Non-mechanistic modelling of complex biofilm reactors and the role of process operation history.
Using the example of a membrane-supported biofilm reactor for industrial effluent treatment, different non-mechanistic approaches for the modelling of complex bioprocesses are presented and evaluated. The models were obtained employing feedforward artificial neural network analysis for the association of process operation with process performance. Three modelling approaches are discussed, i.e. autonomous static (AS) modelling, non-autonomous static (NAS) modelling, as well as a novel approach termed dynamic modelling with embedding of artificial neural network inputs. They are compared with regard to their ability to infer process performance for two different pollutant case studies, employing 1,2-dichloroethane and 3-chloro-4-methylaniline, respectively. The suitability of the different approaches was found to be strongly dependent on process configuration. Especially in configurations where lag times are apparent, the dynamic modelling approach was found to be superior, and process performance prediction was found to be strongly dependent on the history of process operation.
Wolf
Gundula
G
CQFB-REQUIMTE, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
Almeida
Jonas S
JS
Reis
Maria A M
MA
Crespo
João G
JG
D000465:Algorithms; D001419:Bacteria / Q000378:metabolism*; D019149:Bioreactors / Q000382:microbiology*; D018929:Cell Culture Techniques / Q000379:methods; D003198:Computer Simulation; D008954:Models, Biological; D016571:Neural Networks, Computer; D014871:Water Microbiology; D014874:Water Pollutants, Chemical / Q000378:metabolism*; D018508:Water Purification / Q000379:methods
D014874:Water Pollutants, Chemical