Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization

TL;DR

This paper introduces a novel optimization framework combining surrogate models and reinforcement learning to minimize the levelized cost of electricity in heat-pipe microreactors, addressing economic and physical design constraints.

Contribution

It presents a new unifying geometric design optimization approach that integrates techno-economic analysis with surrogate modeling and reinforcement learning for microreactor design.

Findings

LCOE reduced by over 57% using the optimization framework.

Cost of axial reflectors and control drum materials are key cost drivers.

The approach effectively balances design constraints with cost minimization.

Abstract

Microreactors, particularly heat-pipe microreactors (HPMRs), are compact, transportable, self-regulated power systems well-suited for access-challenged remote areas where costly fossil fuels dominate. However, they suffer from diseconomies of scale, and their financial viability remains unconvincing. One step in addressing this shortcoming is to design these reactors with comprehensive economic and physics analyses informing early-stage design iteration. In this work, we present a novel unifying geometric design optimization approach that accounts for techno-economic considerations. We start by generating random samples to train surrogate models, including Gaussian processes (GPs) and multi-layer perceptrons (MLPs). We then deploy these surrogates within a reinforcement learning (RL)-based optimization framework to optimize the levelized cost of electricity (LCOE), all the while imposing constraints on the fuel lifetime, shutdown margin (SDM), peak heat flux, and rod-integrated peaking factor. We study two cases: one in which the axial reflector cost is very high, and one in which it is inexpensive. We found that the operation and maintenance and capital costs are the primary contributors to the overall LCOE particularly the cost of the axial reflectors (for the first case) and the control drum materials. The optimizer cleverly changes the design parameters so as to minimize one of them while still satisfying the constraints, ultimately reducing the LCOE by more than 57% in both instances. A comprehensive integration of fuel and HP performance with multi-objective optimization is currently being pursued to fully understand the interaction between constraints and cost performance.