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Title: Modelling Heterogeneity in Cellulose Properties Predicts the Slowdown Phenomenon during Enzymatic Hydrolysis

Abstract

Fine control of the enzymatic hydrolysis of cellulose is challenging due to complex process dynamics. Conversion rate from cellulose to valuable monomeric products is often significantly slowed down after an initial rapid but short-lived phase. Underlying mechanisms for this process have yet to be fully understood and as a result poorly represented in existing models. Here, we propose a new modelling platform termed Multi-Layered Population Balance Model (ML-PBM), which enables various key aspects of cellulose enzymatic hydrolysis occurring over the entire breakdown process to be captured. As a core component for predicting the slowdown phenomenon, the ML-PBM properly accounts for heterogeneity in cellulose crystallinity and chain lengths across the structural layers of cellulose particles with different morphologies. Beyond a decent quantitative fit to highly nonlinear dynamic experimental data collected across different conditions, the ML-PBM reveals that the rate slowdown phenomenon is potentially due to heterogeneity in cellulose properties coupled with cellulose morphology. Equipped with the unification of various process fundamentals, the ML-PBM is a rational framework with the potential to facilitate sound cellulose engineering.

Authors:
 [1]; ORCiD logo [2];  [1];  [1]
  1. Monash University
  2. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1566762
Report Number(s):
PNNL-SA-143601
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Chemical Engineering Science
Additional Journal Information:
Journal Volume: 206
Country of Publication:
United States
Language:
English

Citation Formats

Ahamed, Firnaaz, Song, Hyun-Seob, Ooi, Chien Wei, and Ho, Yong Kuen. Modelling Heterogeneity in Cellulose Properties Predicts the Slowdown Phenomenon during Enzymatic Hydrolysis. United States: N. p., 2019. Web. doi:10.1016/j.ces.2019.05.028.
Ahamed, Firnaaz, Song, Hyun-Seob, Ooi, Chien Wei, & Ho, Yong Kuen. Modelling Heterogeneity in Cellulose Properties Predicts the Slowdown Phenomenon during Enzymatic Hydrolysis. United States. doi:10.1016/j.ces.2019.05.028.
Ahamed, Firnaaz, Song, Hyun-Seob, Ooi, Chien Wei, and Ho, Yong Kuen. Sat . "Modelling Heterogeneity in Cellulose Properties Predicts the Slowdown Phenomenon during Enzymatic Hydrolysis". United States. doi:10.1016/j.ces.2019.05.028.
@article{osti_1566762,
title = {Modelling Heterogeneity in Cellulose Properties Predicts the Slowdown Phenomenon during Enzymatic Hydrolysis},
author = {Ahamed, Firnaaz and Song, Hyun-Seob and Ooi, Chien Wei and Ho, Yong Kuen},
abstractNote = {Fine control of the enzymatic hydrolysis of cellulose is challenging due to complex process dynamics. Conversion rate from cellulose to valuable monomeric products is often significantly slowed down after an initial rapid but short-lived phase. Underlying mechanisms for this process have yet to be fully understood and as a result poorly represented in existing models. Here, we propose a new modelling platform termed Multi-Layered Population Balance Model (ML-PBM), which enables various key aspects of cellulose enzymatic hydrolysis occurring over the entire breakdown process to be captured. As a core component for predicting the slowdown phenomenon, the ML-PBM properly accounts for heterogeneity in cellulose crystallinity and chain lengths across the structural layers of cellulose particles with different morphologies. Beyond a decent quantitative fit to highly nonlinear dynamic experimental data collected across different conditions, the ML-PBM reveals that the rate slowdown phenomenon is potentially due to heterogeneity in cellulose properties coupled with cellulose morphology. Equipped with the unification of various process fundamentals, the ML-PBM is a rational framework with the potential to facilitate sound cellulose engineering.},
doi = {10.1016/j.ces.2019.05.028},
journal = {Chemical Engineering Science},
number = ,
volume = 206,
place = {United States},
year = {2019},
month = {10}
}