Research progress on modeling of heavy metal adsorption by biochar based on machine learning

Traditional adsorption models are faced with challenges due to various parameters,complex pollution conditions, diverse biochar properties, and the high cost and lengthy duration of research. Machine learning has demonstrated significant potential in handling high-dimensional data and analyzing complex problems. Its clear and mature modeling process provides precise and stable predictions for heavy metal pollution and holds unique value in uncovering hidden adsorption mechanisms, making it an excellent choice for studying heavy metal adsorption by biochar. This paper described the workflow and advantages of machine learning modeling. The application of machine learning in heavy metal adsorption across three aspects of predicting adsorption efficiency, aiding in optimizing experiments, and gaining insights into adsorption mechanisms was summarized. Furthermore, the challenges that machine learning faced in the field of biochar-mediated heavy metal adsorption and interdisciplinary collaboration anticipation were analyzed. Strategies such as establishing a more comprehensive and reliable adsorption database, incorporating surface functional groups as influential factors, emphasizing model accuracy, and balancing computational costs were proposed to deepen the research on modeling heavy metal adsorption by biochar.