MLP Neural Network Modeling of PEG – Dextran, Polymer –Polymer Aqueous Two-Phase Systems

Aqueous two-phase systems (ATPSs) are formed because of the mutual incompatibility of two hydrophilic polymers or a polymer and a salt which are increasingly being applied in extractive separation and purification of macromolecules, particles and biological materials which are fragileand susceptible to denaturation if undergone classical separation processes. This advantageous feature could mainly be attributed to the low surface tension between the two liquid phases and also the generally benign environment of the system. The deployment of ATPSs is especiallypromising in overcoming low product yield in conventional fermentation processes. In this study an artificial intelligence model based on a feedforward back-propagation neural network was employed to predict the partition coefficients of the two polymers comprising ATPSs of PEG –Dextran of varying molecular weights as one of the most common aqueous two-phase system currently employed. These partition coefficients are essential in proper engineering of extractive separation of biomolecules. The results indicate the applicability of the neural network model as areliable technique for the optimization of extraction conditions