Estimation of Reaction Parameters for Phenol Biodegradation Using Trainable Artificial Neural Networks

Biological systems typically respond non-linearly to the external stimuli such as food availability or toxic exposure. Analytical models based on empirical and semi-empirical representations only simulate a narrow range of conditions. Simulation of the developed kinetic laws on wider scale normally fails. A black box approach is normally applied to simulate responses outside of the studied range. One such system that could be used without worrying about the internal mechanisms is the Trainable Artificial Neural Network (TANN). This system offers the capability to predict the next steps in the behaviour of the system using data from the past. In this study, a multi-layer feed forward neural network was capable of simulating phenol biodegradation without a kinetic model. The network consisted of an input layer with three neurons, a single hidden layer with ten neurons and an output layer with one neuron. Network testing achieved a Mean Squared Error (MSE) of 0.001 with the regression coefficient (R-2) of 0.984. The predicted trend was validated by substrate inhibited Monod-type kinetic using actual experimental data from a batch system using a laboratory enrichment of an environmental sample containing Pseudomonas aeruginosa. Both the model and the TANN was further tested against literature data from Chirwa and Wang (2000), where the researchers achieved simultaneous Cr(VI) reduction and phenol degradation using an anaerobic consortium of bacteria containing Escherichia coli ATCC 33456. In all cases tested, using the TANN algorithm ahead of the kinetic model generated a dataset that reduced convergence time during parameter search in the highly non-linear reaction kinetics.