Optimal discrete pipe sizing for tree-shaped CO2 networks

TL;DR

This paper presents an iterative algorithm for optimally sizing discrete pipelines in tree-shaped CO2 transport networks, considering thermophysical effects and real-world constraints, demonstrated on a German network case.

Contribution

It introduces a novel iterative approach combining pipe sizing and thermophysical modeling for CO2 networks, addressing nonlinearities and discrete choices.

Findings

Effective optimization of pipeline diameters in a real-world network

Incorporation of thermophysical effects improves accuracy

Algorithm demonstrates practical applicability in network planning

Abstract

Many energy-intensive industries, like the steel industry, plan to switch to renewable energy sources. Other industries, such as the cement industry, have to rely on carbon capture utilization and storage (CCUS) technologies to reduce their production processes' inevitable carbon dioxide (CO2) emissions. However, a new transport infrastructure needs to be established to connect the point of capture and the point of storage or utilization. Given a tree-shaped network transporting captured CO2 from multiple sources to a single sink, we investigate how to select optimal pipeline diameters from a discrete set of diameters. The general problem of optimizing arc capacities in potential-based fluid networks is already a challenging mixed-integer nonlinear optimization problem. The problem becomes even more complex when adding the highly sensitive nonlinear behavior of CO2 regarding temperature and pressure changes. We propose an iterative algorithm that splits the problem into two parts: a) the pipe-sizing problem under a fixed supply scenario and temperature distribution and b) the thermophysical modeling, including mixing effects, the Joule-Thomson effect, and the heat exchange with the surrounding environment. We show the effectiveness of our approach by applying our algorithm to a real-world network planning problem for a CO2 network in Germany.