Regression-Assisted Ant Lion Optimisation of a Low-Grade-Heat Adsorption Chiller: A Decision-Support Technology for Sustainable Cooling

Growing cooling demand and environmental concerns surrounding mechanical vapour compression systems motivate research into alternative technologies capable of converting low-grade heat into useful cooling. Silica-gel/water single-stage dual-bed adsorption chillers (ADCs) are promising candidates, however, their design must balance conflicting performance targets. This study proposes a regression-assisted multi-objective optimisation framework for low-grade-heat ADC, combining statistically validated surrogate models with the Ant Lion Optimiser and its multi-objective variant. Three co-equal objectives, coefficient of performance (COP), cooling capacity (Q_cc) and waste-heat recovery efficiency (η_e) are jointly maximised to map an operational envelope for sustainable cooling. Two-dimensional Pareto-optimal solutions exhibit a one-dimensional ridge in which η_e declines, and COP and Q_cc increase simultaneously. Within the explored bounds, non-dominated ranges span COP=0.675–0.717, Q_cc=18.3–27.5 kW and η_e=0.118–0.127, with a practical compromise near COP ≈ 0.695, Q_cc ≈ 24 kW and η_(e )≈ 0.122–0.123. While mass flow rate decisions increase Q_cc at the expense of η_e, a one-at-a-time sensitivity analysis with re-optimisation identifies the hot- and chilled-water inlet temperatures and exchanger conductance as the dominant decision variables and maps diminishing-return regions. The proposed framework can effectively use low-grade heat in future low-carbon buildings and processes and supports the configuration of ADC systems.