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Journals / International Advanced Researches and Engineering Journal / 2026 / Volume: 10 Issue: 2

Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems

Hakan Tutumlu(GAZİANTEP ÜNİVERSİTESİ, NACİ TOPÇUOĞLU MESLEK YÜKSEKOKULU)0000-0003-3884-7015
Pages
—
Publication Date
2026-08-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Response-surface analysis identifies u_t=2 m/s with u_e ≥6 m/s as the optimal operating band.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Heat Recovery Ventilation (HRV) systems reduce building energy demand by thermally coupling exhaust and fresh outdoor air streams through a plate heat exchanger core, without auxiliary heating.
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Key Findings & Data:This study presents a machine-learning (ML) framework for predicting and optimizing the passive performance of the Argemsan K-203 cross-flow plate heat exchanger under variable airflow conditions.
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Conclusion & Impact:Response-surface analysis identifies u_t=2 m/s with u_e ≥6 m/s as the optimal operating band.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Carbon reduction · Cross-flow plate heat exchanger · Energy efficiency

Abstract

Heat Recovery Ventilation (HRV) systems reduce building energy demand by thermally coupling exhaust and fresh outdoor air streams through a plate heat exchanger core, without auxiliary heating. This study presents a machine-learning (ML) framework for predicting and optimizing the passive performance of the Argemsan K-203 cross-flow plate heat exchanger under variable airflow conditions. Outdoor fresh air at 7.8oC/81%RH was drawn through the HRV core and supplied to the occupied space, while indoor exhaust air at 21.1oC /55%RH was simultaneously discharged outside through the same core-the built-in 1500W electrical heater was intentionally deactivated throughout. Experiments were conducted over 60 minutes at 13 measurement states spanning u_t∈{2, 4, 6} m/s and u_e∈{2, 4, 6, 7.2} m/s. Three regression models - Random Forest (RF), Gradient Boosting (GB), and Ridge Regression-were evaluated via Leave-One-Out Cross Validation (LOO-CV). RF achieved R2 =0.931, MAE=0.261oC for fresh-air outlet temperature (T4); Gradient Boosting achieved R2=0.880 for sensible efficiency (ηT). Sensible efficiency ranged from 48.2% to 78.8% (mean 58.9%) and a humidity-ratio–based latent effectiveness of 35-59% (mean 44%). Recovered heat power spanned 245-713W, yielding 236-685 kWh/year of annual energy savings and 48-138 (natural gas) or 100-290 (grid) kgCO2/year avoidance per unit. Response-surface analysis identifies u_t=2 m/s with u_e ≥6 m/s as the optimal operating band.

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Cite This Article

Tutumlu, H. (2026). Artificial intelligence-assisted performance prediction and operating-point identification for passive heat recovery ventilation systems. International Advanced Researches and Engineering Journal, Volume: 10 Issue: 2. https://doi.org/10.35860/iarej.1934313

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