Improvement of the /Taguchi/ design optimization using artificial intelligence in three acid azo dyes removal by electrocoagulation
The aim of this research is improvement of the Taguchi design optimization using artificial neural network (ANN) and genetic algorithm (GA) in Acid Orange 7, Acid Brown 14, and Acid Red 18 azo dyes removal by electrocoagulation. For this purpose, 27 tests were undertaken for investigation of five pa...
Saved in:
| Published in | Environmental progress & sustainable energy Vol. 34; no. 6; pp. 1568 - 1575 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Blackwell Publishing Ltd
01.11.2015
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1944-7442 1944-7450 |
| DOI | 10.1002/ep.12145 |
Cover
| Abstract | The aim of this research is improvement of the Taguchi design optimization using artificial neural network (ANN) and genetic algorithm (GA) in Acid Orange 7, Acid Brown 14, and Acid Red 18 azo dyes removal by electrocoagulation. For this purpose, 27 tests were undertaken for investigation of five parameters including current density, reaction time, initial dye concentration, dye type, and initial pH by using Taguchi's orthogonal array. Additionally, according to analysis of variance, dye type and reaction time were the most important parameters for responses of dye removal efficiency and operating costs in Taguchi design, respectively. Prediction and modeling of the dye removal efficiency response were also accomplished by ANN. High R2 values (≥97%) indicated that the accuracy of the Taguchi and ANN models are acceptable. In addition, ANN was used in GA for finding the best elimination conditions for the selected dyes according to the Taguchi design. Dye removal efficiencies of 96.79%, 98.12%, and 76.47% were reported for Acid Orange 7, Acid Brown 14, and Acid Red 18, respectively, in the ANN model at the best elimination conditions. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1568–1575, 2015 |
|---|---|
| AbstractList | The aim of this research is improvement of the Taguchi design optimization using artificial neural network (ANN) and genetic algorithm (GA) in Acid Orange 7, Acid Brown 14, and Acid Red 18 azo dyes removal by electrocoagulation. For this purpose, 27 tests were undertaken for investigation of five parameters including current density, reaction time, initial dye concentration, dye type, and initial pH by using Taguchi's orthogonal array. Additionally, according to analysis of variance, dye type and reaction time were the most important parameters for responses of dye removal efficiency and operating costs in Taguchi design, respectively. Prediction and modeling of the dye removal efficiency response were also accomplished by ANN. High R2 values (≥97%) indicated that the accuracy of the Taguchi and ANN models are acceptable. In addition, ANN was used in GA for finding the best elimination conditions for the selected dyes according to the Taguchi design. Dye removal efficiencies of 96.79%, 98.12%, and 76.47% were reported for Acid Orange 7, Acid Brown 14, and Acid Red 18, respectively, in the ANN model at the best elimination conditions. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1568–1575, 2015 The aim of this research is improvement of the Taguchi design optimization using artificial neural network (ANN) and genetic algorithm (GA) in Acid Orange 7, Acid Brown 14, and Acid Red 18 azo dyes removal by electrocoagulation. For this purpose, 27 tests were undertaken for investigation of five parameters including current density, reaction time, initial dye concentration, dye type, and initial pH by using Taguchi's orthogonal array. Additionally, according to analysis of variance, dye type and reaction time were the most important parameters for responses of dye removal efficiency and operating costs in Taguchi design, respectively. Prediction and modeling of the dye removal efficiency response were also accomplished by ANN. High R 2 values (≥97%) indicated that the accuracy of the Taguchi and ANN models are acceptable. In addition, ANN was used in GA for finding the best elimination conditions for the selected dyes according to the Taguchi design. Dye removal efficiencies of 96.79%, 98.12%, and 76.47% were reported for Acid Orange 7, Acid Brown 14, and Acid Red 18, respectively, in the ANN model at the best elimination conditions. