Title: Reinforcement learning-based exergetic analysis and assessment of a novel cogeneration system for smart urban energy applications

Authors: Asli Tiktas

Addresses: Department of Mechanical Engineering, Faculty of Engineering, Izmir Democracy University, 35140 Karabaglar, İzmir, Turkey

Abstract: This paper presents a novel Kalina-based geothermal cogeneration system for smart urban energy applications, integrated with a reinforcement-learning-based exergetic analysis and control framework implemented in a digital twin environment coupling EES, TRNSYS and COMSOL. The configuration exploits absorber-integrated internal heat regeneration to raise the working-fluid temperature above the geothermal source without auxiliary energy input. The optimisation results indicated that the reinforcement learning (RL)-based control strategy improved overall energy and exergy efficiencies by 0.066 and 0.058, respectively while simultaneously reducing CO₂ emissions by 0.126, levelised cost of electricity by 0.191, and the levelised cost of heating by 0.133.

Keywords: reinforcement learning optimisation; digital twin modelling; exergy-aware control; low-grade heat recovery; geothermal energy; cogeneration systems; exergy analysis; exergoeconomic analysis.

DOI: 10.1504/IJEX.2026.151871

International Journal of Exergy, 2026 Vol.49 No.2, pp.122 - 142

Received: 24 Aug 2025
Accepted: 13 Dec 2025
Published online: 24 Feb 2026 *

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