Generating Energy and Greenhouse Gas Inventory Data of Activated Carbon Production Using Machine Learning and Kinetic Based Process Simulation
Generating Energy and Greenhouse Gas Inventory Data of Activated Carbon Production Using Machine Learning and Kinetic Based Process Simulation
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DOI：
10.1021/acssuschemeng.9b06522
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发表时间：
2020-01-20
影响因子：
8.4
通讯作者：
Yao, Yuan
中科院分区：
文献类型：
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作者：
Liao, Mochen;Kelley, Stephen;Yao, Yuan
了解由不同生物质原料生产的活性碳(AC)对环境的影响对于生物质筛选和工艺优化以实现可持续性至关重要。许多研究已经发展了生物质衍生AC的生命周期评估(LCA)。然而，大多数研究要么侧重于不同工艺条件下的单个生物质物种，要么比较多个生物质原料，而没有研究原料和工艺变化的影响。由于缺乏工艺数据(例如，能量和质量平衡)，从不同的生物质中开发AC的生命周期评价既耗时又具有挑战性。这项研究通过开发一个集成了人工神经网络(ANN)、机器学习方法和基于动力学的过程模拟的建模框架来解决这些知识差距。该集成框架能够生成由73种不同类型的木质生物质生产的AC的生命周期清单数据，具有250个表征数据样本。结果表明，不同生物质种类的能源消耗和温室气体排放差异很大(43.4-277MJ/kg AC和3.96-22.0 kg CO2-eq/kg AC)。敏感性分析表明，生物质组成(如氢和氧含量)和工艺操作条件(如活化温度)对与AC生产相关的能耗和温室气体排放有很大影响。
Understanding the environmental implications of activated carbon (AC) produced from diverse biomass feedstocks is critical for biomass screening and process optimization for sustainability. Many studies have developed Life Cycle Assessment (LCA) for biomass-derived AC. However, most of them either focused on individual biomass species with differing process conditions or compared multiple biomass feedstocks without investigating the impacts of feedstocks and process variations. Developing LCA for AC from diverse biomass is time-consuming and challenging due to the lack of process data (e.g., energy and mass balance). This study addresses these knowledge gaps by developing a modeling framework that integrates artificial neural network (ANN), a machine learning approach, and kinetic-based process simulation. The integrated framework is able to generate Life Cycle Inventory data of AC produced from 73 different types of woody biomass with 250 characterization data samples. The results show large variations in energy consumption and GHG emissions across different biomass species (43.4-277 MJ/kg AC and 3.96-22.0 kg CO2-eq/kg AC). The sensitivity analysis indicates that biomass composition (e.g., hydrogen and oxygen content) and process operational conditions (e.g., activation temperature) have large impacts on energy consumption and GHG emissions associated with AC production.