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1568–1575, 2015 The aim of this research is improvement of the Taguchi design optimization using artificial neural network (ANN) and genetic algorithm (GA) in Acid Orange 7, Acid Brown 14, and Acid Red 18 azo dyes removal by electrocoagulation. For this purpose, 27 tests were undertaken for investigation of five parameters including current density, reaction time, initial dye concentration, dye type, and initial pH by using Taguchi's orthogonal array. Additionally, according to analysis of variance, dye type and reaction time were the most important parameters for responses of dye removal efficiency and operating costs in Taguchi design, respectively. Prediction and modeling of the dye removal efficiency response were also accomplished by ANN. High R super(2) values ( greater than or equal to 97%) indicated that the accuracy of the Taguchi and ANN models are acceptable. In addition, ANN was used in GA for finding the best elimination conditions for the selected dyes according to the Taguchi design. Dye removal efficiencies of 96.79%, 98.12%, and 76.47% were reported for Acid Orange 7, Acid Brown 14, and Acid Red 18, respectively, in the ANN model at the best elimination conditions. copyright 2015 American Institute of Chemical Engineers Environ Prog, 34: 1568-1575, 2015 |
| Author | Taheri, Mahsa Moghaddam, Mohammad Reza Alavi Arami, Mokhtar |
| Author_xml | – sequence: 1 givenname: Mahsa surname: Taheri fullname: Taheri, Mahsa organization: Civil and Environmental Engineering Department, Amirkabir University of Technology (AUT), 15875-4413, Tehran, Iran – sequence: 2 givenname: Mohammad Reza Alavi surname: Moghaddam fullname: Moghaddam, Mohammad Reza Alavi email: alavim@yahoo.com organization: Civil and Environmental Engineering Department, Amirkabir University of Technology (AUT), 15875-4413, Tehran, Iran – sequence: 3 givenname: Mokhtar surname: Arami fullname: Arami, Mokhtar organization: Textile Engineering Department, Amirkabir University of Technology (AUT), 15875-4413, Tehran, Iran |
| BookMark | eNp1kEFP3DAQha2KSgVaqT_Bx16yayd2jI8togsSKkiAerSMPVmmTezU9kKXa_94w261SKicZkb65s2bd0D2QgxAyEfOZpyxeg7jjNdcyDdkn2shKiUk29v1on5HDnL-wVjbCK33yZ-zYUzxHgYIhcaOljug82u7XLk7nFMPGZeBxrHggI-2YAx0lTEsqU0FO3Roe4qhQN_jEoKDaZgkEgC1Dj21j5H6NWSaYIj3E3u7ptCDKym6OB3pN5LvydvO9hk-_KuH5ObryfXxaXV-sTg7_nxeOaGkrDivRatb6QV467TwdVNLeauaGkBZr4SW3raMOdUKqRjzktWg5VHjGqcV75pD8mmrO338awW5mAGzm7zbAHGVDVftkVStatSEzraoSzHnBJ1xWDZmS7LYG87MU9oGRrNJ-1l7tzAmHGxa_w-ttugD9rB-lTMnly94zAV-73ibfponr9J8_7Yw7IpLvbg6NV-av9RLn_Q |
| CitedBy_id | crossref_primary_10_1016_j_ultsonch_2016_01_024 crossref_primary_10_1039_D4RA08485C crossref_primary_10_1007_s40201_022_00835_w crossref_primary_10_1016_j_jmrt_2020_10_107 crossref_primary_10_2166_ws_2017_073 crossref_primary_10_1016_j_eti_2024_103832 crossref_primary_10_1080_1536383X_2018_1563543 crossref_primary_10_1111_exsy_12740 crossref_primary_10_1007_s13762_019_02413_4 crossref_primary_10_1155_2021_5510401 crossref_primary_10_1002_aoc_3857 crossref_primary_10_1002_gch2_202300052 crossref_primary_10_1155_2021_5683433 crossref_primary_10_1016_j_wri_2022_100191 crossref_primary_10_1088_1757_899X_206_1_012090 crossref_primary_10_1016_j_jclepro_2022_135522 crossref_primary_10_1016_j_ultsonch_2017_09_056 crossref_primary_10_3390_mi14071427 crossref_primary_10_1007_s11356_022_22055_3 crossref_primary_10_1155_2021_5526517 crossref_primary_10_61186_jrr_2405_1024 crossref_primary_10_1007_s10876_024_02668_z crossref_primary_10_1016_j_clce_2022_100007 crossref_primary_10_1155_2021_5582598 crossref_primary_10_1016_j_jwpe_2021_101969 crossref_primary_10_1007_s11356_023_31362_2 crossref_primary_10_3390_w14030344 crossref_primary_10_1007_s11356_021_15345_9 |
