Optimization of Shell and Tube Condenser Effectiveness via PSO Algorithm Coupled with Forced Convection Characterization in Multiphase Systems

In the design of shell and tube heat exchangers encompassing a condensing zone, meticulous attention is required due to the complexities surrounding forced convection in multiphase systems. Despite extensive research, the intricacies within these multiphase systems have remained elusive, rendering the heat transfer coefficient unresolved. In this study, a novel methodology is introduced to elucidate the thermal characteristics of forced convection within the condensing region of shell and tube condensers. An amalgamation of theoretical methods, specifically the Logarithmic Mean Temperature Difference (LMTD), and empirical data sourced from industrial operations forms the foundation of this approach. Upon rigorous analysis employing both Power Law Analysis and Logarithmic Linear Regression, a correlation in terms of ${N_u}=C \cdot {Re}^m \cdot {Pr}^{\mathrm{n}}$ within the condensing region was discerned using Buckingham Pi Theorem. Findings revealed coefficients of C=1.15, m=0.893, and n=13.442. For optimization purposes, the Particle Swarm Optimization (PSO) Algorithm was employed. A focused examination of parameters such as tube length, tube outside diameter, baffle spacing, shell diameter, number of tube passings, and tube wall thickness revealed that by attenuating their values by 30%, 46%, 80.3%, 8%, 50%, and 61.9% respectively, a substantial increase in condenser effectiveness was observed, elevating the value from 0.9473 to 4.299.