Sensitivity analysis and stochastic modelling of lignocellulosic feedstock pretreatment and hydrolysis. (2nd November 2017)

• Record Type: Journal Article
• Authors: Verma, Sumit Kumar; Fenila, F.; Shastri, Yogendra
• Is Part Of: Computers & chemical engineering. Volume 106(2017)
• Page Start: 23
• Page End: 39
• Publication Date: 2017-11-02
• Subjects: Lignocellulosic biomass acid pretreatment -- Enzymatic hydrolysis -- Global sensitivity analysis -- Ito process -- Mean reverting process; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2017.05.015
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Performed stochastic analysis of acid pre-treatment and enzymatic hydrolysis of lignocellulosic biomass. Conducted global sensitivity analysis to indentify key uncertain parameters. Peak of xylose obtained at varying time for kinetic and operational parameters. Temperature and activation energy identified as highly sensitive parameters. Ito and mean reverting Ito process can be used for stochastic modelling. Abstract: Pretreatment and hydrolysis of lignocellulosic biomass are affected by several uncertainties, which must be systematically considered for a robust process design. In this work, stochastic simulations for expected uncertainties in feedstock composition, kinetic parameter values, and operational parameter values for these two steps were performed. The results indicated that these uncertainties significantly impacted the concentration profiles, which could also affect the optimal batch time. Global sensitivity analysis was then used to identify the critical uncertain parameters. In the feedstock components, cellulose and xylan fractions for acid pretreatment and cellulose fraction for enzymatic hydrolysis were important. Temperature was the most sensitive operating parameter for both acid pretreatment and hydrolysis. The activation energies for different reactions were ranked in terms of their impact on process output. The selected parameters were used to develop stochastic process models using Ito process and mean reverting process for feed composition and kinetic parameter uncertainty.