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−1m−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.
Highlights
Facing the rapid growth of the global economy and the improvement of the people’s living standards, the demand for water and energy is becoming more and more intense, which promotes the agenda of energy structure optimization tending to be more costeffective and energy-efficient
According to the differential governing Equations (12)–(14) and (18)–(20) which were verified to be robust and reliable for the packed bed humidifier based on the experimental results [8], an integrated solver can be coded in the MATLAB software, as shown in moted to search for the optimal parameter combination and thermodynamic and economic performance of the packing bed humidifier, according to the fitting regression equations
In order to analyze the interactive influences of parameter AD on the unit humidification capacity of volume (UHCV) of the packing bed humidifier, the contour and 3D surface plots of parameter AD are presented in Figure 5 based on the response surface method (RSM)
Summary
Facing the rapid growth of the global economy and the improvement of the people’s living standards, the demand for water and energy is becoming more and more intense, which promotes the agenda of energy structure optimization tending to be more costeffective and energy-efficient. In addition to chemical engineering and office buildings, humidifiers are widely used in people’s daily lives for regulating air humidity. The packed bed humidifier has tremendous applications thanks to its high energy effectiveness and large contact area [1]. Packings such as wooden slates [2], corrugated aluminum sheets [3], polypropylene [4], and ceramic foam [5] have shown great humidification performance. The relationship between humidification capacity and the input cost of the humidifier is proverbially a positive correlation in the physical process
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