| Cites_doi | 10.1016/j.jhydrol.2014.03.057 10.1016/j.jenvman.2013.06.029 10.1016/j.carbpol.2012.08.005 10.1016/j.biortech.2013.02.020 10.1016/j.jiec.2013.11.058 10.1007/s00521-012-1031-1 10.1016/j.jiec.2013.05.034 10.1016/j.desal.2011.01.083 10.1016/j.matdes.2008.10.033 10.1016/j.ibiod.2013.10.022 10.3390/molecules18077646 10.1002/ep.11623 10.1016/j.cej.2012.12.084 10.1186/1735-2746-9-23 10.1016/j.optlaseng.2010.11.005 10.30638/eemj.2014.059 10.1002/ep.11656 |
| ContentType | Journal Article |
| Copyright | 2015 American Institute of Chemical Engineers Environ Prog |
| Copyright_xml | – notice: 2015 American Institute of Chemical Engineers Environ Prog |
| DBID | BSCLL AAYXX CITATION 7ST 7U6 C1K |
| DOI | 10.1002/ep.12145 |
| DatabaseName | Istex CrossRef Environment Abstracts Sustainability Science Abstracts Environmental Sciences and Pollution Management |
| DatabaseTitle | CrossRef Environment Abstracts Sustainability Science Abstracts Environmental Sciences and Pollution Management |
| DatabaseTitleList | CrossRef Environment Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1944-7450 |
| EndPage | 1575 |
| ExternalDocumentID | 10_1002_ep_12145 EP12145 ark_67375_WNG_0S159GSH_B |
| Genre | article |
| GroupedDBID | ..I .3N .DC .GA 05W 0R~ 10A 1L6 1OB 1OC 33P 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHBH AAHQN AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABEML ABJNI ABPVW ACAHQ ACBWZ ACCZN ACGFS ACGOD ACIWK ACPOU ACPRK ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADMGS ADNMO ADOZA ADXAS AEFGJ AEIGN AEIMD AENEX AEUYR AEYWJ AFBPY AFFPM AFGKR AFRAH AFWVQ AFZJQ AGQPQ AGXDD AGYGG AHBTC AIDQK AIDYY AITYG AIURR AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ATUGU AUFTA AZBYB AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EBS EDH EJD F00 F01 F04 F5P G-S G.N GODZA H.T H.X HGLYW HZ~ ITG ITH IX1 J0M JPC LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG P2P P2W P2X P4D Q.N Q11 QB0 QRW R.K ROL RX1 SUPJJ UB1 V2E W8V W99 WBKPD WIH WIK WLBEL WOHZO WQJ WXSBR WYISQ XG1 XV2 ~IA ~WT .Y3 31~ 3V. 8FE AAHHS ABJCF ACCFJ ADZOD AEEZP AEQDE AEUQT AEUYN AFKRA AFPWT AIWBW AJBDE AZFZN BCU BEC BGLVJ CCPQU FEDTE GUQSH HF~ HVGLF M7S PATMY PQQKQ PROAC PTHSS PYCSY RWI S0X SJFOW WRC AAYXX CITATION 7ST 7U6 C1K |
| ID | FETCH-LOGICAL-c4755-11246965d4edac94d23255b732ee7ad7495da600c7645700d502e9583c3c971f3 |
| IEDL.DBID | DR2 |
| ISSN | 1944-7442 |
| IngestDate | Tue Oct 07 09:42:19 EDT 2025 Thu Apr 24 23:03:43 EDT 2025 Sat Oct 25 05:12:01 EDT 2025 Wed Jan 22 17:06:25 EST 2025 Sun Sep 21 06:21:46 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4755-11246965d4edac94d23255b732ee7ad7495da600c7645700d502e9583c3c971f3 |
| Notes | istex:2CDE4F1C08B0C2D5C86D75683ECDF93AEDCB5ED2 ArticleID:EP12145 ark:/67375/WNG-0S159GSH-B ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1768576737 |
| PQPubID | 23462 |
| PageCount | 8 |
| ParticipantIDs | proquest_miscellaneous_1768576737 crossref_citationtrail_10_1002_ep_12145 crossref_primary_10_1002_ep_12145 wiley_primary_10_1002_ep_12145_EP12145 istex_primary_ark_67375_WNG_0S159GSH_B |
| PublicationCentury | 2000 |
| PublicationDate | November/December 2015 |
| PublicationDateYYYYMMDD | 2015-11-01 |
| PublicationDate_xml | – month: 11 year: 2015 text: November/December 2015 |
| PublicationDecade | 2010 |
| PublicationTitle | Environmental progress & sustainable energy |
| PublicationTitleAlternate | Environ. Prog. Sustainable Energy |
| PublicationYear | 2015 |
| Publisher | Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd |
