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- (561b) Techno-Economic Analysis of a Chemical Looping Thermochemical Hydrogen Production
A key challenge in adopting TCH is its economic competitiveness. Hydrogen (grey) produced via traditional steam methane reforming costs between $1.50 and $2.00 per kg [1], whereas green hydrogen produced by electrolysis currently costs around $6.22 per kg [2]. In comparison, analysis for solar thermochemical hydrogen (STCH) production cycles predict hydrogen at an estimated cost of $4.55-$14.37 per kg [3,4], with the primary energy input being the dominant contributor. The inefficiency of current STCH systems is reflected in their low heat-to-fuel efficiency, which is approximately 7% [5].
To mitigate these inefficiencies, we previously introduced the Reactor Train System (RTS) to enhance heat recovery between the reduction (~1500°C) and water-splitting (~800°C) stages of the redox cycle [6], which has been identified as a key factor contributing to the low efficiency, accounting for ~50% of the required heat input [7]. By integrating a TES, this system enables continuous hydrogen production. Preliminary analysis indicates that the RTS can substantially improve heat-to-fuel efficiency, raising it from 7% to over 30% in ceria-based cycles.
To evaluate the techno-economic feasibility of the RTS, the NREL-MIT team developed a comprehensive techno-economic analysis (TEA) framework to determine the levelized cost of hydrogen (LCOH) for TCH production system incorporating RTS and TES. This TEA framework integrates advanced modeling tools for plant design, performance assessment, and cost estimation.
Two primary models were developed to support this TEA framework: (1) 0-D system model that evaluates heat-to-hydrogen efficiency of the RTS for different redox-active materials, incorporating auxiliary subsystems such as oxygen removal, hydrogen–steam separation, and a power cycle. In this model, the thermodynamic properties of the redox-active material are used as input in order to model the energy flow and determine the hydrogen production efficiency (assuming thermodynamic equilibrium). This model optimizes system level efficiency by adjusting key operating parameters, including reduction temperature, pressure, and oxidation extent. The model provides sizing specifications for critical plant components and evaluates the steady-state thermal demand of the RTS. (2) Energy source incorporated in a TES model, which utilizes the output from the 0-D system model to determine the required sizing of TES, primary energy input, and energy dispatch strategies, ensuring effective integration of renewable energy sources with energy storage.
The combined outputs of these models define overall plant specifications, including subsystem sizing, operational strategies, and auxiliary system integration. Cost functions have been developed to quantify capital expenditure and operating costs for all components. Finally, the H2A model utilizes these values, incorporating financial parameters to compute the unit cost of hydrogen production. This integrated framework enables a holistic TEA of an RTS-based TCH plant powered by TES and renewable energy sources.
In this study, we conduct a system-wide techno-economic analysis of the RTS in combination with novel redox-active materials. While ceria serves as the reference material, we also investigate metal-substituted ferrites and perovskites (e.g., CTM55 and LSMA). We analyze various system configurations—differing in TES integration, approaches for heat transfer and energy integration, oxygen pumping and hydrogen separation methods, and power cycle utilization—and assess their impact on LCOH under different energy availability scenarios. Our study provides both near-term and long-term cost projections across multiple locations, offering a detailed cost breakdown for different configurations. The final LCOH estimates help identify the most cost-effective and high-performance system designs, as well as informing about the major cost drivers of such a system. This work aims to establish the commercial viability of advanced TCH systems and pinpoint key areas for further development and optimization in future research.