Chunshuang Liu

Simultaneous removal of sulfide, nitrate and acetate: Kinetic modeling.

Biological removal of sulfide, nitrate and chemical oxygen demand (COD) simultaneously from industrial wastewaters to elementary sulfur (S(0)), N(2), and CO(2), or named the denitrifying sulfide (DSR) process, is a cost effective and environmentally friendly treatment process for high strength sulfide and nitrate laden organic wastewater. Kinetic model for the DSR process was established for the first time on the basis of Activated Sludge Model No. 1 (ASM1). The DSR experiments were conducted at influent sulfide concentrations of 200-800 mg/L, whose results calibrate the model parameters. The model correlates well with the DSR process dynamics. By introducing the switch function and the inhibition function, the competition between autotrophic and heterotrophic denitrifiers is quantitatively described and the degree of inhibition of sulfide on heterotrophic denitrifiers is realized. The model output indicates that the DSR reactor can work well at 0.51000 mg/L influent sulfide, however, the DSR system will break down.

Modeling denitrifying sulfide removal process using artificial neural networks.

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.