Organic solute permeation, sorption, and rejection by reverse osmosis membranes, from aqueous solutions, were Studied experimentally and via artificial neural networks (ANN)-based quantitative structure-property relations (QSPR), for a set of fifty organic compounds for polyamide and cellulose acetate membranes. Membrane solute sorption and passage for dead-end filtration model experiments were quantified based on radioactivity measurements for radiolabeled compounds in the feed, permeate and the membrane, while solute rejection was determined from a mass balance on the permeated solution Volume. Artificial neural networks-based quantitative structure-property relations models were developed for the organic passage (P), sorbed (M) and rejected (R) fractions using the most relevant set of molecular descriptors selected from a pool of 45 molecular descriptors by means of a correlation-based feature selection method and self-organizing maps (SOM). The analysis included pre-screening with principal components analysis and SOM of the chemical domain for the study chemicals as defined by chemical descriptors to identify the applicability domain and chemical similarities. The QSPR models predicted the P and M mass fractions within the range of the standard deviations of measurements for the experimental data set of fifty compounds. Mass balance closure (requiring that M, P and R sum to unity) was satisfactory for the experimental data set of fifty compounds and for an external set of 144 test chemicals, which were not included in the model development. Somewhat higher prediction errors were encountered for a few chemicals that were not well represented within the present chemical domain. The quality of the QSPR/NN models developed suggests that there is merit in extending both the present compound database and the present approach to develop a comprehensive toot for assessing organic solute behavior in RO water treatment processes.
Libotean D., Giralt J., Rallo R., Cohen Y., Giralt F., Ridgway H.F., Rodriguez G., Phipps D. (2008). Organic Compounds Passage through RO Membranes. Journal of Membrane Science, (313)1-2:23-43
Using an ensemble of neural based QSARs for the prediction of toxicological properties of chemical contaminants
An ensemble of neural predictors is used to develop a set of QSAR models for the prediction of the carcinogenicity TD50 index. The proposed approach makes use of the self organizing feature map algorithm to select diverse subsets of molecular descriptors that are used afterwards to train an ensemble of predictive fuzzy ARTMAP networks. The diversity introduced by the predictors trained using different subsets of descriptors produces better generalization results than single models. Comparison of the developed models with published models is to be used to assess the quality of the prediction system.
Rallo, R., Espinosa, G., and F. Giralt (2005) Using an ensemble of neural based QSARs for the prediction of toxicological properties of chemical contaminants, Trans IChemE Part B. Process Safety and Environmental Protection, 83(B4), 387-392