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Title: Net energy ratio for the production of steam pretreated biomass-based pellets

Abstract

In this study, a process model was developed to determine the net energy ratio (NER) for both regular and steam-pretreated pellet production from ligno-cellulosic biomass. NER is a ratio of the net energy output to the total net energy input from non-renewable energy source into the system. Scenarios were developed to measure the effect of temperature and level of steam pretreatment on the NER of both production processes. The NER for the base case at 6 kg h–1 is 1.29 and 5.0 for steam-pretreated and regular pellet production respectively. However, at the large scale NER would improve. The major factor for NER is energy for steam and drying unit. The sensitivity analysis for the model shows that the optimum temperature for steam pretreatment is 200 °C with 50% pretreatment (Steam pretreating 50% feed stock, while the rest is undergoing regular pelletization). Uncertainty result for steam pretreated and regular pellet is 1.35 ± 0.09 and 4.52 ± 0.34 respectively.

Authors:
 [1];  [1];  [1];  [2];  [2];  [3]
  1. Univ. of Alberta, Edmonton, AB (Canada)
  2. Univ. of British Columbia, Vancouver, BC (Canada)
  3. Univ. of British Columbia, Vancouver, BC (Canada); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1334452
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Biomass and Bioenergy
Additional Journal Information:
Journal Volume: 80; Journal Issue: C; Journal ID: ISSN 0961-9534
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; biomass; pellets; process model; energy balance; steam pretreatment; NER

Citation Formats

Shahrukh, Hassan, Oyedun, Adetoyese Olajire, Kumar, Amit, Ghiasi, Bahman, Kumar, Linoj, and Sokhansanj, Shahab. Net energy ratio for the production of steam pretreated biomass-based pellets. United States: N. p., 2015. Web. doi:10.1016/j.biombioe.2015.06.006.
Shahrukh, Hassan, Oyedun, Adetoyese Olajire, Kumar, Amit, Ghiasi, Bahman, Kumar, Linoj, & Sokhansanj, Shahab. Net energy ratio for the production of steam pretreated biomass-based pellets. United States. https://doi.org/10.1016/j.biombioe.2015.06.006
Shahrukh, Hassan, Oyedun, Adetoyese Olajire, Kumar, Amit, Ghiasi, Bahman, Kumar, Linoj, and Sokhansanj, Shahab. Sun . "Net energy ratio for the production of steam pretreated biomass-based pellets". United States. https://doi.org/10.1016/j.biombioe.2015.06.006. https://www.osti.gov/servlets/purl/1334452.
@article{osti_1334452,
title = {Net energy ratio for the production of steam pretreated biomass-based pellets},
author = {Shahrukh, Hassan and Oyedun, Adetoyese Olajire and Kumar, Amit and Ghiasi, Bahman and Kumar, Linoj and Sokhansanj, Shahab},
abstractNote = {In this study, a process model was developed to determine the net energy ratio (NER) for both regular and steam-pretreated pellet production from ligno-cellulosic biomass. NER is a ratio of the net energy output to the total net energy input from non-renewable energy source into the system. Scenarios were developed to measure the effect of temperature and level of steam pretreatment on the NER of both production processes. The NER for the base case at 6 kg h–1 is 1.29 and 5.0 for steam-pretreated and regular pellet production respectively. However, at the large scale NER would improve. The major factor for NER is energy for steam and drying unit. The sensitivity analysis for the model shows that the optimum temperature for steam pretreatment is 200 °C with 50% pretreatment (Steam pretreating 50% feed stock, while the rest is undergoing regular pelletization). Uncertainty result for steam pretreated and regular pellet is 1.35 ± 0.09 and 4.52 ± 0.34 respectively.},
doi = {10.1016/j.biombioe.2015.06.006},
journal = {Biomass and Bioenergy},
number = C,
volume = 80,
place = {United States},
year = {Sun Jun 21 00:00:00 EDT 2015},
month = {Sun Jun 21 00:00:00 EDT 2015}
}

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