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  • Title: Modeling denitrifying sulfide removal process using artificial neural networks.
    Author: Wang A, Liu C, Han H, Ren N, Lee DJ.
    Journal: J Hazard Mater; 2009 Sep 15; 168(2-3):1274-9. PubMed ID: 19359094.
    Abstract:
    The denitrifying sulfide removal (DSR) process has complex interactions between autotrophic and heterotrophic denitrifers; thus, constructing a detailed mechanistic model and proper control architecture is difficult. Artificial neural networks (ANNs) are capable of inferring the complex relationships between input and output process variables without a detailed characterization of the mechanisms governing the process. This work presents a novel ANN that accurately predicts the steady-state performance of an expended granular sludge bed (EGSB)-DSR bioreactor for nitrite denitrification and the complete DSR process. The proposed ANN shows that at a threshold hydraulic retention time (HRT)<7h, influent sulfide concentration markedly affects reactor performance.
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