Real-time water treatment process control with artificial neural networks
- Univ. of Alberta, Edmonton, Alberta (Canada). Dept. of Civil and Environmental Engineering
With more stringent requirements being placed on water treatment performance, operators need a reliable tool to optimize the process control in the treatment plant. In the present paper, one such tool is presented, which is a process control system built with the artificial neural network (ANN) modeling approach. The coagulation, flocculation, and sedimentation processes involve many complex physical and chemical phenomena and thus are difficult to model for process control with traditional methods. Proposed is the use of a neural network process control system for the coagulation, flocculation, and sedimentation processes. Presented is a review of influential control parameters and 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 formed the major component of a software controller and was found to consistently predict the optimum alum and power activated carbon doses for different control actions. With minor modifications, the approach illustrated can be used for building control models for other water treatment processes.
- OSTI ID:
- 323796
- Journal Information:
- Journal of Environmental Engineering, Journal Name: Journal of Environmental Engineering Journal Issue: 2 Vol. 125; ISSN 0733-9372; ISSN JOEEDU
- Country of Publication:
- United States
- Language:
- English
Similar Records
Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network
Fault diagnosis in nuclear power plants using an artificial neural network technique