Machine Learning Applications in Adsorption of Water Pollutants

Abstract

Adsorption remains one of the most effective methods of decontamination of water. Adsorption processes are governed by multiple factors which contribute to the overall efficiency of the process. These include process conditions such as temperature, pH, the concentration of pollutants, and competing ions. The adsorbate properties, such as speciation, polarity, kinetic diameter, and ionic sizes, also affect adsorption performance. The adsorbent properties also play a critical role in assessing the suitability of an adsorbing material. This includes surface areas, pore volumes, chemical compositions, surface charges, etc. The complexity of the interaction between all these parameters makes it cumbersome or near impossible to predict with appreciable precision the performance of an adsorption system. Machine learning provides an opportunity for developing models for concise prediction of adsorption efficiencies for different materials. This chapter discusses the principles of various machine learning models and their application in the adsorption of pollutants from water.