Dynamic neural observers and their application for identification and purification of water by ozone

A dynamic neural network is applied to estimate the state of the “phenol-water-ozone” chemical system. A new method based on dynamic neural observers with sliding mode (signum) term is used to estimate the dynamics of decomposition of phenols by ozone and to identify their kinetic parameters without the use of any process model. Decomposition of phenols and their mixtures by ozone in a semi-batch reactor is regarded as a dynamic process with an uncertain model (“black box”). Only the content of gaseous ozone is measured at the reactor output during ozonization. Variations of this variable are used to construct a total characteristic curve of the ozonization process. A dynamic state observer is used to estimate the phenol ozonization constant at different pH values from 2 to 12. Experimental data on decomposition dynamics are in good agreement with their estimates. Our method is helpful in on-line monitoring of water purification process without the use of special chemical sensors.