Chung, Wonsuk Kim, Sunwoo Al-Hunaidy, Ali S. Imran, Hasan Jamal, Aqil Lee, Jay H. 2024-01-19T08:31:48Z 2024-01-19T08:31:48Z 2023-10-29 2023-10 0098-1354 https://pubs.kist.re.kr/handle/201004/113197 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. English Pergamon Press Ltd. Identification of sustainable carbon capture and utilization (CCU) pathways using state-task network representation Article 10.1016/j.compchemeng.2023.108408 1 Computers & Chemical Engineering, v.178 Computers & Chemical Engineering 178 N scie scopus 001075741000001 2-s2.0-85170703007 Computer Science, Interdisciplinary Applications Engineering, Chemical Computer Science Engineering Article STRUCTURAL OPTIMIZATION APPROACH GLOBAL OPTIMIZATION OPTIMUM DESIGN ASSESSMENT FRAMEWORK GENERAL ALGORITHM BATCH-OPERATIONS CO2 CAPTURE GAS METHODOLOGY UNCERTAINTY Carbon capture and utilization Sustainable pathway Superstructure State -task network representation Mixed-inter nonlinear programming