Journal
ENVIRONMENTAL TECHNOLOGY
Volume 33, Issue 15, Pages 1739-1745Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/09593330.2011.644585
Keywords
experimental design; artificial neural networks; microbial consortium; process optimization; phenol degradation
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Funding
- INCT-CNPq (Brasilia, DF, Brazil)
- FAPESP (Sao Paulo, SP, Brazil)
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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.
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