| References | Amani-Ghadim, A.R., Olad, A., Aber, S., & Ashassi-Sorkhabi, H. (2013). Comparison of organic dyes removal mechanism in electrocoagulation process using iron and aluminum anodes, Environmental Progress & Sustainable Energy, 32, 547-556. Hai, F.I., Yamamoto, K., Nakajima, F., Fukushi, K., Nghiem, L.D., Price, W.E., & Jin, B. (2013). Degradation of azo dye acid orange 7 in a membrane bioreactor by pellets and attached growth of Coriolus versicolour, Bioresource Technology, 141, 29-34. Shoabargh, S., Karimi, A., Dehghan, G., & Khataee, A. (2014). A hybrid photocatalytic and enzymatic process using glucose oxidase immobilized on TiO2/polyurethane for removal of a dye, Journal of Industrial & Engineering Chemistry, 20, 3150-3156. Bhatti, M.S., Kapoor, D., Kali, R.K., Reddy, A.S., & Thukral, A.K. (2011). RSM and ANN modeling for electrocoagulation of copper from simulated wastewater: Multi objective optimization using genetic algorithm approach, Desalination, 274, 74-80. Lim, C.K., Aris, A., Neoh, C.H., Lam, C.Y., Majid, Z.A., & Ibrahim, Z. (2014). Evaluation of macrocomposite based sequencing batch biofilm reactor (MC-SBBR) for decolorization and biodegradation of azo dye Acid Orange 7, International Biodeterioration & Biodegradation, 87, 9-17. Taheri, M., Alavi Moghaddam, M.R., & Arami, M. (2014). A comparative study on removal of four types of acid azo dyes using electrocoagulation process, Environmental Engineering & Management Journal, 13, 557-564. Gen, M., Cheng, R., & Lin, L. (2008). Network models and optimization: Multiobjective genetic algorithm approach (decision engineering), London: Springer. Basiri Parsa, J., Golmirzaei, M., & Abbasi, M. (2014). Degradation of azo dye C.I. Acid Red 18 in aqueous solution by ozone-electrolysis process, Journal of Industrial & Engineering Chemistry, 20, 689-694. Taheri, M., Alavi Moghaddam, M.R., & Arami, M. (2013). Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models, Journal of Environmental Management, 128, 798-806. Constantin, M., Asmarandei, I., Harabagiu, V., Ghimici, L., Ascenzi, P., & Fundueanu, G. (2013). Removal of anionic dyes from aqueous solutions by an ion-exchanger based on pullulan microspheres, Carbohydrate Polymers, 91, 74-84. Xiansheng, N., Zhenggan, Z., Xiongwei, W., & Luming, L. (2011). The use of Taguchi method to optimize the laser welding of sealing neuro-stimulator, Optics & Lasers in Engineering, 49, 297-304. Maleki, A., Daraei, H., Shahmoradi, B., Razee, S., & Ghobadi, N. (2013). Electrocoagulation efficiency and energy consumption probing by artificial intelligent approaches, Desalination & Water Treatment, 52, 1-12. Yildiz, Y.S., Şenyiğit, E., & İrdemez, S. (2013). Optimization of specific energy consumption for Bomaplex Red CR-L dye removal from aqueous solution by electrocoagulation using Taguchi-neural method, Neural Computing & Applications, 23, 1061-1069. Guo, Y., Zhu, Z., Qiu, Y., & Zhao, J. (2013). Enhanced adsorption of acid brown 14 dye on calcined Mg/Fe layered double hydroxide with memory effect, Chemical Engineering Journal, 219, 69-77. Taheri, M., Alavi Moghaddam, M.R., & Arami, M. (2012). Optimization of Acid Black 172 decolorization by electrocoagulation using response surface methodology, Iranian Journal of Environmental Health Science & Engineering, 9, 23. Daneshvar, E., Kousha, M., Koutahzadeh, N., Sohrabi, M.S., & Bhatnagar, A. (2013). Biosorption and bioaccumulation studies of acid Orange 7 dye by Ceratophylum demersum, Environmental Progress & Sustainable Energy, 32, 285-293. Nourani, V., Hosseini Baghanam, A., Adamowski, J., & Kisi, O. (2014). Applications of hybrid Wavelet-Artificial Intelligence models in hydrology, Journal of Hydrology, 514, 358-377. Logothetis, N., & Wynn, H.P. (1994). Quality through design: Experimental design, off-line quality control, and Taguchi's contributions, New York: Oxford University Press. Piuleac, C.G., Curteanu, S., Rodrigo, M.A., Sáez, C., & Fernández, F.J. (2013). Optimization methodology based on neural networks and genetic algorithms applied to electro-coagulation processes, Central European Journal of Chemistry, 11, 1213-1224. Batista, A.C.L., Freitas Silva, M.C., Batista, J.B., Nascimento, A.E., & Campos-Takaki, G.M. (2013). Eco-friendly chitosan production by Syncephalastrum racemosum and application to the removal of Acid Orange 7 (AO7) from wastewaters, Molecules, 18, 7646-7660. Satapathy, A., Patnaik, A., & Pradhan, M.K. (2009). A study on processing, characterization and erosion behavior of fish (Labeo rohita) scale filled epoxy matrix composites, Materials & Design, 30, 2359-2371. 