Sequential modeling of fecal coliform removals in a full-scale activated-sludge wastewater treatment plant using an evolutionary process model induction system

We propose an evolutionary process model induction system that is based on the grammar-based genetic programming to automatically discover multivariate dynamic inference models that are able to predict fecal coliform bacteria removals using common process variables instead of directly measuring fecal coliform bacteria concentration in a full-scale municipal activated-sludge wastewater treatment plant. A sequential modeling paradigm is also proposed to derive multivariate dynamic models of fecal coliform removals in the evolutionary process model induction system. It is composed of two parts, the process estimator and the process predictor. The process estimator acts as an intelligent software sensor to achieve a good estimation of fecal coliform bacteria concentration in the influent. Then the process predictor yields sequential prediction of the effluent fecal coliform bacteria concentration based on the estimated fecal coliform bacteria concentration in the influent from the process estimator with other process variables. The results show that the evolutionary process model induction system with a sequential modeling paradigm has successfully evolved multivariate dynamic models of fecal coliform removals in the form of explicit mathematical formulas with high levels of accuracy and good generalization. The evolutionary process model induction system with sequential modeling paradigm proposed here provides a good alternative to develop cost-effective dynamic process models for a full-scale wastewater treatment plant and is readily applicable to a variety of other complex treatment processes.