Uncertainty‐aware robust optimization of <scp> NH ₃ </scp> / <scp> CO ₂ </scp> cascade refrigeration systems: A path toward sustainable and low‐carbon industrial cooling

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This paper provides a robust optimization framework combining Gaussian Process Regression and Grey Wolf Optimizer for refrigeration systems, with validation against multiple datasets.

🏢実務担当者:The derived high-precision polynomial correlations enable easy implementation on low-cost controllers for improved energy efficiency and reduced environmental impact.

🏛政策担当者:The study demonstrates that robust optimization can significantly reduce Total Equivalent Warming Impact and costs, supporting policies for natural refrigerant adoption.

Abstract The transition toward natural refrigerants is a global imperative under the Kigali Amendment; however, the operational instability of ammonia/carbon dioxide cascade systems due to epistemic uncertainties often hinders their widespread sustainable adoption. This study introduces a novel Uncertainty‐Aware Robust Optimization framework, integrating Gaussian Process Regression with the Grey Wolf Optimizer to enhance the energetic, exergetic, and enviro‐economic performance of industrial refrigeration. Validated against four independent datasets (Mean Relative Deviation &lt;6.4%), the model identified a robust optimal cascade temperature ( T cas ) of −6.2°C for a nominal evaporation temperature of −40°C. A detailed Second Law analysis pinpointed the condenser and high‐temperature circuit compressor as the primary sources of irreversibility, contributing 26.9% and 18.8% to total exergy destruction, respectively. Crucially, a novel Sensitivity Index (SI) analysis revealed that while system efficiency is critically dependent on compressor health (SI &gt;11), the proposed strategy maintains exceptional stability (SI &lt;0.02) against internal control deviations. Enviro‐economic assessments across 3000 off‐design operational data patterns demonstrate that this robust strategy achieves an average reduction of 6.2% in both the Total Equivalent Warming Impact and total life‐cycle costs compared to a practical industrial baseline, while delivering a maximum performance and cost improvement of up to 19.4% under severe environmental fluctuations, thereby offering a scalable and risk‐averse path toward decarbonized industrial cooling. Finally, high‐precision polynomial correlations ( R 2 &gt;0.999) are derived to facilitate real‐time industrial implementation on low‐cost controllers, bridging the gap between theoretical optimization and practical sustainability. Concurrently, while this study is bounded by steady‐state performance assumptions and numerical simplifications, it delivers immediate practical application value.

• openalex https://doi.org/10.1002/ep.70561first seen 2026-06-20 05:33:54