Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some costeffective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Automated summary

It is crucial to reduce pollutants in water environment to safe levels, and adsorption technology is considered a promising and cost-effective solution. However, current batch experiments and adsorption isotherms are inefficient and time-consuming, limiting the development of adsorption technology. This review explores the application of machine learning in pollutant adsorption, summarizing the workflow, common algorithms, and recent progress. Guidelines for machine learning in pollutant adsorption are presented, along with the existing challenges and future perspectives. The review aims to promote the use of machine learning and improve its interpretability in pollutant adsorption.