Thermal conductivity of carbon dioxide (CO₂) is a vital thermophysical parameter that significantly affects the heat transfer modeling related to CO₂ transportation, pipelines design and associated process industries. The current study lays emphasis on implementing powerful soft computing approaches to develop novel paradigms for estimation of CO₂ thermal conductivity. To achieve this, a massive database including 5893 experimental datapoints was acquired from the experimental investigations. The collected data, covering pressure values from 0.097 to 209.763 MPa and temperature between 217.931 and 961.05 K, were employed for establishing various models based on multilayer perceptron (MLP) optimized by different back-propagation algorithms, and radial basis function neural network (RBFNN) coupled with particle swarm optimization (PSO). Then, the two best found models were linked under two committee machine intelligent systems (CMIS) using weighted averaging and group method of data handling (GMDH). The obtained results showed that CMIS-GMDH is the most accurate paradigm with an overall AARD% and R² values of 0.8379% and 0.9997, respectively. In addition, CMIS-GMDH outperforms the best prior explicit models. Finally, the leverage technique confirmed the validity of the model and more than 96% of the data are within its applicability realm.

二氧化碳（CO ₂）的导热系数是至关重要的热物理参数，它会显着影响与CO ₂输送，管道设计和相关工艺行业有关的传热模型。当前的研究重点在于实施强大的软计算方法，以开发新颖的范式来估算CO _2。导热系数。为了实现这一目标，从实验研究中获得了一个包括5893个实验数据点的海量数据库。所收集的数据涵盖从0.097到209.763 MPa的压力值以及217.931到961.05 K的温度，用于建立基于多层感知器（MLP）的各种模型，这些模型通过不同的反向传播算法和径向基函数神经网络（RBFNN）优化结合粒子群优化（PSO）。然后，使用加权平均和数据处理分组方法（GMDH）在两个委员会机器智能系统（CMIS）下将找到的两个最佳模型链接在一起。获得的结果表明，CMIS-GMDH是最准确的范例，总体AARD％和R ²值分别为0.8379％和0.9997。此外，CMIS-GMDH的性能优于以前最好的显式模型。最终，杠杆技术证实了模型的有效性，超过96％的数据在其适用范围内。