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

Abstract

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 °C, 30 °C, 35 °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.

Keywords:

• experimental design
• artificial neural networks
• microbial consortium
• process optimization
• phenol degradation

Acknowledgements

INCT-CNPq (Brasília, DF, Brazil) and FAPESP (São Paulo, SP, Brazil) provided financial support for this study.