2014; 514 2013; 18 2009; 30 2013; 219 2013; 11 2013; 32 2013; 23 2013; 52 2013; 128 2013; 91 2008 2014; 13 2013; 141 1994 2011; 49 2014; 87 2014; 20 2012; 9 2011; 274 e_1_2_6_21_1 Logothetis N. (e_1_2_6_17_1) 1994 Gen M. (e_1_2_6_15_1) 2008 Maleki A. (e_1_2_6_19_1) 2013; 52 Piuleac C.G. (e_1_2_6_20_1) 2013; 11 e_1_2_6_9_1 e_1_2_6_8_1 e_1_2_6_5_1 e_1_2_6_4_1 e_1_2_6_7_1 e_1_2_6_6_1 e_1_2_6_13_1 e_1_2_6_14_1 e_1_2_6_3_1 e_1_2_6_11_1 e_1_2_6_2_1 e_1_2_6_12_1 e_1_2_6_22_1 e_1_2_6_18_1 Taheri M. (e_1_2_6_10_1) 2014; 13 e_1_2_6_16_1 |
| References_xml | – reference: Batista, A.C.L., Freitas Silva, M.C., Batista, J.B., Nascimento, A.E., & Campos-Takaki, G.M. (2013). Eco-friendly chitosan production by Syncephalastrum racemosum and application to the removal of Acid Orange 7 (AO7) from wastewaters, Molecules, 18, 7646-7660. – reference: Lim, C.K., Aris, A., Neoh, C.H., Lam, C.Y., Majid, Z.A., & Ibrahim, Z. (2014). Evaluation of macrocomposite based sequencing batch biofilm reactor (MC-SBBR) for decolorization and biodegradation of azo dye Acid Orange 7, International Biodeterioration & Biodegradation, 87, 9-17. – reference: Nourani, V., Hosseini Baghanam, A., Adamowski, J., & Kisi, O. (2014). Applications of hybrid Wavelet-Artificial Intelligence models in hydrology, Journal of Hydrology, 514, 358-377. – reference: Xiansheng, N., Zhenggan, Z., Xiongwei, W., & Luming, L. (2011). The use of Taguchi method to optimize the laser welding of sealing neuro-stimulator, Optics & Lasers in Engineering, 49, 297-304. – reference: Taheri, M., Alavi Moghaddam, M.R., & Arami, M. (2014). A comparative study on removal of four types of acid azo dyes using electrocoagulation process, Environmental Engineering & Management Journal, 13, 557-564. – reference: Piuleac, C.G., Curteanu, S., Rodrigo, M.A., Sáez, C., & Fernández, F.J. (2013). Optimization methodology based on neural networks and genetic algorithms applied to electro-coagulation processes, Central European Journal of Chemistry, 11, 1213-1224. – reference: Logothetis, N., & Wynn, H.P. (1994). Quality through design: Experimental design, off-line quality control, and Taguchi's contributions, New York: Oxford University Press. – reference: Taheri, M., Alavi Moghaddam, M.R., & Arami, M. (2013). Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models, Journal of Environmental Management, 128, 798-806. – reference: Satapathy, A., Patnaik, A., & Pradhan, M.K. (2009). A study on processing, characterization and erosion behavior of fish (Labeo rohita) scale filled epoxy matrix composites, Materials & Design, 30, 2359-2371. – reference: Gen, M., Cheng, R., & Lin, L. (2008). Network models and optimization: Multiobjective genetic algorithm approach (decision engineering), London: Springer. – reference: Yildiz, Y.S., Şenyiğit, E., & İrdemez, S. (2013). Optimization of specific energy consumption for Bomaplex Red CR-L dye removal from aqueous solution by electrocoagulation using Taguchi-neural method, Neural Computing & Applications, 23, 1061-1069. – reference: Shoabargh, S., Karimi, A., Dehghan, G., & Khataee, A. (2014). A hybrid photocatalytic and enzymatic process using glucose oxidase immobilized on TiO2/polyurethane for removal of a dye, Journal of Industrial & Engineering Chemistry, 20, 3150-3156. – reference: Hai, F.I., Yamamoto, K., Nakajima, F., Fukushi, K., Nghiem, L.D., Price, W.E., & Jin, B. (2013). Degradation of azo dye acid orange 7 in a membrane bioreactor by pellets and attached growth of Coriolus versicolour, Bioresource Technology, 141, 29-34. – reference: Bhatti, M.S., Kapoor, D., Kali, R.K., Reddy, A.S., & Thukral, A.K. (2011). RSM and ANN modeling for