Abstract
Aluminium cold rolling is an energy-intensive process that has a substantial impact on CO₂ emis-sions and production cost, yet plant-level optimization remains challenging due to strong process nonlinearities and various operational constraints. This study develops a physics-consistent hy-brid model that combines a Stone–Hitchcock–Ludwik analytical rolling-energy formulation with a residual deep neural network to predict the daily electricity consumption of three single-stand cold rolling mills. Using plant raw data, the hybrid model achieves lower prediction errors than conventional data driven model and yields line-specific physical parameters that agree well with the observed behaviour of each mill. On this basis, an NSGA-II-based tri-objective optimization is carried out to minimise daily energy use, CO₂ emissions, and specific production cost (SPC) by adjusting pass-wise reduction and tension schedules and line-wise production allocation. Case studies on a representative operating day and additional plant data show that the optimised oper-ating strategy shifts production load from less efficient to more efficient lines and smooths pass-wise operating conditions, thereby consistently reducing daily energy consumption and unit cost while moderately decreasing CO₂ emissions without any hardware modifications. The proposed hybrid prediction–optimization framework thus provides a practical decision-support tool for inte-grated energy–environment–economic optimization in multi-line aluminium cold rolling operations.