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Energy consumption and greenhouse gas emissions data of activated carbon production using different biomass

Citation

Liao, Mochen; Kelley, Stephen; Yao, Yuan (2020), Energy consumption and greenhouse gas emissions data of activated carbon production using different biomass, Dryad, Dataset, https://doi.org/10.5061/dryad.s7h44j152

Abstract

This dataset includes the energy consumption and Greenhouse Gas emissions data of activated carbon production using 73 different types of woody biomass.

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.

Methods

The energy consumption and greenhouse gas emissions data of activated production were generated by the integrated modeling framework that was developed in this study. The modeling framework coupled artificial neural network (ANN), a machine learning approach, and kinetic-based process simulation.

Funding

National Science Foundation, Award: 2038439

North Carolina State University

Division of Chemical, Bioengineering, Environmental, and Transport Systems, Award: 1847182