Evolutionary self-organising modelling of a municipal wastewater treatment plant

Building predictive models for highly time varying and complex multivariable aspects of the wastewater treatment plant is important both for understanding the dynamics of this complex system, and in the development of optimal control support and management schemes. This paper presents a new approach, which is called genetic programming as a self-organising modelling tool, to model dynamic performance of municipal activated-sludge wastewater treatment plants. Genetic programming evolves several process models automatically based on methods of natural selection ('survival of the fittest'), that could predict the dynamics of MLSS and suspended solids in the effluent. The predictive accuracy of the genetic programming approach was compared with a nonlinear state-space model with neural network and a well-known IAWQ ASM2. The genetic programming system evolved some models that were an improvement over the neural network and ASM2 and showed that the transparency of the model evolved may allow inferences about underlying processes to be made. This work demonstrates that dynamic nonlinear processes in the wastewater treatment plant may be successfully modelled through the use of evolutionary model induction algorithms in GP technique. Further, our results show that genetic programming can work as a cost-effective intelligent modelling tool, enabling us to create prototype process models quickly and inexpensively instead of an engineer developing the process model.