This work focuses on mathematical modeling of removal of organic dyes from textile industry waste waters by a white-rot fungus Irpex lacteus in a trickle-bed bioreactor. We developed a mathematical model of biomass and decolorization process dynamics. The model comprises mass balances of glucose and the dye in a fungal biofilm and a liquid film. The biofilm is modeled using a spatially two-dimensional domain. The liquid film is considered as homogeneous in the direction normal to the biofilm surface. The biomass growth, decay and the erosion of the biofilm are taken into account. Using experimental data, we identified values of key model parameters: the dye degradation rate constant, biofilm corrugation factor and liquid velocity. Considering the dye degradation rate constant 1×10⁻⁵ kg m⁻³ s⁻¹, we found optimal values of the corrugation factor 0.853 and 0.59 and values of the liquid velocity 5.23×10⁻³ m s⁻¹ and 6.2×10⁻³ m s⁻¹ at initial dye concentrations 0.09433 kg m⁻³ and 0.05284 kg m⁻³, respectively. A good agreement between the simulated and experimental data using estimated values of the model parameters was achieved. The model can be used to simulate the performance of laboratory scale trickle-bed bioreactor operated in a batch regime or to estimate values of principal parameters of the bioreactor system.
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http://dx.doi.org/10.1016/j.jbiotec.2011.08.027 | DOI Listing |
FEMS Microbes
August 2024
Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, BOKU University, Muthgasse 18, 1190 Vienna, Austria.
Bioresour Technol
November 2024
Bioproducts, Sciences, and Engineering Laboratory, Washington State University, Tri-Cities, Richland, WA 99354, USA; The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA 99164, USA; Biological Systems Engineering Department, L.J. Smith Hall, Washington State University, Pullman, WA 99164, USA. Electronic address:
Biomethanation converts carbon dioxide (CO) emissions into renewable natural gas (RNG) using mixed microbial cultures enriched with hydrogenotrophic archaea. This study examines the performance of a single methanogenic archaeon converting biogas with added hydrogen (H) into methane (CH) using a trickle-bed bioreactor with enhanced gas-liquid mass transport. The process in continuous operation followed the theoretical reaction of hydrogenotrophic methanogenesis (CO + 4 H → CH + 2 HO), producing RNG with over 99 % CH and more than 0.
View Article and Find Full Text PDFBioresour Technol
September 2024
Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431 Ås, Norway; Faculty of Chemistry, Biotechnology, and Food Science, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, 1432 Ås, Norway.
Biomethanation represents a promising approach for biomethane production, with biofilm-based processes like trickle bed reactors (TBRs) being among the most efficient solutions. However, maintaining stable performance can be challenging, and both pure and mixed culture approaches have been applied to address this. In this study, inocula enriched with hydrogenotrophic methanogens were introduced to to TBRs as bioaugmentation strategy to assess their impacts on the process performance and microbial community dynamics.
View Article and Find Full Text PDFMaterials (Basel)
April 2024
Lukasiewicz Research Network-Lodz Institute of Technology, 19/27 Marii Sklodowskiej-Curie Street, 90-570 Lodz, Poland.
Biological wastewater treatment using trickle bed reactors is a commonly known and used solution. One of the key elements of the proper operation of the trickle bed bioreactor is the appropriate selection of biofilm support elements. The respective properties of the bioreactor packing media used can influence, among other things, the efficiency of the treatment process.
View Article and Find Full Text PDFChemosphere
July 2024
Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P.O. Box 3015, 2601DA Delft, Netherlands.
This study presents a generalized hybrid model for predicting HS and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification model, and (ii) prediction of K <100%, based on inlet concentration, time of day, pH and retention time. Global sensitivity analysis (GSA) was used to test the relationships between the inputs and outputs of the K-NN model.
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