A kinetic modeling study for biosurfactant production using cassava wastewater
Tuesday, April 29, 2014
Exhibit/Poster Hall, lower level (Hilton Clearwater Beach)
Cristina Ferraz1, Álvaro A. Araújo2, Rogerio Pagano1, Douglas Santos1, Waldjer Melo1 and Cleide M. F. Soares3, (1)Department of Chemical Engineering, Federal University of Sergipe, São Cristóvão, Brazil, (2)Departamento de Tecnologia de Alimentos, Universidade Federal de Sergipe, São Cristóvão, Brazil, (3)Instituto de Tecnologia e Pesquisa, Universidade Tiradentes, Aracaju - SE, Brazil
Biosurfactants are biological compounds with surface activity, produced by microorganisms using different substrates (Ferraz, Araújo & Pastore, 2002). Manipueira is an agro industrial waste with high content of carbohydrates and low cost. This residue represents an alternative substrate for the production of biosurfactants (Nitschke, Ferraz & Pastore, 2004). This study describes a simplified kinetic model where different models in the literature have been adapted to this process and its parameters were fitted to experimental data. Kinetic models were implemented and solved using subroutines in FORTRAN®. The mathematical model solution was compared with experimental data through the error function. A heuristic algorithm called Particle Swarm Optimization (PSO) was applied. The substrate concentration, cell concentration and surface tension experimental data were adjusted to the parameters provided from the mathematical model and the kinetic constants were determined. The results showed a surface tension decrease in the medium and the kinetic constants estimated provided a agreement between the experimental data and theoretical model. This model is described by differential equations that relate product formation with substrate concentration and microorganisms growth. In order to simulate the phenomenon, the parameters involved in the model must be estimated and the model prediction limit is characterized. Therefore, in order to determine all the parameters involved, in the mathematical model an adjustment of the experimental data using a heuristic optimization Particle Swarm Optimization was realized. A good fit is obtained thereby ensuring good efficiency of the proposed kinetic model to predict the experimental data.