利用RBF神经网络预测沸石分子筛对水分子的吸附能力  

Application of RBF neural network in predicting the adsorption capacity of zeolite molecular sieves for water molecules

作　　者：乔宝韵 乔佳 张军 谢春旭 赵伟立 QIAO Baoyun;QIAO Jia;ZHANG Jun;XIE Chunxu;ZHAO Weili(School of Electronic Engineering,Jiangsu Ocean University,Lianyungang 222005,China;Continental Automotive Electronics(Lianyungang)Co.,Ltd.,Lianyungang 222006,China;Lanzhou Institute of Space Technology and Physics,Lanzhou 730000,China)

机构地区：[1]江苏海洋大学电子工程学院,连云港222005 [2]大陆汽车电子(连云港)有限公司,连云港222006 [3]兰州空间技术物理研究所,兰州730000

出　　处：《航天器环境工程》2025年第1期109-116,共8页Spacecraft Environment Engineering

摘　　要：针对空间望远镜水污染问题,本研究选取4种常见的沸石分子筛材料(ZSM-5、ZSM-22、MCM-41和SAPO-11)为研究对象,利用原子氧和紫外综合模拟实验设备,测试了不同环境下沸石分子筛对水分子的吸附性能,并结合实验结果和机器学习技术,构建了一个基于径向基函数(RBF)神经网络的污染物吸附能力预测模型。分析结果表明,该模型能够有效预测分子筛的吸附性能,其决定系数R^(2)均大于0.99,平均绝对误差和均方根误差均达到10^(-5)量级,优于长短期记忆(LSTM)神经网络、卷积神经网络(CNN)、基于反向传播(BP)算法训练的神经网络等模型。该模型的建立解决了仅通过实验方法研究分子筛吸附性能耗时耗力的难题,并为构建更复杂的数据预估模型奠定了基础。To address the challenge of water contamination in space telescopes,four common zeolite molecular sieve materials:ZSM-5,ZSM-22,MCM-41,and SAPO-11 were investigated in this study.Using a combined simulation facility that provides atomic oxygen exposure and ultraviolet radiation,the adsorption capacities of these zeolites for water molecules under various environmental conditions were evaluated.Furthermore,a predictive model for the adsorption capacity of contaminants was developed using radial basis function(RBF)neural networks,combining experimental data with machine learning techniques.The results demonstrate the model’s effectiveness in predicting the adsorption performance of molecular sieves,achieving R^(2) values greater than 0.99 and mean absolute error and root mean square error values below 10^(-5).This performance surpasses that of other models,including Long Short-Term Memory(LSTM)neural networks,Convolutional Neural Networks(CNN),and neural networks trained with backpropagation(BP)algorithm.The establishment of this model overcomes the time-consuming and labor-intensive challenges of studying molecular sieve adsorption performance through experimental methods alone and lays a foundation for the development of more complex data prediction models.

关 键 词：空间望远镜 水污染控制 沸石分子筛 水分子吸附 机器学习 RBF神经网络 

分 类 号：V484.4[航空宇航科学技术] TQ426.34[化学工程] TP182[自动化与计算机技术—控制理论与控制工程]