title: Online intelligent controllers for an enzyme recovery plant: design methodology and performance.
abstract: This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity.
authors.affiliation: School of Chemical Engineering, Department of Chemical Systems Engineering, University of Campinas (UNICAMP), 13083-970 Campinas, SP, Brazil.
authors.forename: M S
authors.initials: MS
authors.lastname: Leite
authors.forename: T L
authors.initials: TL
authors.lastname: Fujiki
authors.forename: F V
authors.initials: FV
authors.lastname: Silva
authors.forename: A M F
authors.initials: AM
authors.lastname: Fileti