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Evaluating the Variability of Energy...

Liao, Mochen.

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Evaluating the Variability of Energy Consumption and Carbon Footprints of Activated Carbon Production Using Machine Learning Integrated Process Simulation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evaluating the Variability of Energy Consumption and Carbon Footprints of Activated Carbon Production Using Machine Learning Integrated Process Simulation./
Author:	Liao, Mochen.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	96 p.
Notes:	Source: Masters Abstracts International, Volume: 82-05.
Contained By:	Masters Abstracts International82-05.
Subject:	Natural resource management. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28122781
ISBN:	9798664735925

Evaluating the Variability of Energy Consumption and Carbon Footprints of Activated Carbon Production Using Machine Learning Integrated Process Simulation.
Liao, Mochen.

Evaluating the Variability of Energy Consumption and Carbon Footprints of Activated Carbon Production Using Machine Learning Integrated Process Simulation. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 96 p.

Source: Masters Abstracts International, Volume: 82-05.

Thesis (M.Sc.)--North Carolina State University, 2020.

This item must not be sold to any third party vendors.

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.

ISBN: 9798664735925Subjects--Topical Terms:

589570
Natural resource management.
Subjects--Index Terms:

activated carbon

Evaluating the Variability of Energy Consumption and Carbon Footprints of Activated Carbon Production Using Machine Learning Integrated Process Simulation.
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245 1 0 $a Evaluating the Variability of Energy Consumption and Carbon Footprints of Activated Carbon Production Using Machine Learning Integrated Process Simulation.
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300 $a 96 p.
500 $a Source: Masters Abstracts International, Volume: 82-05.
500 $a Advisor: Park, Sunkyu;Kelley, Stephen;Yao, Yuan.
502 $a Thesis (M.Sc.)--North Carolina State University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
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