School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China.
Bioresour Technol. 2024 Jun;402:130776. doi: 10.1016/j.biortech.2024.130776. Epub 2024 May 1.
Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption models was applied to build prediction models for adsorption kinetics rate (i.e., K) and maximum adsorption capacity (i.e., Q) of emerging contaminants (ECs) on biochar. Results demonstrated that the prediction performance of adaptive boosting algorithm significantly improved after data preprocessing (i.e., log-transformation) in the small unbalanced datasets with R of 0.865 and 0.874 for K and Q, respectively. The surface chemistry, primarily led by ash content of biochar significantly influenced the K, while surface porous structure of biochar showed a dominant role in predicting Q. An interactive platform was deployed for relevant scientists to predict K and Q of new biochar for ECs. The research provided practical references for future engineered biochar design for ECs removal.
深入了解吸附速率快、吸附容量高的生物炭的关键特性，对于将生物炭设计为污染应急处理中的吸附剂至关重要。本研究将机器学习（ML）与经典理论吸附模型相结合，构建了用于预测新兴污染物（ECs）在生物炭上吸附动力学速率（即 K）和最大吸附容量（即 Q）的预测模型。结果表明，在小不平衡数据集（即经过对数转换的数据预处理）中，自适应增强算法的预测性能显著提高，K 和 Q 的 R 值分别为 0.865 和 0.874。生物炭的表面化学性质，主要由灰分含量显著影响 K，而生物炭的表面多孔结构在预测 Q 方面起主导作用。本研究还部署了一个交互平台，供相关科学家预测新生物炭对 ECs 的 K 和 Q。该研究为未来针对 ECs 去除的工程化生物炭设计提供了实用参考。
