Optical oxygen sensing with artificial intelligence

TL;DR

This paper introduces a novel AI-based method for optical oxygen sensing using neural networks, eliminating the need for sensor calibration and achieving accuracy comparable to commercial sensors.

Contribution

It demonstrates the feasibility of using machine learning for optical oxygen sensing, moving beyond traditional calibration methods based on the Stern-Volmer equation.

Findings

Neural network predicts oxygen concentration with 0.5% air deviation.

Model trained on synthetic data shows promising accuracy.

Potential for improved performance with experimental training data.

Abstract

Luminescence-based sensors for measuring oxygen concentration are widely used both in industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the classical approach, this change is related to an oxygen concentration using the Stern-Volmer equation. This equation, which in most of the cases is non-linear, is parametrized through device-specific constants. Therefore, to determine these parameters every sensor needs to be precisely calibrated at one or more known concentrations. This work explores an entirely new artificial intelligence approach and demonstrates the feasibility of oxygen sensing through machine learning. The specifically developed neural network learns very efficiently to relate the input quantities to the oxygen concentration. The results show a mean deviation of the predicted from the measured concentration of 0.5 percent air, comparable to many commercial and low-cost sensors. Since the network was trained using synthetically generated data, the accuracy of the model predictions is limited by the ability of the generated data to describe the measured data, opening up future possibilities for significant improvement by using a large number of experimental measurements for training. The approach described in this work demonstrates the applicability of artificial intelligence to sensing of sensors.