Application of natural computation techniques to optimal design of flow injection systems Original Research Article

Flow injection (FI) systems are widely used for on-line monitoring of chemical processes. Several approaches have been made in order to achieve the optimal design of the FI system, mainly based on the approach of deterministic models that describe the process using the mass balances around the system and the corresponding kinetic relations. Although, good results have been obtained with this approach, the complexity of the system and the effort necessary to calculate the parameters that characterize the FI system using a deterministic model, have led to the consideration of more empirical approaches to obtain a model of the process. In this paper, the authors present the results obtained in the application of two techniques, known as natural intelligence techniques, in the optimal design of a flow injection sandwich system for glucose and glycerol analysis. The optimization is performed using a genetic algorithm, in which a population evolves combining the genetic code of the most capable individuals of the previous generation. To evaluate the performance of each individual an artificial neural network is used. The results obtained with this approach are comparable with the one previously developed using a deterministic description of the FI system.