An hybrid neural network based system for optimization of coagulant dosing in a water treatment plant

Artificial neural network techniques are applied to the control of coagulant dosing in a drinking water treatment plant. Coagulant dosing rate is nonlinearly correlated to raw water parameters such as turbidity, conductivity, pH, temperature, etc. An important requirement of the application is robustness of the system against erroneous sensor measurements or unusual water characteristics. The hybrid system developed includes raw data validation and reconstruction based on the Kohonen self-organizing feature map, and prediction of coagulant dosage using multilayer perceptrons. A key feature of the system is its ability to take into account various sources of uncertainty, such as a typical input data, measurement errors and limited information content of the training set. Experimental results with real data are presented