Title: Simulation-based optimisation and analysis for CO₂ pipeline transportation system with uncertainties

Authors: Qunhong Tian; Dongya Zhao; Zhaomin Li; Quanmin Zhu

Addresses: College of Chemical Engineering, China University of Petroleum (East China), Shandong Qingdao, 266580, China ' College of Chemical Engineering, China University of Petroleum (East China), Shandong Qingdao, 266580, China ' School of Petroleum Engineering, China University of Petroleum (East China), Shandong Qingdao, 266580, China ' College of Chemical Engineering, China University of Petroleum (East China), Shandong Qingdao, 266580, China; Department of Engineering Design and Mathematics, University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QY, UK

Abstract: Carbon capture, utilisation and storage (CCUS) technology is one of the main measures to achieve greenhouse gas emission reduction. As an important link of CCUS technology, pipeline transportation is the most cost-effective transportation mode to transport large-scale CO₂. In order to investigate the effect caused by parameter uncertainties, a simulation-based techno-economic optimisation model is proposed to describe the complicated pipeline design problem in this paper, which takes into account engineering and economic modelling. Penalty function and Newton's method are presented to optimise the pipeline, based on the obtained optimal inlet pressure, pipe diameter, wall thickness, number of pump stations and levellised cost, and uncertain parameters analysis is presented to research the pipeline optimisation design. Based on the pipeline optimisation modelling, simulation results demonstrate the effectiveness of the proposed optimisation approach, and analysis gives the levellised cost change caused by uncertainties, which can provide a theoretical basis to design pipeline.

Keywords: CCUS technology; CO₂ transportation; pipeline modelling; simulation; optimisation and analysis.

DOI: 10.1504/IJSPM.2018.091743

International Journal of Simulation and Process Modelling, 2018 Vol.13 No.2, pp.179 - 187

Received: 05 Jun 2017
Accepted: 29 Sep 2017
Published online: 14 May 2018 *

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