title: On-line biomass estimation in biosurfactant production process by Candida lipolytica UCP 988.
abstract: Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation of biomass concentration, in biosurfactant production by Candida lipolytica UCP 988, as a nonlinear function of pH and dissolved oxygen. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consists of one hidden layer with four neurons. The performance of the ANN was checked using experimental data. The results obtained indicate a very good predictive capacity for the ANN-based software sensor with values of R2 of 0.969 and RMSE of 0.021 for biomass concentration. Estimated biomass using the ANN was proved to be a simple, robust and accurate method.
authors.affiliation: Departamento de Estatística e Informática, Universidade Católica de Pernambuco, Rua Nunes Machado, 42, Bloco J, Térreo, Boa Vista, 50050-590, Recife, Pernambuco, Brazil. cdaisy@unicap.br
authors.forename: Clarissa Daisy
authors.initials: CD
authors.lastname: da Costa Albuquerque
authors.forename: Galba Maria
authors.initials: GM
authors.lastname: de Campos-Takaki
authors.forename: Ana Maria Frattini
authors.initials: AM
authors.lastname: Fileti
mesh_terms: D018533:Biomass; D002175:Candida / Q000378:metabolism*; D016571:Neural Networks, Computer; D009862:Online Systems / Q000295:instrumentation; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software; D013501:Surface-Active Agents / Q000378:metabolism*
chemical_list: D013501:Surface-Active Agents