title: Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks.
abstract: A two-stage biological waste gas treatment system consisting of a first stage biotrickling filter (BTF) and second stage biofilter (BF) was tested for the removal of a gas-phase methanol (M), hydrogen sulphide (HS) and α-pinene (P) mixture. The bioreactors were tested with two types of shock loads, i.e., long-term (66h) low to medium concentration loads, and short-term (12h) low to high concentration loads. M and HS were removed in the BTF, reaching maximum elimination capacities (ECmax) of 684 and 33 gm-3h-1, respectively. P was removed better in the second stage BF with an ECmaxof 130 gm-3h-1. The performance was modelled using two multi-layer perceptrons (MLPs) that employed the error backpropagation with momentum algorithm, in order to predict the removal efficiencies (RE, %) of methanol (REM), hydrogen sulphide (REHS) and α-pinene (REP), respectively. It was observed that, a MLP with the topology 3-4-2 was able to predict REMand REHSin the BTF, while a topology of 3-3-1 was able to approximate REPin the BF. The results show that artificial neural network (ANN) based models can effectively be used to model the transient-state performance of bioprocesses treating gas-phase pollutants.
authors.affiliation: Chemical Engineering Laboratory, Faculty of Sciences, University of La Coruña, Rúa da Fraga, 10, E-15008 La Coruña, Spain.
authors.forename: M Estefanía
authors.initials: ME
authors.lastname: López
authors.affiliation: Chemical Engineering Laboratory, Faculty of Sciences, University of La Coruña, Rúa da Fraga, 10, E-15008 La Coruña, Spain; Department of Environmental Engineering and Water Technology, UNESCO-IHE, P.O. Box 3015, 2601 DA Delft, The Netherlands.
authors.forename: Eldon R
authors.initials: ER
authors.lastname: Rene
authors.affiliation: OPTIMAL-Industrial Neural Systems, 54 Rambal St., Be'er Sheva, 84243 Israel.
authors.forename: Zvi
authors.initials: Z
authors.lastname: Boger
authors.affiliation: Chemical Engineering Laboratory, Faculty of Sciences, University of La Coruña, Rúa da Fraga, 10, E-15008 La Coruña, Spain.
authors.forename: María C
authors.initials: MC
authors.lastname: Veiga
authors.affiliation: Chemical Engineering Laboratory, Faculty of Sciences, University of La Coruña, Rúa da Fraga, 10, E-15008 La Coruña, Spain. Electronic address: Kennes@udc.es.
authors.forename: Christian
authors.initials: C
authors.lastname: Kennes
mesh_terms: D000393:Air Pollutants / Q000302:isolation & purification*; D000465:Algorithms; D000081008:Bicyclic Monoterpenes; D001673:Biodegradation, Environmental; D019149:Bioreactors; D003198:Computer Simulation; D006862:Hydrogen Sulfide / Q000302:isolation & purification; D000432:Methanol / Q000302:isolation & purification; D039821:Monoterpenes / Q000302:isolation & purification; D016571:Neural Networks, Computer; D055549:Volatile Organic Compounds / Q000302:isolation & purification*; D018505:Waste Management
chemical_list: D000393:Air Pollutants; D000081008:Bicyclic Monoterpenes; D039821:Monoterpenes; D055549:Volatile Organic Compounds; C005451:alpha-pinene; D000432:Methanol; D006862:Hydrogen Sulfide
keywords: Artificial neural networks; Biofilter; Biotrickling filter; Transient-state performance; Two-stage bioreactor