Journal of CO2 Utilization ( IF 7.7 ) Pub Date : 2020-07-29 , DOI: 10.1016/j.jcou.2020.101256 Amir Dashti , Arash Bahrololoomi , Farid Amirkhani , Amir H. Mohammadi
In recent decades, adsorption of high amounts of carbon dioxide (CO2) in metal-organic frameworks (MOFs) has recived attention and is studied broadly. As a main principle, most scientists have accepted that CO2 can be capured by MOFs in order to prevent atmosphere from green-house gas emissions. In the present work, the potential of Particle Swarm Optimization Adaptive Neuro-Fuzzy Inference System (PSO-ANFIS), Differential Evolution-ANFIS (DE-ANFIS), Radial Basis Function Artificial Neural Network (RBF-ANN) and Least Square Support Vector Machine (LSSVM) to estimate CO2 uptake in 13 different MOFs, as a function of the operational pressure (P) supplemented with the property of MOFs was investigated. The inputs of the models are temperature, pressure, surface area and pore volume of MOFs. An extensive databank containing 506 data gathered from the literature was used for models development. The obtained %AARD values for the developed models are 10.05, 36.6, 35.51 and 8.17 for LSSVM, PSO-ANFIS, DE-ANFIS and RBF models, respectively.The sensitivity analysis demonstrated that operational pressure and pore volume of MOFs are the most effective parameters on CO2 adsorbtion by MOFs. It is found that LSSVM model is an outstanding tool for estimating adsorption of CO2 in comparison with other models. The LSSVM model presents a decent method for estimating CO2 adsorption in the studied MOFs, which is straightforward, capable and cost-efficient.
中文翻译:
高容量金属有机框架中CO 2吸附的估算:在温室气体控制中的应用
近几十年来,金属有机骨架(MOF)中大量二氧化碳(CO 2)的吸附引起了人们的关注,并得到了广泛的研究。作为一项主要原则,大多数科学家已经接受了MOF可以吸收CO 2的作用,以防止大气排放温室气体。在目前的工作中,粒子群优化自适应神经模糊推理系统(PSO-ANFIS),差分进化-ANFIS(DE-ANFIS),径向基函数人工神经网络(RBF-ANN)和最小二乘支持向量机的潜力(LSSVM)估算CO 2研究了13种不同MOF的吸收量,并以此作为补充MOF特性的工作压力(P)的函数。模型的输入是MOF的温度,压力,表面积和孔体积。包含从文献中收集的506数据的广泛数据库用于模型开发。对于LSSVM,PSO-ANFIS,DE-ANFIS和RBF模型,所开发模型的%AARD值分别为10.05、36.6、35.51和8.17。敏感性分析表明,MOF的工作压力和孔体积是最有效的参数对MOF吸附CO 2的影响。发现与其他模型相比,LSSVM模型是评估CO 2吸附的出色工具。LSSVM模型提出了一种估算CO的合适方法2在研究的MOF中的吸附,简单,有效且具有成本效益。