Investigation of potato peel-based bio-sorbent efficiency in reactive dye removal: Artificial neural network modeling and genetic algorithms optimization

Document Type : Original Article

Authors

1 Kurdistan Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran

2 Department of Environmental Health Engineering, School of Public Health, Birjand University of Medical Sciences, Birjand, Iran

Abstract

Over the last few years, a number of investigations have been conducted to explore the low cost sorbents for the decontamination of toxic materials. Undoubtedly, agricultural waste mass is presently one of the most challenging topics, which has been gaining attention during the past several decades. Wastes are very cheap and easily available material in production of sorbent. Therefore, the Reactive Red 198 (RR198) removal efficiency from aqueous solutions by potato peel powder based sorbent (PP) was examined in this study. The Taguchi method was used in combination with full factorial methods to design the experiments. Based on the design of experiment outputs, 18 experimental sets were designed and the experiments were done in accordance with the experimental design. The sorption handmade batch reactor consists of a 200 ml beaker, 100 RPM magnetic stirrer, and a sampling port. Then, the experimental data were collected under desired conditions. In each sample sorbent was separated using a centrifuge (3000 rpm and 5 minutes). Then, dye concentrations were determined based on Beer’s law and calibration plots using a UV-visible spectrophotometer. The wavelength resolution and the bandwidth were, respectively, 1 and 0.4 nm. The length of the optical path in glass cell was 1 cm. The maximum absorption wavelength was determined in each run to compensate the matrix effects. The results revealed that PP is effective for the sorption of RR198 from aqueous solutions. The maximum sorption of PP from RR198 solution was determined as 93 mg/g. Artificial neural network (ANN) model of dye removal efficiency (DR%) was developed based on the experimental data sets. The ANN model was strongly validated using statistical tests. The R2 and RMSE of the test set were 0.98 and 4.3, respectively. The results demonstrate that PP can be successfully used as sorbent for RR198 removal from aqueous solutions. The results revealed that experimental parameters strongly influence the DR% and different experimental conditions cause different DR% (from 0 to 93).   

Keywords


1. Robinson T, McMullan G, Marchant R, Nigam P. Remediation of dyes in textile effluent: a critical review on current treatment technologies with a proposed alternative. Bioresour Technol 2001; 77(3): 247-55.
2. Cheung C, Porter J, McKay G. Sorption kinetics for the removal of copper and zinc from effluents using bone char. Separation and purification technology 2000; 19(1): 55-64.
3. Bell JP, Tsezos M. Removal of hazardous organic pollutants by biomass adsorption. Water Pollution Control Federation 1987; 59(4): 191-8.
4. Aman T, Kazi AA, Sabri MU, Bano Q. Potato peels as solid waste for the removal of heavy metal copper(II) from waste water/industrial effluent. Colloids Surf B Biointerfaces 2008; 63(1): 116-21.
5. Turan NG, Mesci B, Ozgonenel O. The use of artificial neural networks (ANN) for modeling of adsorption of Cu (II) from industrial leachate by pumice. Chemical Engineering Journal 2011; 171(3): 1091-7.
6. Hagan MT, Demuth HB, Beale MH. Neural network design. Kolkata, India: Vikas Publishing House; 2003.
7. Himmelblau DM. Applications of artificial neural networks in chemical engineering. Korean Journal of Chemical Engineering 2000; 17(4): 373-92.
8. Ames A, Rogel-Hernandez E, Lin SW, Flores-L َ◌pez LZ, Castro JR, Wakida FT, et al. Prediction of metal ion rejection in electro-cross-flow ultrafiltration using an artificial neural network. Desalination and Water Treatment 2011; 36(1): 105-18.
9. Saien J, Soleymani AR, Bayat H. Modeling fentonic advanced oxidation process decolorization of direct red 16 using artificial neural network technique. Desalination & Water Treatment 2012; 40(1-3): 174-82.
10. Singh G, Kandasamy J, Shon H, Cho J. Measuring treatment effectiveness of urban wetland using hybrid water quality -- Artificial neural network (ANN) model. Desalination and Water Treatment 2011; 32(1-3): 284-90.
11. K ِ◌rbahti BK, Artut K, Geçgel C, Qzer A. Electrochemical decolorization of textile dyes and removal of metal ions from textile dye and metal ion binary mixtures. Chemical Engineering Journal 2011; 173(3): 677-88.
12. Olmez-Hanci T, Kartal Z, Arslan-Alaton I. Electrocoagulation of commercial naphthalene sulfonates: process optimization and assessment of implementation potential. J Environ Manage 2012; 99: 44-51.
13. Tsutsui S, Goldberg DE. Search space boundary extension method in real-coded genetic algorithms. Information Sciences 2001; 133(1-4): 229-47.
14. Hasseine A, Kabouche A, Meniai AH, Korchi M. Salting effect of NaCl and KCl on the liquid―liquid equilibria of water+ ethyl acetate+ ethanol system and interaction parameters estimation using the genetic algorithm. Desalination and Water Treatment 2011; 29(1-3): 47-55.
15. Sashi Kumar GN, Mahendra AK, Sanyal A, Gouthaman G. Genetic algorithm-based optimization of a multi-stage flash desalination plant. Desalination and Water Treatment 2009; 1(1-3): 88-106.
16. Tabesh M, Hoomehr S. Consumption management in water distribution systems by optimizing pressure reducing valves' settings using genetic algorithm. Desalination and Water Treatment 2009; 2(1-3): 95-100.
17. Ebrahimi R, Maleki A, Shahmoradi B, Daraei H, Mahvi AH, Barati AH, et al. Elimination of arsenic contamination from water using chemically modified wheat straw. Desalination and Water Treatment 2013; 51(10-12): 2306-16.
18. Daraei H, Irandoust M, Ghasemi JB, Kurdian AR. QSPR probing of Na+ complexation with 15-crown-5 ethers derivatives using artificial neural network and multiple linear regression. Journal of Inclusion Phenomena and Macrocyclic Chemistry 2012; 72(3-4): 423-35.
19. Hatami T, Rahimi M, Daraei H, Heidaryan E, Alsairafi AA. PRSV equation of state parameter modeling through artificial neural network and adaptive network-based fuzzy inference system. Korean Journal of Chemical Engineering 2012; 29(5): 657-67.
20. Maleki A, Daraei H, Alaei L, Abasi L, Izadi A. Dye Removal Probing by Electrocoagulation Process: Modeling by MLR and ANN Methods. J Chem Soc Pak 2012; 34(15): 1056-69.
21. Nasri F, Daraei H, Hatami T, Maleki A. Phase equilibrium of binary system carbon dioxide-methanol at high pressure using artificial neural network. J Chem Soc Pak 2012; 34(1): 1070-8.