Artificial neural networks vs linear regression in a fluid mechanics and chemical modelling problem: Elimination of hydrogen sulphide in a lab-scale biofilter

A biofilter is a biological reactor in which a certain pollutant is eliminated by the action of microorganisms. In this work, the removal efficiency of a lab-scale biofilter for eliminating hydrogen sulphide (H2S) has been modelled. To that end, multilayer perceptron (MLP) neural networks and multiple linear regression (MLR) have been used and then, results obtained with both techniques have been compared. The biofilter has been operating during 194 days and for modelling purposes, it has been considered as a system in which changes in the flow and concentration of H2S entering the biofilter are followed by changes in the removal efficiency of the reactor. In all cases, to obtain true representative values corresponding to the different equilibrium situations, be re removal efficiencies (outputs) were measured, 24 hours were allowed after the H2S load was changed by altering the inlet concentration and flow. The results showed that a multilayer perceptron 2-2-1 (MLP) model was able to explain 92% (R-2=0. 92) of the overall variability detected in the removal efficiency of the biofilter corresponding to a wide range of operating conditions. The MLR model yielded a value of R-2=0. 72. The MLP outperforms the MLR though not dramatically. The explanation might be that the combination of a great number of highly non-linear mechanisms tends to linearize the overall effect, at least to a certain extent. As a conclusion, the use of neural networks and more specifically, MLP models can describe the behaviour of a biofilter more accurately than simple linear regression models.