Mathematical Programming Techniques for Designing Minimum Cost Pipeline Networks for CO2 Sequestration

Abstract

It has been proposed that the CO 2 produced at sources such as fossil energy conversion facilities and industrial process plants could be captured, compressed to supercritical pressures, transported via pipelines and stored in underground geologic formations such as depleted oil and natural gas reservoirs and deep saline aquifers. In this paper, we describe the initial phases of a project using mathematical programming techniques to find the minimum cost network for transporting the CO 2 from the sources to the sinks. A nonlinear model of the CO 2 pipeline system is described, with appropriate constraints, and the lowest cost system is found using a state-of-the-art nonlinear optimization software called LOQO. We also present ways to model the behavior of the system over time, and to model the impact of uncertainties. In future work, we plan to extend these methods to look at the larger system of fossil energy complexes with CO 2 capture and sequestration and distribution of hydrogen.

A multi-scale framework for CO2 capture, utilization, and sequestration: CCUS and CCU

“…(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Rubin, 2008;Benson and Ogden., 2003), it remains an open question whether a nationwide CCUS deployment would be economically feasible or not. Diverse emission scenarios, difference in CO 2 emissions, different geographic locations, technological complexities, etc., preclude a simplistic study toward the design of a cost-effective CCUS.…”

Development and application of a multi-stage CCUS source–sink matching model

“…Benson and Ogden [17] considered transport pipeline network as a nonlinear programming problem, and developed a nonlinear optimization software LOQO. Middleton developed SimCCS mathematical models under the constraints of a given CO 2 capture amount or imposing carbon tax, in which several equations are supplied to calculate pressure drop, CO 2 density and viscosity, drilling cost, etc.…”

Steady-state design of CO2 pipeline networks for minimal cost per tonne of CO2 avoided

“…In light of the fact that the cost and design functions associated with a CCS transport network are discrete and non-linear, and that the nature of the network topology is combinatorial, the task of designing a CCS transport network with the minimum cost per tonne of CO 2 avoided may be more suitable for a heuristic technique than for a MILP model (Middleton and Bielicki, 2009), a greedy-type algorithm (Kazmierczak et al, 2009), or a Newton Method-based non-linear optimisation method (Benson et al, 2003). Genetic Algorithms are a metaheuristic technique appropriate for this type of optimisation.…”

“…Nevertheless, a number of studies (Benson et al, 2003;Brunsvold et al, 2011;Chen et al, 2009Chen et al, , 2010Kazmierczak et al, 2009;Middleton and Bielicki, 2009;Morbee et al, 2011) examine how to obtain the optimal CCS transport network by minimising the present value (PV) of the total capital and operating cost of transport. This approach does not consider any extra CO 2 emissions related to operating the transport network and, therefore, does not necessarily yield the lowest CCS network cost per tonne of CO 2 avoided.…”