Biochar can be used for environmental remediation, which includes carbon sequestration and soil quality improvement. Biochar is produced from the thermochemical conversion (i.e., pyrolysis) of biomass under inert conditions. However, there are no general rules regarding the relationship between biochar surface properties and biomass physiochemical properties as well as pyrolysis conditions. Machine learning (ML) algorithms can be used to investigate the relation between data sets and deliver useful decision output. In this work, rough set machine learning (RSML) was applied to generate a prediction model of biochar surface properties based on decisional attributes. The prediction model is a rule-based model that contains if-then rules to classify properties by fulfilling conditions. As a result, the specific surface area, pore volume, and pore diameter of biochar were found to be strongly influenced by pyrolysis conditions which includes temperature and retention time as well as biomass attributes including volatile matter, fixed carbon, and ash content. The results generated from RSML showed that the preferred range for pyrolysis temperature to produce biochar with desired surface properties is in between 425 °C and 625 °C, as well as retention time lower than 0.75 h.

生物炭可用于环境修复，包括固碳和改善土壤质量。生物炭是在惰性条件下通过生物质的热化学转化（即热解）产生的。然而，对于生物炭表面特性与生物质理化特性以及热解条件之间的关系，还没有通用的规则。机器学习 (ML) 算法可用于研究数据集之间的关系并提供有用的决策输出。在这项工作中，应用粗糙集机器学习 (RSML) 来生成基于决策属性的生物炭表面特性预测模型。预测模型是一个基于规则的模型，包含 if-then 规则以通过满足条件对属性进行分类。因此，比表面积、孔容、发现生物炭的孔径和孔径受热解条件的强烈影响，包括温度和保留时间以及生物质属性，包括挥发性物质、固定碳和灰分含量。RSML 生成的结果表明，生产具有所需表面特性的生物炭的热解温度的优选范围在 425 °C 和 625 °C 之间，以及保留时间低于 0.75 小时。