(695d) Evaluating Variability of Energy Consumption and Carbon Emissions of Activated Carbon Production from Wood Using Artificial Neural Network Integrated Process Simulation

Activated carbon (AC), a carbonaceous material with outstanding porosity, absorptivity, and surface reactivity, has broad industrial applications such as water purification and flue gas cleaning.^1,2 AC can be produced from coal and biomass. Previous studies indicated that biomass-based AC is more environmentally sustainable than coal-based AC, but the AC quality and environmental footprints of AC production from different biomass has large variations.^3,4 Understanding such variations and identifying major driving factors is critical for sustainable utilization of diverse biomass resources in large-scale AC production. Although many studies have used process simulation and Life Cycle Assessment (LCA) to quantify AC yields and environmental impacts of different biomass-to-AC systems, few of them have evaluated variations across different feedstock and process options, nor do they evaluated the impacts of biomass characterization and operational variations on AC product quality and overall environmental footprints.^2,5,6

This study addressed the knowledge gap by integrating Artificial Neural Network (ANN) with Bio-PoliMi pyrolysis kinetic model for AC from woody biomass.^7,8 The ANN model trained in this study estimates the overall product yield of steam AC production by inputting data of biomass characterizations and operational conditions. The Bio-PoliMi model provided detailed composition of material flows. To understand the variability of diverse biomass feedstock, 251 characterization data samples were collected from the literature.

The preliminary results show a large variety of product yields, primary energy consumption, and Greenhouse Gas (GHG) emissions of AC production. Biomass species and their property (e.g., H/C ratio) are one of the major contributors to the variations. Another key factor is the energy recovery. Without energy recovery, the average primary energy consumption of these results is 1.5 times higher. The results provide insightful information for LCA practitioners, researchers, and engineers for future energy/environmental analysis, experimental design, biomass screening, and process optimization. The results can also be used to enhance decision making related to research, development, and deployment of biomass-derived AC. Although this study focused on woody biomass to AC, the methods and modeling framework developed can be applied to other bio-based materials and biorefinery systems.

Reference

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