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

被引：15
|
作者：
Perpetuo, Elen Aquino [1 ,2 ]
Silva, Douglas Nascimento [3 ]
Avanzi, Ingrid Regina [1 ]
Gracioso, Louise Hase [1 ]
Galluzzi Baltazar, Marcela Passos [1 ]
Oller Nascimento, Claudio Augusto [2 ]
机构：
[1] Univ Sao Paulo, CEPEMA POLI USP, Environm Microbiol Lab, BR-11573000 Sao Paulo, Brazil
[2] Univ Sao Paulo, Polytech Sch, Dept Chem Engn, LSCP, BR-05508900 Sao Paulo, Brazil
[3] Univ Fed Rio Grande do Norte, Sch Sci & Technol, BR-59078970 Natal, RN, Brazil
来源：
ENVIRONMENTAL TECHNOLOGY | 2012年 / 33卷 / 15期
基金：
巴西圣保罗研究基金会;
关键词：
experimental design; artificial neural networks; microbial consortium; process optimization; phenol degradation; PSEUDOMONAS-PICTORUM; DEGRADATION; SOIL; TEMPERATURE; SUPPLEMENTS; NUTRIENT; KINETICS;
D O I：
10.1080/09593330.2011.644585
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L-1, 2.8 g L-1, 4.2 g L-1), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency of biodegradation.
引用
收藏
页码：1739 / 1745
页数：7
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