The paper provides a review of the different approaches of Model Predictive Control (MPC) to deal with the nonlinearities and transient behavior associated with pH and its control. Firstly a description of the pH system and what makes it difficult to control is presented, followed by the general description of the structure of MPC. The different applications of MPC vary mostly in the way the model is described and how the optimization is carried out to obtain the desired control action. The different modeling techniques applied to the MPC, which is used to describe the behavior of the pH are ranging from simple linear models, multiple linear models like piecewise linear descriptions and fuzzy models, to nonlinear descriptions like Wiener models and the use of artificial neural networks. The models and their respective ways of application are reviewed. Finally, the areas where more research is needed are addressed.

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In the process industry controlling the pH is considered to be one of the toughest tasks among the most commonly controlled variables. This is due to the nonlinear behavior of the pH and the time dependence of the nonlinearity, requiring an advanced controller. In this paper a multi-model nonlinear model predictive control (MMNMPC) scheme is applied to describe and handle the nonlinearities, were the multi-model description gives a piecewise linear description enabling a simple and swift computation of control moves. MPC implementation requires the knowledge, through measurement or by estimation, of the states of the neutralization system, both creating various problems. Here problem is addressed by including integral action in the controller to compensate for the unmeasured states. The MMNMPC combined with integral action is tested by simulation of a pH system in Matlab and the control structures were applied in Matlab/YALMIP showing good control performance.