Waste-heat-to-demand matching in cruise ships through sorption thermal energy storage: Modelling, validation and optimisation

This paper investigates a sorption-based thermal energy storage system using a silica gel/water working pair to improve waste-heat utilisation onboard large cruise ships. Such systems can mitigate the temporal mismatch between waste-heat availability during navigation and thermal demand during port stays. The main contributions are the development and validation of a reduced-order dynamic sorption TES model, its integration into a cruise-ship energy-performance simulator using modular bank-based control, and multi-objective optimisation of the TES configuration under realistic operating profiles. Accordingly, a lumped-parameter dynamic model of the adsorber was developed in MATLAB by coupling wall and adsorbent energy balances, vapour-pressure evolution, adsorption equilibrium, and linear-driving-force mass-transfer kinetics. The in-house model was validated against published experimental data and the authors' laboratory measurements. It reproduced the thermodynamic and dynamic variables of interest, with temperature, uptake, and pressure predictions within the experimental uncertainty ranges. The validated model was then embedded in a ship energy-performance simulation tool to evaluate interactions among recoverable waste heat, thermal demand, oil-fired boiler (OFB) operation, and TES charging and discharging. The proposed non-optimised system, based on 468 t of adsorbent and a useful capacity of about 20.1 MWh, stores 5.0 GWh/y and delivers 4.6 GWh/y of useful heat, reducing the OFB thermal energy supply from 5.2 to about 0.6 GWh/y. A subsequent optimisation of 9408 simulated system layouts identified an optimised TES system with a volume of 930.4 m 3 , corresponding to 421 t of adsorbent, a desorption temperature of 95 °C, and yearly OFB energy savings of 4.69 GWh/y. This optimised configuration reduces fuel use by about 0.49 kt/y, avoids approximately 1.53 kt/y of CO 2 emissions, and lowers NO x , SO x , and PM2.5 emissions by 45.7, 26.5, and 1.38 t/y, respectively.