Forecasting the effect of feast and famine conditions on biological sulphate reduction in an anaerobic inverse fluidized bed reactor using artificial neural networks |
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| Institution: | 1. UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands;2. University of Cassino, Department of Mechanics, Structures and Environmental Engineering, via Di Biasio 43, 03043 Cassino (FR), Italy;1. National Risk Management Research Laboratory, U.S. Environmental Protection Agency, 26 West Martin Luther King Dr, Cincinnati, OH 45268, United States;2. Pegasus Technical Services, Inc., 46 East Hollister St, Cincinnati, OH 45219, United States;3. Department of Chemistry, Louisiana State University, Baton Rouge, LA 70803, United States;4. Department of Environmental Sciences and LSU Superfund Research Center, Louisiana State University, Baton Rouge, LA 70803, United States;1. Department of Geosciences, Virginia Tech, Blacksburg, VA 24061, USA;2. Virginia Tech Center for Sustainable Nanotechnology, Virginia Tech, Blacksburg, VA 24061, USA;3. Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA;4. Department of Material Science and Engineering, Virginia Tech, Blacksburg, VA 24061, USA;5. Nanoscale Characterization and Fabrication Laboratory, Virginia Tech, Blacksburg, VA 24061, USA;6. Infrastructure and Environment, School of Engineering, University of Glasgow, Glasgow G12 8QQ, Scotland, United Kingdom;7. Geosciences Group, Pacific Northwest National Laboratory, Richland, WA 99352, USA;1. Department of Chemical Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines;2. Center for Research in Energy Systems and Technologies, School of Engineering, University of San Carlos, Cebu City 6000, Philippines;3. Department of Environmental Resources Management, Chia-Nan University of Pharmacy and Science, Tainan 71710, Taiwan;1. Commonwealth Scientific and Industrial Research Organisation (CSIRO) Land and Water, 147 Underwood Avenue, Floreat WA, 6014, Australia;2. Department of Environmental Engineering, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China;3. School of Engineering and Information Technology, Murdoch University, Perth WA, Australia;4. School of Biomedical Sciences, University of Western Australia, 35 Stirling Highway, Nedlands, WA 6009, Australia;1. Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, via Gaetano di Biasio 43, 03043 Cassino (FR), Italy;2. Department of Chemistry and Bioengineering, Tampere University of Technology, P.O. Box 541, FI-33101 Tampere, Finland;3. IHE Delft Institute for Water Education, PO Box 3015, 2601 DA Delft, The Netherlands;4. Université Paris-Est, Laboratoire Géomatériaux et Environnement (EA 4508), UPEM, 77454 Marne-la-Vallée, France |
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| Abstract: | The longevity and robustness of bioreactors used for wastewater treatment is determined by the activity of the microorganisms under steady and transient loading conditions. Two identical continuously operated inverse fluidized bed bioreactors (IFB), IFB R1 and IFB R2, were tested for sulphate removal under the same operating conditions for 140 d (Periods I–IV). Later, IFB R1 was used as the control reactor (Period V), while IFB R2 was operated under feast (Period V-A) and famine (Period V-B) feeding conditions for 66 d. The sulphate removal efficiency was comparable in both IFB, <20% in Period I and ～70% during Periods II, III and IV. The robustness of the IFB was evident when the sulphate removal efficiency remained comparable during the feast Period (67 ± 15%) applied to IFB R2 compared to continuous feeding Periods (Period IV (71 ± 4%) for IFB R2 and Period V (61 ± 15%) for IFB R1). The IFB performance was modelled using a three-layered artificial neural networks (ANN) model (5-11-3) and a sensitivity analysis, the sulphate removal was found to be dependent on the COD:sulphate ratio. Besides, the robustness, resilience and adaptation time of the IFB were affected by the degree of mixing and the hydraulic retention time. |
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| Keywords: | Inverse fluidized bed reactor Biological sulphate reduction Feast-famine conditions Artificial neural networks Sensitivity analysis |
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