Predicting and refining acid modifications of biochar based on machine learning and bibliometric analysis: Specific surface area, average pore size, and total pore volume

• Published in: The Science of the total environment Vol. 948; p. 174584
• Main Authors: Zhao, Fangzhou, Tang, Lingyi, Song, Wenjing, Jiang, Hanfeng, Liu, Yiping, Chen, Haoming
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 20.10.2024
• Subjects: Acid-modified biochar; Adsorption; Bibliometrics; Charcoal - chemistry; Extreme gradient boosting; Machine Learning; Optimizing modification conditions; Porosity; Specific surface area; Acid-modified biochar; Specific surface area; Extreme gradient boosting; Optimizing modification conditions
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2024.174584

Acid-modified biochar is a modified biochar material with convenient preparation, high specific surface area, and rich pore structure. It has great potential for application in the heavy metal remediation, soil amendments, and carrying catalysts. Specific surface area (SSA), average pore size (APS), and total pore volume (TPV) are the key properties that determine its adsorption capacity, reactivity, and water holding capacity, and an intensive study of these properties is essential to optimize the performance of biochar. But the complex interactions among the preparation conditions obstruct finding the optimal modification strategy. This study collected dataset through bibliometric analysis and used four typical machine learning models to predict the SSA, APS, and TPV of acid-modified biochar. The results showed that the extreme gradient boosting (XGB) was optimal for the test results (SSA R2 = 0.92, APS R2 = 0.87, TPV R2 = 0.96). The model interpretation revealed that the modification conditions were the major factors affecting SSA and TPV, and the pyrolysis conditions were the major factors affecting APS. Based on the XGB model, the modification conditions of biochar were optimized, which revealed the ideal preparation conditions for producing the optimal biochar (SSA = 727.02 m2/g, APS = 5.34 nm, TPV = 0.68 cm3/g). Moreover, the biochar produced under specific conditions verified the generalization ability of the XGB model (R2 = 0.99, RMSE = 12.355). This study provides guidance for optimizing the preparation strategy of acid-modified biochar and promotes its potentiality for industrial application. [Display omitted] •Four machine learning models were constructed based on dataset collected by bibliometric methods.•Pyrolysis primarily controlled the APS and modification conditions determined the SSA and TPV of biochar.•Optimization of modification conditions was confirmed using the XGB model.