Modeling of bed-to-wall heat transfer coefficient in fluidized adsorption bed by gene expression programming approach

Adsorption cooling and desalination methods with adsorption chillers (AC) are promising in energy technologies. However, the low-performance coefficient and bulkiness of traditional packed-bed ACs, primarily due to the high voidage of the sorbent beds leading to low heat transfer coefficients, pose significant challenges. Despite numerous attempts, a practical solution to this problem is yet to be found.

In response to this challenge, we propose a novel approach: a fluidized adsorbent bed instead of the traditional packed bed. We also introduce gene expression programming (GEP) as an innovative artificial intelligence (AI) method for modeling the bed-to-wall heat transfer coefficient in the adsorption bed. Our study includes calculations and model validation for a heat transfer adsorption bed reactor designed for low-pressure adsorption processes. The fluidizing agent of the adsorbent bed was water vapor generated in the evaporator. Silica gel was used as the parent adsorption material in our tests. The heat transfer coefficient was successfully validated and determined through experiments and estimated using the formulated (b-t-wHTc) meta-model. The data evaluated by the model aligns well with the experimental results. Our calculations demonstrate that the GEP-based model accurately predicts the heat transfer coefficient and is suitable for analyzing the fluidized adsorption bed reactor.

The outlined studies serve as a benchmark for subsequent simulations of the intensified heat transfer adsorption bed reactor, as they are integral to project No. 2018/29/B/ST8/00442, titled “Research on sorption process intensification methods in modified construction of adsorbent beds,” supported by the National Science Center in Poland.