Time-dependent multi-objective framework for heat exchanger network design in batch processes with integrated thermal energy storage
Bibliographic record
Abstract
• 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".