‎Fuzzy Model of Smart Toilet Bowl-Bidet System

This paper proposes an application of a Takagi-Sugeno fuzzy model to the prediction of complex mass transfer behavior in smart toilet bidet systems. The model is constructed through the integration of fuzzy logic theory, nonlinear autoregressive moving average exogenous input models, neural networks, and data clustering algorithms. To develop the model for estimating the air quality of the smart toilet-bidet system, many datasets are collected from a smart toilet bidet model equipped with an automatic odor/bacteria suction system using Sulfur hexafluoride (SF6) gas. Many case studies were carried out as a function of the suction flow rate, suction angle, the number of suction holes, and suction hole size. The inputs for training the fuzzy model are the size, number, and angles of suction holes, whereas its output is the undesirable gas concentration. The trained fuzzy model is tested using different datasets. Modeling and testing results show the effectiveness of the fuzzy model in predicting the gas concentration of the toilet bowl. The proposed fuzzy model is expected to be useful in the implementation of smart toilet bowl systems in the near future.