Journal
JOURNAL OF ENVIRONMENTAL ENGINEERING
Volume 142, Issue 9, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)EE.1943-7870.0001004
Keywords
Sulfate-reducing bacteria; Inversed fluidized bed bioreactor; Sulfide production; Thiosulfate; Artificial neural network
Funding
- Erasmus Mundus Joint Doctorate program in Environmental Technologies for Contaminated Solids, Soils and Sediments (ETeCoS3) [2010-0009]
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Sulfate-reducing bacteria (SRB)-based technologies have gained a lot of attention in the field of wastewater treatment, especially to treat metal-contaminated wastewaters. An inverse fluidized bed (IFB) bioreactor is a versatile bioreactor configuration that uses SRB technology for metal removal and recovery from wastewater. Apart from sulfate, which is commonly used as an electron acceptor, thiosulfate is another potential candidate for this process. In this study, the performance of two IFB bioreactors that were operated at pH 7.0 (R1) and 5.0 (R2) using sulfate and thiosulfate as the electron acceptors were evaluated. The electron donor used in this study was ethanol and the chemical oxygen demand (COD) to electron acceptor ratio (SO42- or S2O32-) was kept constant at 1.0. By using sulfate as the electron acceptor, the average COD removal efficiency was 75.0 and 58.0% at pH 7.0 and 5.0, respectively, while the sulfate removal efficiency was 74.4 and 50.4%, respectively. The average sulfide production was 246.3 and 150.7 mg/L at pH 7.0 and pH 5.0, respectively. Using thiosulfate as the electron acceptor, slightly higher sulfate reduction activities were achieved when compared to sulfate at pH 5.0. The maximum COD removal efficiency was 54.8% and 162.7 mg/L sulfide was produced. The COD and sulfate removal efficiencies as well as the total sulfide production profiles in the IFB reactor fed with sulfate were modeled using a three-layered artificial neural network (ANN). The results showed that the developed ANN model with a topology of 3-7-3 was able to give good predictions of the performance variables. Moreover, the sensitivity analysis from ANN showed that this process is mainly pH dependent.
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