Cost effective water clarification control using a neurally informed control strategy

The abstraction, treatment and supply of potable (drinking) water presents a range of special problems. Early attempts at introducing and extending the range of automatic control at a water treatment plant were invariably compromised by the poor quality of the instrumentation available. Improved sensor technology and the advent of microprocessor based programmable controllers has allowed the implementation of nonstandard solutions. This paper describes the steps followed in developing an accurate feedforward automatic clarification control strategy. The existing strategy was occasionally compromised when a colour measurement was corrupted, usually under conditions of high turbidity. It should be emphasised that the colour monitor used is being asked to operate at and beyond the range of its original design specification. This occurs because there are no other alternative instruments. The paper shows how the use of an artificial neural network produces a solution to a seemingly intractable problem.