Department of Chemical and Environmental Engineering, University of Nottingham Malaysia, Semenyih, Malaysia.
Center for Engineering and Sustainable Development Research, De La Salle University, Manila, Philippines.
Environ Technol. 2024 Jun;45(15):2908-2922. doi: 10.1080/09593330.2023.2192877. Epub 2023 Mar 29.
Biochar is a high-carbon-content organic compound that 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 and 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 conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11-14 wt%, volatile matter of 60-62 wt% and carbon content of 42-45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C-475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.
生物炭是一种高碳含量的有机化合物，在储能和转换领域有潜在的应用。它可以从各种生物质原料（如植物、动物和城市废物）在不同的热解条件下生产。然而，如果不了解生物质类型、操作条件和生物炭特性之间的关系，就很难大规模生产生物炭。因此，有必要使用基于机器学习的数据分析方法来寻找生物炭生产参数与原料特性与生物炭能量特性之间的关系。在这项工作中，应用了基于粗糙集的机器学习（RSML）方法来生成决策规则和分类生物炭特性。条件属性是生物质特性（挥发分、固定碳、灰分、碳、氢、氮和氧）和热解条件（操作温度、加热速率、停留时间），而决策属性则考虑了产率、碳含量和高位发热量。生成的规则经过一组验证数据进行了测试，并评估了它们的科学一致性。基于生成的决策规则，灰分含量为 11-14wt%、挥发分含量为 60-62wt%、碳含量为 42-45.3wt%的生物质，在操作温度为 425°C-475°C 的热解过程中，可以生成具有较高产率、碳含量和高位发热量的生物炭。这项工作为生物炭生产提供了最佳的生物质原料特性和热解条件，以实现高质量和高能量的产量。
