Modeling of the lyotropic cholesteric liquid crystal based toxic gas sensor using adaptive neuro-fuzzy inference systems

Detection of toxic gases is important in a variety of settings, including industrial facilities, laboratories, and even in homes. In these settings, toxic gas detection can help prevent accidents and protect the health and safety of workers, researchers, and others who may be exposed to these gases. This study evaluates an Adaptive NeuroFuzzy Inference System (ANFIS) models in predicting the machining responses in the detection of toxic gases vapor, such as toluene (T), phenol (P) and 1,2 dichloropropane (D) using lyotropic cholesteric crystal (CLC) have been shown to have potential as gas sensors due to their unique optical and liquid crystal (LC) properties, and the ANFIS model may be used to better understand and optimize these properties for toxic gas detection. Experiments were carefully carried out to gather data on the response of a lyotropic CLC toxic gas vapor sensor. The effectiveness of using ANFIS combined with Grid Partitioning (GP) was then carefully studied and evaluated in terms of modeling and predicting the responses of the sensor. The best ANFIS-GP model is chosen from these criteria; RSS, PCC, R2, RMSE, MSE, MAE, and MAPE. In addition, validation was performed between the model and experimental data using the LOOCV method. The results show that the ANFIS-GP5 model with 96 fuzzy inference systems (FIS) rules with high R2 values. According to the ANFIS-GP5 model, R2varied ranges from 0.77 to 1 for train, test, and total data of lyotropic CLC sensor exposed to toluene, phenol and 1,2 dichloropropane toxic gases vapors.