深共晶溶剂
共晶体系
二氧化硫
吸收(声学)
烟气
化学
人工神经网络
平均绝对百分比误差
工艺工程
均方误差
计算机科学
材料科学
机器学习
数学
工程类
有机化学
复合材料
统计
合金
作者
Xiaolei Zhu,Marzieh Khosravi,Behzad Vaferi,Menad Nait Amar,Mohammed Abdelfetah Ghriga,Adil Hussein Mohammed
标识
DOI:10.1016/j.jclepro.2022.132465
摘要
Sulfur dioxide (SO2) is one of the main atmospheric pollutants and an active threat to human health. SO2 separation from industrial flue gases improves air quality, decreases human health problems, and reserves sulfur resources. The capturing and recycling processes demand a green solvent with an effective, selective, and reversible SO2 absorption. Deep eutectic solvents (DESs) have recently been engaged in SO2 capture/recycle processes. The SO2 removal capacity of DESs depends on their ingredients (the hydrogen bond donor, hydrogen bond acceptor, water content), temperature, and pressure. Despite comprehensive experimental investigations, literature presents no clue to compare the SO2 absorption capacity of DESs considering their compositions and operating conditions. Therefore, this work deploys an efficient machine learning model to estimate the SO2 absorption capacity of DESs as a function of their molecular weight, water content, pressure, and temperature. All the laboratory-measured datasets reported in the literature have been included to ensure that the deployed model is reliable and generalized. Furthermore, the proposed model has been selected among five different classes of artificial neural networks. A single hidden-layer neural network with only eleven neurons optimized by the Levenberg-Marquardt is the most precise model predicting 480 DES-SO2 phase equilibria with the mean squared error, mean absolute percentage error, and coefficient of determination of 1.13 × 10−3, 4.76%, and 0.97936, respectively. This research is the first step toward constructing a reliable model for screening available DESs based on their SO2 absorption capacity and finding the best candidate.
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