Numerical investigations and design optimization of cryogenic plate-fin heat exchanger in Electric Arc Furnace using radiative heat transfer model

The most crucial element affecting cryogenic systems' energy efficiency is heat exchangers. In this article, a model for cryogenic plate-fin heat exchanger (PFHE) under thermal-hydraulic performance with various blends is proposed. To reduce five objective functions total heat transfer rate, total weight, total mass flow rate, number of entropy generating units, and whole annual cost the study carried out the PFHE's optimal design. This study innovatively focuses on optimizing PFHE design for cryogenic conditions using Opposition Learning with the Beetle Swarm algorithm. The proposed geometry addresses non-uniform temperature distribution, emphasizing thermal-hydraulic properties in various blends. Additionally, a second-order semi-discretization radiative heat transfer approach enhances boiler simulation accuracy. A multi-objective optimization of Opposition Learning with Beetle Swarm (MO-OBBS) optimization approach was suggested in the article for the design of PFHE. Despite the significant potential for renewable energy the low-temperature waste heat treatment process is rarely examined from a sustainability perspective. Consequently, the study proposed a second-order semi-discretization radiative heat transfer method to simulate a furnace using a water heat recovery process. The cooling panel's numerical analysis was conducted using computed temperature fields. The performance of the various algorithms is compared using the Freidman rank-sum test to determine the statistical significance of the findings obtained. In logarithmic coordinates, the proposed method's faster convergence is compared to GQB-PSO, SLTLBO, Jaya, CKKO, and OLE-SCA algorithms. The proposed method exhibits a maximum average increase of 115.39% for PFHE design. When the numerical simulation results are compared to measurements of output water temperature, the accuracy is highly improved.