Adaptive and Neural pH Neutralization for Strong Acid-Strong Base System

Introduction

Effective modeling of a pH neutralization plant is not a recent issue. However, due to the nonlinear characteristics and complexity of this type of system, research on how to provide a good dynamic model of pH neutralization process, which was first started in the 1970's or earlier, still continues. The pH process can be mainly classified into four groups and they are strong acid-strong base system, strong acid-weak base system, weak acid-strong base system and weak acid-weak base system. The strong acid-strong base pH process is the most highly nonlinear process among the group. The pH value versus the reagent flow has a logarithmic relationship. Away from neutrality, the process gain is relatively small. Near neutrality where pH = 7, the process gain can be a few thousand times higher. Hence it is impossible for a fixed controller like PID to effectively control this process.

pH control is an interesting and challenging research subject which has led to a large number of motivating and interesting published papers. The control of pH process is a classic and difficult nonlinear control problem encountered in the chemical process industry. Various control strategies used for pH control are classified as non-adaptive linear, adaptive linear, model based, non-adaptive non-linear and adaptive nonlinear. For years there has been researches using various control approaches, such as simple PID control, adaptive control, nonlinear linearization control and various model-based control. Conventional control methods rely on the exact mathematical modeling of the plant, which may be tremendously difficult to obtain in many cases.