Identification and online validation of a ph neutralization process using an adaptive network-based fuzzy inference system

Abstract

In this study, the application of adaptive neuro-fuzzy inference system (ANFIS) architecture to build prediction models that represent the pH neutralization process is proposed. The dataset used to identify the process was obtained experimentally in a bench scale plant. The prediction model attained was validated offline and online and demonstrated as able to precisely predict the one step-ahead value of effluent pH leaving the neutralization reactor. The input variables were the current and one past value of the acid and base flow rates and the current value of the output variable. Variance accounted for (VAF) indices greater than 99% were achieved by the model in experiments in which the disturbances in the acid and basic solutions flow rates were applied separately. For tests with simultaneous disturbances, conditions never seen in the training and suffering from reactor level oscillations, the prediction model VAF index was still approximately 96%. The validations demonstrated the capability of ANFIS to build precise fuzzy models from input-output datasets. R-2 values achieved were always larger than 0.96.