A hybrid evolutionary data-driven model for Biofouling assessment in pipelines

Biofouling is exceptionally complex to describe constituting chemical, physical, and biological processes interacting on different spatial and temporal scales. Although a great deal of research has already been conducted on Biofouling, the mechanisms by which microorganisms become attached to solid surfaces are still not well understood, thus creating difficulties in establishing reliable physical models for Biofouling predictions. This study describes the methodology development and application of a data-driven (Model-Trees-MT)-genetic algorithm (GA) scheme for estimating Biofouling formation on pipelines. The methodology was tested on four identical pipeline experimental systems with different types of inlet waters, which were part of a large cooperative project between academia and industry in Israel. Good correlations were achieved using the proposed methodology between modelled and observed cell coverage area within the Biofilm for various experimental runs.