Establishing Surrogate Model to Predict the Optimal Thermodynamic and Economic Performance of a Packed Bed Humidifier via Multi-Objective Optimization

Junjie Chen, Dong Han, Weifeng He and Majid Amidpour
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Junjie Chen: Energy Conservation Research Group (ECRG), Key Laboratory of Thermal Management and Energy Utilization of Aircraft, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Dong Han: Energy Conservation Research Group (ECRG), Key Laboratory of Thermal Management and Energy Utilization of Aircraft, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Weifeng He: Energy Conservation Research Group (ECRG), Key Laboratory of Thermal Management and Energy Utilization of Aircraft, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Majid Amidpour: Department of Mechanical Engineering, K.N. Toosi University of Technology, Energy Systems Division, No. 19, Pardis Street, Molla Sadra Ave., Vanak Sq., P.O. Box 19395-1999, Tehran 1999143344, Iran

Sustainability, 2021, vol. 13, issue 15, 1-18

Abstract: In this paper, to optimize the thermodynamic and economic performance of a packed bed humidifier, a multi-objective optimization combined response surface method with a genetic algorithm is employed. The critical parameters, including geometric and thermodynamic parameters, are designated as the impact factors, and the objective functions contain unit humidification capacity of volume and unit humidification capacity of cost in a Box–Behnken design. The results of the analysis of variance demonstrated that the quadratic regression models of objectives are reliable and robust. It is found that the liquid–gas ratio, the interaction of the liquid–gas ratio, and inlet water temperature are simultaneously the strongest influence factors for the thermodynamic and economic indicators among the independent and interactive parameters. In addition, the optimal parameter group is found out through a genetic algorithm, and the actual optimal results are obtained as 0.11 kgs ?1 m ?3 for thermodynamic performance and 15.86 kg$ ?1 for economic performance. Furthermore, it is shown that the thermodynamic performance improves by 56% and the economic performance increases by 6.55%, compared with optimum experimental design points. During the optimization design process, the computational time to find the optimal values reduces from 69,000 s with previous mathematical models to 10 s with established regression models. Additionally, a series of Pareto-optimal points for possible best thermodynamic and economic performance give the reference for the designers of packed bed humidifiers.

Keywords: packed bed humidifier; response surface method; genetic algorithm; thermodynamic performance; economic performance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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