Abstract
The pH neutralization process is typical in chemical, biological and petrochemical industries. One of the major challenges to control it is to understand its nonlinearities and that requires several fine adjustments from conventional controls. Artificial Intelligence has been used to study these nonlinearities; one of them is Neuro-Fuzzy Logic, which was investigated in this work to develop controls dedicated to this process. These controls are formed by logical structures and may be adjusted to different configurations. In practical applications, it is highly important to adapt control parameters based on artificial intelligence to obtain better performance. The present work studied the effect of different configurations of a neuro-fuzzy control on the performance of a regulatory control to pH neutralization process by means of a virtual plant developed in both Indusoft© and Matlab© environments. For both variables, pH and reactor level control, membership function (MF) = [Gaussian], method “OR” = [probabilistic], method “E” = [product], type of MF output = [linear] and the optimization method = [hybrid], have improved control performance, which confirms the importance of configuration choices in neuro-fuzzy control adjustments. Moreover, the most determining factor in NFC performance is the types of membership functions.
Artificial Intelligence Based Sensor Network Congestion Fuzzy Control Algorithm
