ARTIFICIAL NEURAL NETWORK BASED MODELING OF THE SUPPORTED LIQUID MEMBRANE FOR SIMULTANEOUS EXTRACTION AND RECOVERY OF CADMIUM AND LEAD FROM WASTE WATER

Abstract

With the exponential popularization of modern industries, more products are being produced, leading to water wastage and chemical disposal. These toxic chemicals are submerged in clean water resources, resulting in increased drinkable water toxicity. Separation of poisonous substances from wastewater is a pressing requirement to adopt the proof of concept of clean industrialization. Supported liquid membrane (SLM) is a popular and widely adopted non-dispersive membrane for the recovery and extraction of solutes from aqueous solution. The efficiency of cadmium and lead separation increases with the use of SLM. In this study, we have adopted an ANN-based approach to predict the results related to the recovery and extraction of cadmium and lead using the MATLAB deep learning toolbox. The experimental results are predicted by modeling the experimental data and analyzing the effect of the operating parameter. The accuracy of the predicted model is validated with experimental results, and the variation in the features helped in optimizing the study.