Performance characterization of a solar-powered shell and tube heat exchanger utilizing MWCNTs/water-based nanofluids: An experimental, numerical, and artificial intelligence approach. (25th July 2022)

• Record Type: Journal Article
• Authors: Said, Zafar; Rahman, Shek; Sharma, Prabhakar; Amine Hachicha, Ahmed; Issa, Salah
• Is Part Of: Applied thermal engineering. Volume 212(2022)
• Publication Date: 2022-07-25
• Subjects: Artificial intelligence -- Multi-walled carbon nanotubes -- Shell and tube heat exchanger -- Neural networks -- Nanofluids -- Thermal conductivity; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2022.118633
• Languages: English
• ISSNs: 1359-4311

Abstract

Graphical abstract: Highlights: Enhancement of 31.08% in heat transfer coefficient at 0.3% vol. was obtained. Effectiveness improved by 15.24% with nanofluid at 0.3% vol. fraction. 0.3% MWCNT/water system reduced the area by 5.4% compared to base fluid. Tube side model had excellent R and R 2 values of 0.998 and 0.996. Abstract: In the present work, Multi-Wall Carbon Nanotubes (MWCNT)/water nanofluids are used to increase the performance of a shell and tube heat exchanger (STHX) while reducing energy consumption and overall cost. MWCNT/water with 0.3% and 0.05% volume fractions were studied for stability and thermophysical characteristics. At a 0.3% volume fraction, a substantial improvement in the heat transfer coefficient of around 31.08 % was found compared to the base fluid. Experiments were conducted on STHX, and the results show that using nanofluid at a volume fraction of 0.3% improves heat exchanger efficacy by 5.49% compared to the base fluid. Good agreement was obtained between experimental and analytical results. Furthermore, a numerical model was developed using ANSYS commercial software to study the inclusion of semicircular baffles with nanofluid. Results suggest that MWCNT/water nanofluid at 0.3% volume fraction, along with semicircular baffles, enhanced the overall efficacy of the shell and tube heat exchanger by 15.4%, according to numerical data. Furthermore, comparisons between the proposed heat exchanger (STHX) with previous literature was also carried out. Results suggest a notable enhancement of 7% and 12.4% on heat transfer coefficient and overall efficiency was achieved compared to the previous literature. The experimentally acquired temperature variation data was utilized to create an artificial intelligence-based prognostic model. The multilayer perceptron type artificial neural network (MLP-ANN) was employed to map and forecast the thermal performance of MWCNT nanofluids on the tube side and water on the shell side. The tube side model had excellent R and R 2 values of 0.998 and 0.996, while the shell side model had R and R 2 values of 0.994 and 0.988, indicating a robust predictive model. The Kling-Gupta efficiency of the prediction model as 0.9936 and 0.9865 for tube side and shell side models, respectively, further confirms the MLP-ANN based model as an efficient prognostic model. A life cycle study was additionally performed to assess the framework's total energy usage, carbon footprint emissions, and cost over a 25-year life expectancy. The studies eventually indicated that the solar-assisted STHX is both cost-effective and environmentally beneficial.