The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning.

During the last few years, important advances have been made in big data exploration, complex pattern recognition and prediction of complex variables. Machine learning (ML) algorithms can efficiently analyze voluminous data, identify complex patterns and extract conclusions. In chemical engineering, the application of machine learning approaches has become highly attractive due to the growing complexity of this field.