Research Mapping of Model Development of Coupling Nuclear Power Plants with Seawater Desalination Systems Using AI-Based Literature Mapping

PEMETAAN RISET PENGEMBANGAN MODEL KOPLING PEMBANGKIT LISTRIK TENAGA NUKLIR DENGAN SISTEM DESALINASI AIR LAUT MENGGUNAKAN AI-BASED LITERATURE MAPPING

Abstract

Freshwater scarcity and the demand for low-carbon energy are driving the development of coupled Nuclear Power Plant (NPP)-seawater desalination systems. This paper presents a research mapping of model development for NPP-desalination coupling using an AI-based literature mapping approach. The objective is to map modelling trends, coupling techniques, and research gaps in this field. The methodology uses a systematic literature review accelerated by Rabbit AI, which maps citation relationships between publications (citation mapping), and ChatGPT, which analyses the content of the literature, classifies journals, and helps build a mind map using Xmind. The mapping of ten core publications, representing six research pillars — optimisation, dynamic modelling, simulation-tool development, thermodynamics, coupling technology, and hybrid systems — shows that research on NPP-desalination coupling remains dominated by steady-state thermodynamic modelling and basic optimisation, while dynamic modelling, cross-technology hybrid configurations, integrated safety-economic analysis, artificial intelligence, and digital twins remain open research gaps. The main contribution of this paper is to provide a systematic research map and to demonstrate the application of AI-assisted literature mapping as a new approach to literature review in the field of NPP-desalination coupling. In conclusion, future research should focus on integrating AI/machine learning, developing reactor digital twins, and applying real-time predictive control to support the efficiency, flexibility, and safety of future NPP-desalination coupling systems.