Prediction

In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) or genetic algorithm (GA) have been increasingly used to model membrane fouling and performance. In the present study, we select genetic programming (GP) for modeling and prediction of the membrane fouling

In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) or genetic algorithm (GA) have been increasingly used to model membrane fouling and performance. In the present study, we select genetic programming (GP) for modeling and prediction of the membrane fouling

One of the key transport parameter—permeability, is being used as an intrinsic characteristic of membrane for reverse osmosis process prediction. In this study, the prediction of reverse osmosis process in terms of boron rejection was carried out in two steps. First, the permeability of boron existi

An important challenge for nanofiltration processes is the development of predictive models that convey a fundamental understanding and simple quantification of the governing phenomena in a way that has the potential for industrial application. The paper reviews one such approach, including: the for

We propose a network architecture based on adaptive receptive fields and a learning algorithm that combines both supervised learning of centers and unsupervised learning of output layer weights. This algorithm causes each group of radial basis functions to adapt to regions of the clustered input spa