Investigation of enhancing heat transfer in three Kenics static mixer utilizing muti-objective optimization

The enhancement of heat transfer efficiency in industrial processes remains a critical technological challenge for achieving optimal energy utilization and minimizing environmental impacts. In light of static mixers efficient heat transfer, this study employs experimental and numerical simulations at Re = 2600 ‒ 17,700 to investigate the influence of various geometric parameters in three Kenics static mixer (TKSM), including elevation angles (α = 0°, 3°, 5°, 7°), deflection angles (θ = 0°, 30°, 60°), and aspect ratios (A_r = 1, 1.25, 1.5). Artificial neural networks and multi-objective genetic algorithms are implemented to predict the geometric structure. Results indicate that the optimal heat transfer performance of TKSM occurs at α = 5° and θ = 60°, demonstrating an improvement of 1.69 %–3.7 % compared to α = 0°; when θ = 0° and α = 7°, the overall heat transfer performance of the α = 7° structure is improved by 6.9 %–11.7 % compared to the unmodified TKSM. Through ANNs modeling, correlations were established between structural parameters, heat transfer performance and fluid resistance, achieving prediction accuracies of 93.84 % and 89.6 % for Nusselt number (Nu) and pressure drop(Δp), respectively. In the Re range of 2600–8800, the optimal structures are: α = 9°–9.6°, θ = 0.2°–0.5°, and A_r range from 1.39 to 1.46. Compared with the initial structure under the same operating conditions, the comprehensive heat transfer performance is improved by 7.69 %–11.58 %. In the Re range of 8800–17700, the optimal structures are: α = 2.2°–7.4°, θ = 18.9°–60° and A_r range from 1.46 to 1.5, the comprehensive heat transfer performance is improved by 7.85 %–9.84 %.