Optimization of the cost and efficiency of Mo-99 production in the MARIA reactor

This paper investigates how to reduce the cost and improve the efficiency of Mo-99 production by optimizing the irradiation scheme of uranium targets in the MARIA research reactor (NCBJ, Poland). We combine simplified analytical models that explain how yield depends on irradiation time, number of cycles, and target shuffling with a numerical optimization framework coupling a genetic algorithm (based on DEAP library) with the FISPACT-II inventory code. Core-position neutron flux spectra and reaction-rate inputs are obtained from Serpent2 calculations and corrected using activation-foil measurements, enabling realistic evaluation of candidate loading and shuffling patterns over 1–4 irradiation cycles. Using a cost model that accounts for both irradiation (reactor time/volume) and target material costs, the optimization identifies multi-cycle irradiation of the same targets as consistently preferable to introducing fresh targets each cycle, while shuffling provides only marginal additional benefit. For a representative irradiation-to-target cost ratio ξ = C 1 / C 2 ≈ 0 . 01 , the most cost-effective strategy is irradiation over 2–3 cycles, reducing the relative production cost to ∼ 87 % of the single-cycle baseline. Sensitivity analysis shows that the preferred number of cycles depends on ξ , with multi-cycle operation remaining advantageous up to ξ c ≈ 0 . 19 (2 cycles) and ξ c ≈ 0 . 09 (3 cycles). These results indicate that simple scheduling changes (primarily multi-cycle irradiation with limited reliance on shuffling) can deliver measurable cost reductions for Mo-99 production under MARIA operating conditions. • Theoretical analyses of Mo-99 production from uranium targets were carried out. • The sample and operational costs of the MARIA research reactor were estimated. • An optimization framework was developed, based on FISPACT-II and genetic algorithm. • Production was optimized using the framework, matching theoretical predictions. • It was demonstrated that costs can be reduced by up to 14%.