Zongyu Geng

Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Industrial and Managements Systems Engineering

Committee Chair

Feng Yang

Committee Co-Chair

Wafik Iskander

Committee Member

Majid Jaridi

Committee Member

Natalia A Schmid

Committee Member

Nianqiang Wu

Committee Member

Feng Yang


Traditional sensor calibration is restricted to mathematically relating the steady-state sensor responses to the target analyte concentrations to realize environment monitoring. However, commonly-used chemical sensors usually require a relatively long time, on the order of minutes, to reach steady-state operation, and exhibit nonlinear drifting behaviors. To achieve real-time monitoring of rapidly-changing environments while accommodating drifting behaviors, this work develops statistical methods for both forward descriptive calibration and inverse dynamic calibration of sensor arrays.;Forward calibration is performed based on experimental data. In this work, multivariate Gaussian processes (GPs) were adapted to obtain the forward calibration model, which quantifies the sensor response as a function of the analyte concentration, the drifting variables, and the sensors' exposure time. The multivariate GP method synergistically models all calibration data collected under a range of drifting conditions, and seeks to produce the calibration model of highest quality with the given experimental data. The forward calibration model is a descriptive model, relating sensors' time-dependent responses to a static environment specified by several variables, hence it is not able to assist in real-time monitoring of rapidly-changing environments.;To achieve real-time monitoring of analyte concentrations while fully utilizing the efficiency of forward calibration rendered by multivariate GPs, an inverse calibration method was developed. This inverse model takes the form of a transfer function regression, infers the time-varying analyte concentrations from the dynamic sensor responses, and thus can be coupled with sensors for real-time monitoring. The inverse transfer function model is estimated from the pseudo-calibration data generated by the forward multivariate GP model, which captures the sensors' dynamic and drifting behaviors as reflected in the real experimental data.;Simulated sensor arrays have been developed from real sensor data, and were used to demonstrate the calibration methods developed in this work.