Evolutionary Multivariate Dynamic Process Model Induction for a Biological Nutrient Removal Process
Yoon‐Seok Timothy Hong, Byeong‐Cheon Paik · Journal of Environmental Engineering · 2007
This paper proposes an automatic process model induction system using an evolutionary computational intelligence, called grammar-based genetic programming, that is specially designed to automatically discover multivariate dynamic process models that best fit observed process data. This automatic process model induction system combines an evolutionary self-organizing system of genetic programming paradigm with various mathematical functions for a multivariate nonlinear model evolution using a grammar system via the mechanism of genetics and natural selection. The results demonstrate how the automatic process model induction system based on grammar-based genetic programming can be used to develop accurate and relatively cost-effective multivariate dynamic process models for the full-scale biological nutrient removal process. Multivariate dynamic process models are derived automatically in the form of understandable mathematical formulas that enable engineers to extract important knowledge hidden in the data and develop better operation and control strategies.