Identification of sustainable carbon capture and utilization (CCU) pathways using state-task network representation

•Novel framework for representing CCU pathways as a superstructure with state-task network (STN).•Logic-based outer approximation (LOA) for solving the MINLP.•Optimization based identification of sustainable pathways in a large superstructure model.•A case study to elucidate the main features and fu...

Full description

Saved in:
Bibliographic Details
Published in:Computers & chemical engineering Vol. 178; p. 108408
Main Authors: Chung, Wonsuk, Kim, Sunwoo, Al-Hunaidy, Ali S., Imran, Hasan, Jamal, Aqil, Lee, Jay H.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2023
Subjects:
Carbon capture and utilization
Mixed-inter nonlinear programming
State-task network representation
Superstructure
Sustainable pathway
Mixed-inter nonlinear programming
Carbon capture and utilization
State-task network representation
Superstructure
Sustainable pathway
ISSN:0098-1354, 1873-4375
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Novel framework for representing CCU pathways as a superstructure with state-task network (STN).•Logic-based outer approximation (LOA) for solving the MINLP.•Optimization based identification of sustainable pathways in a large superstructure model.•A case study to elucidate the main features and functionalities of the framework. Carbon capture and utilization (CCU) can be a pertinent solution to avoid millions of tons of carbon emission. The challenge is to identify, among numerous available options of carbon sources capture/utilization technologies, and products, the CCU pathways with best economic and/or CO2 reduction potential. In this work, we propose a novel framework for identifying sustainable CCU pathways, i.e., combinations of sources, processes, and products, using a superstructure based on state-task network (STN) representation. STN allows incorporation of nonlinear models including first-principles or surrogate models into the superstructure representation of potential CCU pathways. The proposed framework solves the superstructure optimization problem of mixed-integer nonlinear programming (MINLP) by introducing logic-based outer approximation (LOA), to reduce the computational time and improve the solvability greatly. A case study using a sizable CCU superstructure demonstrates that LOA can reduce the computational time from hours to minutes while identifying any sustainable pathway from a superstructure with highly nonlinear surrogate models.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2023.108408