithm, kaolinite.

Kaynakça

• [1] Nwankwere E. T., Nwadiogbu J. O., Yileng M. T., Eze K. A., (2012) Kinetic Investigation of the Adsorptive Removal of B-carotene Pigments from Oil Using Unmodified Natural Clay, Advances of Applied Science Research, 3(2), 1122-1125.
• [2] Khan T. A., Dahiya S., Ali I., (2012) Use of Kaolinite as Adsorbent: Equilibrium, Dynamics and Thermodynamic Studies on the Adsorption of Rhodamine B from Aqueous Solution, Applied Clay Science, 69, 58-66.
• [3] Annadurai G., Juang R. S., Lee D. J., (2002) Factorial Design Analysis of Adsorption of Activated Carbon on Activated Carbon Incorporated with Calcium Aginate, Advances in Environmental Research, 6, 191 -198.
• [4] Chew S. C., Tan C. P., Nyam K. I., (2017) Optimization of Neutralization Parameters in Chemical Refining of Kenaf Seed Oil by Response Surface Methodology, Industrial Crops and Products, 95, 742-750.
• [5] Kumar A., Prasad B., Mishra I. M., (2008) Adsorptive Removal of Acrylonitrile Using Powdered Activated Carbon, Journal of Environmental Protection Science, 2, 54-62.
• [6] Fayyazi E., Ghobadian B., Najafi G., Hosseinzadeh B., Mamat R., Hosseinzadeh J., (2015) An Ultrasound-Assisted System for the Optimization of Biodiesel Production from Chicken Fat Oil Using a Genetic Algorithm and Response Surface Methodology, Ultrasonics Sonochemistry, 26, 312-320.
• [7] Singh V., Khan M., Khan S., Tripathi C., (2009) Optimization of Actinomycin V Production by Streptomyces Triostinicus Using Artificial Neural Network and Genetic Algorithm, Applied Microbiology and Biotechnology, 82, 379-385.
• [8] Gupta J. N., Sexton R. S., (1999) Comparing Back Propagation With a Genetic Algorithm for Neural Network Training, Omega, 27, 679-684.
• [9] Shen C., Wang L., Li Q., (2007) Optimization of Injection Molding Process Parameters Using Combination of Artificial Neural Network and Genetic Algorithm Method, Journal of Materials Processing Technology, 183, 412-418.
• [10] Ajemba R. O., (2012) Optimum Activation Conditions of Ughelli Bentonite for Palm Oil Bleaching Using Response Surface Methodology, Australian Journal of Basic and Applied Sciences, 6(12), 186 – 197.
• [11] Ighose B. O., Adeleke I. A., Damos M., Junaid H. A., Okpalaeke K. E., Betiku E., (2017) Optimization of Biodiesel Production from Thevetia Peruviana Seed Oil by Adaptive Neuro-Fuzzy Inference System Coupled with Genetic Algorithm and Response Surface Methodology, Energy conversion and Management, 132, 231- 240.
• [12] Nnanwube I. A., Onukwuli O. D., Ajana, S. U., (2018) Modeling and Optimization of Galena Dissolution in Hydrochloric Acid: Comparison of Central Composite Design and Artificial Neural Network, Journal of Minerals and Materials Characterization and Engineering, 6, 294 – 315.