Computationally predicting the performance of gas sensor arrays for anomaly detection

In many gas sensing tasks, we simply wish to become aware of gas compositions that deviate from normal, “business-as-usual” conditions. We provide a methodology, illustrated by example, to computationally predict the performance of a gas sensor array design for detecting anomalous gas compositions. Specifically, we consider a sensor array of two zeolitic imidazolate frameworks (ZIFs) as gravimetric sensing elements for detecting anomalous gas compositions in a fruit ripening room. First, we define the probability distribution of the concentrations of the key gas species (CO₂, C₂H₄, H₂O) we expect to encounter under normal conditions. Next, we construct a thermodynamic model to predict gas adsorption in the ZIF sensing elements in response to these gas compositions. Then, we generate a synthetic training data set of sensor array responses to “normal” gas compositions. Finally, we train a support vector data description to flag anomalous sensor array responses and test its false alarm and missed-anomaly rates under conceived anomalies. We find the performance of the anomaly detector diminishes with (i) greater variance in humidity, which can mask CO₂ and C₂H₄ anomalies or cause false alarms, (ii) higher levels of noise emanating from the transducers, and (iii) smaller training data sets. Our exploratory study is a step towards computational design of gas sensor arrays for anomaly detection.