Estimating biofilm reaction kinetics using hybrid mechanistic-neural network rate function model

This work describes an alternative method for estimation of reaction rate of a biofilm process without using a model equation. A first principles model of the biofilm process is integrated with artificial neural networks to derive a hybrid mechanistic-neural network rate function model (HMNNRFM), and this combined model structure is used to estimate the complex kinetics of the biofilm process as a consequence of the validation of its steady state solution. The performance of the proposed methodology is studied with the aid of the experimental data of an anaerobic fixed bed biofilm reactor. The statistical significance of the method is also analyzed by means of the coefficient of determination (R2) and model efficiency (ME). The results demonstrate the effectiveness of HMNNRFM for estimating the complex kinetics of the biofilm process involved in the treatment of industry wastewater.