Development of predictive model for biochar surface properties based on biomass attributes and pyrolysis conditions using rough set machine learning

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 degrees C and 625 degrees C, as well as retention time lower than 0.75 h.