Design of a Decision Support System to Operate a NO2 Gas Sensor Using Machine Learning, Sensitive Analysis and Conceptual Control Process Modelling

The most advantageous method for detecting dangerous gases and reducing the risk of potential environmental toxicity effects is the use of innovative gas sensing systems. However, designing effective sensors requires a complex process of synthesizing functional nanoparticles, which is a costly process. Additionally, practical operation of the toxic gas sensors always carries a significant cost along with a considerable risk of hazardous gas emissions. Machine learning algorithms may be used to accurately automate the behavior of the sensors to eliminate the abovementioned deficiencies. In the present research, there are three different factors involved in the optimization of NO2 sensing by means of the response surface methodology (RSM). Two main functions of sensor efficiency, namely sensitivity and response time, are predicted according to the Fe3O4 additive (%), input NO2 (ppm), and response time/sensitivity, and moreover, the execution of a controlling system of the sensor network using the Jacobson model is proposed. The machine learning computations are implemented by Meta.RegressionByDiscretization, M5.Rules, Lazy KStar, and Gaussian Processes algorithms. The outcomes illustrate that the best gas sensor efficiency predictions are related to M5.Rules and Lazy KStar, with a correlation coefficient of more than 96%. The best performance of machine learning computations can be found in the range of 8-10-fold in training and testing arrangements. Meanwhile, the ANOVA assessment confirmed that the most important features in the prediction of response time and sensitivity are NO2 concentration and response time, respectively, with the lowest p-value recorded. The outcomes illustrated that with combinations of RSM, machine learning, and the Jacobson model as a controller, a decision support system can be presented for the NO2 gas sensor system.