In Environmental technology
Biochar is a high carbon content organic compound has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant based, animal based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning based data analysis is necessary to find the relationship between biochar production parameters as well as feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The condition attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, oxygen) and pyrolysis conditions (operating temperature, heating rate residence time) while the decision attributes considered were yield, carbon content, and higher heating value. The rules generated were tested against a set of validation data and evaluated for its scientific coherency. Based on then decision rules generated, biomass with ash content of 11 to 14 wt%, volatile matter of 60 to 62 wt% and carbon content of 42 to 45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via pyrolysis process at operating temperature of 425°C to 475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.
Tang Jia Yong, Chung Boaz Yi Heng, Ang Jia Chun, Chong Jia Wen, Tan Raymond R, Aviso Kathleen B, Chemmangattuvalappil Nishanth G, Thangalazhy-Gopakumar Suchithra
2023-Mar-17
biochar, biochar yield, carbon content, higher heating value, rough set machine learning
