CAJ | 학술논문

针对氢回收膜分离过程中一些重要性能参数难以在线测量，提出一种基于PCA-LSSVM的软测量模型。首先，用PCA分析影响气体膜分离过程的多个非线性变量间的相关性，得到它们各自对目标变量的重要程度。然后，用网格搜索、交叉验证结合贝叶斯估计，得到LSSVM模型的2个最优参数值(gam为0.7，sig2为100)。最后，建立模型并对数据进行预测。仿真结果表明，4输入变量(渗透气压力、尾气侧压力、原料气氢浓度、一段膜流量)PCA-LSSVM模型的预测值，与实测的渗透气氢浓度、渗透气流量和尾气氢浓度符合较好，最大相对误差都在8%以内，而且模型的收敛速度不到1 s，可为气体膜分离过程重要性能参数的在线检测和其过程的优化控制提供指导。
침대경회수막분리과정중일사중요성능삼수난이재선측량，제출일충기우PCA-LSSVM적연측량모형。수선，용PCA분석영향기체막분리과정적다개비선성변량간적상관성，득도타문각자대목표변량적중요정도。연후，용망격수색、교차험증결합패협사고계，득도LSSVM모형적2개최우삼수치(gam위0.7，sig2위100)。최후，건립모형병대수거진행예측。방진결과표명，4수입변량(삼투기압력、미기측압력、원료기경농도、일단막류량)PCA-LSSVM모형적예측치，여실측적삼투기경농도、삼투기류량화미기경농도부합교호，최대상대오차도재8%이내，이차모형적수렴속도불도1 s，가위기체막분리과정중요성능삼수적재선검측화기과정적우화공제제공지도。
To aimed at the difficulty of measuring the performance parameters of hydrogen recovery membrane separation process in real time, a soft measurement model based on PCA-LSSVM was proposed. Firstly, PCA was used to analyze the relevance of multiple nonlinear variables, and obtain their different important degrees for the target variable. Then, combined grid search and cross validation with Bayes estimation were used to obtain the optimal value of two important parameters (gam=0.7, sig2=100) of LSSVM. The simulation results show that the prediction results of the model with four variables (permeate-side pressure, residue-side pressure, feed hydrogen concentration, feed gas flux) are in reasonable agreement with the measurement values. All the maximal relative errors are less than 8%, and the convergence rate of the model is less than 1 s. This study provides a guidance for the on-line detection of important performance parameters of the gas membrane separation process and its process optimal control.