electrocoagulation of copper from simulated wastewater: Multi objective optimization using genetic algorithm approach, Desalination, 274, 74-80. – reference: Daneshvar, E., Kousha, M., Koutahzadeh, N., Sohrabi, M.S., & Bhatnagar, A. (2013). Biosorption and bioaccumulation studies of acid Orange 7 dye by Ceratophylum demersum, Environmental Progress & Sustainable Energy, 32, 285-293. – reference: Maleki, A., Daraei, H., Shahmoradi, B., Razee, S., & Ghobadi, N. (2013). Electrocoagulation efficiency and energy consumption probing by artificial intelligent approaches, Desalination & Water Treatment, 52, 1-12. – reference: Guo, Y., Zhu, Z., Qiu, Y., & Zhao, J. (2013). Enhanced adsorption of acid brown 14 dye on calcined Mg/Fe layered double hydroxide with memory effect, Chemical Engineering Journal, 219, 69-77. – reference: Amani-Ghadim, A.R., Olad, A., Aber, S., & Ashassi-Sorkhabi, H. (2013). Comparison of organic dyes removal mechanism in electrocoagulation process using iron and aluminum anodes, Environmental Progress & Sustainable Energy, 32, 547-556. – reference: Constantin, M., Asmarandei, I., Harabagiu, V., Ghimici, L., Ascenzi, P., & Fundueanu, G. (2013). Removal of anionic dyes from aqueous solutions by an ion-exchanger based on pullulan microspheres, Carbohydrate Polymers, 91, 74-84. – reference: Basiri Parsa, J., Golmirzaei, M., & Abbasi, M. (2014). Degradation of azo dye C.I. Acid Red 18 in aqueous solution by ozone-electrolysis process, Journal of Industrial & Engineering Chemistry, 20, 689-694. – reference: Taheri, M., Alavi Moghaddam, M.R., & Arami, M. (2012). Optimization of Acid Black 172 decolorization by electrocoagulation using response surface methodology, Iranian Journal of Environmental Health Science & Engineering, 9, 23. – volume: 11 start-page: 1213 year: 2013 end-page: 1224 article-title: Optimization methodology based on neural networks and genetic algorithms applied to electro‐coagulation processes publication-title: Central European Journal of Chemistry – volume: 91 start-page: 74 year: 2013 end-page: 84 article-title: Removal of anionic dyes from aqueous solutions by an ion‐exchanger based on pullulan microspheres publication-title: Carbohydrate Polymers – volume: 87 start-page: 9 year: 2014 end-page: 17 article-title: Evaluation of macrocomposite based sequencing batch biofilm reactor (MC‐SBBR) for decolorization and biodegradation of azo dye Acid Orange 7 publication-title: International Biodeterioration & Biodegradation – volume: 32 start-page: 285 year: 2013 end-page: 293 article-title: Biosorption and bioaccumulation studies of acid Orange 7 dye by publication-title: Environmental Progress & Sustainable Energy – volume: 20 start-page: 689 year: 2014 end-page: 694 article-title: Degradation of azo dye C.I. Acid Red 18 in aqueous solution by ozone‐electrolysis process publication-title: Journal of Industrial & Engineering Chemistry – volume: 32 start-page: 547 year: 2013 end-page: 556 article-title: Comparison of organic dyes removal mechanism in electrocoagulation process using iron and aluminum anodes publication-title: Environmental Progress & Sustainable Energy – volume: 219 start-page: 69 year: 2013 end-page: 77 article-title: Enhanced adsorption of acid brown 14 dye on calcined Mg/Fe layered double hydroxide with memory effect publication-title: Chemical Engineering Journal – year: 2008 – volume: 52 start-page: 1 year: 2013 end-page: 12 article-title: Electrocoagulation efficiency and energy consumption probing by artificial intelligent approaches publication-title: Desalination & Water Treatment – volume: 18 start-page: 7646 year: 2013 end-page: 7660 article-title: Eco‐friendly chitosan production by and application to the removal of Acid Orange 7 (AO7) from wastewaters publication-title: Molecules – volume: 141 start-page: 29 year: 2013 end-page: 34 article-title: Degradation of azo dye acid orange 7 in a membrane bioreactor by pellets and attached growth of publication-title: Bioresource