Applied Biochemistry and Biotechnology

, Volume 115, Issue 1, pp 1087–1102

Modeling of carbonic acid pretreatment process using ASPEN-plusŖ

Session 6A Biomass Pretreatment and Hydrolysis

DOI: 10.1385/ABAB:115:1-3:1087

Cite this article as:
Jayawardhana, K. & Peter Van Walsum, G. Appl Biochem Biotechnol (2004) 115: 1087. doi:10.1385/ABAB:115:1-3:1087

Abstract

ASPEN-PlusŖ process modeling software is used to model carbonic acid pretreatment of biomass. ASPEN-Plus was used because of the thorough treatment of thermodynamic interactions and its status as a widely accepted process simulator. Because most of the physical property data for many of the key components used in the simulation of pretreatment processes are not available in the standard ASPEN-Plus property databases, values from an in-house database (INHSPCD) developed by the National Renewable Energy Laboratory were used. The standard non-random-two-liquid (NRTL) or renon route was used as the main property method because of the need to distill ethanol and to handle dissolved gases. The pretreatment reactor was modeled as a “black box” stoichiometric reactor owing to the unavailability of reaction kinetics. The ASPEN-Plus model was used to calculate the process equipment costs, power requirements, and heating and cooling loads. Equipment costs were derived from published modeling studies. Wall thickness calculations were used to predict construction costs for the high-pressure pretreatment reactor. Published laboratory data were used to determine a suitable severity range for the operation of the carbonic acid reactor. The results indicate that combined capital and operating costs of the carbonic acid system are slightly higher than on H2SO4-based system and highly sensitive to reactor pressure and solids concentration.

Index Entries

Acid pretreatment carbonic acid ASPEN-Plus model alcohol fuels biomass 

Copyright information

© Humana Press Inc. 2004

Authors and Affiliations

  1. 1.Department of Environmental StudiesBaylor UniversityWaco

Personalised recommendations