# Techno-economic optimization of a heat-pipe microreactor, part II: multi-objective optimization analysis

arXiv: https://arxiv.org/abs/2601.20079 · HTML: https://mlsanity.com/paper/2601.20079/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 46 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization](https://mlsanity.com/paper/2305.05812/) (2023)
- [Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization](https://mlsanity.com/paper/2312.10194/) (2023)
- [Surpassing legacy approaches to PWR core reload optimization with single-objective Reinforcement learning](https://mlsanity.com/paper/2402.11040/) (2024)
- [Evaluation of Nuclear Microreactor Cost-competitiveness in Current Electricity Markets Considering Reactor Cost Uncertainties](https://mlsanity.com/paper/2506.13361/) (2025)
- [Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization](https://mlsanity.com/paper/2512.16032/) (2025)

## Abstract (arXiv)

Heat-pipe microreactors (HPMRs) are compact and transportable nuclear power systems exhibiting inherent safety, well-suited for deployment in remote regions where access is limited and reliance on costly fossil fuels is prevalent. In prior work, we developed a design optimization framework that incorporates techno-economic considerations through surrogate modeling and reinforcement learning (RL)-based optimization, focusing solely on minimizing the levelized cost of electricity (LCOE) by using a bottom-up cost estimation approach. In this study, we extend that framework to a multi-objective optimization that uses the Pareto Envelope Augmented with Reinforcement Learning (PEARL) algorithm. The objectives include minimizing both the rod-integrated peaking factor (FDelta h) and LCOE -- subject to safety and operational constraints. We evaluate three cost scenarios: (1) a high-cost axial and drum reflectors, (2) a low-cost axial reflector, and (3) low-cost axial and drum reflectors. Our findings indicate that reducing the solid moderator radius, pin pitch, and drum coating angle -- all while increasing the fuel height -- effectively lowers FDelta h. Across all three scenarios, four key strategies consistently emerged for optimizing LCOE: (1) minimizing the axial reflector contribution when costly, (2) reducing control drum reliance, (3) substituting expensive tri-structural isotropic (TRISO) fuel with axial reflector material priced at the level of graphite, and (4) maximizing fuel burnup. While PEARL demonstrates promise in navigating trade-offs across diverse design scenarios, discrepancies between surrogate model predictions and full-order simulations remain. Further improvements are anticipated through constraint relaxation and surrogate development, constituting an ongoing area of investigation.