Real-time water treatment process control with artificial neural networks

The coagulation, flocculation, and sedimentation processes involve many com plex physical and chemical phenomena and thus are difficult to model for pr ocess control with traditional methods. Proposed is the use of a neural net work process control system for the coagulation, flocculation, and sediment ation processes. Presented is a review of influential control parameters an d control requirements for these processes followed by the development of a feed forward neural network control scheme. A neural network process model was built based on nearly 2,000 sets of process control data. This model f ormed the major component of a software controller and was found to consist ently predict the optimum alum and power activated carbon doses for differe nt control actions. With minor modifications, the approach illustrated can be used for building control models for other water treatment processes.