摘要
超临界CO_(2)螯合萃取是近年来出现的一种绿色高效重金属离子回收技术,但过高的萃取压力及动力学模型的缺乏限制了其工业化应用。文中利用自行开发的5釜并联超临界CO_(2)螯合萃取动力学快速测定装置,以磷酸三丁酯(TBP)为螯合剂,开展了不同温度下,超临界CO_(2)低压(7~9 MPa)螯合萃取钴离子动力学快速测定实验;并以温度、压力和时间为输入参数,以钴离子萃取率为输出参数,构建了3-9-1结构的超临界CO_(2)低压螯合萃取钴离子BP神经网络(BPNN)动力学模型。结果表明:在压力8 MPa,温度40℃,时间30 min下的萃取效率可达97.3%;BP神经网络动力学模型的预测平均相对误差均在2%以内。说明该模型预测值与实验值拟合良好,能够为超临界CO_(2)萃取技术在钴离子提取领域的工业化应用提供理论基础。
Supercritical CO_(2)chelation extraction is a burgeoning green and efficient technology for heavy-metal ion recovery.Nevertheless,its industrial implementation is hindered by high extraction pressures and the absence of a kinetic model.In this research,a self-developed five-kettle parallel device for rapidly determining the kinetics of supercritical CO_(2)chelation extraction was utilized.Using tributylphosphate(TBP)as the chelating agent,experiments for the rapid determination of cobalt-ion chelation extraction were conducted under low-pressure(7~9 MPa)supercritical CO_(2)conditions at various temperatures.By taking temperature,pressure,and time as input parameters,and the cobalt-ion extraction rate as the output parameter,a BP neural network(BPNN)dynamic model with a 3-9-1 architecture for the low-pressure chelation extraction of cobalt ions by supercritical CO_(2)was established.The findings indicate that an extraction efficiency of 97.3%can be achieved at a pressure of 8 MPa,a temperature of 40℃,and a duration of 30 minutes.The average relative error of the prediction made by the BP neural network dynamic model is within 2%.This implies a high degree of consistency between the model's predicted values and the experimental data,thereby providing a theoretical foundation for the industrial application of supercritical CO_(2)extraction technology in cobalt-ion extraction.
作者
伦晨晨
李佳友
胡德栋
LUN Chenchen;LI Jiayou;HU Dedong(College of Mechanical and Electrical Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《山东化工》
2025年第16期1-5,共5页
Shandong Chemical Industry
基金
山东省创新创业共同体项目:船舶和海工装备超临界CO_(2)喷涂装备开发(GTP-2402)。
