Neural networks as a tool for control and management of a biological reactor for treating hydrogen sulphide |
| |
| Authors: | A. Elías G. Ibarra-Berastegi R. Arias A. Barona |
| |
| Affiliation: | (1) Department of Chemical and Environmental Engineering, Engineering Faculty, University of the Basque Country, Alda Urkijo s/n., 48013 Bilbao, Spain;(2) Department of Nuclear Engineering and Fluid Mechanics, Engineering Faculty, University of the Basque Country, Alda Urkijo s/n., 48013 Bilbao, Spain |
| |
| Abstract: | Based on an experimental database consisting of 194 daily cases, artificial neural networks were used to model the removal efficiency of a biofilter for treating hydrogen sulphide (H2S). In this work, the removal efficiency of the reactor was considered as a function of the changes in the air flow and concentration of H2S entering the biofilter. In order to obtain true representative values, the removal efficiencies (outputs) were measured 24 h after each input was changed. A MLP (multilayer perceptron 2-2-1) model with two input variables (unit flow and concentration of the contaminant fed into the biofilter) rendered good prediction values with a determination coefficient of 0.92 for the removal efficiency within the range studied. This means that the MLP model can explain 92% of the overall variability detected in the biofilter corresponding to a wide range of operating conditions. |
| |
| Keywords: | Bioreactors Artificial neural networks Numerical analysis Statistical modelling Hydrogen sulphide |
| 本文献已被 PubMed SpringerLink 等数据库收录！ |
|
