Prediction of Succinic Acid Extraction Efficiency in the Emulsion Liquid Membrane by using Machine Learning Techniques

Abstract

This research aims to predict succinic acid concentration in the external phase during the emulsion liquid membrane process by using artificial neural networks along with a popular alternative method: k-nearest neighbor technique. The solute concentration values can be predicted by the proposed method without performing a great number of emulsion liquid membrane experiments. Several computer simulations were performed to demonstrate the success of the system. Simulation results showed that the estimated solute concentration values are very close to the achieved experimental results. The optimal conditions for emulsion liquid membrane were found to be: solvent kerosene, TOPO concentration (1%w/w), Amberlite LA-2 concentration (4%w/w), surfactant concentration (5%w/w), Na2CO3 concentration (5%w/v), modifier (decanol) concentration (2%w/w), mixing speed 300 rpm. The average accuracy percentages achieved by artificial neural network and k-nearest neighbor approaches were 88.75 +/- 1.94% and 90.2 +/- 1.2%, respectively.