MODELING PH NEUTRALIZATION PROCESSES USING FUZZY-NEURAL APPROACHES

This paper is concerned with the modeling and identification of pH-pro cesses via fuzzy-neural approaches. A simplified fuzzy model acting as an approximate reasoner is used to deduce the model output on the bas is of the identified rule-base which is derived by using one of the fo llowing three network-based self-organizing algorithms: unsupervised s elf-organizing counter-propagation network (USOCPN), supervised self-o rganizing counter-propagation network (SSOCPN), and self-growing adapt ive vector quantization (SGAVQ). Three typical pH processes were treat ed including a strong acid-strong base system, a weak acid-strong base system, and a two-output system with buffering taking part in reactio n. Extensive simulations including on-line modeling have shown that th ese nonlinear pH-processes can be modeled reasonably well by the prese nt schemes which are simple but efficient.