Technology – volume: 30 start-page: 2359 year: 2009 end-page: 2371 article-title: A study on processing, characterization and erosion behavior of fish ( ) scale filled epoxy matrix composites publication-title: Materials & Design – volume: 13 start-page: 557 year: 2014 end-page: 564 article-title: A comparative study on removal of four types of acid azo dyes using electrocoagulation process publication-title: Environmental Engineering & Management Journal – volume: 514 start-page: 358 year: 2014 end-page: 377 article-title: Applications of hybrid Wavelet‐Artificial Intelligence models in hydrology publication-title: Journal of Hydrology – volume: 49 start-page: 297 year: 2011 end-page: 304 article-title: The use of Taguchi method to optimize the laser welding of sealing neuro‐stimulator publication-title: Optics & Lasers in Engineering – volume: 23 start-page: 1061 year: 2013 end-page: 1069 article-title: Optimization of specific energy consumption for Bomaplex Red CR‐L dye removal from aqueous solution by electrocoagulation using Taguchi‐neural method publication-title: Neural Computing & Applications – volume: 274 start-page: 74 year: 2011 end-page: 80 article-title: RSM and ANN modeling for electrocoagulation of copper from simulated wastewater: Multi objective optimization using genetic algorithm approach publication-title: Desalination – year: 1994 – volume: 128 start-page: 798 year: 2013 end-page: 806 article-title: Techno‐economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models publication-title: Journal of Environmental Management – volume: 20 start-page: 3150 year: 2014 end-page: 3156 article-title: A hybrid photocatalytic and enzymatic process using glucose oxidase immobilized on TiO /polyurethane for removal of a dye publication-title: Journal of Industrial & Engineering Chemistry – volume: 9 start-page: 23 year: 2012 article-title: Optimization of Acid Black 172 decolorization by electrocoagulation using response surface methodology publication-title: Iranian Journal of Environmental Health Science & Engineering – ident: e_1_2_6_13_1 doi: 10.1016/j.jhydrol.2014.03.057 – ident: e_1_2_6_21_1 doi: 10.1016/j.jenvman.2013.06.029 – ident: e_1_2_6_6_1 doi: 10.1016/j.carbpol.2012.08.005 – ident: e_1_2_6_7_1 doi: 10.1016/j.biortech.2013.02.020 – ident: e_1_2_6_8_1 doi: 10.1016/j.jiec.2013.11.058 – volume: 52 start-page: 1 year: 2013 ident: e_1_2_6_19_1 article-title: Electrocoagulation efficiency and energy consumption probing by artificial intelligent approaches publication-title: Desalination & Water Treatment – ident: e_1_2_6_22_1 doi: 10.1007/s00521-012-1031-1 – ident: e_1_2_6_9_1 doi: 10.1016/j.jiec.2013.05.034 – ident: e_1_2_6_18_1 doi: 10.1016/j.desal.2011.01.083 – volume-title: Network models and optimization: Multiobjective genetic algorithm approach (decision engineering) year: 2008 ident: e_1_2_6_15_1 – ident: e_1_2_6_14_1 doi: 10.1016/j.matdes.2008.10.033 – ident: e_1_2_6_2_1 doi: 10.1016/j.ibiod.2013.10.022 – ident: e_1_2_6_5_1 doi: 10.3390/molecules18077646 – ident: e_1_2_6_3_1 doi: 10.1002/ep.11623 – volume: 11 start-page: 1213 year: 2013 ident: e_1_2_6_20_1 article-title: Optimization methodology based on neural networks and genetic algorithms applied to electro‐coagulation processes publication-title: Central European Journal of Chemistry – ident: e_1_2_6_4_1 doi: 10.1016/j.cej.2012.12.084 – ident: e_1_2_6_12_1 doi: 10.1186/1735-2746-9-23 – ident: e_1_2_6_16_1 doi: 10.1016/j.optlaseng.2010.11.005 – volume: 13 start-page: 557 year: 2014 ident: e_1_2_6_10_1 article-title: A comparative study on removal of four types of acid azo dyes using electrocoagulation process publication-title: Environmental Engineering & Management Journal doi: 10.30638/eemj.2014.059 – volume-title: Quality through design: Experimental design, off‐line quality control, and Taguchi's contributions year: 1994 ident: e_1_2_6_17_1 – ident: e_1_2_6_11_1 doi: 10.1002/ep.11656 |
| SSID | ssj0063499 |
| Score | 2.2193604 |
