Effective and Robust Parameter Identification Procedure of a Two-Site Langmuir Kinetics Model for a Gas Sensor Process.,ACS Sensors

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Effective and Robust Parameter Identification Procedure of a Two-Site Langmuir Kinetics Model for a Gas Sensor Process.
ACS Sensors ( IF 8.2 ) Pub Date : 2020-07-30 , DOI: 10.1021/acssensors.0c00442
Xiaobo Chen ₁ , Weifeng Jin ₂

Affiliation

Gas sensors have received plenty of attention due to various applications, and the methods to model the kinetic processes and estimate the corresponding parameters play a critical role in characterizing the sensor response behavior. In this work, a two-site Langmuir kinetics model is applied to describe the adsorption/desorption response processes of a SnO₂/reduced graphene oxide resistive gas sensor and the pertinent kinetic parameters are optimized based on the genetic algorithm (GA). For the robustness and fast convergence of the GA, the initial values and ranges of kinetic parameters are obtained step-by-step. This a priori knowledge is sufficient to guarantee reasonable parameter identification from experimental data. Moreover, the kinetics model and GA are integrated into graphical user interface software for subsequent application. Eventually, the exploration of improvements to experimental design is uncovered to increase the accuracy and reliability of the estimation.

中文翻译：

用于气体传感器过程的两站点Langmuir动力学模型的有效且鲁棒的参数识别过程。

气体传感器由于各种应用而受到了广泛的关注，而对动力学过程进行建模并估算相应参数的方法在表征传感器响应行为方面起着至关重要的作用。在这项工作中，使用了两点Langmuir动力学模型来描述SnO ₂的吸附/解吸响应过程/还原的氧化石墨烯电阻气体传感器，并基于遗传算法（GA）优化了相关的动力学参数。对于GA的鲁棒性和快速收敛性，逐步获取动力学参数的初始值和范围。该先验知识足以保证从实验数据中合理地确定参数。此外，动力学模型和遗传算法已集成到图形用户界面软件中，可用于后续应用。最终，探索了对实验设计的改进，以提高估计的准确性和可靠性。

更新日期：2020-08-28

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