Towards improved predictions for the enzymatic chain-end scission of natural polymers by population balances: The need for a non-classical rate kernel

Yong Kuen Ho, Christoph Kirse, Heiko Briesen, Mehakpreet Singh, Chung Hung Chan, Kien Woh Kow

Research output: Journal PublicationArticlepeer-review

20 Citations (Scopus)

Abstract

Enzymatic chain-end depolymerization is commonly employed for the transformation of biomass into important products. To date, investigation on the validity of the rate kernel which is critical to model success, has been conveniently avoided. Through a case study with extensive confrontation with experimental data, we uncover this critical relationship by inspecting every minute detail in the mechanistic modelling procedure. Using a newly proposed shape-evolving function for the rate kernel, model calibration reveals that the commonly employed constant rate kernel is inappropriate for modelling the scission step, and that the apparent rate kernel of hydrolysis resembles an endothermic activation energy barrier function. Facilitated by the adoption of this non-classical rate kernel, good predictions are attained by the model at different hydrolysis conditions with a global parameter set. Being the first to predict distributed data, the methodology here serves as a guide for future studies on the enzymatic disruption of polymeric biomass, i.e. for guiding substrate and enzyme structure modifications.

Original languageEnglish
Pages (from-to)329-342
Number of pages14
JournalChemical Engineering Science
Volume176
DOIs
Publication statusPublished - 2 Feb 2018

Keywords

  • Biomass
  • Chain-end depolymerization
  • Enzymatic hydrolysis
  • Population balances
  • Rate kernel

ASJC Scopus subject areas

  • Chemistry (all)
  • Chemical Engineering (all)
  • Industrial and Manufacturing Engineering

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