A unified parameter model based on machine learning for describing microbial transport in porous media

Abstract

The transport and retention of microorganisms are typically described using attachment/detachment and straining/liberation models. However, the parameters in the models varied significantly, posing a significant challenge to describe microbial transport under different environmental conditions. A neural network (ANN) model was developed in this study to link the parameters in the model with the factors influencing microbial transport including the properties of microorganisms such as size and surface potentials, and the properties of porous media such as grain size and porosity, and flow conditions. Exhaustive search of literature renders 420 sets of experimental data of microbial transport, which were fitted using the microbial transport model to obtain model parameters. The model parameters, together with the factors influencing microbial transport, were then used to train an ANN model to search for their relationship. An ANN-based parameter relationship was derived and was then used to simulate microbial transport. The simulated results using the relationship roughly matched with the experimental data under different environmental conditions, indicating that a unified relationship was established between the parameters of the microbial transport model and the factors influencing microbial transport, and that microbial transport can be described using the microbial transport model with the ANN-based unified relationship for model parameters.