Neural networks as a tool for control and management of a biological reactor for treating hydrogen sulphide

作者：Elias A; Ibarra Berastegi G; Arias R; Barona A^*

来源：Bioprocess and Biosystems Engineering, 2006, 29(2): 129-136.

DOI：10.1007/s00449-006-0062-3

• bioreactors
• artificial neural networks
• numerical analysis
• statistical modelling
• hydrogen sulphide

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.

• 出版日期2006-7