透射式光纤氢气传感系统构建与算法优化

氢气作为绿色清洁能源被广泛应用，但由于氢气易燃易爆易泄漏的特性，研发一种安全可靠的光纤氢气传感器是安全使用氢气的前提。采用溶胶-凝胶法制作三氧化钨/铂(WO₃/Pt)氢敏薄膜，基于透射式光纤氢气传感系统进行氢气浓度检测。由于氢敏薄膜与氢气发生化学反应时，容易受温度影响，并且在此过程中会产生水，阻碍反应的进行，因此氢气浓度与光电探测器接收到的光强信号呈现非线性特性。为了提高氢气检测的准确度，使用支持向量回归(support vector regression, SVR)、反向传播(back propagation, BP)和径向基函数(radial basis function, RBF)神经网络等算法对测试数据进行优化，通过计算算法的平均相对误差率和决定系数(R²)来对比3种算法的优劣。与BP和RBF神经网络相比，SVR算法预测样本的平均相对误差率低至3.8%,R²高达0.999 8,对氢气浓度具有更好的预测效果。

Construction of transmitted optical fiber hydrogen sensing system and research on optimization algorithm

In this paper, hydrogen sensitive film of tungsten trioxide/Platinum (WO3/Pt) was prepared by sol-gel method, and the hydrogen concentration was detected based on transmission optical fiber hydrogen sensing system. Due to the hydrogen sensitive film is easily affected by temperature when it reacts with hydrogen, and water will be generated in the process and hinder the reaction. Therefore, the relationship between hydrogen concentration and the transmitted optical intensity signal received by the photodetector is nonlinear. In order to improve the accuracy of detection system, in this paper, Support Vector Regression (SVR), Back Propagation (BP) and Radial Basis Function (RBF) neural network are innovatively used to optimize the data. The effect of the three algorithms are compared by average relative error rate and coefficient of determination (R2). Compared with BP and RBF neural networks, SVR algorithm has a better prediction effect on hydrogen concentration with average relative error of 3.8% and determination coefficient (R2) of 0.9998.