Machine learning insights in predicting heavy metals interaction with biochar.

The use of machine learning (ML) in the field of predicting heavy metals interaction with biochar is a promising field of research, mainly because of the growing understanding of how removal efficiency is affected by characteristic variables, reaction conditions and biochar properties. The practical application in biochar still faces large challenges, such as difficulties in data collection, inadequate algorithm development, and insufficient information. However, the quantity, quality, and representation of data have a large impact on the accuracy, efficiency, and generalizability of machine learning tasks. From this perspective, the present data descriptors, the efficiency of machine learning-aided property and performance prediction, the interpretation of underlying mechanisms and complicated relationships, and some potential ways to augment the data are discussed regarding the interactions of heavy metals with biochar. Finally, future perspectives and challenges are discussed, and an enhanced model performance is proposed to reinforce the feasibility of a particular perspective. Highlights: A high growth rate of studies on the application of machine learning (ML) in biochar in recent years. ML interpretability of heavy metals (HMs) interaction mechanisms with biochar is explicated emphatically. Challenges and perspectives of ML application in the removal of HMs by biochar. Combining an advanced machine learning technique to achieve better predicted performance. [ABSTRACT FROM AUTHOR]