(Invited) Using Machine Learning to Decode Output of a Mixed-Potential Sensor Array for Automotive Exhaust Monitoring

Abstract

Introduction In order to meet the latest requirements for more fuel-efficient cars with low emissions, it is essential to equip vehicles with advanced sensor systems whose readouts can be used to determine the content of key automotive exhaust pollutants such as NOx, NH3 and C3H8 in real time [1]. Arrays of mixed-potential electrochemical sensors (MPES) provide a competitive sensor platform for this task. MPES are electrochemical devices that develop a non-Nernstian potential due to differences in the redox kinetics of various gas species at each electrode/electrolyte gas interface [2,3]. To mitigate signal drift and poor reproducibility, we have developed a patented MPES design utilizing dense electrodes with an overcoat of porous electrolytes such as yttria-stabilized zirconia (YSZ). This design has resulted in highly stable, reproducible, and durable devices which were tested for up to 1000h of operation [4]. In this study, we have employed an array of four mixed-potential sensors (Cr450, Au475, H545, and Cr470) to monitor gas mixtures that mimic automotive exhaust in a laboratory setting.However, even with the latest sensor designs we observe significant cross-specificity and non-linearity in sensor responses to each target gas. Thus, reliable inference of the concentration (or even the presence) of each constituent gas in a complex mixture requires application of advanced machine-learning techniques that will be the focus of this presentation. The ability to decipher the content of gas or liquid mixtures both quickly and reliably has potential applications in many areas of science and technology, including monitoring of various technological processes and continuous observation of air quality in the interests of ecological studies and national security. Our computational methodology can be readily adapted to various sensor array platforms in which gas or liquid mixtures elicit complex response patterns. Method Our previous work has focused on developing computational models that were based on the fundamental electrochemistry of MPES and utilized detailed quantitative descriptions of gas-sensor interactions [5]. We were able to treat both two- and three-gas mixtures (Fig. 1) and take into account the non-linearities of sensor responses. Our model was able to estimate relative concentrations of C3H8, NH3 and NO2 with respect to NO with the maximum error of 14.0% and the average error of 1.8%. Furthermore, we predicted the absolute concentration of each gas in two- and three-gas mixtures with the average error of 3.2%. In this presentation, we discuss application of a general-purpose machine-learning technique called Bayesian regression to sensor array data. The primary advantages of this technique are its versatility (that is, the technique is relatively independent of the sensor platform employed to provide the measurements) and, given a novel measurement, its ability to estimate both the expected concentration of each component in the mixture and the corresponding uncertainty of the predictions. Unlike maximum-likelihood methods, Bayesian regression does not suffer from overfitting and readily lends itself to model comparison, allowing us to choose the optimal model complexity given the available data. Finally, Bayesian regression can be treated using the relevance vector machine approach, which is specifically designed to produce sparse, interpretable solutions. Discussion and Conclusions There is a pressing need for a computational approach designed to infer gas concentrations in complex mixtures such as diesel exhaust on the basis of sensor array outputs. In this presentation, we will discuss Bayesian regression analysis of two-, three- and four-gas mixtures (Fig. 1) using voltage readouts of an array of four mixed-potential sensors as input. Our Bayesian approach is scalable, robust, and easily transferrable to other sensor platforms that are currently employed in numerous scientific and industrial settings. After fitting the model parameters on gas-mixture data generated in the lab under controlled conditions, the resulting model can be employed to predict gas concentrations from novel measurements in real time, as required by real-world applications. Figure 1. Plot of the voltage response (in volts) of three sensors in the array: Cr450, Au475 and H545 (Cr470 was excluded due to its similarity with Cr450). Color coding indicates mixture type.