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Environmental Technology Volume 33, 2012 - Issue 15
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Original Articles

Phenol biodegradation by a microbial consortium: application of artificial neural network (ANN) modelling

Elen Aquino Perpetuo Environmental Microbiology Laboratory, CEPEMA-POLI-USP, University of São Paulo, São Paulo-SP, CEP 11573-000, Brazil; LSCP, Chemical Engineering Department, Polytechnic School, University of São Paulo, São Paulo-SP, CEP 05508-900, BrazilCorrespondence[email protected]
,
Douglas Nascimento Silva School of Science and Technology, Federal University of Rio Grande do Norte, Natal-RN, CEP 59078-970, Brazil
,
Ingrid Regina Avanzi Environmental Microbiology Laboratory, CEPEMA-POLI-USP, University of São Paulo, São Paulo-SP, CEP 11573-000, Brazil
,
Louise Hase Gracioso Environmental Microbiology Laboratory, CEPEMA-POLI-USP, University of São Paulo, São Paulo-SP, CEP 11573-000, Brazil
,
Marcela Passos Galluzzi Baltazar Environmental Microbiology Laboratory, CEPEMA-POLI-USP, University of São Paulo, São Paulo-SP, CEP 11573-000, Brazil
&
Claudio Augusto Oller Nascimento LSCP, Chemical Engineering Department, Polytechnic School, University of São Paulo, São Paulo-SP, CEP 05508-900, Brazil
Pages 1739-1745 | Received 21 Jul 2011, Accepted 20 Nov 2011, Published online: 23 Jan 2012

Citations (15)

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Ayaz Mohd. (2022) Presence of phenol in wastewater effluent and its removal: an overview. International Journal of Environmental Analytical Chemistry 102:6, pages 1362-1384.
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Biswanath Mahanty, Mohd. Zafar & Hung-Suck Park. (2013) Characterization of co-digestion of industrial sludges for biogas production by artificial neural network and statistical regression models. Environmental Technology 34:13-14, pages 2145-2153.
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Iván Machón-González, Hilario López-García, Jesús Rodríguez-Iglesias, Elena Marañón-Maison, Leonor Castrillón-Peláez & Yolanda Fernández-Nava. (2013) Comparing feed-forward versus neural gas as estimators: application to coke wastewater treatment. Environmental Technology 34:9, pages 1131-1140.
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Articles from other publishers (12)

Yasmen A. Mustafa, Sinan J. Mohammed & Mohanad J. M. Ridha. (2022) Polyaromatic hydrocarbons biodegradation using mix culture of microorganisms from sewage waste sludge: application of artificial neural network modelling. Journal of Environmental Health Science and Engineering 20:1, pages 405-418.
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Sinan J. Mohammed, Yasmen A. Mustafa & Mohanad S. Jabbar. (2022) Biodegradation of Naphthalene Using Glass Beads Roller Bioreactor: Application of Artificial Neural Network Modeling. Environmental Processes 9:1.
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Saloni Sharma, Ritika Gupta, Ranjana Bhatia, Amrit Pal Toor & Hema Setia. (2021) Predicting microbial response to anthropogenic environmental disturbances using artificial neural network and multiple linear regression. International Journal of Cognitive Computing in Engineering 2, pages 65-70.
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J. A. Valencia, G. Astray, M. Fernández-González, M. J. Aira & F. J. Rodríguez-Rajo. (2019) Assessment of neural networks and time series analysis to forecast airborne Parietaria pollen presence in the Atlantic coastal regions. International Journal of Biometeorology 63:6, pages 735-745.
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Louise H. Gracioso, Paula B. Vieira, Marcela P. G. Baltazar, Ingrid R. Avanzi, Bruno Karolski, Claudio A. O. Nascimento & Elen A. Perpetuo. (2019) Removal of phenolic compounds from raw industrial wastewater by Achromobacter sp. isolated from a hydrocarbon-contaminated area . Water and Environment Journal 33:1, pages 40-50.
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Nirlipta Saha, Gonzalo Astray & S. Dutta Gupta. (2018) Modelling and Optimization of Biogenic Synthesis of Gold Nanoparticles from Leaf Extract of Swertia chirata Using Artificial Neural Network. Journal of Cluster Science 29:6, pages 1151-1159.
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Amanda Pacholak, Wojciech Smułek, Agata Zdarta, Agnieszka Zgoła-Grześkowiak & Ewa Kaczorek. (2018) Bacterial Biodegradation of 4-Monohalogenated Diphenyl Ethers in One-Substrate and Co-Metabolic Systems. Catalysts 8:10, pages 472.
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Hossein Ebrahimzade, Gholam Reza Khayati & Mahin Schaffie. (2018) Leaching kinetics of valuable metals from waste Li-ion batteries using neural network approach. Journal of Material Cycles and Waste Management 20:4, pages 2117-2129.
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Vivek Narisetty, Gonzalo Astray, Beatriz Gullón, Eulogio Castro, Binod Parameswaran & Ashok Pandey. (2017) Improved 1,3-propanediol production with maintained physical conditions and optimized media composition: Validation with statistical and neural approach. Biochemical Engineering Journal 126, pages 109-117.
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Itzel de Jesus Gonzalez Ojeda, Olivier Patrouix & Yannick Aoustin. (2017) Pressure based approach for Automated Fiber Placement (AFP) with sensor based feedback loop and flexible component in the effector. IFAC-PapersOnLine 50:1, pages 794-799.
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Gonzalo Astray, Beatriz Gullón, Jalel Labidi & Patricia Gullón. (2016) Comparison between developed models using response surface methodology (RSM) and artificial neural networks (ANNs) with the purpose to optimize oligosaccharide mixtures production from sugar beet pulp. Industrial Crops and Products 92, pages 290-299.
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Aramide M. Akintunde, Sheriff O. Ajala & Eriola Betiku. (2015) Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate. Industrial Crops and Products 67, pages 387-394.
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