Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Abstract Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of related synthesis parameters. But the influence factor numerous, traditional experimental enumeration powerless. In recent years, machine learning has been gradually employed biochar, but there no comprehensive review on whole process regulation adsorbents, covering optimization modeling. This article systematically summarized application in adsorbents from perspective all-round first time, including modeling adsorbents. Firstly, overview was introduced. Then, latest advances removal were summarized, prediction yield physicochemical properties, optimal synthetic conditions economic cost. And by reviewed, efficiency, revelation mechanism. General guidelines whole-process presented. Finally, existing problems future perspectives put forward. We hope that this can promote integration thus light up industrialization biochar. Graphical