Predicting Fenton modification of solid waste vegetable oil industry for arsenic removal using artificial neural networks

Journal of the Taiwan Institute of Chemical Engineers

Volumn 43, Issue 6, 2012, Pages 873-878

  Jaafarzadeh, Nemat ^a   Ahmadi, Mehdi ^a   Amiri, Hoda ^b   Yassin, Mohammad Hossein ^c   Martinez, Susana Silva ^d  

^a AHVAZ JUNDISHAPUR UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^b ALBORZ UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^c SHIRAZ UNIVERSITY   (Iran)

^d UNIVERSIDAD AUTÓNOMA DEL ESTADO DE MORELOS   (Mexico)

Author keywords

Arsenate; Arsenite; Artificial neural network; Fenton; Modification

Indexed keywords

ADSORPTION REACTION; ANTHROPOGENIC ACTIVITY; AQUATIC ENVIRONMENTS; ARSENATE; ARSENIC POLLUTION; ARSENIC REMOVAL; ARSENITE; CONTACT TIME; CONTAMINATED DRINKING WATER; EXPERIMENTAL CONDITIONS; FENTON; FENTON PROCESS; FENTON REAGENTS; HUMAN HEALTH; INORGANIC ARSENIC; MODIFICATION; NATURAL SORBENTS; NONIONIC; ORGANIC ARSENIC; REDOX CONDITION; WASTE VEGETABLE OIL;

ADSORPTION; ARSENIC; BIOACTIVITY; COMPUTER SIMULATION; GROUNDWATER; NEURAL NETWORKS; OILS AND FATS; PH EFFECTS; POLLUTION CONTROL; REDOX REACTIONS; SOLID WASTES; WATER POLLUTION;

CHEMICAL CONTAMINATION;

EID: 84870247616     PISSN: 18761070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jtice.2012.05.008     Document Type: Article

References (34)

