Hai Tao, Basem Ali, Alizadeh As'ad, Sharma Kamal, Jasim Dheyaa J, Rajab Husam, Mabrouk Abdelkader, Kolsi Lioua, Rajhi Wajdi, Maleki Hamid, Sawaran Singh Narinderjit Singh
Artificial Intelligence Research Center (AIRC), Ajman University, P.O. Box 346, Ajman, UAE.
Faculty of Data Science and Information Technology, INTI International University, Nilai, 71800, Malaysia.
Sci Rep. 2024 Nov 27;14(1):29524. doi: 10.1038/s41598-024-81044-3.
Optimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature. The optimization objectives include three TPPs: specific heat capacity (SHC), dynamic viscosity (DV), and thermal conductivity (TC). In the proposed hybrid approach, the powerful group method of data handling (GMDH)-type ANN technique is used to model TPPs in terms of input variables. The obtained models are integrated into the multi-objective particle swarm optimization (MOPSO) and multi-objective thermal exchange optimization (MOTEO) algorithms, forming a three-objective optimization problem. In the final step, the TOPSIS technique, one of the well-known multi-criteria decision-making (MCDM) approaches, is employed to identify the desirable Pareto points. Modeling results showed that the developed models for TC, DV, and SHC demonstrate a strong performance by R-values of 0.9984, 0.9985, and 0.9987, respectively. The outputs of MOPSO revealed that the Pareto points dispersed a broad range of MXene MFs (0-0.4%). However, the temperature of these optimal points was found to be constrained within a narrow range near the maximum value (75 °C). In scenarios where TC precedes other objectives, the TOPSIS method recommended utilizing an MF of over 0.2%. Alternatively, when DV holds greater importance, decision-makers can opt for an MF ranging from 0.15 to 0.17%. Also, when SHC becomes the primary concern, TOPSIS advised utilizing the base fluid without any MXene additive.
优化基于MXene的纳米流体的热物理性质（TPPs）对于提高混合太阳能光伏与光热（PV/T）系统的性能至关重要。本研究提出了一种混合方法来优化基于MXene的离子纳米流体的TPPs。输入变量为MXene质量分数（MF）和温度。优化目标包括三种TPPs：比热容（SHC）、动态粘度（DV）和热导率（TC）。在所提出的混合方法中，使用强大的数据处理分组方法（GMDH）型人工神经网络技术根据输入变量对TPPs进行建模。将所得模型集成到多目标粒子群优化（MOPSO）和多目标热交换优化（MOTEO）算法中，形成一个三目标优化问题。在最后一步，采用著名的多准则决策（MCDM）方法之一的TOPSIS技术来确定理想的帕累托点。建模结果表明，所建立的TC、DV和SHC模型分别具有很强的性能，R值分别为0.9984、0.9985和0.9987。MOPSO的输出结果表明，帕累托点分布在较宽范围的MXene MF（0 - 0.4%）内。然而，发现这些最优点的温度被限制在接近最大值（75°C）的较窄范围内。在TC优先于其他目标的情况下，TOPSIS方法建议使用超过0.2%的MF。或者，当DV更为重要时，决策者可以选择0.15至0.17%的MF。此外，当SHC成为主要关注点时，TOPSIS建议使用不含任何MXene添加剂的基础流体。
