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: Contribution to journalArticleResearchpeer-review

    16 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

    Cite this