Relining or Replacement: How Timing, Costs, and Infrastructure Shape U.S. Primary Steel Decarbonization
eess.SY, cs.SY
Submitted: 2026-10-10
Updated: 2026-10-10
License: http://creativecommons.org/licenses/by/4.0/
The gist: Imminent blast furnace relinings create a consequential investment window for U.S.
Abstract
Imminent blast furnace relinings create a consequential investment window for U.S. primary steel decarbonization. Using plant-level capacity planning and stochastic operations modeling, we quantify the costs, infrastructure, and fiscal requirements to transition legacy blast furnace sites to direct reduction under supply-side incentives. Under baseline assumptions, post-subsidy premiums above average blast furnace costs, including relining, are 272 to 427 dollars per ton of steel for near-zero-emission hydrogen-based direct reduction and 115 to 181 dollars for natural-gas-based production. Representative facilities producing two million tons of steel annually at legacy locations require approximately 4 to 6.5 and 0.7 to 1.1 GW of renewable generation and battery storage, respectively. This translates to 65 and 12 GW across the unrelined fleet for hydrogen- and natural gas-based pathways, respectively. These findings connect replacement timing to costs, infrastructure, and public support, motivating a sequenced transition through clean-powered natural-gas-based direct reduction to avert blast furnace relining, reduce emissions by 57 percent relative to blast furnaces, and preserve options for deeper decarbonization through hydrogen substitution or carbon capture.
Related papers
- One Request, Multiple Experts: LLM Orchestrates Domain Specific Models via Adaptive Task Routing
- A Geometric Decision Procedure for STL Feasibility and Repair
- From Asymptotic to Designer-Assigned-Time Control: A Review of Stability Notions, Design Mechanisms, and Controller Architectures
- Battery Second Life: A Review of Experimental Studies
- Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems
- Policy-Level Recursive Self-Improvement for Embodied AI with a Criticality World Model