Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China; Dali Comprehensive Experimental Station of Environmental Protection Research and Monitoring Institute, Ministry of Agriculture and Rural Affairs (Dali Original Seed Farm), Dali 671004, China.
College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
Bioresour Technol. 2024 Jul;403:130861. doi: 10.1016/j.biortech.2024.130861. Epub 2024 May 18.
Developing an optimized and targeted design approach for metal-modified biochar based on water quality conditions and management is achievable through machine learning. This study leveraged machine learning to analyze experimental data on phosphate adsorption by metal-modified biochar from literature published in Web of Science. Using six machine learning models, the phosphate adsorption capacity of biochar and residual phosphate concentration were predicted. After hyperparameter optimization, the gradient boosting model exhibited superior training performance (R > 0.96). Metal load quantity, solid-liquid ratio, and pH were key factors influencing adsorption performance. Optimal preparation parameters indicated that Mg-modified biochar achieved the highest adsorption capacity (387-396 mg/g), while La-modified biochar displayed the lowest residual phosphate concentration (0 mg/L). The results of verification experiments based on optimized process parameters closely aligned with model predictions. This study introduces a new machine learning-based approach for tailoring biochar preparation processes considering different water quality management objectives.
通过机器学习，可以针对水质条件和管理目标，开发一种优化且有针对性的金属修饰生物炭设计方法。本研究利用机器学习分析了来自 Web of Science 发表的文献中金属修饰生物炭对磷酸盐吸附的实验数据。使用六种机器学习模型，预测了生物炭的磷酸盐吸附容量和残留磷酸盐浓度。经过超参数优化后，梯度提升模型表现出了优越的训练性能（R>0.96）。金属负载量、固液比和 pH 是影响吸附性能的关键因素。最佳的制备参数表明，Mg 修饰的生物炭具有最高的吸附容量（387-396mg/g），而 La 修饰的生物炭则表现出最低的残留磷酸盐浓度（0mg/L）。基于优化工艺参数的验证实验结果与模型预测结果非常吻合。本研究引入了一种新的基于机器学习的方法，可以根据不同的水质管理目标来定制生物炭的制备工艺。
