Time-dependent multi-objective framework for heat exchanger network design in batch processes with integrated thermal energy storage

• Developed a dynamic multi-objective optimization framework for greenhouse HEN design. • Integrated thermal energy storage (TES) with direct and indirect heat recovery strategies. • Balanced energy recovery, cost, and GHG emissions using Pareto-based decision support. • Applied the framework to a real Québec greenhouse case, achieving lower TAC and emissions. • Provides a practical pathway for sustainable, year-round greenhouse energy systems. This study presents a novel dynamic multi-objective optimization framework for the design of heat exchanger networks (HENs) in batch processes, with integrated thermal energy storage (TES). Targeting the dual goals of minimizing total annual cost (TAC) and greenhouse gas (GHG) emissions while maximizing heat recovery (HR), the methodology combines direct and indirect heat recovery strategies with a Pareto-based NSGA-II algorithm. While multi-objective optimization is widely applied in HEN design, most studies address steady-state conditions and overlook time-varying thermal loads. The proposed framework overcomes this limitation by capturing time-dependent thermal load variations across TDs derived from clustering analysis and integrating thermal energy storage (TES) into a unified optimization model. It incorporates both economic and environmental trade-offs into the decision-making process, enabling more realistic and practical HEN configurations for dynamic operations. A detailed case study of a greenhouse in Sherbrooke, Canada. The optimized HEN and TES configurations achieved up to 31 % reductions in HR while cutting TAC by over 50% and containing GHG emissions to modest increases, offering a balanced and operationally feasible energy integration solution. This approach enables the systematic design of cost-effective and sustainable thermal systems in dynamic industrial settings.