1. WHO Guidelines for drinking water quality: recommendations 1993, World Health Organization, Geneva.
2. EPA National primary drinking water regulations; arsenic and clarifications to compliance and new source contaminants monitoring: final rule. Fed Regist 2001, 6976-7066.
3. Rahman M.A., Hasegawa H., Rahman M.M., Miah M.A.M., Tasmin A. Arsenic accumulation in rice (Oryza sativa L.): human exposure through food chain. Ecotoxicol Environ Saf 2008, 69:317-324.
4. NRC Arsenic in Drinking Water. NRC Subcommittee to Update the 1999 Arsenic in Drinking Water 2002, Committee on Toxicology, Washington, DC.
5. Pontius F.W., Brown K.G., Chen C.J. Health implications of arsenic in drinking water. Am Water Works Assoc 1994, 86:52-63.
6. Wickramasinghe S.R., Han B., Zimbron J., Shen Z., Karim M.N. Arsenic removal by coagulation and filtration: comparison of groundwaters from the United States and Bangladesh. Desalination 2004, 169:231-244.
7. Borho M., Wilderer P. Optimized removal of arsenate (III) by adaptation of oxidation and precipitation processes to the filtration step. Water Sci Technol 1996, 34:25-31.
8. Meng X., Korfiatis G.P., Christrodoulatos C., Bang S. Treatment of arsenic in Bangladesh well water using a household co-precipitation and filtration system. Water Res 2001, 35:2805-2810.
9. Maleki A., Eslami A. Isotherm and kinetics of arsenic (V) adsorption from aqueous solution using modified wheat straw. IJEHSE 2011, 3:450-539.
10. Kim J., Benjamin M.M. Modeling a novel ion exchange process for arsenic and nitrate removal. Water Res 2004, 38:2053-2062.
11. Ning R.Y. Arsenic removal by reverse osmosis. Desalination 2002, 143:237-241.
12. Han B., Runnells T., Zimbron J., Wickramasinghe R. Arsenic removal from drinking water by flocculation and microfiltration. Desalination 2002, 145:293-298.
13. Ishiguro M., Makino T., Hattori Y. Sulfate adsorption and surface precipitation on a volcanic ash soil (allophonic andisol). J Colloid Interface Sci 2006, 300:504-510.
14. Joshi A., Chaudhuri M. Removal of arsenic from ground water by iron oxide-coated sand. J Environ Eng 1996, 122:769-773.
15. Thirunavukkarasu O.S., Viraraghavan T., Subramanian K.S. Removal of arsenic in drinking water by iron oxide-coated sand and ferrihydrite-batch studies. Water Qual Res J Canada 2001, 36:55-70.
16. Thirunavukkarasu O.S., Viraraghavan T., Subramanian K.S. Removal of arsenic in drinking water by iron oxide-coated sand. Water Air Soil Pollut 2003, 142:95-111.
17. Zouboulis A.I., Katsoyiannis I.A. Arsenic removal using iron oxide loaded alginate beads. Ind Eng Chem Res 2002, 41:6149-6155.
18. Chen W., Parette R., Zou J., Cannon F.S., Dempsey B.A. Arsenic removal by iron-modified activated carbon. Water Res 2007, 41:1851-1858.
19. Hague N., Morrison G., Cano-Aguilera I., Gardea-Torresdey J.L. Iron-modified light expanded clay aggregates for the removal of arsenic(V) from groundwater. Microchem J 2008, 88:7-13.
20. Argun M.E., Dursun S., Karatas M., Guru M. Activation of pine cone using fenton oxidation for Cd(II) and Pb(II) removal. Bioresour Technol 2008, 99:8691-8698.
21. Amiri H., Jaafarzadeh N., Ahmadi M., Silva M.S. Application of LECA modified with Fenton in arsenite and arsenate removal as an adsorbent. Desalination 2011, 272:212-217.
22. 13 as a new multi-metal heterogeneous Fenton-like catalyst for the degradation of organic pollutants. Environ Technol 2010, 31:145-154.
23. 2 process. Environ Technol 2010, 31:165-173.
24. San Sebastian N., Fernandez J.F., Segura X.F., Ferrer A.S. Preoxidation of an extremely polluted industrial wastewater by the Fenton's reagent. J Hazard Mater 2003, 101:315-322.
25. Giri A.K., Patel R.K., Mahapatra S.S. Artificial neural network (ANN) approach for modelling of arsenic (III) biosorption from aqueous solution by living cells of Bacillus cereus biomass. Chem Eng J 2011, 178:15-25.
26. Ranjan D., Mishra D., Hasan S.H. Bioadsorption of arsenic: an artificial neural networks and response surface methodological approach. Ind Eng Chem Res 2011, 50:9852-9863.
27. Chang F.J., Kao L.S., Kuo Y.M., Liu C.W. Artificial neural networks for estimating regional arsenic concentrations in a blackfoot disease area in Taiwan. J Hydrol 2010, 388:65-76.
28. ASTM. Annual book of American Society for Testing and Material standards, General products, chemical specialties, and end use products: refractories, activated carbon, advanced ceramics. Philadelphia, PA, USA; 2002.
29. Namasivayam C., Yamuna R.T., Arasi D.J.S.E. Removal of acid violet from wastewater by adsorption on waste red mud. Environ Geol 2001, 41:269-273.
30. Genc-Fuhrman H.J.C.T., McConchie D. Adsorption of arsenic from water using neutralized red mud. Environ Sci Technol 2004, 38:2428-2434.
31. Silva M.S., Flores V.S., Blass A.G. Preparación de un gel a partir de desechos de aluminio y su uso en la Eliminación de arsénico en agua para consumo humano. Rev Int Contam Ambient 2005, 21:171-181.
32. Bowen W.R., Jones M.G., Yousef H.N.S. Prediction of the rate of crossflow membrane ultrafiltration of colloids: a neural network approach. Chem Eng Sci 1998, 53:3793-3802.
33. Gilbert R.O. Statistical methods for environmental pollution monitoring 1987, Van Nostrand Reinhold, New York, p. 320-30.
34. Haykin S. Neural networks: a comprehensive foundation 1994, Macmillan, Englewood Cliffs, NJ, p. 696.