| Snippet | The aim of this research is improvement of the Taguchi design optimization using artificial neural network (ANN) and genetic algorithm (GA) in Acid Orange 7,... |
| SourceID | proquest crossref wiley istex |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1568 |
| SubjectTerms | acid azo dyes artificial neural network electrocoagulation genetic algorithm Taguchi design |
| Title | Improvement of the /Taguchi/ design optimization using artificial intelligence in three acid azo dyes removal by electrocoagulation |
| URI | https://api.istex.fr/ark:/67375/WNG-0S159GSH-B/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fep.12145 https://www.proquest.com/docview/1768576737 |
| Volume | 34 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library - Core collection (SURFmarket) issn: 1944-7442 databaseCode: DR2 dateStart: 20000101 customDbUrl: isFulltext: true eissn: 1944-7450 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0063499 providerName: Wiley-Blackwell |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT9wwELYquLSHFlqqUihypaqcwmZjO06OFAErpKKqgEDqwfJjghCwWe1DYvfKH2dsJ8uCqFT1FCUaO4k99nxjj78h5JtllTWZYYl2vEzQHpdJAZIlUMgKH6MFCdGEP4_z3hk_uhAXTVSlPwsT-SHmC25-ZIT52g9wbUadR9JQGHhmBO7Pl3dZHryp33PmqJzxkDoSXXSeSM6zlnc2zTptwSeWaNk36t0TmLkIVoO1OXhH_rTfGYNMrncmY7NjZ88oHP_vR1bI2waE0t2oNavkFfTfkzcL1IQfyH1cbQiLh7SuKOJE2jnVlz53Soe6EPdBa5xvbpuDnNRH0F9Sr4mRlIJeLbB94g1WMQSg2l45qmc1dVMY0SHc1qjr1Expk5DH1viSGKC3Rs4O9k_3ekmTsCGxXAqRIHZDbzsXjoPTtuQO4ZoQRrIMQGon0RlzGhGWlTn3vPpOpBmUomCW2VJ2K_aRLPXrPnwiVLosNeD90dJxU6A-lX4HLxW64q7I9TrZbjtP2YbN3CfVuFGRhzlTMFChWdfJ17nkIDJ4vCDzPfT_XEAPr33EmxTq_PhQpScI-g5PeuoHVtYqiMJx6DdXdB_qyUh10W9D3w3LYGWhu__6NrX_K1w__6vgBnmNOE3EI5CbZGk8nMAXxEJjsxW0_gEc3wTl |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1NT9wwEB1ROLQcSj8FLbSuVLWnsCGx40ScAAHbFlZVWVQOlSzHniBE2ayWXQm48scZ28l2qVqp6ilKZDuJPeN5Y4_fALw3aWXKpEwjbXkRkT0uohxlGmEuK3pMFsRHEx72su4x_3wiTuZgsz0LE_ghpgtuTjP8fO0U3C1Id36xhuLQUSNw8QAWeEZuikNE36bcUVnKffJIctJ5JDlPWubZOOm0Ne_ZogXXrVf3gOYsXPX2Zm8JfrRfGsJMztcn43Ld3PxG4vifv_IEHjc4lG0FwXkKczh4Bosz7ITP4TYsOPj1Q1ZXjKAi6_T1qUuf0mHWh36wmqaci-YsJ3NB9KfMCWPgpWBnM4SfdENNjBCZNmeW6Zua2Wu8ZCO8qEncWXnNmpw8pqaXhBi9F3C8t9vf6UZNzobIcClERPCNHO5MWI5Wm4JbQmxClDJNEKW2kvwxqwlkGZlxR61vRZxgIfLUpKaQG1X6EuYH9QCXgUmbxCU6l7SwvMxJpAq3iRcLXXGbZ3oFPrajp0xDaO7yavxUgYo5UThUvltX4N205DCQePyhzAcvANMCenTugt6kUN97-yo-Ity3f9RV29RYKyGKVNHtr-gB1pNLtUGuG7lvVIca8-P917ep3a_--upfC76Fh93-4YE6-NT78hoeEWwT4UTkKsyPRxNcI2g0Lt94FbgDb3sJBg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fTxQxEJ4gJEYfUFQCCFoTo0_LLbvtdjc8iXAc_rgQgcgDSdNtZwlBbi_HXSK8-o87bXfPw2hifNrsZtrdbWfab9rpNwCvTVqZMinTSFteRDQfF1GOMo0wlxU9phnERxN-7me9E_7hVJzOwXZ7FibwQ0wX3Jxl-PHaGTgObdX5xRqKQ0eNwMU9WOCiyF083-6XKXdUlnKfPJKcdB5JzpOWeTZOOm3JO3PRgmvW73eA5ixc9fNN9xGctV8awkwuNyfjctPc_kbi-J-_8hgWGxzK3gXFWYI5HDyBhzPshE_hR1hw8OuHrK4YQUXWOdbnLn1Kh1kf-sFqGnKumrOczAXRnzOnjIGXgl3MEH7SDVUxQmTaXFimb2tmb_CajfCqJnVn5Q1rcvKYml4SYvSewUl37_h9L2pyNkSGSyEigm_kcGfCcrTaFNwSYhOilGmCKLWV5I9ZTSDLyIw7an0r4gQLkacmNYXcqtJlmB_UA1wBJm0Sl-hc0sLyMieVKtwmXix0xW2e6VV42_aeMg2hucur8U0FKuZE4VD5Zl2FV1PJYSDx-IPMG68AUwE9unRBb1Kor_19FR8R7ts_6qkdqqzVEEWm6PZX9ADrybXaIteN3DcqQ5X5_v7r29Teob-u_avgS7h_uNtVnw76H5_DA0JtIhyIXIf58WiCG4SMxuULbwE_AQtKCIo |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Improvement+of+the+%2FTaguchi%2F+design+optimization+using+artificial+intelligence+in+three+acid+azo+dyes+removal+by+electrocoagulation&rft.jtitle=Environmental+progress+%26+sustainable+energy&rft.au=Taheri%2C+Mahsa&rft.au=Moghaddam%2C+Mohammad+Reza+Alavi&rft.au=Arami%2C+Mokhtar&rft.date=2015-11-01&rft.pub=Blackwell+Publishing+Ltd&rft.issn=1944-7442&rft.eissn=1944-7450&rft.volume=34&rft.issue=6&rft.spage=1568&rft.epage=1575&rft_id=info:doi/10.1002%2Fep.12145&rft.externalDBID=n%2Fa&rft.externalDocID=ark_67375_WNG_0S159GSH_B |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1944-7442&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1944-7442&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1944-7442&client=summon |